With each passing year, it is becoming increasingly clear to mortgage credit investors that climate change is emerging as a non-trivial risk factor that must be accounted for. Questions around how precisely to account for this risk, however, and who should ultimately bear it, remain unanswered.
Current market dynamics further complicate these questions. Late last year, Politico published this special report laying out the issues surrounding climate risk as it relates to mortgage finance. Even though almost everyone agrees that underinsured natural disaster risk is a problem, the Politico report outlines several forces that make it difficult for anyone to do anything about it. The massive undertaking of bringing old flood zone maps up to date is just one example. As Politico puts it:
The result, many current and former federal housing officials acknowledge, is a peculiar kind of stasis — a crisis that everyone sees coming but no one feels empowered to prevent, even as banks and investors grow far savvier about assessing climate risk.
At some point, however, we will reach a tipping point – perhaps a particularly devastating event (or series of events) triggering significant losses. As homeowners, the GSEs, and other mortgage credit investors point fingers at one another (and inevitably at the federal government) a major policy update will become necessary to identify who ultimately bears the brunt of mispriced climate risk in the market. Once quantified and properly assigned, the GSEs will price in climate risk in the same way they bake in other contributors to credit risk — via higher guarantee fees. For non-GSE (and CRT) loans, losses will continue to be borne by whoever holds the credit risk.
Recognizing that such an event may not be far off, the GSEs, their regulator, and everyone else with credit exposure are beginning to appreciate the importance of understanding the impact of climate events on mortgage performance. This is not easily inferred from the historical data record, however. And those assessing risk need to make informed assumptions about how historically observed impacts will change in the future.
The first step in constructing these assumptions is to compile a robust historical dataset. To this end, RIskSpan began exploring the impact of certain hurricanes a few years ago. This initial analysis revealed a significant impact on short-term mortgage delinquency rates (not surprisingly), but less of an impact on default rates. In other words, affected borrowers encountered hardship but ultimately recovered.
This research is preliminary, however, and more data will be necessary to build scenario assumptions as climate events become more severe and widespread. As more data covering more events—including wildfires—becomes available, RiskSpan is engaged in ongoing research to tease out the impact each of these events has on mortgage performance.
It goes without saying that climate scenario assumptions need to be grounded in reality to be useful to credit investors. Because time-series data relationships are not always detectable using conventional means, especially when data is sparse, we are beginning to see promise in leveraging various machine learning techniques to this end. We believe this historical, machine-learning-based research will provide the backbone for an approach that merges historical effects of events with inputs about the increasing frequency and severity of these events as they become better understood and more quantifiable.
Precise forecasting of severe climate events by zip code in any given year is not here yet. But an increasingly reliable framework for gauging the likely impact of these events on mortgage performance is on the horizon.
