How Bad Are Weather Disasters for Banks?
What this paper finds — and why it matters
Using FEMA disaster declarations matched to SHELDUS property-damage estimates and Call Report data for 1995–2018, this paper finds that weather disasters — even at their most severe — have had modest effects on U.S. bank safety over the last quarter century. For single-county banks exposed to 95th-percentile disasters, Z-scores decline by roughly 9 percent at a five-year horizon under the panel estimates; reaching failure thresholds from sample mean Z-score levels would require a disaster approximately 6.7 standard deviations more destructive than a 95th-percentile event. Federal disaster aid does not appear to be the primary driver of this resilience, since banks exposed to weather events without FEMA declarations exhibit similar stability. Instead, the paper points to a loan demand channel — multi-county bank lending increases roughly 0.25 percentage points per standard deviation of damage at five years without an accompanying interest-rate increase — and to local banks’ apparent avoidance of mortgage lending in flood-prone areas beyond what official flood maps predict, consistent with local information about true flood risk limiting exposure before disasters strike.
Summary of a forthcoming paper, AI-assisted and human-reviewed. See the linked original for the authoritative claims and full conditions.
Q1. How severe are weather disaster effects on bank safety?
The paper finds that weather disasters at any severity level produce small and often statistically insignificant effects on the key bank safety measures — charge-offs, capital ratios, return-on-assets volatility, and Z-scores — at single-county banks, with the largest measured effect being roughly a 9 percent decline in Z-scores at the 95th percentile of disaster damage at a five-year horizon. The regression framework uses bank and state-year fixed effects, with SHELDUS damage as the continuous severity measure and FEMA disaster declarations as a binary indicator. For multi-county banks, charge-offs increase by roughly 10 percent at five years, but net income also rises, suggesting disaster-area loan demand partially offsets credit losses. The paper’s calculation is that pushing a typical bank from its mean Z-score of 135.9 to the failure threshold would require a Z-score decline of 127.9 — far exceeding the estimated −9 percent impact of a 95th-percentile disaster, which would need to be approximately 6.7 standard deviations more destructive to close that gap.
Q2. Is bank resilience an artifact of federal disaster aid?
The paper presents evidence that federal disaster aid is not the primary source of bank resilience, since banks exposed to weather events that did not receive FEMA disaster declarations exhibit similarly modest effects on bank safety measures. The test is designed to separate the insurance mechanism (FEMA aid replacing household income and debt service capacity) from intrinsic bank resilience. The fact that non-FEMA disasters produce comparable stability redirects attention to the demand-side and local-knowledge channels as the more fundamental explanations for the resilience finding.
Q3. What is the loan demand channel and how large is it?
Multi-county banks experience an increase in lending of roughly 0.25 percentage points per standard deviation of SHELDUS damage at a five-year horizon, and the authors find no accompanying increase in loan interest rates, which is consistent with a demand-side shift rather than a tightening of lending standards. The demand interpretation is that disasters create a wave of borrowing demand as households and firms repair or replace damaged assets, and the increased loan volume helps offset the increase in charge-offs. The pattern is found at multi-county banks — which can serve affected and unaffected areas simultaneously — but not at single-county banks, consistent with lending capacity mattering for capturing the demand increase.
Q4. What does “local knowledge” mean in this context?
Local banks originate approximately 6.4 percent fewer log mortgage dollars per application in FEMA flood zones than would be predicted by the official flood map classifications alone, with the gap widening to 7–8 percent in areas that have experienced more than five FEMA flood declarations compared to areas with fewer than three, which is consistent with local lenders holding information about true flood risk not captured in official maps. The finding is consistent with local banks having access to community-level information — observed flooding history, property-level characteristics, local drainage and elevation — that is not incorporated into official FEMA flood zone classifications. This pre-disaster selectivity limits mortgage accumulation in the highest-risk areas before disasters occur.
Q5. What are the implications for climate risk assessment?
The paper explicitly frames the historical resilience documented for 1995–2018 as informing rather than settling assessments of physical risk to banks from future climate change, since more frequent or more severe disasters could overwhelm the demand-offset and local-knowledge mechanisms that the paper identifies as sustaining bank performance. The key qualification is temporal scope: the demand-side recovery effect requires that affected areas have the income and economic capacity to service new loans, and the local-knowledge effect requires that banks have experienced enough repeated flooding to develop accurate private flood risk assessments. Both conditions could become less reliable as climate change alters the frequency, geography, and severity of weather events relative to the historical distribution.
Key concepts
Z-score : a bank-level distance-to-insolvency measure equal to (return on assets + capital ratio) divided by return-on-assets volatility; higher values indicate greater distance from failure; used here as the primary measure of disaster impact on bank safety.
SHELDUS : the Spatial Hazard Events and Losses Database for the United States, providing county-level property damage estimates for weather events; used in this paper as the continuous measure of disaster severity in panel regressions.
single-county bank : a bank whose entire depositor base is drawn from one county, making it fully exposed to local disaster effects with no geographic diversification across other counties.
loan demand channel : the mechanism by which disasters increase demand for credit from households and firms repairing or replacing damaged assets, generating new loan volume that partially offsets credit losses at banks serving affected areas.
local knowledge : the paper’s label for the informational advantage that local banks appear to have about true flood risk beyond what official FEMA flood zone classifications capture, inferred from lower mortgage originations in areas with a history of repeated flooding.