# Welcome **WELCOME** Welcome to AlphaGeo’s documentation library. Here you’ll find product guides, methodology details, data sources, and other reference materials. **OUR SOLUTIONS** Explore AlphaGeo’s data and analytics solutions: 1. [Climate Resilience Suite](/climate-resilience-suite/climate-risk-and-resilience-index) — assess physical climate risk, resilience, and financial impact. 2. [Global Adaptation Layer](/global-adaptation-layer/methodology-2-3-global-adaptation-layer) — measure local adaptation capacity and resilience infrastructure. 3. [Climate Price](/climate-price/climate-price) — quantify climate-related value impacts. 4. [Macro Suite](/macro-suite/location-macro-signals) — forecast climate-driven economic impacts and location dynamics. **PLATFORM GUIDES** SaaS subscribers will also find guides to our native app, AlphaGeo Explorer, in the **Platform Guides** section. **FOR DEVELOPERS** Documentation for accessing our data via API is available in the **For Developers** section. **TRIAL ACCOUNT** You can create a trial account to access AlphaGeo Explorer application and selected data products [here](https://app.alphageo.ai/trial_setup?_gl=1*bfqcwa*_ga*Mzg3NTI2MjIuMTczOTMzOTM3Ng..*_ga_GJ386VZ4Y1*MTczOTMzOTM3Ni4xLjAuMTczOTMzOTM3Ni4wLjAuMA..*_ga_H5PF8KS554*MTczOTMzOTM3Ni4xLjAuMTczOTMzOTM3Ni4wLjAuMA..). A guide to the trial (including limits) can be found [here](https://docs.alphageo.ai/platform-guides/trial). # Client Onboarding Guide Getting started with AlphaGeo Welcome to AlphaGeo. Use this guide to complete your initial setup and start using the platform with confidence. ### 1. Login credentials Login credentials are sent by email to your organization's designated admin user. ### 2. Getting started * Log in using your credentials at [AlphaGeo Explorer](https://app.alphageo.ai/login). * Reset your password. Your password must: * contain at least 12 characters * include an uppercase letter, a lowercase letter, a number, and a special character * not contain your username * not be too similar to your personal information * not be a commonly used password * not be entirely numeric * not match your last 5 passwords ### 3. Upload your data Upload your data in our Data Manager to begin asset-level or portfolio-level analysis. * Refer to our [Data Manager](/alphageo-platform/data-manager) user guide for more instructions. * If you are using an API, refer to our guide on [Data APIs](/for-developers/alphageo-data-api). ### 4. Navigating our platform AlphaGeo’s native application, [**AlphaGeo Explorer**](https://app.alphageo.ai/), allows users to analyze existing assets at both asset and portfolio level, and explore new locations for further diligence. Our [Platform Overview](/alphageo-platform/platform-overview) and other related user guides will help your team get started quickly. If you would like to schedule live demos for your teams, please contact or your direct client contact. Key guides include: * [Data Manager](/alphageo-platform/data-manager) * [Portfolio Analytics](/alphageo-platform/portfolio-analytics) * [Location Explorer](/alphageo-platform/location-explorer) You can access all **User Guides** at the **bottom-left of our app,** alongside the **Admin Panel**, **Customer Support**, **Feedback & Suggestions**, and **Methodology** tabs.

### 5. Understanding our data Use our product and methodology documentation to understand the analytics behind the platform. Our public product pages provide a high-level overview for users and stakeholders: [AlphaGeo Public Docs](https://www.docs.alphageo.ai). For deeper detail on model logic, inputs, and assumptions, refer to the **Methodology Docs** accessible from within the bottom-left panel of our app. ### 6. For admins: manage your organization Our [Admin Panel](/alphageo-platform/admin-panel) guide explains how designated admins can add team members and manage permissions. ### 7. Support and feedback The **Customer Support** and **Feedback & Suggestions** options in the bottom-left of our app allow you to contact us directly. You can also email . If you require *urgent* assistance, please email our Client Relations and Tech leads Xiaoyou at and Mehroz at . ### 8. Staying connected Book your first quarterly consultation, or share feedback at any time by emailing . # Enterprise Trial Onboarding Guide Getting started with AlphaGeo Welcome to AlphaGeo. This guide covers the resources you need to begin your trial. ### 1. Getting Started * Log in to [AlphaGeo Explorer](https://app.alphageo.ai/login). You should have received your login credentials from the AlphaGeo team. * Reset your password if preferred. Your password must: * be at least 12 characters long * include uppercase and lowercase letters, a number, and a special character * not contain your username * not closely resemble your personal information * not be commonly used or entirely numeric * differ from your previous five passwords ### 2. Navigating our platform Access our platform [User Guides](/alphageo-platform/platform-overview) from the app’s bottom-left panel.

Some key features you may wish to explore include: * [Location Explorer](/alphageo-platform/location-explorer) — Search an address to assess climate risk, resilience, and financial impact. * [Portfolio Analytics](/alphageo-platform/portfolio-analytics) — Assess risk across assets and portfolios. Upload assets through **Data Manager** or add them from **Location Explorer**. * [Data Manager](/alphageo-platform/data-manager) — Upload assets for stress testing in **Portfolio Analytics**. * [Hazard Alerts](/climate-resilience-suite/hazard-alerts) — Monitor real-time and near-term hazards across your portfolio. If you would like to schedule live walkthroughs for your teams, please contact or your direct AlphaGeo contact. ### 3. Understanding our data Use our product and methodology documentation to understand the analytics behind the platform. [AlphaGeo Public Docs](https://www.docs.alphageo.ai) provide a high-level product overview. For detailed model logic, inputs, and assumptions, refer to **Methodology Docs** in the app’s bottom-left panel. ### 4. Support and feedback Use **Customer Support** or **Feedback & Suggestions** in the app’s bottom-left panel to contact us. You can also email . For urgent assistance, please send an email to our Client Relations lead [Xiaoyou](mailto:xiaoyou@alphageo.ai) and Tech lead [Mehroz](mailto:mehroz@alphageo.ai). # Access to Our Methodology Docs AlphaGeo's detailed methodology papers are available on request. Please fill out our request form here, and a member of our team will get back to you, typically within one business day. Request form # Climate Risk and Resilience Index A resilience-adjusted approach to climate risk {% columns %} {% column valign="middle" %} Access Free Trial {% endcolumn %} {% column %}

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{% endcolumn %} {% endcolumns %} ## Overview AlphaGeo’s **Climate Risk and Resilience Index (CRRI)** measures climate risk for any location through a resilience-adjusted lens, modelling both physical climate risk as well as resilience-adjusted risk that factors in the adaptation capacity of the location, society, and asset. This allows users to model likely real-world impact, not just hazard exposure. The result is a view of **both** **unadapted risk** **and** **adapted risk**, as well as a **quantification of the location's adaptive capacity and resilience**. The CRRI can be used to **screen locations, compare assets, plan for adaptation, and support climate disclosure and reporting requirements**.

The CRRI provides a physical risk and resilience-adjusted risk score for 9 acute and chronic hazards, over 4 time periods.

## Our approach: Resilience-adjusted risk Traditional climate risk models focus on hazard intensity. They often miss how local adaptation changes outcomes on the ground. CRRI addresses that gap with AlphaGeo’s **resilience-adjusted risk** methodology. It first computes physical climate risk. It then calculates the location's mitigated, resilience-adjusted risk using a hazard-specific offset coefficient, based on the adaptation measures in place at that location. This approach produces two complementary views: 1. **Physical Risk Scores** — the hazard-only baseline derived from climate models *(i.e., unmitigated, or undefended risk)* 2. **Resilience-Adjusted Risk Scores** — the likely real-world impact after accounting for local adaptation capacity *(i.e., mitigated, or defended risk)* This helps users move beyond risk identification. It supports adaptation planning, capital allocation, and resilience strategies. ## Triple-layer adaptation offset CRRI's unique Resilience-adjusted Risk Framework applies a **triple-layer adaptation offset** to move from hazard exposure to likely real-world impact. 1. It first adjusts Physical Risk (or "Hazard Score") using **local adaptation capacity**, such as flood defenses or drainage systems. 1. We quantify hazard-specific adaptation capacity using our proprietary [Global Adaptation Layer](/global-adaptation-layer/methodology-2-3-global-adaptation-layer), the world's first commercially available, multi-hazard database on global adaptation capacity. 2. It then applies **societal resilience** factors, such as fiscal capacity, and demographic vulnerability. 3. Where asset data is available, it adds a third layer through an **asset-level remediation workflow.** This third layer is powered by the [**Remediation Checklist**,](/alphageo-platform/portfolio-analytics/remediation-checklist) which captures the mitigation measures in place at the building or asset level. That makes the Remediation Checklist a key feature of CRRI, extending the index from location-level risk screening to asset-level adaptation planning and helping users quantify how specific resilience measures can further reduce risk.

AlphaGeo's proprietary Resilience-adjusted Risk score reflects *adapted* risk, where hazard intensity is offset by (1) local adaptations; (2) societal resilience; and (3) asset-level remediations.

In this example, the initial Heat Stress Hazard (i.e., Physical Risk) score is adjusted by the "triple-layer" adaptation offset to result in a Resilience-adjusted Risk Score of 40/100.

## Hazard categories The CRRI includes data on risk, adaptation, and resilience for 9 acute and chronic hazard categories:

The Climate Risk and Resilience Index's feature matrix. Risk, adaptation, and resilience features are provided by AlphaGeo, while asset-level remediations are an optional, user-reported input.

* **Heat Stress**: Evaluates the risk of extreme heat on buildings, productivity, and thermal comfort. Adaptation capacity data includes **building density** and **urban greenery**, which shape local heat island effects and cooling potential. * **Drought**: Assesses the risk of prolonged water stress, supply disruption, and land degradation. Adaptation capacity data includes **water works**, **water storage**, **water amenities**, and **groundwater well access**. * **Inland Flooding**: Identifies risk from riverine flooding, flash flooding, and heavy rainfall runoff. Adaptation capacity data includes **surface porosity**, **flood barriers**, **drainage systems**, **storage and control infrastructure**, and **nature-based flood solutions**. * **Coastal Flooding**: Analyzes risk from sea-level rise, storm surge, coastal inundation, and erosion. Adaptation capacity data includes **coastal defenses**, **natural buffers**, **drainage capacity**, and **coastal flood control infrastructure**. * **Wildfire**: Assesses the likelihood and potential impact of wildfire on structures, infrastructure, and surrounding land. Adaptation capacity data includes **fire response infrastructure**, **fire prevention measures**, and **fire detection systems**. * **Hurricane**: Assesses the risk of tropical cyclones, including damaging wind, heavy rainfall, and compound flood impacts. Adaptation capacity data includes **building strength**, **storage and control infrastructure**, and **flood barrier proximity**. * **Hail**: Assesses the risk of damaging hailstorms that can impact roofs, facades, glazing, vehicles, and exposed equipment. Adaptation capacity data includes **building strength** and other local protection proxies that reduce surface damage exposure. * **Landslide**: Evaluates the risk of slope failure caused by unstable terrain, saturated soils, and ground movement. Adaptation capacity data includes **vegetation cover** and **proximity to manmade barriers** that help stabilize slopes. * **Earthquake**: Assesses seismic risk from ground shaking and related ground failure affecting structures and infrastructure. Adaptation capacity data includes **building strength** and **building sparsity**, which help indicate likely structural resilience and spillover vulnerability. ## Use cases * Site selection and acquisition due diligence * Portfolio risk assessment and management * Adaptation planning and resilience investment * Climate risk disclosures and reporting, including GRESB, TCFD, IFRS S2, ISSB, EU Taxonomy, and others * Stakeholder engagement and communications ## Data details * **Resolution:** Up to 100 meters in spatial resolution * **Timescales**: 2025, 2035, 2050, 2100 * **Emission Scenarios**: SSP245, SSP370, SSP585 ## Detailed Methodology Please fill out our Methodology Request Form, and a member of our team will get back to you, typically within one business day. Request form # Indexing & Score Interpretation {% columns %} {% column valign="middle" %} Access Free Trial {% endcolumn %} {% column %}

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{% endcolumn %} {% endcolumns %} We translate the expected damage for each feature calculated from the [Resilience-Adjusted Risk Framework](/climate-resilience-suite/climate-risk-and-resilience-index#our-approach-resilience-adjusted-risk) into a set of scores that capture both the shifting intensity of risk from the current period to 2100 as well as the adaptation capacity of each location, resulting in an overall "ground-truth" resilience profile.

These scores work like risk ratings based on a score card that scales with the intensity of the hazard, giving the absolute risk exposure of a location for one type of risk at any given scenario and time period on a scale of 0-100. Please refer to the score card above for how the scores and categories are assigned for each index. You may also [**download a PDF version** **here**](https://alphageosingapore.sharepoint.com/:b:/s/DataManagement/IQCUhSbbTKnCR4YxJFdXSYn7AavkAJakQ1ulNY0fMRj4zJY?e=aqpCcz)**.** The category breaks are based on the Mean Damage Ratio (MDR) for the respective features. The higher the risk scores and category assigned for each feature, the higher the expected mean damage ratio caused by the contributing feature, and the more advanced the mitigation strategies required. The overall scores, i.e. the **Overall Physical Climate Risk Score** and **Overall Resilience-adjusted Risk Score**, are global percentile scores that measures the overall risk profile of a location in comparison to other locations, allowing users to compare the overall risk profile of one location against another. ### Example The platform offers two set of scores: the **Physical Risk Scores**, where the hazard exposure is scored on a scale of 0-100, and the **Resilience-Adjusted Risk Score**, where local adaptation features, and societal resilience are applied to reduce the physical risk scores where applicable. The image below shows the visualization of the dashboard for one sample location.

# AlphaGeo versus the Market: Climate Risk and Resilience Index (CRRI) AlphaGeo versus the market {% columns %} {% column valign="middle" %} Access Free Trial {% endcolumn %} {% column %}

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{% endcolumn %} {% endcolumns %} ## **Summary** Unlike existing products focused purely on climate physical risk, AlphaGeo's Climate Risk and Resilience Index (CRRI) is a unique two-in-one scoring suite of (1) Physical Risk and (2) Resilience-adjusted Risk. Together, these deliver risk *and* resilience assessments at scale. Furthermore, AlphaGeo is much more than a "defensive" climate risk data provider. We are also a predictive location analytics platform with an "offensive" toolkit that includes [Climate Financial Impact Metrics](/climate-resilience-suite/financial-impact-analytics), Location Signals, and other bespoke metrics useful for investment strategy and portfolio construction. ## How we differentiate ### **(1) Resilience-Adjusted Risk Methodology: a two-in-one scoring suite comprising physical risk and resilience that captures the "ground truth"** Unlike traditional approaches to physical risk, CRRI uses a unique **Resilience-Adjusted Risk Methodology** that accounts for local adaptation measures (or the lackthereof) that offset climate hazards, thus providing a “ground-truth” view on the likely real-world impact of climate change. Our approach helps users go *beyond* assessing risk to guide remediation measures, make strategic decisions, and promote overall resilience. | What our competitors provide | How AlphaGeo differentiates | | -------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | **Climate physical risk scores** |

Two-in-one scoring suite comprising:

(1) Climate Physical Risk Scores, based on pure physical risk
(2) Resilience-Adjusted Risk Scores, accounting for local adaptation measures

| ### **(2) Global adaptation layer: a unique dataset of local adaptation measures that underpins our resilience-adjusted scores** Underpinning our Resilience-adjusted Risk Scores is the [Global Adaptation Layer](/global-adaptation-layer/methodology-2-3-global-adaptation-layer), a unique dataset of globally consistent, hyperlocal geospatial data layers on the adaptation capacity of any coordinate on earth, enabling "ground truth” analysis of climate resilience down to the neighborhood or asset level. | What our competitors provide | How AlphaGeo differentiates | | -------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | (1) **Risk data** used for development of physical risk scores |

(1) Risk data used for development of Physical Risk Scores

(2) Adaptation data used for development of Resilience-adjusted Risk Scores

| ### **(3) Go beyond climate risk: predictive location analytics for defensive and offensive use cases** | What our competitors provide | How AlphaGeo differentiates | | ------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------- | | 1) **Climate physical risk scores and underlying data** |

(1) CRRI

(2) Financial Impact Analytics
(3) Location Dynamism Signals

| ## AlphaGeo vs. the market

Feature	AlphaGeo	Jupiter Intelligence	Climate X	First Street
Climate physical risk scores	true	true	true	true
Underlying risk data	true	true	true	true
Resilience-adjusted risk scores	true	false	false	false
Underlying resilience (i.e., adaptation capacity) data	true	false	false	false
Global coverage	true	true	true	true
Near-term (2030)	true	true	true	true
Mid-term (2050)	true	true	true	true
Long-term (2100)	true	true	true	true
[Financial impact] Expected asset losses (e.g., CVaR)	false	true	true	true
[Financial impact] Climate impact on cashflows (Link to Product)	true	false	false	false

# Financial Impact Analytics Modelling the financial impact of climate change {% columns %} {% column valign="middle" %} Access Free Trial {% endcolumn %} {% column %}

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{% endcolumn %} {% endcolumns %} ## Overview AlphaGeo’s **Financial Impact Analytics** model climate impacts on the key drivers of cashflow (including revenues, OpEx, CapEx) and asset value to enable more robust, future-ready financial modelling and planning. ## Our approach: Beyond risk management to strategic finance Most climate financial analytics focus on expected losses, using metrics like Climate Value-at-Risk (CVaR) or Average Annual Losses. While useful for risk management, they don’t capture climate’s impact on cashflows—critical for valuations, investments, and capital budgeting. Our **Financial Impact Analytics** fill this gap, delivering cashflow-based insights that plug directly into models like DCF to support strategic financial decisions. ## Product features * **Asset types:** Residential, Commercial, Power Plants, Electricity T\&D, Water & Wastewater, Transport (Road & Rail), Airports, Seaports, Data Centers * **Geographic coverage:** Global * **Emission Scenarios**: SSP245, SSP370, SSP585 ## Asset-level Metrics

AlphaGeo's Financial Impact Analytics module provides a transparent and explainable approach to climate-informed cashflow modelling, valuations, and underwriting.

| Category | Metric | Description | Unit | | -------------------------- | ------------------------- | ---------------------------------------------------------------------------------------------------------------- | --------------------- | | **Section 1 — Valuation** | 10-Year NPV Loss (CVaR) | Expected NPV loss under adverse but plausible climate scenarios (CVaR) over a 10-year hold. | % NPV loss | | | Adaptation Alpha | Quantifies the projected NPV loss that is recoverable if targeted adaptation measures are implemented | % NPV recovered | | | Average Annual Loss (AAL) | Aggregating all climate-driven OpEx, CapEx, and revenue impacts, the all-in annualized cost of climate exposure. | % loss/yr | | | Climate Risk Discount | Additional discount rate to reflect future climate uncertainty based on the climate profile. | % rate added | | **Section 2 — Insurance** | Insurance Premiums | Climate-driven net effect on insurance premiums through 2050 (driven by fire and flood risk). | Annual % change | | | Insurability Risk | Risk of a location exceeding standard insurance market thresholds (99th percentile of today's global risk). | Insurable / High Risk | | **Section 3 — OpEx Costs** | Utility Demand | Increase in cooling energy demand outpacing savings from reduced heating requirements. | Annual % change | | | Maintenance Costs | Additional maintenance budget required due to changing climate conditions and extreme weather impacts. | Annual % change | | **Section 4 — Income** | Operational Efficiency | Reduction in operational efficiency as climate conditions strain building systems. | Annual % change | | | Operational Downtime | Additional downtime days per year due to extreme heat, precipitation, winds, and dry spells. | Days/yr | | | Workforce Productivity | Decline in workforce output due to extreme heat reducing safe operating hours for outdoor/manual workers. | Annual % change | | **Section 5 — CapEx** | Retrofit Costs | Recommended allocation of annual income to proactive climate retrofits to mitigate physical and stranding risk. | % of income | ## Portfolio-level Metrics

NPV Loss and Adaptation Alpha is also aggregated at the portfolio-level using a simple average. If asset values are provided by the user, we calculated a weighted average. ## Use cases * Valuation and investment analyses * Financial planning * Capital planning * Public budgeting and economic modelling ## Detailed Methodology Please fill out our Methodology Request Form, and a member of our team will get back to you, typically within one business day. Request form # Hazard Alerts AlphaGeo’s **Hazard Alerts** monitors your asset portfolio for hazards happening now and forecast over the next seven days, anywhere in the world. It continuously aggregates authoritative public hazard feeds and matches them to each asset, presenting the result as a single map, alert list, and asset-by-hazard summary. Where the [Climate Risk and Resilience Index (CRRI)](/climate-resilience-suite/climate-risk-and-resilience-index) measures a location’s long-term, modelled climate risk, Nowcasting answers the operational question alongside it: ***what is affecting my assets today, and what is coming this week**?* Both use the same nine hazard categories, so the live picture lines up directly with the underlying risk profile. ### Our approach: Live hazard monitoring Traditional climate risk models look decades into the future. They tell you how much risk a location carries over its lifetime — but not whether a storm is bearing down on it this week. Real-time Hazard Nowcasting closes that gap. It draws on established government and scientific hazard feeds, matches each event to the assets it affects, and produces two complementary views: 1. **Active hazards** — events that monitoring networks have already detected and that are affecting an asset now. 2. **Forecasted hazards** — events expected at an asset over the next seven days, shown with how many days ahead the peak is expected. ### Hazard categories Nowcasting tracks the same nine acute and chronic hazard categories as the CRRI: * **Heat Stress**: Dangerously high temperatures that pose health risks to people and assets. * **Inland Flooding**: Overflowing rivers, flash floods, and surface-water inundation, including **river-discharge** surges. * **Coastal Flooding**: Storm surge, tidal flooding, and tsunami inundation affecting coastal areas. * **Wildfire**: Active fire and extreme fire-weather conditions, detected from **satellite hotspots** and fire warnings. * **High Wind**: Damaging winds from windstorms, **tropical cyclones**, tornadoes, and severe thunderstorms. * **Drought**: Prolonged precipitation deficit causing water stress. * **Hail**: Large hail, ice storms, and severe winter weather. * **Earthquake**: Ground shaking from seismic events, and related ground failure. * **Landslide**: Slope failure, debris flow, and mudslide risk. Each alert carries one of four severity labels — **Extreme**, **Severe**, **Moderate**, or **Minor** — to help you triage quickly. ### Data sources As with the rest of the AlphaGeo platform, sources are selected for being authoritative, well-documented, and widely trusted in the field. Nowcasting aggregates the following openly published hazard feeds: | Source | Provider | What it brings | | ----------------------- | ---------------------------------- | ------------------------------------------------- | | **NWS Alerts** | NOAA — US National Weather Service | Official US weather warnings and watches | | **Hurricane Tracks** | NOAA — National Hurricane Center | Tropical cyclone forecast cones | | **Weather Forecast** | Open-Meteo | 7-day heat, wind, and rainfall outlook | | **Flood Forecast** | Open-Meteo / GloFAS | River-flood (discharge) outlook | | **Earthquakes** | USGS | Global earthquake monitoring | | **Multi-hazard Alerts** | GDACS (UN / EU) | Global disaster alerts across hazard types | | **Active Fires** | NASA FIRMS | Satellite-detected wildfire hotspots | | **European Alerts** | Meteoalarm | National meteorological-service warnings (Europe) | | **Public Alerts** | Google Public Alerts | Government public-safety alerts where available | ### What you see The dashboard has three connected panels: * **Risk Nowcasts** — an alert list grouped by hazard, showing the headline, affected assets, source, and severity. Active events are listed ahead of forecast ones. * **Asset Map** — an interactive world map with assets as pins and hazard areas drawn as coloured layers. Pin colour shows status at a glance: **red** for an active hazard, **yellow** for a forecast hazard, and **grey** for no current hazard. * **Asset–Hazard Table** — a grid of assets against the nine hazard categories, showing the most severe alert for each combination. ### Data details * **Coverage:** Global, with added depth in the United States (NWS) and participating European countries (Meteoalarm). * **Active hazards:** Observed within the last 24 hours to 7 days, depending on the source. * **Forecast horizon:** Up to 7 days ahead. * **Refresh:** On demand — each source reflects its provider’s latest published data. The table below shows the time window each source contributes: | Hazard signal | Source | Looks at | | ---------------------- | ------------------- | ------------------------ | | US weather warnings | NWS | Current / active | | Earthquakes | USGS | Last 24 hours | | Active fires | NASA FIRMS | Last 24 hours | | Global disaster alerts | GDACS | Last 7 days | | European warnings | Meteoalarm | Current / active | | Public alerts | Google | Current / active | | Heat, wind & rainfall | Open-Meteo | Next 7 days (forecast) | | River flooding | Open-Meteo / GloFAS | Next 7 days (forecast) | | Hurricane tracks | NOAA NHC | Forecast cone (\~5 days) | ### Use cases * Operational monitoring of asset portfolios for live and emerging hazards * Early warning and preparedness for forecast events in the week ahead * Situational awareness during active disasters across multiple regions * Pairing live conditions with CRRI scores for context-aware decisions ### Limitations We document limitations explicitly so users can interpret the nowcast with appropriate context. * **Forecasts are planning aids, not guarantees.** Confidence decreases with lead time — a hazard flagged for tomorrow is far more reliable than one flagged six or seven days out. Treat distant forecasts as early signals to watch rather than firm predictions. * **Forecasts change between updates.** As new data arrives, an event may grow, shrink, shift, or drop off entirely. The view always reflects the latest available outlook. * **Active alerts are observations.** Hazards on the "now" side reflect what monitoring networks have actually detected, not a prediction. * **Severity labels are indicative.** They flag relative seriousness to support triage; they are not a precise damage estimate. For modelled, asset-specific impact, use the CRRI. * **Coverage depends on source feeds.** Some hazards are strongest in specific regions — NWS alerts cover the United States, and Meteoalarm covers participating European countries — while sources such as USGS, GDACS, NASA FIRMS, and Open-Meteo provide global coverage. # Use Case: Regulatory Disclosures {% columns %} {% column valign="middle" %} Access Free Trial {% endcolumn %} {% column %}

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{% endcolumn %} {% endcolumns %} ## Overview Climate data and reporting tools are becoming essential as disclosure requirements move into the mainstream worldwide. This article explains how AlphaGeo's climate risk, resilience, and financial impact analytics support regulatory compliance and voluntary disclosure across multiple jurisdictions and frameworks. Most physical climate risk disclosure requirements ask the same core questions: Which assets are exposed? How resilient are they? What is the financial impact? AlphaGeo's Climate Resilience Suite is built to answer all three, mapped below to each framework. ## Frameworks at a glance The table below summarizes the key frameworks covered on this page and the AlphaGeo products that address each. Detailed requirement-by-requirement mappings follow.

