Moody’s Analytics has introduced its Spatial Areas datasets to help organizations manage the growing challenges of physical risk. With climate-related events becoming more frequent and severe, decision-makers in government, finance, and investment are facing increased pressure to assess exposure and potential costs across a variety of locations.

The Spatial Areas datasets offer detailed risk metrics aggregated at multiple geographic levels, including sovereign nations, administrative regions, urban clusters, postal codes, and custom geographies. This enables users to evaluate risks relevant to their portfolios or operations—whether they are underwriting municipal bonds or managing assets with uncertain locations.

The datasets cover six major perils: floods, heat stress, hurricanes and typhoons, sea level rise, water stress, and wildfires. For each peril, Moody’s provides analysis based on current conditions as well as future climate scenarios such as RCP 4.5 and RCP 8.5. This approach allows organizations to anticipate how exposures may change over time.

Moody’s catastrophe modeling technology underpins the new offering. The models have been refined over three decades serving the global (re)insurance market valued at $2.5 trillion. These stochastic models quantify expected damage using Exceedance Probability (EP) curves that capture both frequent minor events and rare catastrophic losses.

Risk data is first calculated at a granular location level before being aggregated for broader areas like cities or countries. This process supports applications such as evaluating securities backed by governments or pricing mortgage-backed securities when collateral details are imprecise.

Three main metrics are included in the datasets:

- Annualized Damage Rate (ADR): The average expected annual loss relative to asset value.

- Standard Deviation (Std Dev): Year-over-year volatility in ADR indicating risk variability.

- Impact Scores: A percentile ranking from 0 to 100 comparing an area’s risk against a global reference set.

According to Moody’s Analytics: “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.”

Visualizations produced from the data can show changes in financial impact metrics between years—for example comparing state-level risks globally for 2020 versus projected conditions in 2100 under high-warming scenarios. While these visualizations highlight areas of greatest relative risk, actual damage costs will be highest where high exposure coincides with higher annualized damage rates.

Moody’s states that its Spatial Areas datasets provide “granular, location-relevant risk metrics across multiple perils,” built on industry-standard modeling methods and designed for consistent comparisons across locations and asset classes.

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