Empowering smarter risk decisions: How Spatial Areas data transforms physical risk management

Leverage Moody’s Spatial Areas datasets for informed, confident financial decisions

Physical Risks are manifesting more frequent and intense events and thereby transforming the risk landscape for decision-makers across industries—whether you are a government agency, a financial institution, or an investor. Accurately assessing physical risk is no longer optional; it is essential to protecting portfolios, guiding strategic investments, and ensuring resilient communities. Yet, the challenge lies in the sheer complexity of risk factors: multiple perils, diverse geographies, and evolving scenarios.

That’s where Moody’s Spatial Areas datasets come in, delivering robust, actionable risk metrics at the scales that matter to real-world decisions. In this blog, we’ll explore the value that the Spatial Areas dataset brings to organizations aiming to better understand and manage physical risk, and why it is a game-changer for anyone making risk-informed decisions.

 

Why “Spatial Areas” data matters in Physical Risk Management

When it comes to managing physical risk, the key question is: “How exposed am I, and what could it cost me?” The answer is rarely simple. Real-world assets and portfolios are seldom confined to single buildings or precise coordinates. Instead, exposure often spans entire cities, postal codes, urban clusters, and administrative regions—sometimes even crossing borders.

Traditional risk assessment tools may struggle to provide insight at these meaningful scales. Moody’s Spatial Areas datasets bridge this gap by providing detailed risk data that is aggregated for a wide range of geographic areas:

• Sovereign nations
• Sub-sovereign administrative boundaries
• Non-political boundaries (like urban areas or Metropolitan Statistical Areas)
• Postal codes and other custom geographies

This flexibility empowers decision-makers to evaluate risk where it is most relevant—whether you’re underwriting municipal bonds, valuing mortgage-backed securities, or managing assets with uncertain or imprecise locations.

 

Comprehensive peril coverage for today and tomorrow

Physical risk is multi-dimensional, shaped by both present-day and future warming scenarios. The Spatial Areas dataset offers comprehensive coverage across six major perils:

• Floods
• Heat Stress
• Hurricanes & Typhoons
• Sea Level Rise
• Water Stress
• Wildfires

For each peril, risk analysis is performed using both current conditions (including all warming to date) and future projections, allowing you to anticipate how exposures may evolve under different scenarios (such as RCP 4.5 and RCP 8.5). This future-looking approach enables organizations to stress-test strategies for resilience and long-term value protection.

 

Proven Risk Modeling—Trusted by the world’s largest insurers

The backbone of the Spatial Areas datasets is Moody’s state-of-the-art catastrophe modeling technology, honed over 30 years of supporting the US$2.5 trillion (re)insurance global market. These advanced stochastic catastrophe models are widely trusted for quantifying expected damage from complex perils and enabling probabilistic risk analysis.

At their core, these models calculate risk as an Exceedance Probability (EP) curve, quantifying the annual probability of exceeding specific damage thresholds. This allows for a nuanced understanding of risk distributions—including both frequent, low-severity events and rare, high-severity catastrophes.

 

From single site to geographic area: Aggregating risk for real-world applications

Every dataset begins with location-level risk modeling: each peril is evaluated at fine spatial resolution, leveraging the same modeling capabilities underpinning physical risk solutions. Then, risks are aggregated across the entire geographic area of interest—whether it’s a postal code, a city, or a country—yielding actionable insights at the operational scale.

This aggregation is critical for applications like:

• Evaluating securities backed by governments (e.g., municipal bonds or debt)
• Pricing and managing derivative financial instruments (e.g., mortgage-backed securities)
• Assessing assets where collateral locations are uncertain or imprecise
• Strategic planning for infrastructure, supply chains, and public services
• Assessing indirect area-level impacts on a facility’s ability to operate due to disruptions in critical transportation, energy, and water infrastructure.

By delivering risk metrics at the scale of real-world decision-making, the Spatial Areas datasets reduce uncertainty and improve the reliability of financial and strategic analysis.