Framework	Jurisdiction / scope	Core physical risk requirement	AlphaGeo solution
ISSB IFRS S2	Global baseline (adopted across UK, EU, APAC and other markets)	Identify climate risks, assess financial effects, and assess strategic resilience.	Climate Risk and Resilience Index; Financial Impact Analytics
EU Taxonomy	EU	Robust climate risk and vulnerability assessment plus adaptation solutions.	CRRI; Adaptation Layer
EU CSRD / ESRS	EU	Assets and revenue at material physical risk, adaptation actions, and financial effects.	CRRI; Financial Impact Analytics; Location Explorer
EU SFDR	EU financial market participants	Exposure of investments to physical climate hazards (principal adverse impacts).	CRRI; Financial Impact Analytics
California SB 261	US (entities doing business in California)	TCFD-aligned climate-related financial risk report.	CRRI; Financial Impact Analytics
GRESB	Global real estate and infrastructure funds (voluntary benchmark)	Systematic identification and financial-impact assessment of physical climate risk; resilience targets.	CRRI; Financial Impact Analytics

## International Sustainability Standards Board (ISSB) IFRS S2 Climate Disclosures IFRS S2 is the global baseline for climate-related disclosures, developed by the ISSB. It has absorbed the monitoring responsibilities of the TCFD and is being adopted or referenced by regulators across the UK, EU, Asia-Pacific, and other markets.

Relevant Disclosure Requirement (Abbreviated)	AlphaGeo Solution
Identify the climate-related risks and opportunities that could reasonably be expected to affect the entity's prospects.	Our Climate Risk & Resilience Index provides a comprehensive assessment of the risks, exposure and vulnerability of any location under multiple timescales and scenarios, including detailed data on the adaptation capacity of each location to offset the key physical climate risks.
Assess the current and anticipated effects of those climate-related risks and opportunities on the entity's financial position.	Our Financial Impact Analytics calculate the impact of climate risks on cashflow via CapEx costs (e.g. retrofits) and OpEx costs (e.g. insurance and utilities). Our climate-adjusted discount multiplier can be integrated into NPV modelling and exit valuation forecasting.
Assess the climate resilience of the entity's strategy and its business model.	Our CRRI provides a comprehensive assessment of the risks, exposure and vulnerability of any location under multiple timescales and scenarios, including detailed data on the adaptation capacity of each location to offset the key physical climate risks. Our Adaptation Layer data measures the adaptation capacity of any location to climate hazards and suggests remediation measures to reduce vulnerability.
Measure the amount and percentage of assets or business activities vulnerable to climate-related physical risks.	Our Location Explorer platform can cluster locations according to their risk rating categories (high / med / low) as well as by asset value or percentage of a fund or portfolio.
Assess the amount and percentage of assets or business activities aligned with climate-related opportunities.	Our Adaptation Layer data suggests remediation measures to reduce vulnerability. Our Financial Impact Analytics identify key areas for investment to manage CapEx costs (e.g. retrofits and maintenance) and OpEx costs (e.g. insurance and utilities).
Calculate the amount of capital expenditure, financing or investment deployed towards climate-related risks and opportunities.	Our Financial Impact Analytics calculate the impact of climate risks on cashflow via CapEx costs (e.g. retrofits) and OpEx costs (e.g. insurance and utilities). Our climate-adjusted discount multiplier can be integrated into NPV modelling and exit valuation forecasting.
Explain the quantitative and qualitative climate-related targets the entity has set.	Our CRRI scores are standardized globally and our software displays benchmarks for the global, national, provincial and city level, allowing for clear target setting.

## Task Force on Climate-Related Financial Disclosures (TCFD) {% hint style="info" %} The TCFD was disbanded in 2023 and its disclosure-monitoring responsibilities transferred to the ISSB. The TCFD recommendations remain widely referenced — including by California SB 261 and the GRESB Resilience Module — so they are retained here for reference. For new reporting, the [ISSB IFRS S2](#international-sustainability-standards-board-issb-ifrs-s2-climate-disclosures) framework above is the current standard. {% endhint %}

Relevant Disclosure Requirements (Abbreviated)	AlphaGeo Solution
Describe climate-related risks and opportunities the organization has identified over the short, medium, and long-term.	Our Climate Risk & Resilience Index provides risk scores and adaptation opportunities under multiple scenarios and time periods from the present to 2100.
Describe the impact of climate-related risks and opportunities on the organization's businesses, strategy and financial planning.	Our Financial Impact Analytics calculate the impact of climate risks on cashflow via CapEx costs (e.g. retrofits) and OpEx costs (e.g. insurance and utilities). Our climate-adjusted discount multiplier can be integrated into NPV modelling and exit valuation forecasting.
Describe the resilience of the organization's strategy, taking into consideration different climate-related scenarios, including a 2C or lower scenario.	Our Resilience-Adjusted Risk Scores and Adaptation Layer data measure any location's resilience to physical climate risk under multiple scenarios and provide suggested remediation measures to enhance organizational resilience.
Describe the targets used by the organization to manage climate-related risks and opportunities and performance against targets.	Our CRRI scores are standardized globally and our software displays benchmarks for the global, national, provincial and city level, allowing for clear target setting.

## EU Taxonomy Regulation

Relevant Disclosure Requirement (Abbreviated)	AlphaGeo Solution
The climate projections and assessment of impacts are based on best practice and available guidance … in line with the most recent IPCC reports, scientific peer-reviewed publications and open source or paying models.	AlphaGeo models are developed based on IPCC as well as scientific, peer-reviewed research as detailed in our public technical documentation.
The physical climate risks that are material to the activity have been identified by performing a robust climate risk and vulnerability assessment.	Our Climate Risk & Resilience Index provides a comprehensive assessment of the risks, exposure and vulnerability of any location under multiple timescales and scenarios, including detailed data on the adaptation capacity of each location to offset the key physical climate risks.
The economic activity has implemented adaptation solutions that substantially reduce the most important physical climate risks that are material to that activity.	Our Adaptation Layer data measures the adaptation capacity of any location to climate hazards and suggests remediation measures to reduce vulnerability.

## EU Corporate Sustainability Reporting Directive (CSRD) European Sustainability Reporting Standards (ESRS)

Relevant Disclosure Requirement (Abbreviated)	AlphaGeo Solution
Report assets at material / acute / chronic risk before considering climate change adaptation actions, as well as percentage of assets at risk. Disclose locations of significant assets at material physical risk (disaggregated by NUTS codes). Report revenue from business activities at material physical risk, as well as percentage.	Our Climate Risk & Resilience Index provides a comprehensive assessment of the risks, exposure and vulnerability of any location under multiple timescales and scenarios, including detailed data on the adaptation capacity of each location to offset the key physical climate risks. Our Financial Impact Analytics calculate the impact of climate risks on cashflow via CapEx costs (e.g. retrofits) and OpEx costs (e.g. insurance and utilities). Our climate-adjusted discount multiplier can be integrated into NPV modelling and exit valuation forecasting.
Disclose percentage of assets at material physical risk addressed by climate change adaptation actions.	Our CRRI provides a comprehensive assessment of the risks, exposure and vulnerability of any location under multiple timescales and scenarios, including detailed data on the adaptation capacity of each location to offset the key physical climate risks. Our Adaptation Layer data, part of CRRI, suggests remediation measures to reduce vulnerability.
Disclose whether and how anticipated financial effects for assets and business activities at material physical risk have been assessed. Disclose magnitude of anticipated financial effects in terms of margin erosion for business activities at material physical risk.	Our Financial Impact Analytics calculate the impact of climate risks on cashflow via CapEx costs (e.g. retrofits) and OpEx costs (e.g. insurance and utilities). Our climate-adjusted discount multiplier can be integrated into NPV modelling and exit valuation forecasting.
Conduct at least one high-emission scenario to be used in identification of business hazards.	We offer three IPCC aligned scenarios, including the high-emission scenario (SSP5-8.5 / RCP 8.5).

## EU Sustainable Finance Disclosure Regulation (SFDR) SFDR requires financial market participants — including real estate and infrastructure fund managers — to disclose the principal adverse impacts (PAI) of their investments on sustainability factors. This includes exposure to physical climate hazards at the asset and portfolio level.

Relevant Disclosure Requirement (Abbreviated)	AlphaGeo Solution
Assess and disclose the exposure of investments to physical climate hazards (acute and chronic) as part of principal adverse impact reporting.	Our Climate Risk & Resilience Index (CRRI) provides a comprehensive assessment of the risks, exposure and vulnerability of any location under multiple timescales and scenarios, covering acute and chronic physical climate hazards.
Identify the share of investments in assets exposed to material physical climate risk.	Our Portfolio Analytics tool can cluster locations according to their risk rating categories (high / med / low) as well as by asset value or percentage of a fund or portfolio.
Describe actions taken to address and reduce the adverse impacts identified.	Our Adaptation Layer data measures the adaptation capacity of any location to climate hazards and suggests remediation measures to reduce vulnerability. Our Financial Impact Analytics identify key areas for investment to manage CapEx and OpEx costs associated with climate risk.

Relevant Disclosure Requirement (Abbreviated)

AlphaGeo Solution

Assess and disclose the exposure of investments to physical climate hazards (acute and chronic) as part of principal adverse impact reporting.

Our Climate Risk & Resilience Index (CRRI) provides a comprehensive assessment of the risks, exposure and vulnerability of any location under multiple timescales and scenarios, covering acute and chronic physical climate hazards.

Identify the share of investments in assets exposed to material physical climate risk.

Our Portfolio Analytics tool can cluster locations according to their risk rating categories (high / med / low) as well as by asset value or percentage of a fund or portfolio.

Describe actions taken to address and reduce the adverse impacts identified.

Our Adaptation Layer data measures the adaptation capacity of any location to climate hazards and suggests remediation measures to reduce vulnerability.

Our Financial Impact Analytics identify key areas for investment to manage CapEx and OpEx costs associated with climate risk.

## California SB 261 (Climate-Related Financial Risk Act) California's SB 261 requires entities doing business in California with annual revenues above 500 million USD to publish a biennial climate-related financial risk report aligned with the TCFD framework or an equivalent standard such as IFRS S2.

Relevant Disclosure Requirement (Abbreviated)	AlphaGeo Solution
Disclose climate-related physical risks to the entity, consistent with the TCFD framework or an equivalent standard.	Our Climate Risk & Resilience Index provides risk scores and adaptation opportunities for any location under multiple scenarios and time periods, consistent with TCFD and IFRS S2 expectations.
Describe the measures the entity has adopted to reduce and adapt to the climate-related financial risk disclosed.	Our Resilience-Adjusted Risk Scores and Adaptation Layer data measure resilience to physical climate risk and provide suggested remediation measures to reduce vulnerability.
Disclose the anticipated financial impact of material physical climate risks on the entity.	Our Financial Impact Analytics calculate the impact of climate risks on cashflow via CapEx costs (e.g. retrofits) and OpEx costs (e.g. insurance and utilities). Our climate-adjusted discount multiplier can be integrated into NPV modelling and exit valuation forecasting.

## GRESB Assessment The GRESB Assessment is the global standard for ESG benchmarking and reporting for listed property companies, private property funds, developers, and investors that invest directly in real estate. It aligns with international frameworks including TCFD, GRI, and PRI, and a parallel assessment exists for infrastructure. Several scored indicators address the systematic identification, assessment, and management of physical climate risk and resilience — areas where location-level data is directly required.

Relevant Indicator Area (Abbreviated)	AlphaGeo Solution
Demonstrate a systematic process for identifying physical climate risks (acute and chronic) that could have a material financial impact, at the asset and entity level.	Our Climate Risk & Resilience Index provides a systematic, location-level assessment of acute and chronic physical climate risks for any asset or portfolio, under multiple timescales and scenarios.
Demonstrate a systematic process for assessing the material financial impact of physical climate risks on the business and financial planning of the entity.	Our Financial Impact Analytics quantify the financial impact of physical climate risks via CapEx costs (e.g. retrofits) and OpEx costs (e.g. insurance and utilities), including effects on insurability and operating costs in high-risk locations.
Disclose the outcomes of asset-level risk assessments and how they are integrated into overall risk management.	Our Portfolio Analytics module can cluster assets by risk rating category (high / med / low) and by asset value or share of a fund or portfolio, producing entity-level outputs that support evidence requirements.
Set and report climate risk and resilience-related targets and goals, including reducing vulnerability to physical climate risk (broadly aligned with TCFD Metrics and Targets).	Our Climate Risk and Resilience Index scores are standardized globally with benchmarks at the global, national, provincial and city level, supporting clear target setting. Our Resilience-Adjusted Risk Scores and Adaptation Layer data track vulnerability reduction over time and suggest remediation measures.

# Use Case: Climate-adjusted Valuation How analysts can use Financial Impact Analytics to forecast climate-adjusted cashflows ## Use Case: AlphaGeo for Climate-adjusted Valuation ### Overview At AlphaGeo, our Financial Impact Analytics suite provides 9 bottom-up metrics covering insurance, OpEx, income, and CapEx impacts. All of these metrics can be easily incorporated into standard financial frameworks, including Discounted Cash Flow (DCF) models, to determine a climate-adjusted valuation. To illustrate how this works in practice, the simplified use case below demonstrates applying a **subset** of these features to a DCF model: 1. Annual rate of Insurance Premiums increase 2. Annual rate of Utility Demand increase 3. Additional Retrofit Costs for thermal comfort/hazard reinforcement retrofits 4. Additional Exit Cap Rate (Climate Risk Discount) based on future shifts in climate hazards While a complete analysis on our platform also factors in Maintenance Costs, Operational Efficiency, Operational Downtime, Workforce Productivity, and Insurability Risk, we have omitted them here for simplicity. By applying these forecasted changes to a DCF model, analysts can forecast the climate-adjusted NPV of an asset or portfolio. We then calculate the Financial Impact Analytics as the percentage difference between the 'Climate-adjusted NPV' and the 'Baseline NPV'. ### Financial Impact Analytics in DCF Modelling We illustrate with two simplified DCF models below. #### Step 1: Build Baseline Scenario

In the baseline scenario, we model the NPV of the asset based on the assumptions illustrated in the table above. The cash flow assumptions do not take climate change induced hazards and retrofits into consideration. The assumed Discount Rate is 8% and the Exit Cap Rate, which influences the Terminal Value, is assumed at 5%. The Terminal Value is calculated as Next Year NOI/Exit Cap Rate. #### Step 2: Develop Climate-adjusted Scenario Using AlphaGeo's Financial Impact Analytics

In this climate-adjusted scenario, due to climate change, insurance premiums and utility costs are expected to increase at a rate higher than gross income. For this asset, flood insurance is expected to have a YoY increase of **3%**, fire insurance an increase of **2%**, and utilities an increase of **2%.** This increase is applied yearly to the baseline value as **"Baseline value \* (1 + %increase)".** In addition, climate change might also induce necessary CapEx, mostly in the form of thermal retrofits and on-site reinforcements against acute risks (such as flooding). In the baseline scenario, we have set a side **4%** of gross income as CapEx. Based on the climate risk profile of this location, we recommend adding another **2%** to CapEx to account for the additional retrofits, giving the new CapEx as 6% of gross income. Lastly, the long-term effect of climate change on a location should be accounted for during the exit transaction. This can be factored in using the Climate Risk Discount given by the model. In this location, the Climate Risk Discount is **0.25%** as calculated based on the [methodology](broken://pages/Et2rzTY0xvozPbFkaqSy#climate-risk-discount) in the previous page. We add the **0.25%** Climate Risk Discount to the existing exit cap rate of 5% giving a climate-adjusted exit cap rate of **5.25%.** This adjusted exit cap rate accounts for future climate related hazards, downtimes, and CapEx beyond the holding period. The final NPV in the figure factors in the different financial metrics and yields an **effective Climate-adjusted impact on NPV of -6.51%** compared to the baseline scenario. In our platform, this final figure (calculated over a standard 10-year period with all available metrics) is exactly what is presented as the **10-Year NPV Loss (CVaR)** metric. If we average this value over the holding period, we arrive at the **Average Annual Loss (AAL)**. This demonstrates how the bottom-up climate adjustments feed directly into the top-level valuation metrics. # FAQ: Climate Resilience Suite {% tabs %} {% tab title="Climate Risk and Resilience Index (CRRI)" %} **Overall** **Are there plans to develop risk analytics for other climate hazards other than the current hazards?**\ Yes, AlphaGeo plans to enhance the Climate Risk and Resilience Index (CRRI) by incorporating additional hazard types such as hail, landslides, cold storms, etc. and even non-climate hazards such as earthquakes. *** **Data sources** **What are your primary data sources?**\ Our CRRI integrates data from 85 unique sources, encompassing satellite observations, climate model projections, historical weather records, and socio-economic datasets. Notable sources include CMIP6 climate projections, Aqueduct 4.0, Copernicus, IPCC reports, NOAA's IBTrACS, OpenStreetMap, WorldPop, and the World Bank. **Why do you choose to use open-source data?**\ Utilizing open-source data ensures transparency, reproducibility, and broad accessibility. It allows AlphaGeo to build upon peer-reviewed and well-documented datasets, fostering trust and enabling users to understand and verify the underlying data and methodologies. **What is the value you provide, if underlying data is open-source?**\ AlphaGeo adds value through rigorous data curation, advanced geospatial data processing and engineering, and the calculation of resilience-adjusted risk scores for actionable insights that go beyond raw data. Learn more about how we transform raw adaptation data into standardized, actionable insight here: [Global Adaptation Layer](/global-adaptation-layer/methodology-2-3-global-adaptation-layer) **What is the global coverage of your data?**\ AlphaGeo's datasets offer global coverage, enabling assessments of climate risk and resilience for any location worldwide. **What is the data resolution?**\ The data resolution reaches up to 30 meters. For a detailed breakdown on data source resolution, please request a copy of our [Data Dictionary](/for-developers/data-dictionary). **What GCMs are used for future scenarios? How are they downscaled?** We use an ensemble of GCMs such as CanESM5, MRI-ESM1 and downscale them according to the methods described by[ (carbon)plan.](https://carbonplan.org/research/cmip6-downscaling) **The native resolution of some data sources are far coarser than the building/parcel level resolution of your analytics. How have you downscaled this data?** We employ a combination of machine learning algorithms and spatial interpolation techniques. These methods enable us to estimate finer resolution data by identifying patterns and distributing data values appropriately across smaller units. Additionally, we validate the downscaled data by comparing it with high-resolution reference datasets to ensure accuracy and reliability. **Is the resolution of your features uniform across all global locations? Specifically, how granular is your coverage for developing countries or rural zones? Does this differ depending on risk versus adaptation features?** All features share the same resolution globally. **How frequent are data updates?**\ Quarterly. **Do you have historical or current risk data?** Yes, we have historic, current and future climate data from 1975-2100. **Does your data incorporate historical weather events, i.e., incidents? Does it include the severity of the weather event and how is that measured?** We have access to publicly available historical data on events such as hurricanes, wildfires, and major floods around the world. However, we do not incorporate these events into our scoring or forecasts due to the limited number of observed occurrences. Instead, we rely on simulated events, such as hurricane tracks and flood inundation levels, to provide a more comprehensive assessment of risk across all areas, rather than focusing solely on locations with a history of hazardous events. Historical events are utilized for accuracy benchmarking rather than directly influencing projections. **How is AlphaGeo's data different from what FEMA provides?** We have more granular data, and cover multiple SSP scenarios and time periods: 1. AlphaGeo's data is more granular. Our data is provided at the building/parcel-level, compared to FEMA's neighbourhood or block-level data. 2. AlphaGeo covers 3 emissions scenarios (SSP245, SSP370, SSP585) and 4 time periods (2025, 2035, 2050, 2100). FEMA focuses on current and historical flood risk based on observed data and past events. They do not project future flood risk under climate scenarios, and are less suitable for scenario analysis or forecasting. **How do flood models account for precipitation?** Our scores are based on rainfall intensity and flood inundation levels. For example, compared to Singapore, Bangkok receives approximately half the amount of precipitation and has fewer days with extreme precipitation, leading to lower levels of inundation due to rainfall. *** **Methodology** **What framework or methodology do you use to select and group risk features for each hazard?** Features are grouped based on their correlation to key components of climate risk: * **Exposure**: The degree to which a location is exposed to climate-related hazards. * **Frequency**: How often a location might experience these hazards. * **Intensity**: The severity of the impacts when these hazards occur. **What is your hazard forecasting methodology?** Drawing from risk engineering practices for each risk category, we assign a damage function that maps the intensity (I) of the exposure to the Mean Damage Ratio (MDR). The resulting MDR calculated from the intensity (I) represents the estimated damage on an exposed asset or a location given a certain climate hazard. As intensity of the hazard changes over time under different climate change scenarios for the target location, the MDR of a location change over time as well. **How do you define, or where do you obtain, the damage functions for each hazard, and its change over time/scenarios?** We define the range, threshold, and type of damage functions for each type of hazard through existing scientific literature and domain experience. **Is the change in MDR modelled at the hazard (e.g., coastal flooding) or feature level (e.g., mean sea level rise)?** Feature level. Each feature has a corresponding damage function. **How are the resilience-adjusted risk scores calculated?**\ We apply an adaptation offset coefficient to the intensity of each hazard, based on data on the local adaptation measures in place. **How do you determine the degree of adaptation offsets? What thresholds are used, and is this based on any established method?** The adaptation offset is based on an aggregated Resilience Score for each location. Each hazard has its own corresponding resilience sub-index, which is comprised of its underlying adaptation features. For example, the aggregate Resilience Score for Hurricane Resilience is computed from the sum of **Building Strength, Flood Defenses,** and all features under **General Societal Resilience**. This produces a "resilience aggregate" of each adaptation measure. The Resilience-adjusted Risk score is derived by subtracting this aggregated "Resilience Score" from the Physical Climate Risk score. This adjustment is weighted, with 70% assigned to the physical risk and 30% to the resilience measures (15% for local adaptation measures, 15% for societal resilience), ensuring that both aspects are appropriately considered in the final assessment. **Are your methods peer-reviewed? How can we trust your methodology?** AlphaGeo's analytics are built upon the evolving peer-reviewed data sources and literature. With the open access nature of our methodology and documentation, we invite the scrutiny and feedback from all experts in the field to continuously enhance our methods. **What are the biases and limitations of your methodology?** 1. **Data Limitations**: Our methodology relies on the quality and quantity of available data. Insufficient or biased data can impact the results. 2. **Assumptions**: The analysis is based on certain assumptions that may not hold true in every scenario. 3. **Model Bias**: The algorithms used may have inherent biases, affecting the objectivity of predictions or outputs. 4. **Static Framework**: The methodology may not account for immediate changes in the environment or context. Understanding these limitations helps in interpreting results more accurately and applying improvements to future iterations. **Do you provide confidence intervals?** To facilitate straightforward comparative analysis across assets and portfolios, we present the midpoint of our projected risk range. Values for the 95% confidence intervals are available upon request. *** **Indexing and scoring** **Key links:** [Indexing & Score Interpretation](/climate-resilience-suite/climate-risk-and-resilience-index/methodology-3-3-indexing) **What do your scores represent, and how are they calculated?** Our hazard-specific risk scores (e.g., Hurricane Risk) are absolute risk scores, while the overall scores (e.g., Overall Physical Climate Risk) are relative scores. * **Hazard risk scores**: **Absolute risk profiles for any o**ne type of risk for any given scenario and time period, on a scale of 0-100. Each score is assigned based on thresholds determined for each risk feature. The risk category thresholds are based on the Mean Damage Ratio (MDR) for the respective features. * **Overall scores**: Global percentile scores that measure overall risk profile of a location relative to other locations, allowing users to compare across space. Details are available at our scorecard here: [Indexing & Score Interpretation](/climate-resilience-suite/climate-risk-and-resilience-index/methodology-3-3-indexing) **Do the overall risk scores change if a different benchmark is selected? Or is it always relative to a global distribution?** No, the overall risk score are percentile scores on a global distribution. The benchmark helps the user to compare the performance of the selected location to a narrower geographic region (i.e. country or city). **What are the implications in your CRRI scorecard based on?**\ The implications are based on the expected damages caused by the hazard for each risk category. This is based on industry standards and the thresholds in the damage functions **How are features benchmarked?** All features are benchmarked globally, i.e., the indicators are scored based on datasets that are globally available. For example, FEMA Flood Zone data is not used in scoring Singapore or the US. We continue to include these datasets, however, for users who wish to pull these engineered risk features to create their own models. **How are "regional averages" calculated?** We calculate the regional average by aggregating data from the sub-provincial level. In most countries, this means the data is aggregated at the city/county/municipality level. In the case of Singapore, for example, since it's a city, the sub-provincial level will be the five regions. When you access data for a property in Singapore, regional-level data aggregates data in those regions. If the access point is Asia Square, then the sub-provincial level is the average of the Central Region of Singapore. *** **Scenarios** **Which scenarios do you support, and why?** We support three emissions scenarios: SSP2-4.5, SSP3-7.0, SSP5-8.5. These are the CMIP6/IPCC AR6 scenarios. *** **Regulatory alignment** **Key links:** [Broken mention](broken://pages/Pcxg2tWcAMs7QxpPmDEz) **Are your hazards aligned to the EU taxonomy?**\ Yes, AlphaGeo's assessments align with the EU Taxonomy Regulation by identifying material physical climate risks and evaluating adaptation measures that substantially reduce these risks. The methodology incorporates best practices and guidance from the IPCC and other scientific bodies. **How does your data help with regulatory disclosures?**\ AlphaGeo's Climate Risk and Resilience Index supports compliance with various regulatory frameworks, including the Task Force on Climate-Related Financial Disclosures (TCFD), International Sustainability Standards Board (ISSB) IFRS S2, EU Taxonomy Regulation, and the EU Corporate Sustainability Reporting Directive (CSRD). The platform provides standardized risk scores, adaptation assessments, and financial impact metrics, facilitating comprehensive and transparent climate-related disclosures. *** If you have further questions, please feel free to reach out to us at ****. {% endtab %} {% tab title="Financial Impact Analytics" %} **Methodology** **Key links:** [Broken mention](broken://pages/Et2rzTY0xvozPbFkaqSy) **How is physical risk translated into financial impact?** Each financial impact metric is developed with its own unique methodology. In general, we define a change in the risk profile of a location, and apply an impact function to that delta in risk. These damage functions are derived from empirical, academic research. Only relevant risks (e.g., fire and flooding for insurance cost impact) are modelled in related to each metric. {% endtab %} {% endtabs %} # Limitations, Assumptions, and Data Transparency At AlphaGeo, we aim to provide robust, transparent, and financially relevant insights into climate risk. While our methodology is grounded in the best available science, financial modeling, and engineering evidence, it is important for users to understand the inherent **limitations, assumptions, and uncertainties** in our approach. This ensures results are interpreted with appropriate context and caution. *** ### 1. Climate data and projections * **Historical data gaps:** Observational datasets vary in coverage and resolution across regions. In some areas, climate baselines are interpolated from limited data points. * **Uncertainty in projections:** Our models use IPCC-endorsed scenarios (SSP245, SSP370, SSP585) to explore a range of plausible climate futures. These do not represent precise predictions but rather structured scenarios. * **Downscaling methods:** We translate global or continental-scale climate model outputs into local impacts using statistical downscaling and proxy measures (e.g., degree days, damage ratios). These methods balance accuracy and practicality but inevitably smooth over local variability. *** ### 2. Model scope and use * **Asset classes:** Our methodology covers a broad set of asset classes (residential, commercial, data centers, infrastructure). It does not account for unique bespoke designs or adaptive management strategies at individual sites. * **Temporal scope:** Most projections are benchmarked against 2025–2050 horizons. Beyond this, uncertainty compounds rapidly, and results should be viewed as indicative rather than predictive. * **Systemic interactions:** Our model assesses direct asset-level impacts. Wider economic feedback loops (e.g., supply chain disruptions, policy shifts, insurance retreat at systemic scale) are not explicitly modeled. *** ### 3. Methodological assumptions * **Two-Step framework:** Our results combine (1) location-based climate exposure metrics with (2) asset-specific sensitivity adjustments. This simplifies complexity but may underrepresent unique micro-level asset conditions. * **Sensitivity coefficients:** Asset class vulnerabilities are represented by multipliers on a 1–5 scale. This approach ensures transparency and scalability but does not capture every engineering nuance. * **Aggregation choice:** Metrics (e.g., Maintenance Cost Increase) are calculated as blended scores before sensitivity adjustment, rather than adjusting each underlying climate driver separately. This avoids false precision but may mask cases where opposing sensitivities exist. *** ### 4. Financial modeling assumptions * **Insurance premiums:** Future premiums are projected using long-term mean damage ratios with a fixed average loss ratio (70%) to avoid short-term volatility. Real-world insurance markets may deviate significantly from this assumption. * **Discount rates:** Climate discount rates are capped (e.g., 0.75% to 2.0% depending on time horizon) to reflect tail risks. These assumptions are consistent with current financial practice but may shift as markets evolve. * **CapEx for retrofits:** Additional capital expenditures are estimated on an exponential scale (up to 10% of annual income per hazard). Actual retrofit costs may vary widely depending on design choices, regulatory changes, and supply chain dynamics. * **Operational impacts:** Metrics such as downtime or workforce productivity loss are simplified as percentage changes in annual outputs. Real-world impacts may be non-linear and event-specific. *** ### 5. Uncertainty beyond the model * **Extreme events:** While acute hazard metrics are included, unprecedented “black swan” events cannot be reliably quantified. * **Policy & regulation:** Future changes in building codes, carbon pricing, or adaptation subsidies may materially alter cost structures. * **Market dynamics:** Insurance, financing, and valuation markets are dynamic. Our assumptions represent today’s best estimates but may diverge from future market behavior. *** ### Ethical and transparency commitment AlphaGeo subscribes to the principle of **“decision-useful transparency”**: providing clear, interpretable, and bounded insights rather than overstated precision. We recommend using our analytics as one input among others — complemented by local expertise, engineering assessments, and market intelligence — for robust decision-making. # Why AlphaGeo for Climate Resilience ## Overview AlphaGeo is the **ultimate adaptation and resilience workflow for asset management** — from risk analytics to portfolio construction. Most platforms just project your exposure to climate risk – but they neglect what's been done about it already and don't tell you what to do next. AlphaGeo closes that gap – and much more. We are built for teams who need to move from reporting to action. Our datasets span economic, demographic, climate and many other themes, guiding both "defensive" monitoring and reporting as well as forward-looking "offensive" investment strategy. ## Our strengths

Proprietary resilience-adjusted risk methodology

The [Climate Risk and Resilience Index](/climate-resilience-suite/climate-risk-and-resilience-index) models both unmitigated and mitigated risk, so you see true exposure rather than a raw hazard score — globally and at hyper-local resolution. We achieve this through the market’s most comprehensive quantification of adaptation and resilience across three layers: * location-level adaptation capacity * societal resilience * asset-level remediation This approach refines risk estimates and identifies where adaptation investments can deliver the greatest impact. **Why this matters:** The result is a more realistic, resilience-adjusted view of current risk, helping investors avoid misinformed decisions such as premature divestments or misallocated adaptation capital. A raw hazard score can overstate risk where meaningful resilience measures are already in place, while understating risk where adaptive capacity and defenses remain weak. By accounting for existing resilience, AlphaGeo provides investors with a **more accurate baseline** for screening opportunities, pricing risk, prioritizing adaptation investments, and making portfolio-level decisions.

