The NRI: An Emerging Tool for Quantifying Climate Risk in Mortgage Credit
Climate change is affecting investment across virtually every sector in a growing number of mostly secondary ways. Its impact on mortgage credit investors, however, is beginning to be felt more directly.
Mortgage credit investors are investors in housing. Because housing is subject to climate risk and borrowers whose houses are destroyed by natural disasters are unlikely to continue paying their mortgages, credit investors have a vested interest in quantifying the risk of these disasters.
To this end, RiskSpan is engaged in leveraging the National Risk Index (NRI) to assess the natural disaster and climate risk exposure of mortgage portfolios.
This post introduces the NRI data in the context of mortgage portfolio analysis (loans or mortgage-backed securities), including what the data contain and key considerations when putting together an analysis. A future post will outline an approach for integrating this data into a framework for scenario analysis that combines this data with traditional mortgage credit models.
The National Risk Index
The National Risk Index (NRI) was released in October 2020 through a collaboration led by FEMA. It provides a wealth of new geographically specific data on natural hazard risks across the country. The index and its underlying data were designed to help local governments and emergency planners to better understand these risks and to plan and prepare for the future.
The NRI provides information on both the frequency and severity of natural risk events. The level of detailed underlying data it provides is astounding. The NRI focuses on 18 natural risks (discussed below) and provides detailed underlying components for each. The severity of an event is broken out by damage to buildings, agriculture, and loss of life. This breakdown lets us focus on the severity of events relative to buildings. While the definition of building here includes all types of real estate—houses, commercial, rental, etc.—having the breakdown provides an extra level of granularity to help inform our analysis of mortgages.
The key fields that provide important information for a mortgage portfolio analysis are bulleted below. The NRI provides these data points for each of the 18 natural hazards and each geography they include in their analysis.
- Annualized Event Frequency
- Exposure to Buildings: Total dollar amount of exposed buildings
- Historical Loss Ratio for Buildings (Bayesian methods to derive this estimate, such that every geography is covered for its relevant risks)
- Expected Annual Loss for Buildings
- Population estimates (helpful for geography weighting)
Grouping Natural Disaster Risks for Mortgage Analysis
The NRI data covers 18 natural hazards, which pose varying degrees of risk to housing. We have found the framework below to be helpful when considering which risks to include in an analysis. We group the 18 risks along two axes:
1) The extent to which an event is impacted by climate change, and
2) An event’s potential to completely destroy a home.
Earthquakes, for example, have significant destructive potential, but climate change is not a major contributor to earthquakes. Conversely, heat waves and droughts wrought by climate change generally do not pose significant risk to housing structures.
When assessing climate risk, RiskSpan typically focuses on the five natural hazard risks in the top right quadrant below.
Immediate Event Risk versus Cumulative Event Risk
Two related but distinct risks inform climate risk analysis.
- Immediate Event Analysis: The risk of mortgage delinquency and default resulting directly from a natural disaster event. A home severely damaged or destroyed by a hurricane, for example.
- Cumulative Event Risk: Less direct than immediate event risk, this is the risk of widespread home price declines across an entire area communities because of increasing natural hazard risk brought on by climate change. These secondary effects include:
- Heightened homebuyer awareness or perception of increasing natural hazard risk,
- Property insurance premium increases or areas becoming ‘self-insured,’
- Government policy impacts (e.g., potential flood zone remapping), and
- Potential policy changes related to insurance from key players in the mortgage market (i.e., Fannie Mae, Freddie Mac, FHFA, etc.).
NRI data provides an indication of the probability of immediate event occurrence and its historic severity in terms of property losses. We can also empirically observe historical mortgage performance in the wake of previous natural disaster events. Data covering several hurricane and wildfire events are available.
Cumulative event risk is less observable. A few academic papers attempt to tease out these impacts, but the risk of broader home price declines typically needs to be incorporated into a risk assessment framework through transparent scenario overlays. Examples of such scenarios include home price declines of as much as 20% in newly flood-exposed areas of South Florida. There is also research suggesting that there are often long term impacts to consumer credit following a natural disaster.
Geography Normalization
Linking to the NRI is simple when detailed loan pool geographic data are available. Analysts can merge by census tract or county code. Census tract is the more geographically granular measure and provides a more detailed analysis.
For many capital markets participants, however, that level of geographic specific detail is not available. At best, an investor may have a 5-digit or 3-digit zip code. Zip codes do not directly match to a given county or census tract and can potentially span across those distinctions.
There is no perfect way to perform the data link when zip code is the only available geographic marker. We take an approach that leverages the other data on housing stock by census tract to weight mortgage portfolio data when multiple census tracts map to a zip code.
Other Data Limitations
The loss information available represents a simple historical average loss rate given an event. But hurricanes (and hurricane seasons) are not all created equal. The same is true of other natural disasters. Relying on averages may work over long time horizons but could significantly underpredict or overpredict loss in a particular year. Further, the frequency of events is rising so that what used to be considered 100 year event may be closer to a 10 or 20 year event. Lacking data about what losses might look like under extreme scenarios makes modeling such events problematic.
The data also make it difficult to take correlation into account. Hurricanes and coastal flooding are independent events in the dataset but are obviously highly correlated with one another. The impact of a large storm on one geographic area is likely to be correlated with that of nearby areas (such as when a hurricane makes its way up the Eastern Seaboard).
The workarounds for these limitations have limitations of their own. But one solution involves designing transparent assumptions and scenarios related to the probability, severity, and correlation of stress events. We can model outlier events by assuming that losses for a particular peril follow a normal distribution with set standard deviations. Other assumptions can be made about correlations between perils and geographies. Using these assumptions, stress scenarios can be derived by picking a particular percentile along the loss distribution.
A Promising New Credit Analysis Tool for Mortgages
Notwithstanding its limitations, the new NRI data is a rich source of information that can be leveraged to help augment credit risk analysis of mortgage and mortgage-backed security portfolios. The data holds great promise as a starting point (and perhaps more) for risk teams starting to put together climate risk and other ESG analysis frameworks.
Buyout efficiency for 90+ day delinquent loans, data as of January 2021. 
Buyout efficiency for 90+ day delinquent loans, data as of January 2021.