 

Three key metrics: A clear view of financial impact

Effective risk management requires translating complex modeling into clear, actionable metrics. The Spatial Areas datasets deliver three core measures—each quantifying a distinct aspect of physical risks, and each comparable across asset classes, locations, and time horizons.

 

Annualized Damage Rate (ADR)

ADR estimates the average expected damage per year, expressed as the ratio between annual damage cost and the total value of the assets exposed. This provides a straightforward view of expected loss, enabling organizations to budget, price, and plan for related impacts.

 

Standard Deviation (Std Dev)

Not all risks are created equal, even if their averages are the same. Std Dev measures the year-over-year volatility in annual damage rates—highlighting locations where rare, high-severity events can drive substantial financial losses. 

Locations  with higher ADR values see, on average, greater cumulative annual impacts from event occurrences than facilities with lower ADR values. Std Dev is the standard deviation of the ADR and indicates the variability and uncertainty around the ADR. It is used to measure volatility in ADR year-on-year and can be used to compare different locations, with higher Std Dev indicating higher risk volatility.

 

Impact Scores

To make comparisons easy, the Impact Score benchmarks each area’s risk (ADR + Std Dev) against a global reference universe of economically relevant locations. Each area receives a score from 0 (lowest relative risk) to 100 (highest relative risk), calculated via percentile ranking. This simple, intuitive measure enables rapid assessment and prioritization—making “hot spots” of risk immediately visible.

For example, if a city’s risk metric lies at the 75th percentile of the global distribution, it receives an Impact Score of 75. This makes it easy for stakeholders to compare risks across geographies, scenarios, and time horizons using a common language.

 

Transparent methodology, consistent comparability

Consistency is key for decision-makers who need to compare risk across diverse portfolios and asset types. The risk metrics and benchmarking methodology used in the Spatial Areas datasets are aligned with other Moody’s datasets—enabling seamless comparison between asset-level, location-level, and corporate-level risk analytics.

This transparency builds trust and facilitates clear communication with stakeholders, regulators, and rating agencies. The metrics are calculated robustly and are supported by decades of peer-reviewed science and decades of insurance industry (claims) validation.

 

Real-world visualization: Bring the data to life

To support analysis and communication, the Spatial Areas datasets provide insight that bring risk data to decision-making. For example, side-by-side maps of total peril risk metrics (the sum of ADR and Std Dev) can be produced for different years—such as 2020 and 2100 under a high-warming scenario (RCP 8.5). These visualizations help stakeholders see how risk profiles may shift over time and inform strategies for adaptation, mitigation, and investment.

The example maps below show state-level annualized financial impact metrics globally, for all physical risk perils combined. Specifically, the maps show the financial risk metric, calculated as the sum of the ADR and Std Dev, for 2020 and 2100 under RCP 8.5 scenario conditions. These financial impact risk metrics are damage relative to the exposed value (with the lowest metric at 0% and the highest at 100% of exposed value). 

 

Figure 1: Global state-level financial risk metric (annualized damage ratio plus standard deviation, relative to exposed value) for all six perils combined (excluding Earthquakes which does not vary with climate change) for (a) 2020 and (b) 2100 under RCP8.5.

(a) 2020

(b) 2100

 

While the figure above shows areas of highest risk, this may not be reflective of areas of highest damage costs.  Note that annual damage costs (ADR times exposure value) will be highest in regions with both high annualized damage rate (ADR) and high exposed value. As a result, damage costs in developed countries (with high exposures) can be significant.

 

Unlock the value: Make confident, informed decisions

In a world where risk is a financial reality, leveraging best-in-class data is the foundation of responsible decision-making. Moody’s Spatial Areas datasets arm organizations with:

• Granular, location-relevant risk metrics across multiple perils
• Proven, industry-standard catastrophe modeling methodology
• Consistent, comparable measures to inform strategic choices

Whether you are structuring financial products, developing resilience strategies, or managing portfolios, the Spatial Areas datasets provide the insight and confidence needed to navigate an uncertain future.

Are you ready to make better-informed decisions and manage physical risk with confidence? Explore how Moody’s Spatial Areas datasets can support your organization’s journey toward a more resilient tomorrow.