Triple-layer adaptation workflow for proactive asset management

AlphaGeo doesn't stop at identifying risk — it also supports adaptation planning. Once risk has been assessed, our [Asset Remediation Checklist](/alphageo-platform/portfolio-analytics/remediation-checklist) helps asset owners and investors identify adaptation gaps, evaluate remediation options, and quantify both their costs and expected benefits. This enables adaptation measures to be prioritized and incorporated into capital planning processes with greater confidence. **Why this matters:** This helps teams determine what to fix, what to fund, and where adaptation investments can generate the greatest risk reduction and value creation. By linking adaptation actions to measurable financial and resilience outcomes, it supports more effective capital allocation and adaptation planning.

Comprehensive financial impact metrics purpose-built for real assets

Industry-standard metrics such as Climate Value-at-Risk (CVaR) — which we also provide — can be difficult to integrate into real asset cash flow and valuation models. Their underlying assumptions are also often complex, opaque, and challenging to trace or defend. Our [Financial Impact Analytics](/climate-resilience-suite/financial-impact-analytics) addresses this challenge by translating climate risk into decision-ready financial outputs such as insurance premiums, OpEx and CapEx costs. The results can be readily integrated into real estate and infrastructure models while remaining easy to explain, validate, and defend. These analytics are further underpinned by bespoke methodologies calibrated to specific real estate and infrastructure asset classes: * **Real estate**: Residential, Commercial * **Infrastructure**: Power Plants, Electricity T\&D, Water & Wastewater, Transport (Road & Rail), Airports, Seaports, Data Centers **Why this matters:** Real estate and infrastructure investors need to understand how climate risk affects asset values, operating performance, and capital requirements specific to their sector and asset type. Financial impacts must be quantified in a transparent and defensible manner that can be integrated into investment workflows and withstand investment committee scrutiny.

Transparent and defensible methodology

AlphaGeo prioritizes explainability over unnecessary complexity. Our financial impact metrics are transparent, traceable, and designed to be easily understood by both technical and non-technical stakeholders. All methodologies, assumptions, and data sources are clearly documented and shared with clients, providing a clear audit trail from underlying data to final outputs. **Why this matters:** Transparent and traceable analytics make it easier for sustainability teams to communicate results to internal committees, regulators, and external stakeholders. At the same time, investment teams need outputs they can clearly explain, validate, and defend when making capital allocation, risk management, and portfolio decisions.

Forward-looking market research and site selection

AlphaGeo's approach to location resilience is holistic, and goes beyond climate. Our complete location intelligence suite - including the [Dynamism Signals](broken://pages/de0YtYA7X9qaRFDxEDFn) and [Alpha Finder](https://alphageo.ai/alpha-finder/) - provides novel insights to guide real estate and infrastructure investors with forward-looking site selection and market research. **Why this matters:** Real estate and infrastructure returns depend on more than climate resilience. Investors need a forward-looking view of whether or not a location's economic momentum is robust — and whether demand is strengthening or weakening. With our comprehensive data lake covering the multivariate factors affecting location performance, AlphaGeo helps teams identify truly future-proof markets and assets with conviction.

## Who we're for AlphaGeo is a strong fit for: * **Real estate and infrastructure** owners, operators, investors, and portfolio managers seeking **asset-level and portfolio-wide** climate intelligence. * Organizations that want **existing resilience and adaptation measures reflected in risk assessments**, rather than relying solely on raw hazard exposure. * Investment teams that need to translate **climate risk into tangible financial impacts**, including insurance premiums, operating expenditure (OpEx), capital expenditure (CapEx), revenue and income effects, asset valuation impacts, and net present value (NPV) implications. * Portfolio managers requiring **portfolio screening**, **aggregation**, **monitoring**, **alerts**, and **seamless collaboration** with internal teams and external stakeholders, such as portfolio companies, consultants, and advisors, within a single platform. * Analysts and decision-makers who **value transparency, traceability, and defensibility**, and who prefer **clear, explainable outputs** over black-box methodologies. * Teams looking for a solution that supports **both climate risk reporting and practical adaptation planning**, helping move from assessment to action. * Investors seeking **novel and proprietary datasets** to gain first-mover advantage in prime markets and underpin fund formation and capital allocation. ## Feature set

Feature	AlphaGeo
Resilience-adjusted risk scoring	✅
Asset-level adaptation workflow	✅
Top-down financial impact modelling (e.g., CVaR, AAL, NPV Loss)	✅
Granular financial impact metrics for real assets	✅
Bespoke methodologies across real estate and infrastructure	✅
Real-time and near-term hazard alerts	✅
Purpose-built for real-asset investors	✅
Corporate issuer-level risk and EBITDA-at-risk assessment (via the Corporate Monitor product)	✅

## Why this matters With AlphaGeo, you can: * Distinguish between physical hazard exposure and real-world, resilience-adjusted risk, providing a more accurate view of current and future vulnerability. * Identify, prioritize, and justify adaptation investments across individual assets and portfolios based on their potential risk-reduction and value-creation impact. * Connect climate risk directly to asset valuation, operating performance, and capital planning through decision-ready financial metrics tailored to real estate and infrastructure assets. * Support both strategic decision-making and day-to-day risk management, combining portfolio-level analysis with ongoing monitoring, alerts, and stakeholder collaboration within a single platform. In summary, AlphaGeo helps organizations move beyond hazard mapping and climate reporting toward resilient investing, asset management, and adaptation strategy. # Global Adaptation Layer Proprietary suite of hyper-local, multi-hazard global climate adaptation datasets {% columns %} {% column valign="middle" %} Access Free Trial {% endcolumn %} {% column %}

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{% endcolumn %} {% endcolumns %} ## Overview AlphaGeo’s **Global Adaptation Layer** is a **global,multi-hazard dataset that identifies and quantifies hazard-specific adaptation infrastructure.** It captures the local features that shape real-world resilience, from drainage systems and flood barriers to urban greenery, water infrastructure, and fire response coverage. This gives users a clearer view of **defended conditions on the ground,** not just hazard exposure. The Global Adaptation Layer supports **resilience-adjusted risk analysis, site screening, adaptation planning, and downstream climate impact models**. ## Our approach: Measuring adaptation capacity Most climate datasets measure hazard. They do not measure whether a location is protected. The Global Adaptation Layer addresses this gap by translating raw geospatial data into hazard-specific adaptation signals. It identifies, then quantifies both (1) proximity to; and (2) adequacy of the infrastructure, land cover, and built-environment features that reduce physical vulnerability or improve local resilience. This allows users to distinguish between places that are equally exposed but differently defended. It also provides a core input into AlphaGeo’s resilience-adjusted modelling stack, including the [Climate Risk and Resilience Index](/climate-resilience-suite/climate-risk-and-resilience-index) and [Climate GDP Impact Forecast](/macro-suite/clima-metrics).

An overview of how the Global Adaptation Layer is developed.

Raw spatial features (e.g., filtration systems, wastewater treatment plants, and water storage solutions) are categorized into its relevant thematic feature groups (e.g., "Availability and Capacity of Water Works") that correspond to specific hazards.These adaptation feature groups are then aggregated into hazard-specific Adaptation Scores. The maps below show examples of different adaptation feature groups for drought, coastal flooding, inland flooding, and wildfire in Santa Cruz, California, USA.

## What's included The Global Adaptation Layer includes: * **Hazard-specific adaptation scores** for major acute and chronic hazards * **Underlying feature layers** that explain the drivers of adaptation capacity * **Global coverage** at high spatial resolution for asset- and neighborhood-level analysis (up to 30m) These outputs are designed for direct use in resilience-adjusted (i.e.,defended) climate risk modelling, resilience screening, as well as adaptation gap analysis and decision-making.

Example of our Inland Flooding Adaptation score and underlying adaptation data features for a location

## Hazard coverage The Global Adaptation Layer currently includes adaptation data for 9 hazard categories, plus societal resilience: * **Heat Stress Adaptation** — building density, urban greenery, and surface context * **Inland Flooding Adaptation** — porosity, drainage systems, barriers, and storage infrastructure * **Coastal Flooding Adaptation** — coastal defences, natural buffers, and drainage capacity * **Hurricane Wind Adaptation** — building strength and nearby protective infrastructure * **Drought Adaptation** — water treatment, storage, amenities, and access * **Wildfire Adaptation** — fire response, fire prevention, and fire detection features * **Hail Adaptation** — building strength and local protection proxies * **Landslide Adaptation** — vegetation cover and manmade slope barriers * **Earthquake Adaptation** — building strength and building sparsity ## Use cases * Resilience-adjusted climate risk and impact modelling * Adaptation planning and resilience investment * Adaptation gap and vulnerability analysis * Resilience-adjusted insurance underwriting ## Data details * **Geographic coverage:** Global * **Resolution:** Up to 30 meters * **Output types:** Adaptation scores and underlying feature layers * **Hazard coverage:** 9 hazard categories plus societal resilience ## Detailed Methodology Please fill out our Methodology Request Form, and a member of our team will get back to you, typically within one business day. Request form # Use Case: Thematic Investing in Adaptation How geospatial data can drive investments in climate adaptation {% columns %} {% column valign="middle" %} Access Free Trial {% endcolumn %} {% column %}

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{% endcolumn %} {% endcolumns %} ## **Overview** A United Nations study projects that the market for private investments in climate adaptation will reach US$1.3 a year by 2030. To seize this opportunity, investors must navigate complex, localized climate risks and adaptation needs. Geospatial climate risk and adaptation data provides a powerful lens to identify high-potential markets, quantify demand, and uncover expansion whitespace. This article explores how private equity investors can leverage AlphaGeo’s geospatial data to drive strategic investment decisions in climate adaptation. ### **The role of geospatial data in climate adaptation** Climate adaptation is inherently location-specific, making geospatial data critical compared to other investment sectors. To assess market demand and opportunities, investors need geospatial data to: 1. **Map climate risks**: Risks are a prime driver of adaptation demand. Using datasets on hazards like flooding, heatwaves, or droughts is key when identifying areas with high demand for adaptation. 2. **Assess adaptation capacity**: If risks proxy demand, then existing adaptation capacity (e.g., presence flood barriers) reveals supply gaps. 3. **Overlay local market intelligence**: Combining climate data with relevant market or socioeconomic factors helps prioritize those with strong tailwinds. This data empowers investors to: 1. **Generate investment themes**: Identify high-potential markets or expansion opportunities 2. **Model financials**: Quantify demand and market size based on supply-demand gaps 3. **Engage stakeholders**: Measure the risk and adaptation impact of investments ### **Illustrative use case: Identifying markets for flood-related investments** This illustrative example demonstrates how AlphaGeo’s risk and adaptation data can be used to help identify high-potential markets for flood-related solutions. **1)** **Map “resilience-adjusted” flood risks** — ***i.e., areas that are high-risk, yet poorly adapted*** First, analysts can tap into AlphaGeo’s Resilience-adjusted Risk scores (part of our Climate Risk and Resilience Index) to identify areas that have **high flood risk, but insufficient adaptive capacity**. This pinpoints markets where demand for flood defenses is expected to grow.

Figure 1: Resilience-adjusted Inland Flood Risk in the United States

**2)** **Assess adaptation capacity:** Next, analysts can use our Global Adaptation Layer dataset that covers measures for nine hazards, including inland and coastal flooding. This dataset helps investors pinpoint gaps in hazard-specific measures (e.g., drainage systems or nature-based barriers), to identify and size the market for high-demand adaptation products or services. Our datasets cover measures that are both **human-engineered** or **nature-based**, proving equally useful for the many emerging players who are investing in nature-based solutions. **Table 1** below summarizes AlphaGeo’s flood-related adaptation dataset, showing how it can size markets for specific solutions, including both engineered and nature-based approaches.

### **Conclusion** As climate change intensifies, climate adaptation will become both an inevitable need as well as a significant investment opportunity. Because adaptation requires an understanding of localized environmental risks and gaps, investors who successfully integrate geospatial risk and adaptation data into more traditional analytical processes will have a competitive advantage. By mapping risks, identifying high-potential markets, and quantifying impact, this data enables investors to uncover untapped opportunities and build portfolios that deliver both financial returns and societal value. # Use Case: Insurance Risk Models and Underwriting Integrate resilience into underwriting and guide clients towards adaptation {% columns %} {% column valign="middle" %} Access Free Trial {% endcolumn %} {% column %}

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{% endcolumn %} {% endcolumns %} ## Integrate resilience into underwriting Insurers already model physical hazard well. The next step is to model how local adaptation changes likely loss. AlphaGeo’s **Global Adaptation Layer** helps insurers enhance their own physical climate risk models with hazard-specific resilience data. This makes it possible to assess **physical climate risk (undefended)** and **resilience-adjusted risk** **(defended)** side-by-side for more precise underwriting and pricing. ### Why adaptation data matters for insurers Two properties can face the same physical hazard but very different outcomes. The difference often comes from local adaptation capacity and asset-level resilience. That gap matters for underwriting. A model that only captures hazard intensity can miss where defenses, drainage, building strength, or response capacity reduce likely damage. ### Model unmitigated and mitigated risk together AlphaGeo’s [Climate Risk and Resilience Index](/climate-resilience-suite/climate-risk-and-resilience-index) shows how physical risk changes once adaptation is included. This dual view helps insurers: * benchmark baseline hazard exposure against mitigated risk * identify pockets of resilience inside high-risk markets * improve pricing precision without losing hazard transparency The California wildfire example below shows how resilience-adjusted scoring can reveal relatively safer locations inside a high-risk state.

Figure 2: Resilience-adjusted risk highlights locations where adaptation reduces likely damage.

Underlying hazard drivers remain fully visible. This lets insurers compare the physical signal with the resilience signal rather than replacing one with the other. ### Global Adaptation Layer for underwriting models The [Global Adaptation Layer](/global-adaptation-layer/methodology-2-3-global-adaptation-layer) is a hazard-specific dataset of local adaptation capacity. It covers more than 20 engineered and nature-based adaptation measures worldwide. These layers can be used directly in insurer workflows to strengthen internal view-of-risk models. They help quantify whether a location is protected by features such as flood defenses, drainage infrastructure, fire response capacity, natural buffers, or stronger building stock. ### What this enables With AlphaGeo, insurers can: 1. Enhance internal peril models with local adaptation variables. 2. Model unmitigated and mitigated risk in parallel. 3. Support more precise underwriting at quote, renewal, and portfolio level. This supports decisions such as: * refining technical pricing in exposed geographies * differentiating resilient properties from lookalike risks * guiding policyholders toward targeted remediation ### From risk selection to resilience incentives Adaptation data is useful beyond risk selection. It also helps insurers design products and engagement strategies that reward resilience. With precise data quantifying adaptation capacity, underwriters can identify adaptation gaps, support targeted loss prevention, and track how mitigation measures may improve risk quality over time. This creates a clearer path to resilience-linked pricing, and stronger retention in climate-exposed markets. # Climate Price AlphaGeo's **Climate Price** quantifies climate-driven **EBITDA-at-Risk for publicly listed companies**. It rolls AlphaGeo's asset-level Financial Impact Analytics (FIA) up to the issuer level, so equity and credit investors can see how physical climate risk flows through a company's revenues, operating costs, and valuation — expressed as a single, decomposable percentage of EBITDA.

### Our approach: from asset risk to issuer risk Top-down macroeconomic models establish that climate losses are large, but cannot say whose earnings are exposed. Bottom-up asset models pinpoint where a site floods, but stop at first-order damage — they miss how that damage rolls up through a company's operating footprint into revenues, operating costs, and valuation. Neither lens, on its own, explains why two companies with similar physical exposure can face very different financial outcomes. Climate Price closes that gap. It identifies a company's physical footprint from public filings, and applies AlphaGeo's existing location-level analytics at the scale of the issuer. For every facility in a company's footprint, it draws on two proven, globally-available layers at that exact location: * **Climate Risk & Resilience Index (CRRI)** — resilience-adjusted physical hazard exposure (heat, flood, wind, wildfire, drought). * **Financial Impact Analytics (FIA)** — the translation of that exposure into financial impact across six EBITDA transmission channels. The roll-up then proceeds in three steps: 1. **Per-facility impact** — combine the FIA channel rates at each location into a single facility-level EBITDA-at-Risk, scaled by the facility's asset-class vulnerability. 2. **Company aggregation** — weight facilities (equally, or by revenue or asset value) and aggregate into one company-level figure, preserving the channel and hazard decomposition. 3. **Derived metrics** — express the result as Climate VaR, incremental CapEx, insurance and insurability measures, a composite risk score, and a within-sector percentile. ### Product features * **Coverage universe:** Publicly listed issuers across the GICS sector taxonomy (initial universe of marquee names across Real Estate, Industrials, Utilities, Information Technology, Energy, and Communication Services; expanding) * **Geographic coverage:** Global (via FIA) * **Emission scenarios:** SSP2-4.5, SSP3-7.0, SSP5-8.5 * **Time horizons:** 2035, 2050 Climate Price produces a single climate financial profile per issuer. Headline metrics are decomposed into the channels, hazards, and facilities that drive them. | Category | Metric | Description | Unit | | -------------------------- | ------------------------------ | ---------------------------------------------------------------------------------------- | --------------- | | **Headline** | EBITDA-at-Risk | Annual EBITDA exposed to physical climate risk across the company's operating footprint. | % of EBITDA | | **Valuation** | Climate VaR (10-Year NPV Loss) | Discounted cash-flow loss versus a no-climate baseline over a 10-year hold. | % NPV loss | | | Average Annual Loss (AAL) | The annualized form of Climate VaR. | % loss/yr | | | Climate Valuation Discount | Enterprise-value haircut implied by the company's climate risk profile. | bps | | **Cost & Insurance** | Incremental CapEx | Adaptation capital implied by the risk profile. | % of revenue | | | Insurance Annual Increase | Climate-driven growth in insurance premiums. | annual % change | | | Insurability Exposure | Share of facilities at risk of exceeding standard insurance-market thresholds. | % of facilities | | **Scoring & Benchmarking** | Composite Risk Score | 0–100 score blending EBITDA, insurance, CapEx, and insurability exposure. | 0–100 | | | Sector Percentile | Rank within the company's GICS sector. | percentile | | | Adaptation Opportunity | Share of modelled risk recoverable through adaptation CapEx, with implied ROI. | % / ratio | ### Channel & hazard decomposition EBITDA-at-Risk is built from six **transmission channels**, split between revenue erosion and operating-cost inflation: * **Revenue channels** — Operational Downtime, Operational Efficiency, Workforce Productivity * **OpEx channels** — Insurance, Utility, Maintenance Each channel is attributed across six physical hazards — extreme heat, flood (inland and coastal), wind, wildfire, and drought — so users can see not just *how much* EBITDA is at risk, but *through which mechanism* and *from which hazard*. ### Interpreting the output * **EBITDA-at-Risk is reported as a negative percentage** — the share of annual EBITDA eroded under the selected scenario. A larger negative number means greater exposure. * **Every basis point is traceable** — the headline figure decomposes by facility, channel, and hazard, each tied back to a source quote in the filing. * **Every figure carries a confidence range** of ±30%, reflecting uncertainty in hazard projections, facility completeness, and sensitivity calibration. A higher headline number does not mean a company is uninvestable — it means more of its earnings sit in the path of physical climate risk, and that the adaptation and pricing-power response becomes material to the thesis. ### Use cases * Equity research and security selection * Credit and fixed-income analysis * Portfolio screening, construction, and stress testing * Issuer-level regulatory disclosure (TCFD, IFRS S2, California SB 261) ### See next: Methodology * Access our methodology docs [**Climate Price — EBITDA-at-Risk**](/methodology/access-to-our-methodology-docs) # Dynamism Signals Geospatial macro-intelligence for market research and investment strategy {% columns %} {% column valign="middle" %} Access Free Trial {% endcolumn %} {% column %}

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{% endcolumn %} {% endcolumns %} ## Overview AlphaGeo's Dynamism Signals is a **curated, geospatial macro-intelligence dashboard** for global investors, corporates, and strategists evaluating international markets and investment opportunities. By **consolidating, harmonizing, and downscaling critical indicators across macroeconomic, fiscal, socioeconomic, geopolitical, and environmental domains**, the platform delivers a comprehensive, local view of macro conditions — enabling faster, smarter, and more confident decision-making. ## The challenge Evaluating markets in today’s complex environment requires a multidimensional view of a location’s macroeconomic stability, fiscal health, demographic profile, institutional quality, geopolitical risk, environmental resilience, and more. Yet, this process is often inefficient and hindered by: * **Fragmented and siloed data:** Key indicators are scattered across disparate sources and data formats, making it time-consuming and costly to build a comprehensive picture. * **Geographic inconsistencies:** Coverage varies across global locations, especially at subnational levels, limiting accurate benchmarking and localized analysis. ## Our solution Dynamism Signals solve these challenges through a **unified database of more than 100 curated, macro indicators across over a dozen key themes** — all integrated into a single, intuitive dashboard. Our clients receive: * On-demand access to harmonized macro and inter-disciplinary data from curated global sources across countries and regions. * Proprietary enhancements such as geospatial downscaling to granular resolution and predictive analytics powered by machine learning. ## Use cases Dynamism Signals equips users with the intelligence needed to spot trends, identify opportunities, assess risks, and optimize strategy across global markets. Key applications include: * **Market research and investment strategy**\ Identify emerging trends and predict market dynamics to guide asset allocation. * **Advisory and consulting**\ Accelerate project delivery and decision support with access to on-demand, cross-market indicators for comparative analysis. * **Policy analysis**\ Benchmark performance across key metrics to inform strategic planning and public policy. ## Dynamism Signals data catalog

Theme	Indicator	Description	Frequency	Resolution	Unit
Demographic Trends	Total National Population	Estimate of total resident population, providing harmonized and current demographic figures suitable for international comparison.	Annual	Country	Number of people
Demographic Trends	Population Density	Population density calculated using official population and land area data to ensure comparability across countries.	Annual	4 Sq KM	Number of people
Demographic Trends	Population Growth	Annual percentage change in national population incorporating natural population gain/loss and net migration.	Annual	Country	%
Demographic Trends	Dependent Population	Proportion of each country’s population aged 65 and older, reflecting aging demographics and dependency ratio.	Annual	Country	%
Environmental Sustainability	Per Capita Emissions	CO₂ emissions per capita.	Annual	Country	Tonnes
Environmental Sustainability	Environmental Performance Index	Country-level performance on the Environmental Performance Index, based on 58 performance indicators on climate change performance, environmental healthy, and ecosystem vitality.	Annual	Country	0-100 score (Higher is better)
Environmental Sustainability	Air Pollution	Annual average PM2.5 concentration.	Annual	1 Sq KM	micrograms per cubic meter
Environmental Sustainability	Food Security Index	Country-level performance on the Food Security Index which measures food affordability, availability, quality, safety, sustainability and adaptation across 68 indicators.	Annual	Country	0-100 score (Higher is better)
Energy Security & Reliability	Renewable Energy Generation	Total primary energy generated from renewable sources including solar, wind and hydropower.	Annual	Country	Terrawatt-hours
Energy Security & Reliability	Renewables Share of Generation	Share of electricity generated by renewables including solar, wind, hydropower, bioenergy, geothermal, wave and tidal sources.	Annual	Country	%
Energy Security & Reliability	Electricity Access (% of Population)	Share of population with access to electricity.	Annual	Country	%
Energy Security & Reliability	Total Power Capacity	Total power plant capacity in a 50km radius.	Annual	50 Sq KM	Megawatt-hours
Energy Security & Reliability	Energy Trilemma Index	Country-level performance on the Energy Trilema Index, which quantifies performance across three dimensions: energy security, energy equity and environmental sustainability.	Annual	Country	0-100 score (Higher is better)
Fiscal Health	Current Account Balance (% of GDP)	Balance of current transactions (counting goods and services, earned income and transfer income) between residents and non-residents.	Annual	Country	%
Fiscal Health	General Gross Government Debt (% of GDP)	Ratio of gross public debt to GDP.	Annual	Country	%
Fiscal Health	Government Expenditure (% of GDP)	General government spending as a percentage of GDP.	Annual	Country	%
Labor Market Dynamics	Employment-Population Ratio	Share of employed persons as a percent of the total of working-age population.	Annual	Country	%
Labor Market Dynamics	Youth Population	Percentage of population aged 0-14	Annual	Country	%
Labor Market Dynamics	Working Age Population	Share of population aged 15-64.	Annual	Country	%
Labor Market Dynamics	Unemployment Rate	The number of unemployed persons as a share of the total workforce.	Annual	Country	%
Labor Market Dynamics	Educational Attainment	Share of population (aged 25 and above) with at least a Bachelor's or equivalent.	Annual	Country	%
Infrastructure & Connectivity	Airport Passenger Volume	Number of air passengers carried by airlines registered in each country, including domestic and international travel.	Annual	Country	Number of passengers
Infrastructure & Connectivity	Infrastructure Quality	Country-level performance on the Logistics Performance Index, which scores six dimensions of trade including customs performance, infrastructure quality, and timeliness of shipments.	Annual	Country	0-100 score (Higher is better)
Infrastructure & Connectivity	Fixed Capital Formation Ratio (FCFR)	Gross fixed capital formation as a share of GDP, reflecting national investment in infrastructure and productive assets.	Annual	Country	%
Infrastructure & Connectivity	Number of Data Centers	Total number of data centers in a 50km radius; includes data center that have been permitted and under construction.	Annual	50 KM	Number
Economic Complexity & Innovation	Economic Complexity Index (ECI)	Country-level performance on the Economic Complexity Index, which measures the relative knowledge intensity of an economy, based on trade data.	Annual	Country	0-100 score (Higher is better)
Economic Complexity & Innovation	AI Preparedness Index	Country-level performance on the AI Preparedness Index (AIPI), which assesses a country’s digital infrastructure, human capital and labor market policies, innovation and economic integration, and regulation and ethics.	Annual	Country	0-1 score (Higher is better)
Economic Complexity & Innovation	Global Innovation Index	Country-level performance on the Global Innovation Index, which quantifies over 80 indicators including policy environment, education, infrastructure, and knowledge creation.	Annual	Country	0-100 score (Higher is better)
Economic Complexity & Innovation	ICT Development Index	Country-level performance on the ICT Development Index, which assesses the level of information and communication technology (ICT) development across countries.	Annual	Country	0-100 score (Higher is better)
Governance & Regulatory Environment	Government Effectiveness	Worldwide Governance Indicator for Governance Effectiveness, which captures perceptions on the quality of public and civil services.	Annual	Country	0-100 score (Higher is better)
Governance & Regulatory Environment	Legal and Regulatory Risk	Equally-weighted combination of World Bank Worldwide Governance Indicators for Rule of Law and Regulatory Quality.	Annual	Country	0-100 score (Higher is better)
Geopolitics & Diplomacy	Global Soft Power Index	Country-level performance on Global Soft Power Index ranking countries by influence, familiarity and reputation without military or economic coercion.	Annual	Country	0-100 score
Geopolitics & Diplomacy	Political Stability and the Absence of Violence / Terrorism	Worldwide Governance Indicator for Political Stability and the Absence of Violence / Terrorism, which measures perceptions of the likelihood that the government will be destabilized or overthrown.	Annual	Country	0-100 score (Higher is better)
Macroeconomic Performance	PPP-Adjusted GDP	Gross domestic product (GDP) expressed in current international dollars, converted by purchasing power parities (PPPs).	Annual	Country	Current international dollars
Macroeconomic Performance	Nominal GDP Growth Rate	Annual percentage change in nominal GDP.	Annual	Country	%
Macroeconomic Performance	GDP Per Capita Growth Rate	Annual percentage change in GDP per capita.	Annual	Country	%
Macroeconomic Performance	Climate GDP Impact (Unadapted)	Projected GDP loss from six climate hazards (heat, flash flooding, riverine flooding, coastal inundation, wind, and drought) under the SSP3.7.0 scenario.	Annual	Country	%
Macroeconomic Performance	Climate GDP Impact (Adapted)	Projected GDP loss from six climate hazards (heat, flash flooding, riverine flooding, coastal inundation, wind, and drought), after factoring in the country's existing adaptation capacity.	Annual	Country	%
Macroeconomic Performance	Sovereign Credit Rating	Aggregated sovereign credit rating calculated from key ratings agencies such as Moody’s, S&P and Fitch.	Monthly	Country	0-100
Macroeconomic Performance	Foreign Direct Investment (FDI)	Net foreign direct investment (FDI) inflows in current terms.	Annual	Country	USD
Macroeconomic Performance	Currency Volatility	5-year standard deviation of percentage change in annual bilateral exchange rates relative to the USD (nominal).	Annual	Country	%
Macroeconomic Performance	Inflation Volatility	5-year standard deviation of the annual percentage change in inflation (consumer prices).	Annual	Country	%
Macroeconomic Performance	Inflation Rate Risk	Absolute deviation of the annual change in Consumer Price Index (%) from a 2% target rate.	Annual	Country	%
Macroeconomic Performance	Total Inflation Risk (Rate and Volatility)	Equally-weighted combination of inflation volatility (5-year standard deviation of annual percentage change in inflation) and inflation rate risk (absolute deviation of annual change in inflation from a 2% target rate).	Annual	Country	%
Macroeconomic Performance	Trade (% of GDP)	Total trade as a percentage of GDP, reflecting economic openness and trade dependency.	Annual	Country	% of GDP
Market & Business Conditions	Corporate Tax Rate	National corporate tax rate.	Annual	Country	%
Market & Business Conditions	Residential Property Price Growth	Year-on-year change in real residential property prices.	Annual	Country	%
Market & Business Conditions	Domestic Credit to Private Sector (% of GDP)	Loans to the private sector by domestic financial institutions.	Annual	Country	%
Migration & Talent Flows	Net Migration	Estimate of each country’s net total international migrants, calculated as the difference between immigrants and emigrants, including citizens and noncitizens.	Annual	Country	Number of people
Migration & Talent Flows	Migration Openness Ranking	Ranking of all countries and territories by number of visa-free destinations for their nationals.	Annual	Country	Number of countries
Migration & Talent Flows	Digital Nomad Attractiveness	Top 500 cities for digital nomads based on attractiveness to remote workers according to cost, Wi-Fi speed, safety, weather, community and user reviews.	Annual	City	Rank
Migration & Talent Flows	Global Wealth Mobility Framework (GWMF)	Henley & Partners Global Wealth Mobility Framework (GWMF) competitiveness score.	Annual	Country	Rank
Livability & Social Wellbeing	Life Expectancy	Total life expectancy at birth.	Annual	Country	Years
Livability & Social Wellbeing	Healthcare Spending (% of GDP)	Annual health expenditure as a percentage of GDP.	Annual	Country	%
Livability & Social Wellbeing	Life Satisfaction	Average national life satisfaction score (0–10) from the World Happiness Report.	Annual	Country	0-10 score (Higher is better)
Livability & Social Wellbeing	Violent Crime	Nationally reported statistics on violent offences per 100,000 residents.	Annual	Country	Incidents per 100,000 residents
Livability & Social Wellbeing	Inequality-adjusted Human Development Index (IHDI)	Measures a country's achievements in health, education and income, while accounting for inequalities in their distribution across the population.	Annual	50 KM	0-1 score (Higher is better)
Livability & Social Wellbeing	High-Quality Affordable Retirement	Top 96 destinations across 24 countries for retirement attractiveness, based on costs, amenities, health care, language, crime and climate risk.	Annual	City	Y/N
Societal Income & Wealth	Gross National Income (GNI) Per Capita	Gross national income per capita measured in current USD.	Annual	Country	USD
Societal Income & Wealth	Median Post-Tax Income	Estimated median household disposable income after taxes and transfers, adjusted for inflation and PPP.	Annual	Country	International dollars
Societal Income & Wealth	Poverty Rate (% of population)	Share of population living below poverty rates, as measured according to national poverty lines.	Annual	Country	%
Societal Income & Wealth	Income Inequality	Income inequality measured by the national Gini coefficient.	Annual	Country	Gini coefficent
Societal Income & Wealth	Household Debt (% of GDP)	Household debt (loans and securities) as a percentage of GDP.	Annual	Country	%
Societal Income & Wealth	Cost of Living Composite Index	Composite cost of living comprising rent, groceries, restaurants and other local purchases.	Annual	Country	% over/under baseline (USA = 100)

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{% endhint %} # Dynamism Signals (US-only) Predictive indicators of real estate market demand and dynamism {% columns %} {% column valign="middle" %} Access Free Trial {% endcolumn %} {% column %}

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{% endcolumn %} {% endcolumns %} AlphaGeo’s Dynamism Signals (US-only) ("Signals") are forward-looking indicators of real estate market dynamism and demand, designed to help investors identify high-potential markets with confidence. Our Signals leverage data science to address long-standing blind spots in real estate analytics – such as outdated, inconsistent or incomplete data – to provide a holistic and predictive outlook on future location performance at granular scale. [Sign up for a free trial to check out Signals in action](https://app.alphageo.ai/trial_setup). ### **The challenge: Real estate lacks future-ready foresight** Traditional data sources fail to provide a future-ready view on market potential due to: 1. **Inconsistent market data:** Real estate data varies across geographies and asset classes, limiting meaningful, high-conviction comparisons. 2. **Undifferentiated, lagging insights:** Traditional metrics (e.g., pricing, census data) are backward-looking and undifferentiated, offering little strategic edge. 3. **A static view of market drivers:** Emerging drivers such as greenfield corporate investment and climate migration are missing from conventional datasets, leading to mis-priced risks and overlooked growth opportunities. ### Our solution: Forward-looking, standardized intelligence Signals is a predictive scoring system engineered to provide investors with a **comprehensive** and **comparative** view of market **potential**. **How it works** Signals deliver a 0–100 score on the demand potential of all addresses across the US, offering an intuitive view of a location's: * **Relative potential** (market-beating, average, underperforming): Identify top-performing locations across the US * **Absolute potential** (above or below trend): Assess location potential relative to historical performance ### **Our advantage: A consistent and predictive information edge** We apply advanced data science and machine learning methods to engineer a scoring system that offers users: 1\. **Consistency:** We clean and integrate disparate data sources into one sector-specific, investment-ready signal, enabling like-for-like comparison across geographies and markets. 2\. **Predictive Insight:** Using machine learning, we forecast market movement with a wide-ranging, multidisciplinary feature set. 3\. **Information Edge:** We combine widely recognized metrics (e.g., macroeconomic data) with alternative datasets of both traditional and emerging market drivers (e.g., climate, investment, migration, energy) for a comprehensive view with competitive advantage. ### The Dynamism Signals The factors that make a location attractive are different for every asset class. A great residential neighborhood may not be an optimal logistics hub. To address this, we disaggregate our analysis into three distinct, purpose-built Signals, each tailored to a specific real estate sector: * **Residential Dynamism:** Quantifies the drivers of housing demand, integrating economic health, quality of life, market performance, and long-term growth potential to identify desirable and sustainable places to live. * **Commercial Dynamism:** Measures the forces that determine the success of office and retail assets, focusing on consumer and corporate economic vitality, location value, financial performance, and future growth. * **Industrial Dynamism:** Assesses the strength of locations for logistics and manufacturing, evaluating transportation infrastructure, workforce accessibility, operational efficiency, and indicators of industrial expansion.

### How to Use Signals Our Signals are designed to be an active tool to drive smarter, faster, and more confident investment decisions. Here are a few ways our clients integrate them into their workflows. **Market research and analysis** * **Action:** Use the 0–100 scores to perform a rapid, data-driven screening of the entire national landscape and systematically identify emerging opportunities and undervalued markets before they become mainstream with their potential priced in. * **Example:** A multifamily fund analyst can instantly find all addresses with a **Residential Dynamism score > 80** but where the **median home value is below the state average**. This query surfaces a shortlist of high-potential, undervalued submarkets, focusing due diligence efforts on where they can generate the highest yield. **Geographic asset allocation** * **Action:** Use the standardized 0–100 scoring framework to objectively compare the potential of any market on a like-for-like basis. This empowers portfolio managers to optimize geographic and asset-class exposures based on quantitative evidence. * **Example:** A REIT's investment team can map the **Industrial, Commercial, and Residential Dynamism scores** for every asset in its portfolio. They might discover that several industrial assets are in locations with declining scores and make a strategic decision to re-weight the portfolio, divesting from weakening locations and increasing investment in improving ones. **Augmenting proprietary data science models** * **Action:** Integrate **Signals** directly into your internal platforms as a powerful, pre-engineered input. This leverages our data engineering infrastructure, dramatically accelerating your model development and allowing your team to focus on unique alpha-generating strategies. * **Example:** A quantitative fund building a model to forecast retail NOI growth can ingest the single **Commercial Dynamism score** as one feature. The Signal acts as a powerful, condensed representation of market momentum, significantly boosting the model's predictive accuracy while saving the team thousands of hours of development work. ### How are the Signals Constructed Dynamism Signals (US-only) are computed in a disciplined, multi-stage process that transforms a vast collection of raw data into a coherent and predictive set of signals: * **Data integration and standardization:** We solve the problem of "inconsistent market data" by ingesting information from dozens of public and private sources. Our data engineering platform systematically cleans, validates, and standardizes these disparate sources, mapping all data to a consistent and granular resolution. This creates a unified dataset where every location can be compared on a true like-for-like basis. * **Intelligent feature engineering:** We transform raw data into more meaningful variables that better represent underlying market patterns. For example, instead of just using raw rent and income data, we engineer the `RENT_TO_INCOME_RATIO` to give the model a direct measure of affordability, which is more predictive of market sustainability. * **Proprietary data:** The final component of our advantage comes from unique, proprietary datasets that are not available from any other source. This allows our model to capture emerging trends and risks that others cannot see, such as our **Resilience-Adjusted Climate Risk Score (OVERALL\_RAJ\_SCORE)** and **Forward-Looking Growth Indicators (FUTURE\_POP\_GROWTH, GREENFIELD\_INVESTMENT)**, as well as realtime datasets tracking data centers, micro-grids, EV charging stations, land slated for privatization and other indicators of market momentum. ### Key Features #### Residential Dynamism Signal – Feature Composition The Residential Dynamism Signal provides a comprehensive measure of a location's attractiveness as a place to live, built around four key indicators. * **Economic vitality:** A vibrant local economy is the primary engine of residential demand. We measure this through job, population, and income growth rates, affordability metrics, and the rate of new business formation. * **Livability:** While economic opportunity draws people to an area, quality of life determines where they choose to live. This indicator quantifies the desirability of a neighborhood through education levels, health outcomes, and access to amenities. * **Market performance:** This indicator provides a real-time barometer of the local real estate market, measuring transactional activity and the balance of supply and demand through metrics like price appreciation, days on market, and sale-to-list price ratios. * **Growth potential:** This indicator provides a forward-looking perspective, assessing the factors that will shape a market's future trajectory, including residential building permits, proprietary long-term population projections, and our resilience-adjusted climate risk score.

Indicator	Feature	Description
Economic Vitality	Population growth rate	Annual rate of population growth at census tract level. A higher rate of growth indicates stronger demand in the market.
Economic Vitality	Job growth rate	Annual rate of job growth. A higher rate of growth indicates stronger demand in the market.
Economic Vitality	Income growth rate	Annual rate of income growth at census tract level. A higher rate of growth indicates stronger demand in the market.
Economic Vitality	Rent as percentage of income	Measures average rental cost as a percentage of per capita income. A lower percentage indicates higher affordability. A higher percentage indicates higher housing demand.
Economic Vitality	Housing to income ratio	Measures housing affordability by comparing the median house price to the median household income. A lower value indicates higher affordability. A higher value indicates higher housing demand.
Economic Vitality	Annual change in business establishments	Net annual change in business estabilishments calculated from new business registrations and closures. A positive rate of change indicates a growing local economy.
Economic Vitality	Under-5 population growth rate	Ratio of under-5 population change to overall population change from 2005-2024. A higher ratio indicates a growing youth demographic driven by fertility and in-migration.
Livability	Education attainment	Percentage of population with bachelor's degree or higher. Higher education attainment correlates to higher income, lower crime, and livability.
Livability	Health index	Access to health infrastructure and healthiness of the local population, measured by aggregating disease prevalence, access to hospitals, and insurance peneration. A higher index score indicates better healthcare quality.
Livability	Amenity and walkability index	Measures the walkability of the neighbourhood, access to amenities, and commuter distance to work. More amenities and walkability increases the desirability of a location.
Livability	Social mobility	Ratio of household income change (for bottom 25th percentile) to HPI change (base year 2000). Lower values indicate decreasing social mobility.
Livability	Unhealthy air quality	Days per year with Air Quality Index (AQI) above 100. Population groups with sensitivities such as heart or lung conditions may experience health effects and the general public should limit outdoor activity.
Market Performance	Year-on-year price appreciation	Average yearly rate of appreciation of residential properties in the area.
Market Performance	Average days on market	Average number of days the residential property has been on the market, with a higher number negatively correlating to transaction value.
Market Performance	Ratio of sold price to list price	A ratio of higher than 1 indicates a market in high demand.
Market Performance	Percentage of listings sold above list price	A higher percentage indicates a stronger market with more listings sold above list price.
Market Performance	El Niño Southern Oscillation (ENSO) impact	[Proprietary] ENSO-driven impact on HPI performance (2015-2023). Seven-tier categorical scoring with Low Minus indicating locations least sensitive to ENSO impact, and High Plus indicating locations with the highest sensitivity.
Market Performance	P(CD): Probability of climate default	[Proprietary] Likelihood of increase in mortgage delinquency due to physical climate risks compounding existing economic fragility between the present and 2050.
Growth Potential	Residential building permits	Number of residential building permits issued in a year, with higher number of issued permits indicating rising supply.
Growth Potential	Net Gravity Score (NGS)	[Proprietary] A composite migration index that captures fundamental push-pull drivers, local sentiment and spatial influence. Higher scores indicate stronger conditions for population inflow.
Growth Potential	Climate Risk	[Proprietary] Resilience-adjusted climate risk score. Lower values indicate lower risk, suggesting increasing in-migration and long-term real estate market demand.
Growth Potential	EV charging stations	Number of EV charging stations in the ZIP code.
Growth Potential	Potential federal land privatization	[Proprietary] Percentage of federally owned land eligible for privatization and suitable for residential real estate development.

#### Commercial Dynamism Signal – Feature Composition The Commercial Dynamism Signal measures the forces that drive demand for office and retail real estate, deconstructed into four essential indicators. * **Economic Vitality:** A growing population with disposable income forms the customer base for retail, while a dynamic business environment fuels demand for office space. * **Location Value:** This indicator quantifies the intrinsic, place-based attributes that make an area a valuable hub for commerce, such as population density, foot traffic, and walkability. * **Market Performance:** This indicator assesses the financial health and transactional velocity of the local commercial real estate market through rental/NOI growth, occupancy rates, and transaction volumes. * **Growth Potential:** This forward-looking indicator assesses the pipeline of future demand, using residential construction as a proxy for future retail customers and leveraging our proprietary long-term population and climate risk forecasts.

Indicator	Feature	Description
Economic Vitality	Income growth rate	Annual rate of income growth at census tract level. A higher rate of growth indicates stronger demand in the market.
Economic Vitality	Population growth rate	Annual rate of population growth at census tract level. A higher rate of growth indicates stronger demand in the market.
Economic Vitality	Job growth rate	Annual rate of job growth. Higher rate of job growth indicates higher spending and consumption.
Economic Vitality	Annual change in business establishments	Net annual change in business establishments calculated from new business registrations and closures. A positive rate of change indicates a growing local economy.
Location Value	Population density	Number of people per acre. Higher population density indicates increased demand and location value.
Location Value	Pedestrian and commuter traffic	An indexed score from the EPA calculated from the frequency of transits on foot, vehicles, and public transport. Higher traffic indicates increased demand and location value.
Location Value	Amenity and walkability index	Measures the walkability of the neighbourhood, access to amenities, and commuter distance to work. Better amenities and walkability increases the desirability of the location.
Market Performance	Rental growth rate	Annual growth in rental income of office and retail assets in this market. A higher growth rate indicates higher demand.
Market Performance	NOI growth rate	Annual growth in Net Operating Income of office and retail assets in this market. A higher growth rate indicates higher demand.
Market Performance	Occupancy rate	Average occupancy rate of commercial properties in the market. A higher occupancy rate indicates higher demand.
Market Performance	Price per square foot	Average weighted market value per square feet for office and retail assets in this market.
Market Performance	Property transactions	Number of property transactions in the last quarter. A higher number of transactions indicates a desirable market.
Growth Potential	Units planned and under construction	Number of residential building permits issued in the prior year. A higher number indicates rising demand for office and retail assets.
Growth Potential	Net Gravity Score (NGS)	[Proprietary] A composite migration index that captures fundamental push-pull drivers, local sentiment and spatial influence. Higher scores indicate stronger conditions for population inflow.
Growth Potential	Climate risk	[Proprietary] Resilience-adjusted climate risk score. Lower values indicate lower risk, correlating to increasing in-migration and long-term real estate market demand.
Growth Potential	Venture capital investment	Amount of venture capital invested in the city in 2024. (Only top 25 cities qualify.)

#### Industrial Dynamism Signal – Feature Composition The Industrial Dynamism Signal identifies locations optimally configured for logistics, manufacturing, and data infrastructure, constructed around four key indicators. * **Logistics:** This indicator measures the quality of physical infrastructure that enables the efficient movement of goods, including highway, rail, and air connectivity, as well as energy resilience through micro-grid capacity. * **Workforce Accessibility:** This indicator assesses the health, size, and suitability of the local labor market for industrial employers. * **Operational Efficiency:** This indicator quantifies key input costs and infrastructure quality factors that directly impact an industrial tenant's profitability, such as electricity rates, grid reliability, and internet speed. * **Growth Potential:** This indicator identifies signals of future industrial expansion and innovation through our proprietary greenfield investment tracker, local patent activity, and our long-term climate risk score.

Indicator	Feature	Description
Logistics	Highway endpoint density	Number of highway endpoints within proximity of each census tract. A higher density indicates higher connectivity of the region.
Logistics	Rail nodes density	Rail nodes within proximity of each census tract. A higher density indicates higher connectivity of the region.
Logistics	Airport density	Number of ports within proximity of each census tract. A higher density indicates higher connectivity of the region.
Logistics	Truck stop density	Number of truck stops within proximity of each census tract. A higher density indicates higher connectivity of the region.
Logistics	Micro-grid capacity	Total capacity of local micro-grids in kW. A higher capacity enhances local resilience to black-outs.
Workforce Accessibility	Education attainment	Percentage of population with bachelor's degree or higher. Higher education attainment correlates to better access to skilled workforce.
Workforce Accessibility	Unemployment rate	Lower unemployment generally indicates economic growth which fuels more demand for production in the industrial sector.
Workforce Accessibility	Manufacturing employment	Total number of eligible workers across all sectors. A higher number indicates strong production capacity.
Workforce Accessibility	Workforce population	Total number of eligible workers across all sectors.
Operational Efficiency	Electricity price	Average electricity rate in the state. A lower rate indicates lower operational expense.
Operational Efficiency	Energy generation	Total energy generation in the state. Higher power generation indicates lower rates and increased resilience to blackouts.
Operational Efficiency	Energy grid interruptions	Calculated from Customer Average Interruption Duration Index (CAIDI). A lower number indicates higher grid reliability.
Operational Efficiency	Internet speed	Internet speed of local broadband. A higher internet speed indicates better connectivity and reliability to support internet-related activities such as data centers.
Operational Efficiency	Land cost per acre	Cost of land per acre in the census tract. A lower cost of land indicates higher supply and lower demand.
Growth Potential	Greenfield investment	Cumulative value of new corporate investment by county since 2022. Higher value indicates growth momentum.
Growth Potential	Climate risk	[Proprietary] Resilience-adjusted climate risk score. Lower values indicate lower risk, correlating to increasing in-migration and long-term real estate market demand.
Growth Potential	Patents per capita	Total number of patents per capita in 2020. Higher patent output is a proxy for the innovation potential of the market.
Growth Potential	Data centers	Total number of data centers in a 50km radius; includes data center that are currently under construction.

# Net Gravity Score (NGS) Proprietary forecast of all-factor migration potential for every US census tract ## Overview AlphaGeo’s **Net Gravity Score (NGS)** is a proprietary census tract-level migration index that measures how strongly a place is positioned to attract or lose residents. By combining three independent layers – structural fundamentals, real-time local sentiment, and spatial spill-over effects – it captures migration pressure in a way that is more timely and realistic than lagging population counts or single-variable proxies. The result is a clearer signal of where local growth or decline may emerge next. NGS helps users understand not just where migration conditions are strong or weak today, but where changing fundamentals, public perception, and proximate location dynamics may begin to reshape population trends, housing demand, and market momentum. The proprietary layers make NGS both diagnostic and explanatory, revealing why people move. ## Our approach The migration literature shows that residential mobility is shaped by the interaction of economic opportunity, affordability, place reputation, and geographic proximity, rather than by any single factor in isolation. NGS translates this multi-causal view of migration into a census tract-level composite score built from three layers: structural conditions, local sentiment, and spatial influence. The final score is defined as:

NGS Score = Structural Score + α · Sentiment Score + β · Spatial Influence

where the structural score captures measurable push and pull conditions within the census tract, sentiment score captures county-level local reputation and momentum, and spatial influence captures the average structural attractiveness of immediate neighboring tracts. The parameters *α* and *β* control the contribution of sentiment and spatial influence to the final composite score. Together, these layers produce a census tract-level migration signal that is multi-dimensional, spatially aware, and more responsive to emerging change than conventional migration measures. #### Structural Layer The structural layer is the backbone of the NGS score, split into push and pull factors. Eighteen structural variables in total feed into this layer, chosen after correlation analysis to avoid redundant signaling. This breadth distinguishes NGS from single-metric approaches. The full set of variables is detailed in the table below.

Every push and pull variable rolls up into this one equation, giving each tract a measurable structural score.

Structural Score = Pull Score - Push Score

The structural layer serves as the measurable, data-driven foundation of NGS, providing the statistical base that is then enhanced by the sentiment and spatial layers. #### Sentiment Layer The sentiment layer models how media coverage shapes a location's reputation in real time with respect to economic, safety, climate and other factors. The layer runs in four steps: local news is collected through Google News RSS, scored by a Large Language Model on a -1 to +1 scale, aggregated to a county-level sentiment score, and applied uniformly to all tracts within that county.

The sentiment layer captures momentum and reputation, sometimes ahead of the structural numbers. It is both integrated into the Net Gravity Score (NGS) and available as a *standalone proprietary dataset.* #### Spatial Layer The spatial layer reflects the fact that no census tract moves in isolation. A tract's score is adjusted based on the performance of its immediate neighbors, since growth or decline often spills across tract boundaries. Neighboring-tract influence is calculated as the average structural score of adjacent tracts identified through queen contiguity, then added to the final score using an empirically calibrated spatial weight.

#### National Gravity Score: Combining the Layers The three layers are combined into a single composite Net Gravity Score (NGS) for each census tract. Each layer contributes something distinct: structural gives the measurable fundamentals, sentiment gives real-time perception, and spatial accounts for how growth and decline spill across neighboring areas.

The NGS score is not a black box. Each tract's score can be traced back to its structural drivers, sentiment signal, and spatial context, allowing users to diagnose whether a tract is being pushed down by local conditions, pulled up by attractive features, strengthened by positive sentiment, or influenced by nearby areas. This makes NGS useful not only for ranking places, but also for understanding what is driving each score. ## Product features * **Geographic coverage:** United States * **Spatial unit:** Census Tract * **Model structure:** Structural factors + sentiment + spatial spillover * **Structural inputs:** More than 15 variables across economics, climate, health, education, infrastructure, and investment * **Sentiment source:** County-level sentiment derived from local news coverage * **Date range:** 2026 ## Core outputs The dataset includes five core outputs: * **Net Gravity Score:** Composite gravitational score per tract * **Net Gravity Rank:** Percentile ranking across all tracts * **Structural Score:** Underlying tract-level push-pull score * **Sentiment Score:** Local news-based reputation and momentum signal * **Factor decomposition:** Key drivers behind each tract's push or pull profile ## Interpreting the forecast The key field is **Net Gravity Score** * **Positive values** mean the tract is more likely to attract residents * **Values near zero** mean overall migration conditions are relatively balanced * **Negative values** mean the tract faces stronger out-migration pressure NGS is designed to be unpacked: a tract's score can be traced back to opportunity and investment, affordability or climate strain, sentiment, or the pull of its neighbors. ## Use cases * Market analysis and site selection * Real estate and investment screening * Institutional investment and portfolio risk * Policy and economic development * Within-county equity analysis * Factor decomposition and attribution * Risk monitoring # FAQs In progress 1. What measures are in place to validate and ensure the accuracy and reliability of the Climate Risk Index? 1. Slide on data selection criteria to ensure data quality and reliability 2. Validation of data with users. Especially for the near term risks. 3. Function for users to report questions on accuracy and helpfulness of our data. 2. How exactly do you normalize event intensities of different perils? What are the underlying assumptions when combining normalized values from different perils? 1. Damage functions mapping intensity to 0-100. 2. Assume damage functions pegging to 10th and 90th percentile of intensity. 3. According to the Whitepaper, AlphaGeo has a resolution of 300 meter for Flood and 25 km for storm – why do you think this is adequate enough? 1. Source data can be higher res. For example, flood can go up to 30-100 meters. 2. But as our insight goes, 300 meters is sufficiently good to capture the general risk trend in a location. 3. Higher res data is helpful if blended with other property info, which we don’t have globally. 4. Resolution is enhanced using resilience (adaptive capacity data). 5. The concept of scale here matters. 4. How is the tropical cyclone risk considered in areas where no storms made landfall in the historic reference period? Would additional tracks in IBTrACS change the final risk maps? 1. If both IBTrACS and future simulations did not touch, then no risk. 5. How is the change of annual expected losses for future scenarios calibrated? What is the baseline? 1. Baseline is current period of 2015-2025 6. Explain the downscaling process AlphaGeo is applying in more details 1. Slide. DeepSD, + GAN based models 2. How does the methodology used in the AlphaGeo Climate Risk Index ensure accurate and comprehensive risk assessments? 7. What are the biases and limitations in the methodology? 8. How do the fire-related sub-indexes correlate with observed fire occurrence? Why not using fire-related indices such as the [FWI](https://climate.copernicus.eu/fire-weather-index) instead? 1. Limited by data availability. FWI is a daily index which requires PR, RH, etc. 2. How are the sub-indexes (e.g., Heat Stress, Drought, Inland Flooding) calculated? 3. In the paper. Intensity + Frequency 9. What GCM variables are used for assessing the expected change for the different perils? 1. TAS, TASMAX, TASMIN, PR 10. What GCMs are used for the future scenarios? How are they downscaled to 25km resolution? 1. DeepSD. Ensemble of couple of GCMs such as CanESM5, MRI-ESM1 11. How does the approach for estimating the adaptive capacity perform in data-sparse (typically non-resilient) regions? 1. Not yet. But we have plans to estimate the data using our generative model. 12. What are the most relevant data sources for adaptation measures besides OSM? 1. Global Reservoir, EarthEnv Land Cover, WUDAPT Local Climate Zone 13. Could you be more specific on the “comprehensive spatial analysis” of the adaptation measures? 14. What’s the update cycle? * Incorporate GAM Feedback to data * 1\. Add STORM dataset to hurricane map * 2\. Add 30m flood data to the flood * 3\. Insurance calculations ( risk premium - annual expected damage 50%) (overheads = 50%) * * Loss ration, expense ratio * Impact \* loss ratio * Check out some of the annual report from insurance companies on their loss ratio vs expense ratio * Uncertainty modeling for adaptation curves. * Collab with GAM on sending # Clima-Metrics Proprietary forecast of climate-driven GDP loss across hazards and sectors {% columns %} {% column valign="middle" %} Access Free Trial {% endcolumn %} {% column %}

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{% endcolumn %} {% endcolumns %} ## Overview AlphaGeo’s **Clima-Metrics** models how climate hazards reduce economic output across sectors and geographies. It translates physical hazard intensity into subnational GDP loss, with both unadapted and resilience-adjusted outputs. This product helps users move from high-level climate flags to quantified, top-down economic impact. ## Our approach: From physical hazard to economic impact Most climate macro models stay too high level. They hide local variation, flatten sector differences, and miss how multiple hazards interact. **Clima-Metrics** addresses that gap by estimating GDP loss at subnational resolution, across hazards and sectors, with adaptation built into the view. This creates two complementary views of climate impact: 1. **Unadapted GDP loss** — baseline economic exposure to climate hazards 2. **Resilience-adjusted GDP loss** — expected loss after accounting for existing adaptation capacity This helps users compare gross exposure, defended exposure, and the economic value of resilience. ## Product features * **Geographic coverage:** Global * **Spatial unit:** GID\_1 subnational units, with country-level aggregates * **Hazards:** Heat, flash flood, riverine flood, coastal flood, wind, drought, and combined multi-hazard * **Sectors:** Agriculture, manufacturing, services * **Forecast horizons:** Multiple time horizons ## Core outputs The forecast includes both subnational and country-level outputs: * **GDP loss percent** — combined and per-hazard GDP loss * **Sectoral breakdown** — agriculture, manufacturing, and services contribution to loss * **Resilience dividend** — loss avoided through existing adaptation capacity * **National aggregates** — GRP-weighted country-level summaries * **Adaptation gap ranking** — adaptation efficiency relative to exposure * **Global snapshot** — highest-risk countries and regions by hazard Outputs are available in both **unadapted** and **resilience-adjusted** form. ## Use cases * Macro-level sector and geographic analysis * Sectoral and geographic asset allocation strategy * Climate finance allocation and adaptation prioritization # Global Migration Modeler (GMM) ## Introduction AlphaGeo's Global Migration Modeler (GMM) provides a comprehensive corridor-level view of population movements and forecasts international flows between origin and destination country pairs for distinct demographic groupings including (a) investors (b) professionals (c) students, and (d) tourists. The GMM gives decision makers corridor-level insights into population mobility trends, demographic pressures, labor market dynamics, demand for goods and services, and other consequences of shifting migration flows at a global scale. Explore Global Migration Modeler ## Gaps in Global Migration Data No single dataset captures bilateral migration at a global level. Most that exist have one or more of the following shortcomings: * Old and lagging data. Prominent data sources such as UN DESA capture bilateral flow pairs at erratic intervals and often represent estimates. * Failure to account for sudden reversals. Geopolitical and policy shifts have led to mass return movements (e.g. Syrians from Turkey, Afghans from Pakistan and Iran, US suspension of refugee admission) that defy linear historical trends. * Uneven coverage. Regional sources (such as Europe’s OECD and Eurostat) offer reliable and continuous data, but do not cover some of the largest corridors (such as the Gulf states, South Asia and Southeast Asia). Furthermore, some of these regions do not provide the composition of immigrants by nationality. * Conflicting definitions. Foreign-born, foreign-citizen, and registration-based counts differ materially for the same country (e.g., Japan counts 4.1M foreign residents versus 3.4M foreign-born). Merging these categories produces incoherent corridor totals and spurious year-over-year swings that reflect definitional artifacts rather than real movement. ## AlphaGeo’s Approach ### Estimating flows We begin by constructing a migration stock baseline from international and government sources, upon which we layer estimated flows from a range of sources (both official and unofficial such as media and monitoring groups). Note that changes in stock reveals net movement, not gross flow, with the residual accounted for by migrant mortality, naturalization and reclassification. Where gross churn matters — notably the high-turnover Gulf labor corridors — we supplement our dataset with visa issuance and work-permit data rather than relying on stock deltas alone. ### Data refresh Our dataset incorporates timely overlays of new information from the latest national figures and other sources to update our historical baselines for each country. ### Reconciling sources Given discrepancies in the definition of various migrant categories, we transparently tag each datapoint with a source, date, confidence tier, and annotation explaining the figure, allowing users to filter and weight corridors by data quality. ## Modeling Migrant Flows AlphaGeo’s generative AI pipeline converts unstructured sources (e.g. government policies, media reports, industry publications, etc.) into structured corridor-level +/- modifiers to projected future flows, each with a confidence tier. Each destination country also has a dynamic weight associated with its attractiveness to the four major demographic groups – investors, professionals, students, tourists – based on AlphaGeo’s proprietary migration competitiveness framework. We combine the harmonized series of corridor indicators with the country-level migration competitiveness scores to forecast near-term origin-destination migration flows for all country pairs calibrated to each demographic segment. Clients may provide supplemental data or request modifications to country-level weights to customize the projected migration forecasts. ## Sources * UN DESA — International Migrant Stock (bilateral stock baselines) * OECD — International Migration Database * Eurostat — EU migration statistics and residence permits * UNHCR — refugee, asylum, and returnee data * IOM — Displacement Tracking Matrix and flow monitoring * GLMM — Gulf Labor Markets and Migration program * Frontex — Irregular border-crossing detections * ILO — international labor migration statistics * UNESCO Institute for Statistics * National statistical agencies, census bureaus, and immigration authorities * National visa issuance and work permit registries # Migration Modeler -OLD Proprietary forecast of flood-driven population change across U.S. census blocks {% columns %} {% column valign="middle" %} Access Free Trial {% endcolumn %} {% column %}

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{% endcolumn %} {% endcolumns %} ## Overview AlphaGeo’s **Climate Migration Forecast** estimates how flood risk changes future population trajectories across U.S. census blocks. It separates baseline demographic change from flood-driven displacement, so users can identify where flooding is likely to accelerate out-migration or slow future growth. This product turns flood exposure into a forward-looking demographic signal. It helps users understand not just where flood risk exists, but where that risk may begin to reshape local demand, market momentum, and community stability. ## Our approach Most population forecasts capture demographic change, but not the incremental effect of climate hazard. The Climate Migration Forecast addresses that gap by isolating the portion of future population change associated specifically with flood exposure. This creates two distinct views: baseline population change under standard demographic assumptions, and climate-adjusted population change after accounting for flood risk. The result is a clearer signal for site selection, market screening, and long-term demand analysis. ## Product features * **Geographic coverage:** United States * **Spatial unit:** Census block * **Hazard coverage:** Flood inundation * **Forecast horizon:** 2020 to 2050 * **Scenario baseline:** SSP2 population projections ## Core outputs The dataset includes four core outputs: * **Baseline future change** — expected population change without flooding * **Projected population** — climate-adjusted future population estimate * **Projected future change** — annualized change based on the climate-adjusted forecast * **Climate consequence** — the additional population change attributable to flood risk These outputs let users compare natural demographic momentum with the incremental effect of flooding. ## Interpreting the forecast The key field is **climate consequence**. * **Negative values** mean flood risk is pushing population outcomes downward. * **Values near zero** mean baseline demographic trends dominate. * **Positive values** mean the area still grows despite flood risk. A positive value does not mean the location is low risk. It means growth pressure still outweighs the flood-related drag in the model. ## Use cases * Site selection and market screening * Portfolio strategy and market prioritization * Market demand analysis * Public-sector planning and resilience investment ## See next: Methodology * [Methodology: Climate Migration Projection Model](broken://pages/xEso4YuBMPoHVhGOD5Wr) # Global Data Marketplace {% columns %} {% column valign="middle" %} Access Free Trial {% endcolumn %} {% column %}

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{% endcolumn %} {% endcolumns %} ## Overview AlphaGeo’s Global Data Marketplace is a comprehensive, curated catalog of **proprietary geospatial datasets** designed to answer critical questions around **location dynamism and resilience**. From **climate risk and adaptation** to **macroeconomic, demographic, real estate, and socioeconomic** indicators, our data enables a wide range of analytical use cases. The Marketplace also provides direct, modular access to the full library of proprietary datasets that power our core analytics products — from the Climate Risk and Resilience Index (CRRI) to the Location Dynamism Signals. This gives users the flexibility to access specific datasets without needing to subscribe to the full analytics stack. ## Data features * **Update Frequency:** Depends on dataset (Quarterly to annually) * **Supported Data Formats:** Parquet, GeoPackage * **Query Granularity:** Can be downscaled to address-level * **Resolution**: Various; refer to our [Data Dictionary](/for-developers/data-dictionary) for more details ## Data themes (Links to section) 1. [Climate Risk](#climate-risk) 2. [Climate Adaptation](#climate-adaptation) 3. [Climate Financial Impact](#climate-financial-impact) 4. [Climate Real Estate Impact](#climate-real-estate-impact) 5. [Climate Macroeconomic Impact](#climate-macroeconomic-impact) 6. [Demographic Trends](#demographic-trends) 7. [Economic Complexity & Innovation](#economic-complexity-innovation) 8. [Environmental Sustainability](#environmental-sustainability) 9. [Energy Reliability & Security](#energy-reliability-security) 10. [Fiscal Health](#fiscal-health) 11. [Governance & Regulatory Environment](#governance-and-regulatory-environment) 12. [Geopolitics & Diplomacy](#geopolitics-and-diplomacy) 13. [Infrastructure & Connectivity](#infrastructure-and-connectivity) 14. [Labor Market Dynamics](#labor-market-dynamics) 15. [Liveability & Social Wellbeing](#liveability-and-social-wellbeing) 16. [Macroeconomic Performance](#macroeconomic-performance) 17. [Market & Business Conditions](#market-business-conditions) 18. [Migration & Talent Flows](#migration-talent-flows) 19. [Real Estate Market Data](#real-estate-market-data) 20. [Societal Income & Wealth](#societal-income-wealth) ## Pricing Global Data Marketplace datasets are bespoke based on the following, with a discount for multiple datasets purchased: * Dataset(s) selected * Degree of proprietary engineering or analytical transformation. * Granularity * Geographic coverage Interested customers should contact us at for a quote. ## Climate Risk

Theme	Dataset	Bundled or Individual?	Description	Type of Dataset	Geographic Coverage	Date Range
Climate Risk	Climate Risk and Resilience Index (CRRI)	Bundled	Full access to all Climate Risk and Resilience-adjusted Risk scores, as well as underlying Climate Risk and Climate Adaptation datasets used to drive these metrics.	Proprietary Analytics	Global	2005 - 2100
Climate Risk	Overall Climate Physical Risk Score	Individual	Global percentile rank of a location for climate physical risk on a scale of 0 - 100. A higher score indicates greater risk.	Proprietary Analytics	Global	2005 - 2100
Climate Risk	Overall Climate Resilience-adjusted Risk Score	Individual	Global percentile rank of a location for resilience-adjusted climate risk on a scale of 0 - 100. A higher score indicates greater risk.	Proprietary Analytics	Global	2005 - 2100
Climate Risk	Mean Climate Physical Risk Score	Individual	Mean physical climate risk score taking the average of all hazard-specific risk scores (heat stress, inland flooding, coastal flooding, drought, hurricane wind, and wildfire) for a location. A higher score indicates greater risk.	Proprietary Analytics	Global	2005 - 2100
Climate Risk	Mean Climate Resilience-adjusted Risk Score	Individual	Mean resilience-adjusted risk score taking the average of all hazard-specific resilience-adjusted risk scores (heat stress, inland flooding, coastal flooding, drought, hurricane wind, and wildfire) for a location. A higher score indicates greater risk.	Proprietary Analytics	Global	2005 - 2100
Climate Risk	Climate Physical Risk Score: Heat Stress	Individual	Heat risk score with a scale of 0 - 100. Calculated from Annual Cooling Degree Days (CDD). A higher score indicates greater risk.	Proprietary Analytics	Global	2005 - 2100
Climate Risk	Climate Physical Risk Score: Inland Flooding	Individual	Inland flooding risk score, on a scale of 0 - 100. Calculated from annual days with heavy precipitation (PR_DAYS_10MM) and 1000-year riverine flood inundation depth (R_INUN_RP1000). A higher score indicates greater risk.	Proprietary Analytics	Global	2005 - 2100
Climate Risk	Climate Physical Risk Score: Coastal Flooding	Individual	Coastal flooding risk score, on a scale of 0 - 100. Calculated from mean sea level rise (SLR_CHG) and coastal inundation levels (C_INUN_RP1000). A higher score indicates greater risk.	Proprietary Analytics	Global	2005 - 2100
Climate Risk	Climate Physical Risk Score: Drought	Individual	Drought risk score, on a scale of 0 to 100. Calculated from the increase in the number of dry days (MAX_CONS_DRYDAYS), hot days (HOTDAYS105), and water demand (WS). A higher score indicates greater risk.	Proprietary Analytics	Global	2005 - 2100
Climate Risk	Climate Physical Risk Score: Hurricane Wind	Individual	Hurricane wind risk score, on a scale of 0 - 100. Calculated from the annual frequency (HU_AF) of hurricanes. A higher score indicates greater risk.	Proprietary Analytics	Global	2005 - 2100
Climate Risk	Climate Physical Risk Score: Wildfire	Individual	Wildfire risk score, on a scale of 0 - 100. Calculated from the increase in hot days (HOTDAYS105) and dry days (MAX_CONS_DRYDAYS) in vegetated areas (AVG_VC). A higher score indicates greater risk.	Proprietary Analytics	Global	2005 - 2100
Climate Risk	Resilience-adjusted Risk Score: Heat Stress	Individual	Resilience-adjusted heat risk score, on a scale of 0 to 100. This score incorporates the Urban Heat Island effect, as measured by building density and green coverage in a location, into the HEAT_SCORE. A higher score indicates a greater risk.	Proprietary Analytics	Global	2005 - 2100
Climate Risk	Resilience-adjusted Risk Score: Inland Flooding	Individual	Resilience-adjusted inland flooding risk score, on a scale of 0 - 100. Calculated by factoring elements of surface porosity, flood control measures and defense infrastructures to the INLAND_SCORE. A higher score indicates greater risk.	Proprietary Analytics	Global	2005 - 2100
Climate Risk	Resilience-adjusted Risk Score: Coastal Flooding	Individual	Resilience-adjusted coastal flooding risk score, on a scale of 0 to 100. This score incorporates elements of coastal flood control measures and defense infrastructures into the COASTAL_SCORE. A higher score indicates a greater risk.	Proprietary Analytics	Global	2005 - 2100
Climate Risk	Resilience-adjusted Risk Score: Drought	Individual	Resilience-adjusted drought flooding risk score, on a scale of 0 to 100. This score incorporates the availability and capacity of water works and emergency water supplies into the DROUGHT_SCORE. A higher score signifies a greater risk.	Proprietary Analytics	Global	2005 - 2100
Climate Risk	Resilience-adjusted Risk Score: Hurricane Wind	Individual	Resilience-adjusted hurricane wind risk score, on a scale of 0 to 100. This score incorporates the strength of buildings and water control measures in a location into the WIND_SCORE. A higher score denotes a greater risk.	Proprietary Analytics	Global	2005 - 2100
Climate Risk	Resilience-adjusted Risk Score: Wildfire	Individual	Resilience-adjusted wildfire score, on a scale of 0 to 100. This score incorporates the effectiveness of fire monitoring and fire defense infrastructure into the FIRE_SCORE. A higher score signifies a greater risk.	Proprietary Analytics	Global	2005 - 2100
Climate Risk	Cooling Degree Days	Individual	Annual Cooling Degree Days (CDD), measuring cumulative heat exposure where daily averages exceed 22°C.	Proprietary Derived Dataset	Global	N/A
Climate Risk	Number Of Days With Daily Maximum Temperature > 95 Degrees Fahrenheit	Individual	Number of days annually with maximum temperatures exceeding 95°F, indicating the frequency of extreme heat events.	Proprietary Derived Dataset	Global	N/A
Climate Risk	Number Of Days With Daily Maximum Temperature > 105 Degrees Fahrenheit	Individual	Number of days annually with maximum temperatures exceeding 105°F, signaling high exposure to extreme heat.	Proprietary Derived Dataset	Global	N/A
Climate Risk	Annual Maximum Temperature In Fahrenheit	Individual	Annual maximum temperature (°F), capturing peak recorded heat for the year.	Proprietary Derived Dataset	Global	N/A
Climate Risk	Annual Maximum Temperature In Celsius	Individual	Annual maximum temperature (°C), providing a globally standardized heat extreme indicator.	Proprietary Derived Dataset	Global	N/A
Climate Risk	Annual Days With Precipitation More Than 10Mm	Individual	Number of days per year with precipitation over 10mm, indicating the frequency of heavy rainfall events.	Proprietary Derived Dataset	Global	N/A
Climate Risk	Average Daily Mean Precipitation In Inches	Individual	Average daily mean precipitation in inches, reflecting long-term rainfall patterns.	Proprietary Derived Dataset	Global	N/A
Climate Risk	Average Daily Mean Precipitation In Millimeters	Individual	Average daily mean precipitation in millimeters, providing a metric for moisture accumulation.	Proprietary Derived Dataset	Global	N/A
Climate Risk	100-Year Riverine Flooding Inundation Depth	Individual	Modeled 100-year riverine flood depth (meters), indicating inundation risk for 1-in-100 year flood events.	Proprietary Derived Dataset	Global	N/A
Climate Risk	250-Year Riverine Flooding Inundation Depth	Individual	Modeled 250-year riverine flood depth (meters), reflecting more extreme, lower-probability inland flooding.	Proprietary Derived Dataset	Global	N/A
Climate Risk	1000-Year Riverine Flooding Inundation Depth	Individual	Modeled 1000-year riverine flood depth (meters), assessing catastrophic inland flood scenarios.	Proprietary Derived Dataset	Global	N/A
Climate Risk	100-Year Coastal Flooding Inundation Depth	Individual	Modeled 100-year coastal flood depth (meters), estimating flood levels during 1-in-100 year coastal events.	Proprietary Derived Dataset	Global	N/A
Climate Risk	250-Year Coastal Flooding Inundation Depth	Individual	Modeled 250-year coastal flood depth (meters), reflecting rare but impactful coastal flood scenarios.	Proprietary Derived Dataset	Global	N/A
Climate Risk	1000-Year Coastal Flooding Inundation Depth	Individual	Modeled 1000-year coastal flood depth (meters), indicating risk from the most extreme sea-level rise and surge events.	Proprietary Derived Dataset	Global	N/A
Climate Risk	Mean Sea Level Change In The Coastal Area Compared To 2015 In Meters	Individual	Projected mean sea level rise since 2015 (meters), indicating long-term exposure for coastal areas.	Proprietary Derived Dataset	Global	N/A
Climate Risk	Maximum Number Of Consecutive Dry Days	Individual	Maximum consecutive dry days annually, highlighting prolonged drought conditions.	Proprietary Derived Dataset	Global	N/A
Climate Risk	Water Stres	Individual	Water Stress Index (WSI), calculated as total water demand relative to renewable supply. Values above 1 indicate scarcity.	Proprietary Derived Dataset	Global	N/A
Climate Risk	Average Maximum Sustained Wind Speed	Individual	Historical average of maximum sustained wind speeds, reflecting a location’s storm intensity exposure.	Proprietary Derived Dataset	Global	N/A
Climate Risk	Hurricane Category (Saffir-Simpson Hurricane Wind Scale)	Individual	Hurricane category based on the Saffir-Simpson scale, associated with local wind exposure profiles.	Proprietary Derived Dataset	Global	N/A
Climate Risk	Hurricane 100-Year Return Period	Individual	100-year hurricane return period, indicating the likelihood of a major hurricane impacting the area.	Proprietary Derived Dataset	Global	N/A
Climate Risk	Average Percentage Burnable Fuel Across Different Vegetation Covers	Individual	Average percentage of burnable vegetation within a 2km radius, used to assess wildfire fuel load.	Proprietary Derived Dataset	Global	N/A

{% hint style="success" %} [**Back to top**](#data-themes-links-to-section) {% endhint %} ## Climate Adaptation

Theme	Dataset	Bundled or Individual?	Description	Type of Dataset	Geographic Coverage	Date Range
Climate Adaptation	Global Adaptation Layer	Bundled	Full access to hazard-specific adaptation scores and all underlying data inputs used to derive resilience metrics (see below).	Proprietary Analytics	Global	N/A
Climate Adaptation	Heat Stress Adaptation	Individual	Hazard-specific adaptation score derived from location-specific physical and infrastructural features, and underlying data features used to derive hazard-specific Adaptation Score.	Proprietary Analytics	Global	N/A
Climate Adaptation	Hurricane Adaptation	Individual	Hazard-specific adaptation score derived from location-specific physical and infrastructural features, and underlying data features used to derive hazard-specific Adaptation Score.	Proprietary Analytics	Global	N/A
Climate Adaptation	Inland Flooding Adaptation	Individual	Hazard-specific adaptation score derived from location-specific physical and infrastructural features, and underlying data features used to derive hazard-specific Adaptation Score.	Proprietary Analytics	Global	N/A
Climate Adaptation	Coastal Flooding Adaptation	Individual	Hazard-specific adaptation score derived from location-specific physical and infrastructural features, and underlying data features used to derive hazard-specific Adaptation Score.	Proprietary Analytics	Global	N/A
Climate Adaptation	Drought Adaptation	Individual	Hazard-specific adaptation score derived from location-specific physical and infrastructural features, and underlying data features used to derive hazard-specific Adaptation Score.	Proprietary Analytics	Global	N/A
Climate Adaptation	Wildfire Adaptation	Individual	Hazard-specific adaptation score derived from location-specific physical and infrastructural features, and underlying data features used to derive hazard-specific Adaptation Score.	Proprietary Analytics	Global	N/A
Climate Adaptation	Urban Building Density Index	Individual	Urban building density index reflecting the concentration of built structures. Higher values indicate increased vulnerability to Urban Heat Island (UHI) effects.	Proprietary Derived Dataset	Global	N/A
Climate Adaptation	Urban Greenery Density Index	Individual	Urban greenery density measuring vegetation coverage in built environments. Higher values help mitigate UHI effects and support microclimate regulation.	Proprietary Derived Dataset	Global	N/A
Climate Adaptation	Surface Porosity Index	Individual	Ratio of porous ground surfaces, such as permeable pavement and soil. Higher values reduce runoff and help manage flash flood risk.	Proprietary Derived Dataset	Global	N/A
Climate Adaptation	Inland Flood Barrier Proximity Index	Individual	Proximity to manmade inland flood barriers, such as dykes and retaining walls. Higher scores indicate stronger local flood protection infrastructure.	Proprietary Derived Dataset	Global	N/A
Climate Adaptation	Inland Flood Storage & Control Infrastructure Index	Individual	Proximity to dedicated flood storage and control systems, including reservoirs and retention basins.	Proprietary Derived Dataset	Global	N/A
Climate Adaptation	Inland Drainage Capacity Index	Individual	Assessment of nearby drainage infrastructure, such as ditches and storm drains. Higher values indicate better drainage capacity and flood mitigation.	Proprietary Derived Dataset	Global	N/A
Climate Adaptation	Nature-Based Inland Flood Protection Index	Individual	Proximity to nature-based flood defense systems, including wetlands and bioswales, which offer sustainable flood control.	Proprietary Derived Dataset	Global	N/A
Climate Adaptation	Coastal Defense Infrastructure Proximity Index	Individual	Proximity to manmade coastal flood defense structures such as seawalls, breakwaters, and groynes. Higher values reflect stronger coastal protection.	Proprietary Derived Dataset	Global	N/A
Climate Adaptation	Natural Coastal Buffer Proximity Index	Individual	Proximity to natural coastal buffers, such as beaches, dunes, reefs, and atolls. Higher values indicate increased resilience to coastal flooding.	Proprietary Derived Dataset	Global	N/A
Climate Adaptation	Coastal Drainage Capacity Index	Individual	Proximity to stormwater drainage systems, including open channels and piped networks, aiding local water runoff management.	Proprietary Derived Dataset	Global	N/A
Climate Adaptation	Coastal Flood Control Infrastructure Index	Individual	Proximity to stormwater and flood control infrastructure like basins, floodgates, and water level monitoring stations.	Proprietary Derived Dataset	Global	N/A
Climate Adaptation	Building Strength Index	Individual	Building material strength index estimating structural resilience. Higher values reflect presence of wind-resistant buildings, such as high-rise concrete structures.	Proprietary Derived Dataset	Global	N/A
Climate Adaptation	Water And Wastewater Facility Proximity Index	Individual	Proximity to water and wastewater treatment or monitoring facilities. Higher values indicate stronger water system infrastructure.	Proprietary Derived Dataset	Global	N/A
Climate Adaptation	Water Storage Infrastructure Index	Individual	Proximity to water collection and storage infrastructure, including reservoirs and tanks, supporting local supply capacity.	Proprietary Derived Dataset	Global	N/A
Climate Adaptation	Emergency Water Distribution Amenity Index	Individual	Proximity to emergency water distribution amenities such as public taps and drinking fountains. Higher values indicate local readiness.	Proprietary Derived Dataset	Global	N/A
Climate Adaptation	Groundwater Well Access Index	Individual	Proximity to underground water wells, a critical freshwater source especially in under-resourced or drought-prone areas.	Proprietary Derived Dataset	Global	N/A
Climate Adaptation	Fire Response Infrastructure Index	Individual	Proximity to firefighting infrastructure, including hydrants and emergency water tanks. Higher values reflect stronger fire response capacity.	Proprietary Derived Dataset	Global	N/A
Climate Adaptation	Fire Prevention Measure Coverage Index	Individual	Presence of fire prevention infrastructure such as forest firebreaks and cutlines designed to halt fire spread.	Proprietary Derived Dataset	Global	N/A
Climate Adaptation	Fire Detection Infrastructure Index	Individual	Presence of fire detection infrastructure such as lookout towers. Higher values indicate improved early-warning capability.	Proprietary Derived Dataset	Global	N/A

{% hint style="success" %} [**Back to top**](#data-themes-links-to-section) {% endhint %} ## Climate Financial Impact

Theme	Dataset	Bundled or Individual?	Description	Type of Dataset	Geographic Coverage	Date Range
Climate Financial Impact	Financial Impact Analytics	Bundled	Full access to all Climate Financial Impact metrics and underlying data inputs used to derive these metrics (see below).	Proprietary Analytics	Global	N/A
Climate Financial Impact	Climate Insurance Premium Impact - Fire	Individual	Projected annual insurance premium growth rate for fire insurance based on the change in estimated damage due to wildfire in the next 25 years.	Proprietary Analytics	Global	N/A
Climate Financial Impact	Climate Insurance Premium Impact - Flood	Individual	Projected annual insurance premium growth rate for flood insurance on the change in estimated damage due to coastal and/or inland flooding and/or hurricane in the next 25 years.	Proprietary Analytics	Global	N/A
Climate Financial Impact	Climate Insurance Premium Impact	Individual	Projected total annual insurance premium growth rate for all type of risks (fire, flood, hurricane).	Proprietary Analytics	Global	N/A
Climate Financial Impact	Climate Cooling Demand Impact	Individual	Projected annual change in cooling needs based on estimated cooling degree day changes through the next 25 years.	Proprietary Analytics	Global	N/A
Climate Financial Impact	Climate Heating Demand Impact	Individual	Projected annual change in heating requirements based on estimated heating degree day changes through the next 25 years.	Proprietary Analytics	Global	N/A
Climate Financial Impact	Climate Utility Demand Impact	Individual	Projected annual net change in overall utility consumption, combining cooling and heating demand shifts through the next 25 years.	Proprietary Analytics	Global	N/A
Climate Financial Impact	Fire Retrofit Expenditure Forecast	Individual	Recommended percentage of income to be set aside for retrofit expenditure based on the likelihood of future fire related hazards.	Proprietary Analytics	Global	N/A
Climate Financial Impact	Flood Retrofit Expenditure Forecast	Individual	Recommended percentage of income to be set aside for retrofit expenditure based on the likelihood of future flood and hurricane related hazards.	Proprietary Analytics	Global	N/A
Climate Financial Impact	Thermal Retrofit Expenditure Forecast	Individual	Recommended percentage of income to be set aside for retrofit expenditure based on the increase of cooling demand in the future.	Proprietary Analytics	Global	N/A
Climate Financial Impact	Climate-Related Retrofit Expenditure Forecast	Individual	Sum of all capex calculations (CAPEX_FIRE, CAPEX_FLOOD, CAPEX_CDD).	Proprietary Analytics	Global	N/A
Climate Financial Impact	Climate Discount Rate	Individual	Recommended climate discount rate to be added to the prevailing discount rate. Applicable to assets with a holding period up till 2035.	Proprietary Analytics	Global	N/A

{% hint style="success" %} #### [Back to top](#data-themes-links-to-section) {% endhint %} ## Climate Real Estate Impact

Theme	Dataset	Bundled or Individual?	Description	Type of Dataset	Geographic Coverage	Date Range
Climate Real Estate Impact	El Niño Southern Oscillation (ENSO) impact	Individual	ENSO-driven impact on HPI performance (2015-2023). Seven-tier categorical scoring with Low Minus indicating locations least sensitive to ENSO impact, and High Plus indicating locations with the highest sensitivity.	Proprietary Analytics	USA	2015-2023
Climate Real Estate Impact	P(CD): Probability of climate default	Individual	Likelihood of increase in mortgage delinquency due to physical climate risks compounding existing economic fragility between the present and 2050.	Proprietary Analytics	USA	2025-2050

{% hint style="success" %} [**Back to top**](#data-themes-links-to-section) {% endhint %} ## Climate Macroeconomic Impact

Theme	Dataset	Bundled or Individual?	Description	Type of Dataset	Geographic Coverage	Date Range
Climate Macroeconomic Impact	Climate GDP Impact (Unadapted)	Individual	Projected GDP loss from six climate hazards (heat, flash flooding, riverine flooding, coastal inundation, wind, and drought) at the national and sub-national levels including sectoral (agriculture, manufacturing, services) impact.	Proprietary Analytics	Global	2030-2035 Projection
Climate Macroeconomic Impact	Climate GDP Impact (Adapted)	Individual	Projected GDP loss from six climate hazards (heat, flash flooding, riverine flooding, coastal inundation, wind, and drought) after factoring in the country's existing adaptation capacity.	Proprietary Analytics	Global	2030-2035 Projection

{% hint style="success" %} [**Back to top**](#data-themes-links-to-section) {% endhint %} ## Demographic Trends

Theme	Dataset	Bundled or Individual?	Description	Type of Dataset	Geographic Coverage	Date Range
Demographic Trends	Population Growth Rate	Individual	Annual rate of population growth in the local market, with higher rates indicating rising demand.	Proprietary Derived Dataset	USA	2023
Demographic Trends	Net Gravity Score (NGS)	Individual	A composite migration index that captures fundamental push-pull drivers, local sentiment and spatial influence.	Proprietary Analytics	USA	2026
Demographic Trends	Total National Population	Individual	Estimate of total resident population, providing harmonized and current demographic figures suitable for international comparison.	Proprietary Derived Dataset	Global	1960-2024
Demographic Trends	Population Density	Individual	Population density calculated using official population and land area data to ensure comparability across countries.	Proprietary Derived Dataset	Global	2020-2020
Demographic Trends	Population Growth	Individual	Annual percentage change in national population incorporating natural population gain/loss and net migration.	Proprietary Derived Dataset	Global	1960-2024
Demographic Trends	Dependent Population	Individual	Proportion of each country’s population aged 65 and older, reflecting aging demographics and dependency ratio.	Proprietary Derived Dataset	Global	1960-2024
Demographic Trends	Population Density Per Acre	Individual	Number of people per acre, signaling demand for services and infrastructure.	Proprietary Derived Dataset	USA	2023
Demographic Trends	Under-5 Population Growth Rate	Individual	Ratio of under-5 population change to overall population change from 2005-2024. A higher ratio indicates a growing youth demographic driven by fertility and in-migration.	Proprietary Derived Dataset	USA	2005-2024

{% hint style="success" %} [**Back to top**](#data-themes-links-to-section) {% endhint %} ## Economic Complexity & Innovation

Theme	Dataset	Bundled or Individual?	Description	Type of Dataset	Geographic Coverage	Date Range
Economic Complexity & Innovation	Patents Per Capita	Individual	Patents per capita (2020), used to measure innovation output and technological potential.	Proprietary Derived Dataset	USA	2022
Economic Complexity & Innovation	Economic Complexity Index (ECI)	Individual	Country-level performance on the Economic Complexity Index, which measures the relative knowledge intensity of an economy, based on trade data.	Proprietary Derived Dataset	Global	1998-2023
Economic Complexity & Innovation	AI Preparedness Index	Individual	Country-level performance on the AI Preparedness Index (AIPI), which assesses a country’s digital infrastructure, human capital and labor market policies, innovation and economic integration, and regulation and ethics.	Proprietary Derived Dataset	Global	2023-2023
Economic Complexity & Innovation	Global Innovation Index	Individual	Country-level performance on the Global Innovation Index, which quantifies over 80 indicators including policy environment, education, infrastructure, and knowledge creation.	Proprietary Derived Dataset	Global	2024-2024
Economic Complexity & Innovation	ICT Development Index	Individual	Country-level performance on the ICT Development Index, which assesses the level of information and communication technology (ICT) development across countries.	Proprietary Derived Dataset	Global	2025-2025

## Environmental Sustainability

Theme	Dataset	Bundled or Individual?	Description	Type of Dataset	Geographic Coverage	Date Range
Environmental Sustainability	Per Capita Emissions	Individual	CO₂ emissions per capita.	Proprietary Derived Dataset	Global	1750-2023
Environmental Sustainability	Environmental Performance Index	Individual	Country-level performance on the Environmental Performance Index, based on 58 performance indicators on climate change performance, environmental healthy, and ecosystem vitality.	Proprietary Derived Dataset	Global	2024-2024
Environmental Sustainability	Air Pollution	Individual	Annual average PM2.5 concentration.	Proprietary Derived Dataset	Global	2022-2022
Environmental Sustainability	Food Security Index	Individual	Country-level performance on the Food Security Index which measures food affordability, availability, quality, safety, sustainability and adaptation across 68 indicators.	Proprietary Derived Dataset	Global	2022-2022
Environmental Sustainability	Unhealthy Air Quality	Individual	Days per year with Air Quality Index (AQI) above 100. Population groups with sensitivities such as heart or lung conditions may experience health effects and the general public should limit outdoor activity.	Proprietary Derived Dataset	USA	2023

{% hint style="success" %} [**Back to top**](#data-themes-links-to-section) {% endhint %} ## Energy Reliability & Security

Theme	Dataset	Bundled or Individual?	Description	Type of Dataset	Geographic Coverage	Date Range
Energy Reliability & Security	Renewable Energy Generation	Individual	Total primary energy generated from renewable sources including solar, wind and hydropower.	Proprietary Derived Dataset	Global	1965-2024
Energy Reliability & Security	Renewables Share of Generation	Individual	Share of electricity generated by renewables including solar, wind, hydropower, bioenergy, geothermal, wave and tidal sources.	Proprietary Derived Dataset	Global	1985-2023
Energy Reliability & Security	Electricity Access (% of Population)	Individual	Share of population with access to electricity.	Proprietary Derived Dataset	Global	1960-2023
Energy Reliability & Security	Total Power Capacity	Individual	Total power plant capacity in a 50km radius.	Proprietary Derived Dataset	Global	2013-2017
Energy Reliability & Security	Energy Trilemma Index	Individual	Country-level performance on the Energy Trilema Index, which quantifies performance across three dimensions: energy security, energy equity and environmental sustainability.	Proprietary Derived Dataset	Global	2023-2023
Energy Reliability & Security	Electricity Rate	Individual	Average electricity price at the state level. Lower rates indicate lower operating costs.	Proprietary Derived Dataset	USA	2023
Energy Reliability & Security	Energy Generation	Individual	Total state-level energy generation capacity, associated with grid stability and affordability.	Proprietary Derived Dataset	USA	2023
Energy Reliability & Security	Energy Grid Interruptions	Individual	Average service outage duration based on CAIDI. Lower values indicate higher grid reliability.	Proprietary Derived Dataset	USA	2023

{% hint style="success" %} [**Back to top**](#data-themes-links-to-section) {% endhint %} ## Fiscal Health

Theme	Dataset	Bundled or Individual?	Description	Type of Dataset	Geographic Coverage	Date Range
Fiscal Health	Current Account Balance (% of GDP)	Individual	Balance of current transactions (counting goods and services, earned income and transfer income) between residents and non-residents.	Proprietary Derived Dataset	Global	1960-2024
Fiscal Health	General Gross Government Debt (% of GDP)	Individual	Ratio of gross public debt to GDP.	Proprietary Derived Dataset	Global	1980-2025
Fiscal Health	Government Expenditure (% of GDP)	Individual	General government spending as a percentage of GDP.	Proprietary Derived Dataset	Global	1800-2023

{% hint style="success" %} [**Back to top**](#data-themes-links-to-section) {% endhint %} ## Geopolitics & Diplomacy

Theme	Dataset	Bundled or Individual?	Description	Type of Dataset	Geographic Coverage	Date Range
Geopolitics & Diplomacy	Global Soft Power Index	Individual	Country-level performance on Global Soft Power Index ranking countries by influence, familiarity and reputation without military or economic coercion.	Proprietary Derived Dataset	Global	2022-2025
Geopolitics & Diplomacy	Political Stability and the Absence of Violence / Terrorism	Individual	Worldwide Governance Indicator for Political Stability and the Absence of Violence / Terrorism, which measures perceptions of the likelihood that the government will be destabilized or overthrown.	Proprietary Derived Dataset	Global	1996-2023

{% hint style="success" %} [**Back to top**](#data-themes-links-to-section) {% endhint %} ## Governance & Regulatory Environment

Theme	Dataset	Bundled or Individual?	Description	Type of Dataset	Geographic Coverage	Date Range
Governance & Regulatory Environment	Government Effectiveness	Individual	Worldwide Governance Indicator for Governance Effectiveness, which captures perceptions on the quality of public and civil services.	Proprietary Derived Dataset	Global	1996-2023
Governance & Regulatory Environment	Legal and Regulatory Risk	Individual	Equally-weighted combination of World Bank Worldwide Governance Indicators for Rule of Law and Regulatory Quality.	Proprietary Derived Dataset	Global	1996-2023

{% hint style="success" %} [**Back to top**](#data-themes-links-to-section) {% endhint %} ## Infrastructure & Connectivity

Theme	Dataset	Bundled or Individual?	Description	Type of Dataset	Geographic Coverage	Date Range
Infrastructure & Connectivity	Gross Fixed Capital Formation	Individual	Gross Fixed Capital Formation as a share of GDP, measuring investment in infrastructure, machinery, and assets.	Proprietary Derived Dataset	Global	N/A
Infrastructure & Connectivity	Highway Endpoint Density	Individual	Number of highway endpoints near a tract, indicating regional road network connectivity.	Proprietary Derived Dataset	USA	2023
Infrastructure & Connectivity	Rail Nodes Density	Individual	Density of rail nodes near each tract, reflecting access to rail freight and passenger infrastructure.	Proprietary Derived Dataset	USA	2023
Infrastructure & Connectivity	Airport Density	Individual	Number of seaports in proximity, enhancing logistics and trade access.	Proprietary Derived Dataset	USA	2023
Infrastructure & Connectivity	Truck Stop Density	Individual	Count of nearby truck stops, indicating regional freight movement support.	Proprietary Derived Dataset	USA	2023
Infrastructure & Connectivity	Micro-Grid Capacity	Individual	Total installed capacity of microgrids (kW), contributing to local energy resilience.	Proprietary Derived Dataset	USA	2023
Infrastructure & Connectivity	EV Charging Stations	Individual	Number of EV charging stations in the ZIP code.	Proprietary Derived Dataset	USA	2023-2025
Infrastructure & Connectivity	Airport Passenger Volume	Individual	Number of air passengers carried by airlines registered in each country, including domestic and international travel.	Proprietary Derived Dataset	Global	2000-2024
Infrastructure & Connectivity	Infrastructure Quality	Individual	Country-level performance on the Logistics Performance Index, which scores six dimensions of trade including customs performance, infrastructure quality, and timeliness of shipments.	Proprietary Derived Dataset	Global	2018-2023
Infrastructure & Connectivity	Fixed Capital Formation Ratio (FCFR)	Individual	Gross fixed capital formation as a share of GDP, reflecting national investment in infrastructure and productive assets.	Proprietary Derived Dataset	Global	1960-2024
Infrastructure & Connectivity	Transportation Infrastructure	Individual	Geocoded linear transportation infrastructure (roads, railways) underpinning calculations of logistics efficiency and economic multiplier effects.	Proprietary Derived Dataset	Global	Current
Infrastructure & Connectivity	Energy Infrastructure	Individual	Geocoded energy infrastructure (power generation and transmission) underpinning calculations of grid resilience.	Proprietary Derived Dataset	Global	Current
Infrastructure & Connectivity	Number of Data Centers	Individual	Total number of data centers in a 50km radius; includes data center that are currently under construction.	Proprietary Derived Dataset	Global	2023 - 2025

{% hint style="success" %} [**Back to top**](#data-themes-links-to-section) {% endhint %} ## Labor Market Dynamics

Theme	Dataset	Bundled or Individual?	Description	Type of Dataset	Geographic Coverage	Date Range
Labor Market Dynamics	Education Attainment	Individual	Share of the population with a bachelor’s degree or higher. Higher values correlate with livability and earnings.	Proprietary Derived Dataset	USA	2023
Labor Market Dynamics	Manufacturing employment	Individual	Total number of current employees in the manufacturing and warehousing sector.	Proprietary Derived Dataset	USA	2023
Labor Market Dynamics	Workforce Population	Individual	Total number of eligible workers across all sectors.	Proprietary Derived Dataset	USA	2023
Labor Market Dynamics	Employment-Population Ratio	Individual	Share of employed persons as a percent of the total of working-age population.	Proprietary Derived Dataset	Global	1948-2023
Labor Market Dynamics	Youth Population	Individual	Percentage of population aged 0-14	Proprietary Derived Dataset	Global	1960-2024
Labor Market Dynamics	Working Age Population	Individual	Share of population aged 15-64.	Proprietary Derived Dataset	Global	1960-2024
Labor Market Dynamics	Unemployment Rate	Individual	The number of unemployed persons as a share of the total workforce.	Proprietary Derived Dataset	Global	1980-2025
Labor Market Dynamics	Educational Attainment	Individual	Share of population (aged 25 and above) with at least a Bachelor's or equivalent.	Proprietary Derived Dataset	Global	1970-2022

{% hint style="success" %} [**Back to top**](#data-themes-links-to-section) {% endhint %} ## Liveability & Social Wellbeing

Theme	Dataset	Bundled or Individual?	Description	Type of Dataset	Geographic Coverage	Date Range
Livability & Social Wellbeing	Amenity And Walkability Index	Individual	Walkability index incorporating access to amenities and commute distance. Higher scores reflect livability.	Proprietary Derived Dataset	USA	2023
Livability & Social Wellbeing	Global Share Of Vulnerable Population	Individual	Share of population under 15 or over 65, reflecting dependency ratio and service needs.	Proprietary Derived Dataset	Global	N/A
Livability & Social Wellbeing	Human Development Index	Individual	Human Development Index (HDI) score, capturing education, health, and income performance.	Proprietary Derived Dataset	Global	N/A
Livability & Social Wellbeing	Health Index	Individual	Access to health infrastructure and healthiness of the local population, measured by aggregating disease prevalence, access to hospitals, and insurance peneration. A higher index score indicates better healthcare quality.	Proprietary Derived Dataset	USA	2023
Livability & Social Wellbeing	Life Expectancy	Individual	Total life expectancy at birth.	Proprietary Derived Dataset	Global	1960-2023
Livability & Social Wellbeing	Healthcare Spending (% of GDP)	Individual	Annual health expenditure as a percentage of GDP.	Proprietary Derived Dataset	Global	2000-2022
Livability & Social Wellbeing	Life Satisfaction	Individual	Average national life satisfaction score (0–10) from the World Happiness Report.	Proprietary Derived Dataset	Global	2011-2025
Livability & Social Wellbeing	Violent Crime	Individual	Nationally reported statistics on violent offences per 100,000 residents.	Proprietary Derived Dataset	Global	2015-2023
Livability & Social Wellbeing	Inequality-adjusted Human Development Index (IHDI)	Individual	Measures a country's achievements in health, education and income, while accounting for inequalities in their distribution across the population.	Proprietary Derived Dataset	Global	1990-2023
Livability & Social Wellbeing	High-Quality Affordable Retirement	Individual	Top 96 destinations across 24 countries for retirement attractiveness, based on costs, amenities, health care, language, crime and climate risk.	Proprietary Derived Dataset	Global	2025

{% hint style="success" %} [**Back to top**](#data-themes-links-to-section) {% endhint %} ## Macroeconomic Performance

Theme	Dataset	Bundled or Individual?	Description	Type of Dataset	Geographic Coverage	Date Range
Macroeconomic Performance	Gross National Income	Individual	Downscaled 2022 Gross National Income within a 500m radius using satellite nightlight imagery.	Proprietary Derived Dataset	Global	N/A
Macroeconomic Performance	Job Growth Rate	Individual	Annual job growth rate at the census tract level, used to gauge local economic momentum.	Proprietary Derived Dataset	USA	2023
Macroeconomic Performance	Income Growth Rate	Individual	Annual income growth rate at the census tract level, reflecting improving living standards.	Proprietary Derived Dataset	USA	2023
Macroeconomic Performance	Rent As Percentage Of Income	Individual	Average rental cost as a percentage of per capita income, measuring housing affordability.	Proprietary Derived Dataset	USA	2023
Macroeconomic Performance	Housing To Income Ratio	Individual	Ratio of median house price to median income, used to assess housing market affordability and demand.	Proprietary Derived Dataset	USA	2023
Macroeconomic Performance	US Greenfield Investment Tracker (2022 To Present)	Individual	Cumulative value of new corporate investment by county since 2022, indicating recent economic growth and locational momentum.	Proprietary Dataset	USA	2022 - 2025
Macroeconomic Performance	PPP-Adjusted GDP	Individual	Gross domestic product (GDP) expressed in current international dollars, converted by purchasing power parities (PPPs).	Proprietary Derived Dataset	Global	1990-2024
Macroeconomic Performance	Nominal GDP Growth Rate	Individual	Annual percentage change in nominal GDP.	Proprietary Derived Dataset	Global	1961-2024
Macroeconomic Performance	GDP Per Capita Growth Rate	Individual	Annual percentage change in GDP per capita.	Proprietary Derived Dataset	Global	1961-2024
Macroeconomic Performance	Sovereign Credit Rating	Individual	Aggregated sovereign credit rating calculated from key ratings agencies such as Moody’s, S&P and Fitch.	Proprietary Derived Dataset	Global	2025-2025
Macroeconomic Performance	Foreign Direct Investment (FDI)	Individual	Net foreign direct investment (FDI) inflows in current terms.	Proprietary Derived Dataset	Global	1970-2024
Macroeconomic Performance	Currency Volatility	Individual	5-year standard deviation of percentage change in annual bilateral exchange rates relative to the USD (nominal).	Proprietary Derived Dataset	Global	2024
Macroeconomic Performance	Inflation Volatility	Individual	5-year standard deviation of the annual percentage change in inflation (consumer prices).	Proprietary Derived Dataset	Global	2023
Macroeconomic Performance	Inflation Rate Risk	Individual	Absolute deviation of the annual change in Consumer Price Index (%) from a 2% target rate.	Proprietary Derived Dataset	Global	2023
Macroeconomic Performance	Total Inflation Risk (Rate and Volatility)	Individual	Equally-weighted combination of inflation volatility (5-year standard deviation of annual percentage change in inflation) and inflation rate risk (absolute deviation of annual change in inflation from a 2% target rate).	Proprietary Derived Dataset	Global	2023
Macroeconomic Performance	Trade (% of GDP)	Individual	Total trade as a percentage of GDP, reflecting economic openness and trade dependency.	Proprietary Derived Dataset	Global	1960-2024
Macroeconomic Performance	Country Risk Premium	Individual	Equity country risk premium estimating the default spread for each country's local currency sovereign rating over a default-free government bond rate.	Derived Dataset	Global	2026

{% hint style="success" %} [**Back to top**](#data-themes-links-to-section) {% endhint %} ## Market & Business Conditions

Theme	Dataset	Bundled or Individual?	Description	Type of Dataset	Geographic Coverage	Date Range
Market & Business Conditions	Corporate Tax Rate	Individual	National corporate tax rate.	Proprietary Derived Dataset	Global	1980-2024
Market & Business Conditions	Domestic Credit to Private Sector (% of GDP)	Individual	Loans to the private sector by domestic financial institutions.	Proprietary Derived Dataset	Global	1960-2024
Market & Business Conditions	Internet Speed	Individual	Average broadband internet speed, measuring digital infrastructure strength and connectivity.	Proprietary Derived Dataset	USA	2023
Market & Business Conditions	Annual Change In Business Establishments	Individual	Net annual change in business establishments, indicating local economic dynamism.	Proprietary Derived Dataset	USA	2023

{% hint style="success" %} [**Back to top**](#data-themes-links-to-section) {% endhint %} ## Migration & Talent Flows

Theme	Dataset	Bundled or Individual?	Description	Type of Dataset	Geographic Coverage	Date Range
Migration & Talent Flows	Net Migration	Individual	Estimate of each country’s net total international migrants, calculated as the difference between immigrants and emigrants, including citizens and noncitizens.	Proprietary Derived Dataset	Global	1960-2024
Migration & Talent Flows	Migration Openness Ranking	Individual	Ranking of all countries and territories by number of visa-free destinations for their nationals.	Proprietary Derived Dataset	Global	2025-2025
Migration & Talent Flows	HNW Migration	Individual	Net millionaire migration into the country.	Proprietary Derived Dataset	Global	2025-2025
Migration & Talent Flows	Digital Nomad Attractiveness	Individual	Top 500 cities for digital nomads based on attractiveness to remote workers according to cost, Wi-Fi speed, safety, weather, community and user reviews.	Proprietary Derived Dataset	Global	2025-2025
Migration & Talent Flows	Global Wealth Mobility Framework (GWMF)	Individual	Henley & Partners Global Wealth Mobility Framework (GWMF) competitiveness score.	Proprietary Analytics	Global	2026

{% hint style="success" %} [**Back to top**](#data-themes-links-to-section) {% endhint %} ## Real Estate Market Data

Theme	Dataset	Bundled or Individual?	Description	Type of Dataset	Geographic Coverage	Date Range
Real Estate Market Data	Residential Building Permits	Individual	Annual count of residential building permits, signaling housing supply pipeline and development pace.	Proprietary Derived Dataset	USA	2023
Real Estate Market Data	Pedestrian And Commuter Traffic	Individual	An indexed score from the EPA calculated from the frequency of transits on foot, vehicles, and public transport.	Proprietary Derived Dataset	USA	2023
Real Estate Market Data	Units Planned And Under Construction	Individual	Residential permit issuance rate, correlated with demand for office and retail property.	Proprietary Derived Dataset	USA	2023
Real Estate Market Data	Land Costs Per Acre	Individual	Average land cost per acre at the census tract level. Lower values reflect higher availability or lower demand.	Proprietary Derived Dataset	USA	2023
Real Estate Market Data	Potential Federal Land Privatization	Individual	Percentage of federally owned land potentially slated for privatization and suitable for residential real estate development.	Proprietary Derived Dataset	USA	2025
Real Estate Market Data	Residential Property Price Growth	Individual	Year-on-year change in real residential property prices.	Proprietary Derived Dataset	Global	1960-2025

{% hint style="success" %} [**Back to top**](#data-themes-links-to-section) {% endhint %} ## Societal Income & Wealth

Theme	Dataset	Bundled or Individual?	Description	Type of Dataset	Geographic Coverage	Date Range
Societal Income & Wealth	Gross National Income (GNI) Per Capita	Individual	Gross national income per capita measured in current USD.	Proprietary Derived Dataset	Global	2024
Societal Income & Wealth	Median Post-Tax Income	Individual	Estimated median household disposable income after taxes and transfers, adjusted for inflation and PPP.	Proprietary Derived Dataset	Global	2023
Societal Income & Wealth	Poverty Rate (% of population)	Individual	Share of population living below poverty rates, as measured according to national poverty lines.	Proprietary Derived Dataset	Global	1985-2024
Societal Income & Wealth	Income Inequality	Individual	Income inequality measured by the national Gini coefficient.	Proprietary Derived Dataset	Global	1963-2024
Societal Income & Wealth	Household Debt (% of GDP)	Individual	Household debt (loans and securities) as a percentage of GDP.	Proprietary Derived Dataset	Global	2024
Societal Income & Wealth	Cost of Living Composite Index	Individual	Composite cost of living comprising rent, groceries, restaurants and other local purchases.	Proprietary Derived Dataset	Global	2026

{% hint style="success" %} [**Back to top**](#data-themes-links-to-section) {% endhint %} # Platform Overview AlphaGeo's SaaS platform enables on-demand location, asset, and portfolio analysis. Use it to upload and organize asset data, run analysis, assess assets, share results, and export reports. If you use AlphaGeo through direct data delivery, see our [Data APIs](/for-developers/alphageo-data-api) page. The following pages guide you through each platform feature in more detail: * [Admin Panel](/alphageo-platform/admin-panel) — manage team members and permissions. * [Data Manager](/alphageo-platform/data-manager) — upload, tier, edit, and export your data. * [Location Explorer](/alphageo-platform/location-explorer) — run on-demand analysis for any location. * [Portfolio Analytics](/alphageo-platform/portfolio-analytics) — assess climate risk across portfolios and assets. * [Multi-asset View](/alphageo-platform/portfolio-analytics/multi-asset-view) * [Single-asset View](/alphageo-platform/portfolio-analytics/single-asset-property-map) * [Hazard Alerts View](/alphageo-platform/portfolio-analytics/hazard-alerts-view) * [Remediation Checklist](/alphageo-platform/portfolio-analytics/remediation-checklist) * [Microsites](/alphageo-platform/sharing-analytics) — share dashboards with external collaborators. * [Creating and sharing microsites](/alphageo-platform/sharing-analytics/creating-and-sharing-microsites) * [Accessing and viewing microsites](/alphageo-platform/sharing-analytics/accessing-and-viewing-microsites) * [Exporting Analytics](/alphageo-platform/exporting-analytics) — download PDF reports and XLS files. * [AlphaGeo Explorer](/alphageo-platform/alphageo-explorer) — *beta* module combining Portfolio Analytics and Location Explorer workflows. # Admin Panel Manage team members and permissions. The Admin Panel allows users with Admin permissions to add team members and configure their access permissions. Watch the **guided demo** below to learn more (we recommend viewing this in **full-screen mode**). {% embed url="" %} # Data Manager Upload, tier, edit, and export your data. ## **Uploading your data** There are two ways to upload your data: bulk upload via our data upload template, or directly on the app via our Data Manager tool. Instructions for bulk uploads via our Data Upload Template are below. ![](https://9348326.fs1.hubspotusercontent-na1.net/hubfs/9348326/Screenshot%202023-11-14%20at%204-18-00%E2%80%AFPM-png.png) **Note: Drag & Drop / Browse files method should only be utilized once your data is formatted in the designated template provided.** To download the template used for your upload, click the hyperlinked Excel file within the first line of instruction "Data Upload Template." ![](https://9348326.fs1.hubspotusercontent-na1.net/hubfs/9348326/Screenshot%202023-11-14%20at%204-49-19%E2%80%AFPM-png.png) *Tip: The data fields marked with a red asterisk are compulsory!* ***Tiering your data*** When uploading your properties, you are required to create your first tier. Tiering is essential for building parent/child hierarchies within your dataset. These hierarchies capture nuances like internal/ external reference data surrounding fund/ portfolio composition, including, but not limited to: * Firm, * Fund, * Strategy * Manager/Operator, * Portfolio/Investment/Pool/Deal, * Capital Contribution, * Etc.\ \ ![](https://9348326.fs1.hubspotusercontent-na1.net/hubfs/9348326/Screenshot%202024-02-05%20at%2011-43-57%E2%80%AFAM-png.png)\ ![](https://9348326.fs1.hubspotusercontent-na1.net/hubfs/9348326/Screenshot%202024-02-05%20at%2011-31-04%E2%80%AFAM-png.png) *Tip: Swap "INSERT TIER 1 NAME (ex. Strategy)," etc., for your desired nomenclature - this will reflect in the UI & subsequent exports!* **Note: Each Tier 1, 2, and 3 has its own child tiers, and while the names could be the same, parent tiers are always unique.** **Caution: Deleting a tier will remove the associated underlying data since the tier serves as a grouping mechanism. Alternatively, to retain the underlying data, update the current tier to another, and then proceed with the deletion of the now-empty, i.e., former parent tier.** ## **Editing your data** Once your data is uploaded into the application, it will appear in the "Assets" tab of the Data Manager. Here, you can filter your data entries, check their upload status and review their composition for potential edits. ![Data Manager "Assets" tab](https://9348326.fs1.hubspotusercontent-na1.net/hubfs/9348326/Screenshot%202023-11-20%20at%208-41-39%E2%80%AFAM-png-1.png) *Tip: After selecting an asset (or multiple assets), right-click to either edit, duplicate or delete the entry(-ies). You can create another row by clicking "Add Property," with option to add further granularity by ticking "Show additional fields," and quick-search for properties by address, state, ZIP code, etc.* **Caution: Data fields with material impact on the location of your asset (Street Address, City, State/Provence, Country, ZIP Code and/or Lat/Long Coordinates) will consume another credit if changed!** ***Bulk-editing Your Data*** To apply a change across multiple assets at once, select a single asset in a given dataset within the Data Manager, right click (as shown above), select edit, edit the desired field and hit "Apply to All," and submit the change using checkmark as depicted below.

*Tip: Make sure you've "selected" and "applied" filters to your data as to not inadvertently affect another other assets when applying a bulk edit!* ***Exporting your data***

To export your data from the Data Manager, navigate to the middle "button" in the upper-right hand corner of the interface. *Tip: Remove any filters if you'd like to export ALL your property data.* # Exporting Analytics Download PDF reports and XLS files.

Download a PDF Report or XLS of your results

To export your analysis, navigate to the middle "button" in the upper-right hand corner of the interface. # Portfolio Analytics Assess climate risk across portfolios and assets. Portfolio Analytics is a comprehensive portal to analyze the climate risk and opportunities of your existing investment portfolio. Click through to the following sub-pages to learn how to navigate its key features: * [Multi-asset View](/alphageo-platform/portfolio-analytics/multi-asset-view) * [Single-asset View](/alphageo-platform/portfolio-analytics/single-asset-property-map) * [Hazard Alerts View](/alphageo-platform/portfolio-analytics/hazard-alerts-view) * [Remediation Checklist](/alphageo-platform/portfolio-analytics/remediation-checklist) # Multi-asset View Multi-asset View in Portfolio Analytics helps you assess and report on climate risk across your portfolio. Watch the **guided demo** below to learn more (we recommend viewing this in **full-screen mode**). {% hint style="info" %} For details on the scoring methodology behind the analytics that you will see in this dashboard, please see our documentation for the [Climate Risk and Resilience Index](/climate-resilience-suite/climate-risk-and-resilience-index) and [Financial Impact Analytics](/climate-resilience-suite/financial-impact-analytics). {% endhint %} {% embed url="" %} # Single-asset View Conduct asset-level climate risk and financial impact assessments. Single-asset View in Portfolio Analytics helps you assess climate risk and financial impact at the asset level. Watch the **guided demo** below to learn more (we recommend viewing this in **full-screen mode**). {% hint style="info" %} For details on the scoring methodology behind the analytics that you will see in this dashboard, please see our documentation for the [Climate Risk and Resilience Index](/climate-resilience-suite/climate-risk-and-resilience-index) and [Financial Impact Analytics](/climate-resilience-suite/financial-impact-analytics). {% endhint %} {% embed url="" %} # Hazard Alerts View Monitor live and near-term hazards across your portfolio. Hazard Alerts View in Portfolio Analytics helps you monitor live and near-term hazard risk across assets in your portfolio. Watch the **guided demo** below to learn more (we recommend viewing this in **full-screen mode**). {% hint style="info" %} This is a navigational guide on accessing and using Hazard Alerts on our platform. For product details, hazard coverage, and data sources, please see [Hazard Alerts overview](/climate-resilience-suite/hazard-alerts). {% endhint %} {% embed url="" %} # Remediation Checklist Assess asset-level adaptation measures and understand how they affect resilience-adjusted risk. Use the Remediation Checklist to incorporate asset-level remediation measures into resilience-adjusted risk analysis. Your responses also help identify next steps for adaptation, as measures that are not yet implemented can be prioritized for future adaptation planning. Watch the **guided demo** below to learn more (we recommend viewing this in **full-screen mode**). {% hint style="info" %} For methodological details on how asset-level remediations are used in our resilience-adjusted risk calculations, please see [Remediation Checklist: Quantifying Asset-level Adaptation](broken://pages/xxzKUuRgr3wkPiaRixkp). {% endhint %} {% embed url="" %} # Location Explorer Run on-demand analysis for any location. Location Explorer is a powerful yet easy to use platform for on-demand spatial analytics. With a single click or search, you will be able to access all the data that AlphaGeo offers for the location. It can also automatically aggregate data in an administrative area, such as an entire city, so that you can access data at any granularity you'd like. Watch the **guided demo** below to learn more (we recommend viewing this in **full-screen mode**). {% hint style="info" %} For details on the scoring methodology behind the analytics that you will see in this dashboard, please see our documentation for the [Climate Risk and Resilience Index](/climate-resilience-suite/climate-risk-and-resilience-index) and [Financial Impact Analytics](/climate-resilience-suite/financial-impact-analytics). {% endhint %} {% embed url="" %} # Microsites Share dashboards with external collaborators. Microsites allow you to share your AlphaGeo dashboard directly with collaborators, including with stakeholders who do not have an AlphaGeo subscription or account. They provide a more interactive experience than PDF or Excel exports. Collaborators can view your data filters, charts, visualizations, and platform features such as the Remediation Checklist. Click through the following sub-pages to learn how to create, share, access, and navigate microsites: * [Creating and sharing microsites](/alphageo-platform/sharing-analytics/creating-and-sharing-microsites) * [Accessing and viewing microsites](/alphageo-platform/sharing-analytics/accessing-and-viewing-microsites) (hint: share this page with external collaborators who are new to AlphaGeo and need support with their microsite) {% hint style="warning" %} Changes to the underlying data appear in any active shared link until it expires. Set filters before you share. Always set an expiration date to reduce the risk of exposing confidential data. {% endhint %} # Creating and sharing microsites ### Overview Microsites allow you to share your AlphaGeo dashboard directly with collaborators. They provide a more interactive experience than PDF or Excel exports. Collaborators can view your data filters, charts, visualizations, and platform features such as the Remediation Checklist. ### Guidelines Because microsites are often shared with recipients who are not AlphaGeo customers, certain restrictions apply: * Microsite recipients can only see the filtered view you share with them. They cannot see any assets or portfolios that have been filtered out. * Microsite recipients can access the following features: * **Portfolio Analytics**: Multi-asset view and Single-asset view * **Asset Remediation Checklist**: Use microsites to share asset checklists with recipients. Any answers saved by a recipient also appear in your dashboard and are saved to the relevant asset and portfolio. * The following features are not available to microsite recipients: * **Location Explorer**: The ability to search for and add new assets is limited to AlphaGeo subscribers only. ### Guided Demo Use the **guided demo** below to learn how to create and share an AlphaGeo microsite. {% embed url="" %} # Accessing and viewing microsites Use the **guided demo** below to learn how to access and navigate an AlphaGeo microsite that has been shared with you. ### Guidelines Because microsites are often shared with recipients who are not AlphaGeo customers, certain restrictions apply: * Microsite recipients can only see the filtered view you share with them. They cannot see any assets or portfolios that have been filtered out. * Microsite recipients can access the following features: * **Portfolio Analytics**: Multi-asset view and Single-asset view * **Asset Remediation Checklist**: Use microsites to share asset checklists with recipients. Any answers saved by a recipient also appear in your dashboard and are saved to the relevant asset and portfolio. * The following features are not available to microsite recipients: * **Location Explorer**: The ability to search for and add new assets is limited to AlphaGeo subscribers only. {% embed url="" %} # AlphaGeo Explorer Get oriented in the app and learn the main workflows. AlphaGeo Explorer provides a single integrated view to view your portfolio assets and search for new locations. Explore our user guides for AlphaGeo Explorer below.

START HERE: OVERVIEW

{% embed url="" %}

SINGLE AND MULTI-ASSET ANALYSIS

{% embed url="" %}

CONDUCT PORTFOLIO-LEVEL ANALYSIS

{% embed url="" %}

COMPARE ASSETS AND PORTFOLIOS

{% embed url="" %}

SEARCH AND SAVE NEW ASSETS / LOCATIONS

{% embed url="" %}

# Trial Access Interested in trialing our product? Here's how. Register to create an account using this link: . **Please note the following trial restrictions:** * Access expires 7 days after initial login * Location Explorer is limited to 5 search attempts * Data Analysis cannot be exported * Portfolio Analytics users are limited to 1 Single Asset Report, which can be downloaded from the pre-uploaded default portfolio (any of the 20 assets therein). * Trial users cannot edit assets within the Data Manager.

Once form details have been filled out and our [Terms and Conditions](https://alphageo.ai/legal-items-and-disclosures/) acknowledged, you will be directed to the user interface.

[Location Explorer](/alphageo-platform/location-explorer) will be your trial landing page, where you can search up to 5 locations in the world by address, town, city, county or province/state to analyze against one another. This data will be available for 7 days after your first successful login. You also have access to [Portfolio Analytics](/alphageo-platform/portfolio-analytics), which simulates multi-asset and single-asset portfolio assessment. Within Single Asset View, you can hone in on a location within the Demo Portfolio.

Multi-asset View within Portfolio Analytics Trial Instance

If a sample location within the Demo Portfolio piques interest, we encourage you to export it into a visualized report using the "Single Asset Report" feature.

# FAQ: Navigating Our SaaS Platform Frequently asked questions about our native SaaS application, AlphaGeo Explorer **Does the company have a customer portal for clients to access and manage their accounts?** Yes, within the Admin Panel of the application software. During onboarding, clients must elect administrators to manage accounts (users, permissions, credit consumption) on their behalf. See [Admin Panel](/alphageo-platform/admin-panel). **What data do I need to provide to run my analysis?** The more precise the data you provide, the more detailed your results will be. * **Preferred input**: Coordinates (longitude and latitude) yield the most accurate analysis. * **Alternate input**: If coordinates aren't available, you can enter a street address, city, state, and country. The system will generate approximate coordinates from this information. * **Minimum required**: At least a city name and country. **Note:** Higher-level inputs (like a city name alone) will return more generalized results. For example, entering a city name provides an average across all ZIP/postal codes in that area, while entering a ZIP/postal code yields an average across street addresses within that zone. **What is the largest amount of users for your product at a given site?** Our application software can support any number of users within a given microsite – it all depends on how the client wishes to utilize the product (multiple users under a single company profile with individual logins and member administrators or a single login for the entire company). **How can clients receive support from AlphaGeo?** To address any concerns related to the [Services ](https://alphageo.ai/legal-items-and-disclosures/)or to request additional information about the use of the Services, please contact us at . **How do "credits" work?** Clients purchase **credits** to access our datasets — including the *Climate Risk & Resilience Index*, *Financial Impact Analytics*, and *Location Dynamism Signals*. If a client has access to all datasets, a single credit unlocks results across all of them. A **credit** (i.e., a *search* or *query*) is consumed when results are rendered for a location or asset — regardless of whether the information is saved to the user’s Data Manager (we highly recommend doing so!). To **retain** the analysis, the asset must be added to the user’s portfolio.\ When uploading assets in bulk, credits are deducted based on the total number of assets uploaded. **What does this error message mean? "In column header 'Tier 1,' please assign an existing Tier 1 grouping to all assets or add a new one."** If you encounter this error when [uploading your assets](/alphageo-platform/data-manager), please check that, in your upload template, the Tier-1 Column Name (Yellow text “INSERT TIER 1 NAME”) has been updated. For more information, visit [Data Manager](/alphageo-platform/data-manager). **What types of assets do you cover?** AlphaGeo’s data is asset-type agnostic. Data fields like “Property Type,” “Valuation,” and “Year of Valuation” are user-led reference data. It does not impact the analytics delivered by AlphaGeo, as the data only considers the surrounding “area” and not the asset itself. # Data Delivery Access AlphaGeo data via the following options. ### Platform access: The AlphaGeo Explorer provides a user-friendly, map-based interface that supports on-demand address and coordinate searches and portfolio management with the functionality of data export via excel. [Register for a free trial here.](https://app.alphageo.ai/trial_setup?_gl=1*1g7lrdh*_ga*MTIwMTA2NzQ5MC4xNzE1ODY1NTE3*_ga_GJ386VZ4Y1*MTczODgzMDI4MS4zOS4wLjE3Mzg4MzAyODEuMC4wLjA.*_gcl_au*MTY2ODE4MzkyNS4xNzMyNDQzMzYx*_ga_H5PF8KS554*MTczODgzMDI4MS4zOS4wLjE3Mzg4MzAyODEuMC4wLjA.) ### API and data cloud access: For large volume data access, we support address-based query using our [REST API for developers.](/for-developers/alphageo-data-api) For Snowflake users, we support data delivery via the [AlphaGeo Snowflake Native App](/for-developers/snowflake-native-application). ### Aggregated data access: We support aggregation of our address level data to various administrative boundaries (e.g. census tract, zip code, county, city/metro, state/province, etc.) or custom areas using polygon shapefiles. Contact [info@alphageo.ai ](mailto:undefined)for more information. # Data APIs ## Overview The AlphaGeo API suite provides powerful tools for programmatically accessing climate risk scoring data for global locations and assets uploaded to the AlphaGeo platform. With these APIs, you can seamlessly integrate climate risk information into your applications, enabling data-driven decision-making and risk assessment.\ \ We categorize our API offerings into two primary suites: Location-Level Data APIs and Portfolio APIs. This structure enables you to either query climate risk data for individual assets and locations dynamically or access aggregated insights across entire asset portfolios uploaded to the AlphaGeo platform. The Location-Level Data APIs are further structured into three primary services: Scores, Physical Risk, and Resilience-adjusted Risk. Each service provides distinct capabilities tailored to address specific climate risk assessment needs. #### Location-Level Data APIs


	Climate Risk Scores API	/pages/ruWTIOw8bDrkE4lh1MZy
	Physical Risk API	/pages/PalaMM5XLXs00lxosXTT
	Resilience-adjusted Risk API	/pages/gF5QZSTzxBLYfYZvGT9M
Financial Impact API		/pages/7ke9xacHlumWT6Hq5Kfi

#### Portfolio APIs


	Portfolio Scores API		/pages/ruWTIOw8bDrkE4lh1MZy
	Institution Assets API		/pages/PalaMM5XLXs00lxosXTT

## Access Tokens To access AlphaGeo APIs, you need to obtain an access token. Access tokens are unique identifiers that grant you permission to use our services. Follow these steps to obtain and use your access token: **Sign Up or Log In** If you haven't already, [sign up for an AlphaGeo account](/alphageo-platform/trial). If you already have an account, log in to the AlphaGeo dashboard. (You will need an [Enterprise Subscription](/partnerships-and-pricing/pricing-guide) to access this feature!) #### Ensure you have adequate in-app permissions You can easily check this in your account. Once logged in, navigate to the upper right-hand corner of the dashboard.

**Get Access Token** Copy the token found upon clicking "API" in the upper-right section of your account. Treat this token as sensitive information and keep it secure.

**Use Access Token in Requests** In your API requests, include the access token in the headers. Add an Authorization header with the value "\[your\_access\_token]". Replace "\[your\_access\_token]" with the actual access token you obtained.

$ curl -X GET \
  'https://app.alphageo.ai/api/public/v1/physical_risk/?address=New+York&period=2050&scenario=SSP585&longitude=-74.0059&latitude=40.7128' \
  -H 'Authorization: 123xxxx'

**Refresh Token (Optional)** we recommend that clients routinely "refresh" their token as an added security measure.

## Rate Limits Every interaction with AlphaGeo APIs is subject to rate limits, which restrict the number of requests allowed per endpoint. These limits are in place to ensure fair usage and maintain system performance. Here's what you need to know about rate limits: **What Happens If You Exceed the Limit?** If you exceed the rate limit, you'll receive an HTTP 429 Too Many Requests response from the API. This response indicates that your request cannot be processed immediately due to exceeding the allowed rate. | API | Default requests per minute | | ------------------------ | --------------------------- | | Scores | 60 requests per minute | | Physical Risk | 60 requests per minute | | Resilience-adjusted Risk | 60 requests per minute | | Analytics | 60 requests per minute | | Portfolio Scores | 30 requests per minute | | Institution Assets | 60 requests per minute | **Need a Higher Limit?** If your application requires a higher rate limit than the default – don't worry! Simply reach out to our dedicated [support team](mailto:support@alphageo.ai), and we'll be happy to assist you. # Climate Risk and Resilience Index (CRRI) API The CRRI Scores API allows you to retrieve physical climate risk and resilience-adjusted risk scores for any location globally. ## Endpoint

GET https://app.alphageo.ai/api/public/v1/scores

## Parameters

Parameters	Required	Type	Description
asset_id	false	string (UUID)	The unique identifier of an asset uploaded to your institution's account.
address	false	string	Full address of the location
period	true	string	Time period. Available values : 2025, 2035, 2050, 2100
scenario	true	string	Identifier for CMIP6 Emission Scenarios. Available values : SSP245, SSP370, SSP585
longitude	false	number	Longitude of the location that being queried.
latitude	false	number	Latitude of the location that being queried.

### Query Validation Priority When calling this endpoint, you must provide **at least one** way to identify the location. If multiple parameters are supplied, the API resolves the query using the following precedence: 1. **`asset_id`**: If provided, the API looks up the asset under your institution's profile. 1. **`latitude` & `longitude`**: If `asset_id` is not provided, the API looks up data by coordinates. 2. **`address`**: If neither of the above are provided, the API performs geocoding to resolve coordinates. If none of these parameters are supplied, the API returns a `400 Bad Request` with an error message. ## Example requests #### cURL ```bash $ curl --location 'https://app.alphageo.ai/api/public/v1/scores?longitude=-97.2805288&latitude=32.7376509&period=2035&scenario=SSP245' \ --header 'Authorization: dse3X8de878c37b2fd6835c52390303fk15fc3' ``` #### Python ```python import requests url = "https://app.alphageo.ai/api/public/v1/scores?longitude=-97.2805288&latitude=32.7376509&period=2035&scenario=SSP245" payload = {} headers = { 'Authorization': 'dse3X8de878c37b2fd6835c52390303fk15fc3' } response = requests.request("GET", url, headers=headers, data=payload) print(response.text) ``` ## Example response A successful response containing physical climate risk and resilience-adjusted risk scores of the given location. ```json { "identifiers": { "SCENARIO": "SSP245", "PERIOD": "2035" }, "overall_indicators": { "OVERALL_RISK_SCORE_PCT": 78, "OVERALL_RAJ_SCORE_PCT": 73, "OVERALL_RISK_SCORE": 40, "OVERALL_RAJ_SCORE": 27 }, "heat_stress": { "HEAT_SCORE": 80, "HEAT_RAJ_SCORE": 60 }, "inland_flooding": { "INLAND_SCORE": 10, "INLAND_RAJ_SCORE": 10 }, "coastal_flooding": { "COASTAL_SCORE": 0, "COASTAL_RAJ_SCORE": 0 }, "drought": { "DROUGHT_SCORE": 30, "DROUGHT_RAJ_SCORE": 20 }, "hurricane_wind": { "WIND_SCORE": 80, "WIND_RAJ_SCORE": 40 }, "wildfire": { "FIRE_SCORE": 0, "FIRE_RAJ_SCORE": 0 }, "hail": { "HAIL_SCORE": 80, "HAIL_RAJ_SCORE": 60 }, "data_version": "Aug 2024" } ``` # Global Adaptation Layers Risk API The risk data API endpoint allows you to retrieve detailed climate risk data under the Global Adaptation Layers data product for any location globally. ## Endpoint ```rest GET https://app.alphageo.ai/api/public/v1/risk_data ``` ## Parameters

Parameters	Required	Type	Description
asset_id	false	string (UUID)	The unique identifier of an asset uploaded to your institution's account.
address	false	string	Full address of the location.
period	true	string	Time period. Available values : 2025, 2035, 2050, 2100
scenario	true	string	Identifier for CMIP6 Emission Scenarios. Available values : SSP245, SSP370, SSP585
longitude	false	number	Longitude of the location that being queried.
latitude	false	number	Latitude of the location that being queried.

### Query Validation Priority When calling these endpoints, you must provide **at least one** way to identify the location. If multiple parameters are supplied, the API resolves the query using the following precedence: 1. **`asset_id`**: If provided, the API looks up the asset under your institution's profile. 2. **`latitude` & `longitude`**: If `asset_id` is not provided, the API looks up data by coordinates. 3. **`address`**: If neither of the above are provided, the API performs geocoding to resolve coordinates. If none of these parameters are supplied, the API returns a `400 Bad Request` with an error message. ## Example requests #### cURL ```bash $ curl --location 'https://app.alphageo.ai/api/public/v1/risk_data?longitude=-97.2805288&latitude=32.7376509&period=2035&scenario=SSP245' \ --header 'Authorization: dse3X8de878c37b2fd6835c52390303fk15fc3' ``` #### Python ```python import requests url = "https://app.alphageo.ai/api/public/v1/risk_data?longitude=-97.2805288&latitude=32.7376509&period=2035&scenario=SSP245" payload = {} headers = { 'Authorization': 'dse3X8de878c37b2fd6835c52390303fk15fc3' } response = requests.request("GET", url, headers=headers, data=payload) print(response.text) ``` ## Example response A successful response containing physical climate risk data of the given location. ```json { "identifiers": { "SCENARIO": "SSP245", "PERIOD": "2035" }, "heat_stress": { "CDD": 1921.39, "HOTDAYS_ABV90P": 66.64 }, "inland_flooding": { "HEAVY_PR_DAYS": 17.44, "R_INUN_RP0100": 0, "R_INUN_RP1000": 0 }, "coastal_flooding": { "C_INUN_RP0100": 0, "C_INUN_RP1000": 0, "SUBSIDENCE": 0, "COASTAL_EROSION": 0 }, "drought": { "MAX_CONS_DRYDAYS_1MM": 32.62, "HOTDAYS40C": 21.76, "WS": 1.1 }, "hurricane_wind": { "HU_MAX_SPEED": 74, "HU_AF": 0.01 }, "wildfire": { "MAX_CONS_DRYDAYS_1MM": 32.62, "HOTDAYS40C": 21.76, "AVG_VC": 0 }, "hail": { "HAILDAYS": 7.16, "PR_DAYS_10MM": 28.5, "MAXTEMP": 43.71 }, "data_version": "Aug 2024" } ``` # Global Adaptation Layers Resilience API The Resilience data API allows you to retrieve detailed resilience data under the Global Adaptation Layers data product for any location globally. ## Endpoint ```rest GET https://app.alphageo.ai/api/public/v1/resilience_data ``` ## Parameters

Parameters	Required	Type	Description
asset_id	false	string (UUID)	The unique identifier of an asset uploaded to your institution's account.
address	false	string	Full address of the location
period	true	string	Time period. Available values : 2025, 2035, 2050, 2100
scenario	true	string	Identifier for CMIP6 Emission Scenarios. Available values : SSP245, SSP370, SSP585
longitude	false	number	Longitude of the location that being queried.
latitude	false	number	Latitude of the location that being queried.

$ curl --location 'https://app.alphageo.ai/api/public/v1/resilience_data?longitude=-97.2805288&latitude=32.7376509&period=2035&scenario=SSP245' \
--header 'Authorization: dse3X8de878c37b2fd6835c52390303fk15fc3'

#### Python ``` import requests url = "https://app.alphageo.ai/api/public/v1/resilience_data?longitude=-97.2805288&latitude=32.7376509&period=2035&scenario=SSP245" payload = {} headers = { 'Authorization': 'dse3X8de878c37b2fd6835c52390303fk15fc3' } response = requests.request("GET", url, headers=headers, data=payload) print(response.text) ``` ## Example response A successful response containing resilience-adjusted climate risk data of the given location. ```json { "identifiers": { "SCENARIO": "SSP245", "PERIOD": "2035" }, "heat_stress_adaptation": { "BUILDING_DENSITY": 38.62, "URBAN_GREENERY": 61.4 }, "inland_flooding_adaptation": { "POROSITY": 61.4, "IF_DIRECT_BARRIER": 16.33, "IF_DRAINAGE": 8.16, "IF_NATURE_BASED_SOLUTION": 95.92, "IF_STORAGE_AND_CONTROL": 100 }, "coastal_flooding_adaptation": { "CF_COASTAL_DEFENSE": 0, "CF_NATURAL_BUFFER": 0, "CF_DRAINAGE": 0, "CF_STORAGE_AND_CONTROL": 0 }, "hurricane_wind_adaptation": { "BUILDING_STRENGTH": 38.6, "IF_STORAGE_AND_CONTROL": 100, "IF_DIRECT_BARRIER": 16.33 }, "drought_adaptation": { "WATER_WORKS": 100, "WATER_STORAGE": 100, "WATER_AMENITY": 20.41, "WATER_WELL": 0 }, "wildfire_adaptation": { "FIRE_RESPONSE": 100, "FIRE_PREVENTION": 0, "FIRE_DETECTION": 0 }, "societal_resilience": { "LOC_GNI": 85639301.43, "VUL_POP": 0.33, "HDI": 0.91, "GFCP": 21.53 }, "data_version": "Aug 2024" } ``` # Financial Impact Analytics API This endpoint provides data for real estate cashflow modelling. The metrics translate climate risk and resilience features into various cashflow items such as rate of increase in insurance, projected utility costs, and discount rates. ## Endpoint

GET https://app.alphageo.ai/api/public/v1/analytics

## Parameters

Parameters	Required	Type	Description
asset_id	false	string (UUID)	The unique identifier of an asset uploaded to your institution's account.
address	false	string	Full address of the location
scenario	true	string	Identifier for CMIP6 Emission Scenarios. Available values : SSP245, SSP370, SSP585
longitude	false	number	Longitude of the location that being queried.
latitude	false	number	Latitude of the location that being queried.

### Query Validation Priority When calling these endpoints, you must provide **at least one** way to identify the location. If multiple parameters are supplied, the API resolves the query using the following precedence: 1. **`asset_id`**: If provided, the API looks up the asset under your institution's profile. 2. **`latitude` & `longitude`**: If `asset_id` is not provided, the API looks up data by coordinates. 3. **`address`**: If neither of the above are provided, the API performs geocoding to resolve coordinates. If none of these parameters are supplied, the API returns a `400 Bad Request` with an error message. ## Example requests #### cURL ```bash $ curl --location 'https://app.alphageo.ai/api/public/v1/analytics?longitude=-97.2805288&latitude=32.7376509&period=2035&scenario=SSP245' \ --header 'Authorization: dse3X8de878c37b2fd6835c52390303fk15fc3' ``` #### Python ```python import requests url = "https://app.alphageo.ai/api/public/v1/analytics?longitude=-97.2805288&latitude=32.7376509&period=2035&scenario=SSP245" payload = {} headers = { 'Authorization': 'dse3X8de878c37b2fd6835c52390303fk15fc3' } response = requests.request("GET", url, headers=headers, data=payload) print(response.text) ``` ## Example response A successful response containing analytics of the given location. ```json { "identifiers": { "SCENARIO": "SSP245" }, "insurance": { "INS_RATE_FIRE": 0, "INS_RATE_FLOOD": -1.07, "INS_RATE_ANNUAL": -1.07 }, "utility": { "UTIL_RATE_COOLING": 0.38, "UTIL_RATE_HEATING": -0.41, "UTIL_RATE_ANNUAL": -0.03 }, "capex": { "CAPEX_FIRE": 0, "CAPEX_FLOOD": 0, "CAPEX_CDD": 0, "CAPEX_ANNUAL": 0 }, "discount": { "10_YEAR_DISCOUNT": 0.09, "25_YEAR_DISCOUNT": 0.22, "75_YEAR_DISCOUNT": 0.38 }, "operation_downtime": { "DOWNTIME_DAYS_ANNUAL": 0.08 }, "operation_efficiency": { "EFFICIENCY_LOSS_ANNUAL": 0.68 }, "workforce_productivity": { "PRODUCTIVITY_LOSS_ANNUAL": 1.26 }, "maintenance_cost": { "MAINT_RATE_ANNUAL": -0.04 }, "insurability": { "UNINSURABLE_COASTAL": 0, "UNINSURABLE_FIRE": 0, "UNINSURABLE_INLAND": 0, "UNINSURABLE_TOTAL": 0 }, "data_version": "Aug 2024" } ``` # Portfolio Scores API The Portfolio Scores API endpoint allows you to retrieve aggregated (averaged) climate risk, resilience-adjusted risk, and financial impact metrics across all assets within your institution's portfolios. You can also restrict the aggregation to specific portfolios using optional query filters. ## Endpoint

GET https://app.alphageo.ai/api/public/v1/portfolio

## Parameters

Parameters Required Type Description

tier_one_names false string Comma-separated names of Tier 1 portfolios to include. Example: Fund A,Fund B. If omitted, averages are calculated across all assets in all portfolios.

period

true

string

Parameters	Required	Type	Description
tier_one_names	false	string	Comma-separated names of Tier 1 portfolios to include. Example: `Fund A,Fund B`. If omitted, averages are calculated across all assets in all portfolios.
period	true	string	Time period. Available values : 2025, 2035, 2050, 2100
scenario	true	string	Identifier for CMIP6 Emission Scenarios. Available values : SSP245, SSP370, SSP585

Time period.

Available values : 2025, 2035, 2050, 2100

scenario

true

string

Identifier for CMIP6 Emission Scenarios.

Available values : SSP245, SSP370, SSP585

## Example requests #### cURL ```bash $ curl --location 'https://app.alphageo.ai/api/public/v1/portfolio?period=2035&scenario=SSP245' \ --header 'Authorization: dse3X8de878c37b2fd6835c52390303fk15fc3' ``` #### Python ```python import requests url = "https://app.alphageo.ai/api/public/v1/portfolio?period=2035&scenario=SSP245" payload = {} headers = { 'Authorization': 'dse3X8de878c37b2fd6835c52390303fk15fc3' } response = requests.request("GET", url, headers=headers, data=payload) print(response.text) ``` ## Example response ```json { "identifiers": { "SCENARIO": "SSP370", "PERIOD": "2035" }, "overall_indicators": { "OVERALL_RISK_SCORE_PCT": 62.4, "OVERALL_RAJ_SCORE_PCT": 54.1, "OVERALL_RISK_SCORE": 31.2, "OVERALL_RAJ_SCORE": 22.8 }, "heat_stress": { "HEAT_SCORE": 75.0, "HEAT_RAJ_SCORE": 60.5 }, "inland_flooding": { "INLAND_SCORE": 18.2, "INLAND_RAJ_SCORE": 15.1 }, "coastal_flooding": { "COASTAL_SCORE": 5.0, "COASTAL_RAJ_SCORE": 4.2 }, "drought": { "DROUGHT_SCORE": 30.0, "DROUGHT_RAJ_SCORE": 24.0 }, "hurricane_wind": { "WIND_SCORE": 45.0, "WIND_RAJ_SCORE": 38.0 }, "wildfire": { "FIRE_SCORE": 10.0, "FIRE_RAJ_SCORE": 8.0 }, "hail": { "HAIL_SCORE": 55.0, "HAIL_RAJ_SCORE": 42.0 }, "landslide": { "LANDSLIDE_SCORE": 2.0, "LANDSLIDE_RAJ_SCORE": 1.5 }, "earthquake": { "EARTHQUAKE_SCORE": 0.0, "EARTHQUAKE_RAJ_SCORE": 0.0 }, "insurance": { "INS_RATE_FIRE": 0.0, "INS_RATE_FLOOD": -1.07, "INS_RATE_ANNUAL": -1.07 }, "utility": { "UTIL_RATE_COOLING": 0.38, "UTIL_RATE_HEATING": -0.41, "UTIL_RATE_ANNUAL": -0.03 }, "capex": { "CAPEX_FIRE": 0.0, "CAPEX_FLOOD": 0.0, "CAPEX_CDD": 0.0, "CAPEX_ANNUAL": 0.0 }, "discount": { "10_YEAR_DISCOUNT": 0.09, "25_YEAR_DISCOUNT": 0.22, "75_YEAR_DISCOUNT": 0.38 }, "operation_downtime": { "DOWNTIME_DAYS_ANNUAL": 0.08 }, "operation_efficiency": { "EFFICIENCY_LOSS_ANNUAL": 0.68 }, "workforce_productivity": { "PRODUCTIVITY_LOSS_ANNUAL": 1.26 }, "maintenance_cost": { "MAINT_RATE_ANNUAL": -0.04 }, "insurability": { "UNINSURABLE_COASTAL": 0.0, "UNINSURABLE_FIRE": 0.0, "UNINSURABLE_INLAND": 0.0, "UNINSURABLE_TOTAL": 0.0 }, "data_version": "Dec 2025", "portfolio_summary": { "total_assets": 4320, "portfolios_included": [ "Fund A", "Fund B" ] } } ``` *Note: The fields from `insurance` down to `insurability` will only be present if your institution has access to the Financial Impact Analytics module.* # Institution Assets API The Institution Assets API allows you to retrieve a paginated list of all assets uploaded under your institution profile. This endpoint is particularly useful for retrieving assets' unique UUIDs (`id`), which can then be passed as the `asset_id` parameter to location-level APIs. ## Endpoint

GET https://app.alphageo.ai/api/public/v1/assets

## Parameters

Parameters	Required	Type	Description
page	false	Integer	The page number to retrieve.
page_size	false	Integer	Number of assets to return per page. Allowed choices: `10`, `25`, `50`, `100`, `200`. Default: `25`.

## Example requests #### cURL ```bash $ curl --location 'https://app.alphageo.ai/api/public/v1/assets/?page=1&page_size=25' \ --header 'Authorization: dse3X8de878c37b2fd6835c52390303fk15fc3' ``` #### Python ```python import requests url = "https://app.alphageo.ai/api/public/v1/assets/?page=1&page_size=25" payload = {} headers = { 'Authorization': 'dse3X8de878c37b2fd6835c52390303fk15fc3' } response = requests.request("GET", url, headers=headers, data=payload) print(response.text) ``` ## Example response ```json { "count": 15, "next": "https://docs.alphageo.ai/api/public/v1/assets/?page=2&page_size=25", "previous": null, "results": [ { "id": "e0axc2a7-0355-412d-bdx4-dssxd4ex419a", "asset_name": "NY Office", "address": "420 5th Ave.", "longitude": 1-73.985381, "latitude": 40.751835, "portfolio_name": "Sample Portfolio", "tier_2_name": "North America", "tier_3_name": "", "tier_4_name": "", "city": "New York", "state": "New York", "zip_code": "10018", "country": "USA", "asset_type": "Office", "valuation": null, "year_valuation": null, "tags": [] } ] } ``` # Data Dictionary Request a link to AlphaGeo's Data Dictionary by filling out the following form: {% embed url="" %} \*\*The dictionary contains all features available in the Data APIs. # Snowflake Native Application On-Demand Climate Risk and Resilience Analytics **AlphaGeo's Snowflake Native Application** is designed to provide in-depth predictive analytics for your investment portfolio. This application delivers advanced insights into climate risk and resilience for any coordinate worldwide, analyzed at various resolutions such as addresses, census tracts, postal codes, counties, MSAs/CBSA, and provinces. With our tool, you can access location-specific climate risk scores, adaptation metrics, and overall resilience profiles to support both defensive ESG assessments and offensive investment strategies targeting resilient geographies. Visit our [Snowflake Native Application ("On-Demand Climate Risk and Resilience Analytics with Automatic Geocoding")](https://app.snowflake.com/marketplace/listing/GZSWZ4BBU2OL/alphageo-on-demand-climate-risk-and-resilience-analytics-with-automatic-geocoding) ### Key features * **Flexible location input**: Accepts any location entry or a table of real estate assets directly from your Snowflake database. * **Comprehensive climate risk scores**: Computes risk scores covering overall risk and specific risks (e.g., heat, inland flooding, coastal flooding, wind, drought, and fire). Analyses span four time periods up to 2100 and include three climate scenario pathways. * **Adaptation metrics**: Offers granular location-specific adaptation scores for each risk factor, guiding targeted remediation measures. * **Resilience profiles**: Generates overall resilience profiles to rank and prioritize locations based on future performance expectations. * **Enhanced database output**: Outputs an enriched table with detailed analytics directly into your Snowflake database. * **Automatic geocoding**: Utilizes the Mapbox Geocoding API to populate missing geographical coordinates (latitude and longitude). ### Input table requirements The input table in your Snowflake database must include the following columns: * **identifier** *(STRING, optional):* Internal identifier of the asset. Example: `US-001`. * **address** *(STRING, required):* Street address of the asset. Example: `4510 Main St`. * **address\_second\_line** *(STRING, optional):* Additional address details. Example: `Suite 500`. * **latitude** *(FLOAT, optional):* Latitude of the asset. Example: `27.9922679`. * **longitude** *(FLOAT, optional):* Longitude of the asset. Example: `-82.6190901`. * **zip\_code** *(STRING, required):* ZIP or postal code of the asset. Example: `42911`. * **city** *(STRING, required):* City where the asset is located. Example: `Tampa`. * **state** *(STRING, required):* State or province of the asset. Example: `FL`. * **country** *(STRING, required):* Country where the asset is located. Example: `USA`. ### Output table description The application generates an output table in your Snowflake database with the following structure: #### Metadata * **identifier**: Original identifier of the asset. * **address**: Street address of the asset. * **address\_second\_line**: Additional address details. * **city**: City where the asset is located. * **zip\_code**: ZIP or postal code of the asset. * **state**: State or province of the asset. * **country**: Country where the asset is located. * **latitude**: Latitude of the asset. * **longitude**: Longitude of the asset. #### Climate risk metrics * **scenario**: CMIP6 Emission Scenarios identifier (SSP245, SSP370, SSP585). * **period**: Time periods identifier (2025, 2035, 2050, 2100). * **overall\_risk\_score**: Overall climate risk score (0-100, higher scores indicate greater risk). * **overall\_raj\_score**: Overall resilience-adjusted risk score (0-100, higher scores indicate greater risk). * **mean\_risk\_score**: Mean climate risk score across all indicators. * **mean\_raj\_score**: Mean resilience-adjusted risk score across all indicators. #### Specific risk and resilience metrics For each risk factor (heat, inland flooding, coastal flooding, wind, drought, fire): * **\[factor]\_score**: Risk score (0-100, higher scores indicate greater risk). * **\[factor]\_raj\_score**: Resilience-adjusted risk score (0-100). #### Additional outputs * **result\_precision**: Precision of results based on geocoding accuracy. * **feedback**: Explanation of result precision. * **data\_validation**: Indicates validation errors in the provided data. * **data\_version**: AlphaGeo data version. * **created\_at**: Timestamp of record creation. ## Matching Levels and Feedback The `result_precision` column represents the level of granularity. Below are the possible matching levels and their corresponding feedback: #### Exact Match (`address`) The input address matched exactly with the provided coordinates and database entry. #### Point of Interest Match (`poi`) The input address did not match exactly; results are based on the nearest point of interest. #### Neighborhood Match (`neighborhood`) The input address did not match exactly; results are based on the nearest neighborhood. #### Locality Match (`locality`) The input address did not match exactly; results are based on the nearest locality. #### City Match (`place`) The input address did not match exactly; results are based on the nearest city. #### District Match (`district`) The input address did not match exactly; results are based on the nearest district or region. #### Region Match (`region`) The input address did not match exactly; results are based on the nearest region. #### Postcode Match (`postcode`) The input address did not match exactly; results are based on the nearest postcode. #### Country Match (`country`) The input address did not match exactly; results are based on the country-level data. #### Unmatched If the `feature_type` is unknown or unsupported, the matching level is set to unmatched, and the feedback indicates that the input address could not be matched with any geographic entity in the database. ### Installation and usage Watch our YouTube video for a step-by-step guide on installing and using the application. {% embed url="" %} ### How it works 1. **Data input**: Input your location data table into your Snowflake database. Ensure the required columns are included. 2. **Analysis**: The app processes the input data, calculates various risk and resilience scores, and generates adaptation metrics for each location. 3. **Output table**: The enriched output table is generated in your Snowflake database, providing actionable insights for decision-making. ### **Support** For any issues or questions during installation or usage, please contact our support team at .\ For more information on our data, please visit . For more information about AlphaGeo, please visit the [official website](https://alphageo.ai/). # Compliance FAQ **What compliance certifications does AlphaGeo have?** We have achieved SOC 2 Type I and II certifications. We are also GDPR and CCPA-CCPR aligned. **How does AlphaGeo ensure regulatory compliance?** AlphaGeo ensures regulatory compliance through tailored processes and procedures aligned with security laws and standards applicable to its operations. It implements information security policies, conducts risk assessments, and monitors control effectiveness. **How does AlphaGeo handle incident response and data management?** AlphaGeo has established incident response plans, automated data backup policies, and a disaster recovery plan. It manages data retention through encryption, backup integrity checks, and compliance with data protection laws. **What is AlphaGeo’s approach to risk management?** AlphaGeo takes a systematic approach to risk management, involving risk identification, assessment of likelihood and impact, treatment plans, and reporting to leadership. It conducts vulnerability management and regularly reviews business processes for internal controls. **How does AlphaGeo adapt with the failover to a Disaster Recovery environment?** Our current data backup and recovery protocols are well-aligned with the requirement to failover to a Disaster Recovery environment within 1 week for a Tier 4 low criticality system. The use of our cloud provider’s automated and point-in-time backup features, combined with our daily backup frequency and stringent integrity checks, ensures that we can meet this requirement effectively and reliably. **How does AlphaGeo ensure data security?** AlphaGeo ensures data security through several measures: 1. Encryption of data at rest and in transit using industry-standard protocols. 2. Strict access controls and monitoring through AWS IAM policies. 3. Secure handling of user data in AWS RDS encrypted databases. 4. Regular security audits, vulnerability scans, and penetration testing. 5. Incident response procedures and employee training on security best practices. 6. Is there a physical security program in place, along with established offsite storage and visitor policy procedures? No, AlphaGeo operates with a remote working model and does not maintain physical office spaces or provide company-supplied hardware. **Has your company suffered data loss or security breach within the last 3 years?** No, the company has not suffered any data loss or any security breach. # Data Management FAQ **What Cloud Hosting Tiers are provided as part of this service?** Software as a Service **How is client data stored? Is it kept separate from other clients’ data?** All client data is stored in separate AWS databases. **Are clients allowed to manage access to their own systems and data?** Yes, they can create and delete entries, and change credentials in the system, i.e., permission users with “view/edit” capabilities as well as add/delete users to/within their microsite. **Do you have a backup strategy?** Yes, we have a backup strategy in place to ensure the protection and availability of critical data and systems. **How do you protect your backups?** Our backups are stored on cloud servers that have physical security measures in place to prevent unauthorized access, tampering, and data loss. **How do you deliver data?** Most modes of data delivery, i.e., direct download, API, FTP, S3, Snowflake Marketplace (Private Listing), are feasible. Please reach out to us at if unsure. # Strategic Advisory Partnerships A streamlined draft for consulting and advisory partners evaluating AlphaGeo. ## Overview AlphaGeo’s Strategic Advisory Partnerships are for consulting and advisory firms that need climate and physical risk data for business development, proposal work, and project delivery. Partners receive enterprise-wide platform access with no seat limitations, complimentary query capacity for business development, and flexible billing that begins when project needs are confirmed. ### Who this is for This partnership is a fit for firms that need to: * strengthen proposals with real data * scope client mandates before budget approval * collaborate across teams and with clients * avoid procurement friction during business development and project execution ### What partners get {% columns %} {% column %} **Strategic partner status** Upon execution of a partnership agreement, AlphaGeo will confer formal Strategic Partner status. This includes: * visibility within AlphaGeo’s partner network * support for joint market positioning * institutional visibility across relevant market channels **Enterprise platform access** Partners receive an enterprise account with unrestricted seat allocation across practice areas and geographies. This includes: * unlimited seats across the organization * access across teams, offices, and regions * no initial charges or commercial commitment until project needs are confirmed {% endcolumn %} {% column %} **External collaboration tools** AlphaGeo microsites enable partners to share analysis directly through the platform. This includes: * data visualization and report generation tools * collaboration with clients who do not have AlphaGeo accounts * support for cross-functional and external sharing **Complimentary queries and flexible billing** Partners receive an annual allocation of complimentary queries for proposals, early-stage scoping, and business development. Commercial terms are designed to align with partner billing and cost recovery practices: * annual licenses with a fixed query allocation * pay-as-you-go usage * project-based pricing * custom structures on request {% endcolumn %} {% endcolumns %} ### How it works {% stepper %} {% step %} **1. Sign the partnership agreement** Agree and sign a Strategic Advisory Partnership structured around the partner’s preferences. {% endstep %} {% step %} **2. Activate enterprise access** Your organization gets access across offices, practice areas, and project teams. {% endstep %} {% step %} **3. Use complimentary queries for BD and scoping** Teams start supporting proposals and early client conversations with real data. {% endstep %} {% step %} **4. Start paid usage when projects convert** Commercial charges begin once project needs are confirmed, based on the partnership terms. {% endstep %} {% endstepper %} ### Value Proposition ✅ **Access without commitment:** Integrate climate risk data into client proposals prior to budget confirmation or engagement award. ✅ **Faster business development cycles:** Reduce friction during BD and scoping phases, so teams can access data without procurement overhead. ✅ **Proposals backed by real data:** Use real analysis in proposals instead of relying on vendor branding alone. ✅ **Better collaboration:** Share analysis internally and externally from one platform, including with non-subscribers. ✅ **Billing flexibility:** Usage charges are structured to align with client billing cycles and project confirmation timelines. ✅ **Aligned incentives:** Costs track confirmed engagements rather than speculative pipeline, creating shared incentive to support successful mandates. ### Related resources * [Watch Demo Video](https://demo.alphageo.ai/demo/cmj8x477v05gmf6zpo5vrlnh7?utm_source=link) * [Access Trial Account](https://app.alphageo.ai/trial_setup) * [See our Partnerships](https://alphageo.ai/partnerships/)