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Disasters and lending signals: From borrower information to community characteristics

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  • Beyene, Winta

Abstract

I study the informational value of community resilience in credit markets during natural disasters. Exploiting a severe flood in Germany in 2013, I combine loan-level data on car loans with a composite measure of community resilience based on structural local characteristics linked to disaster recovery capacity. After the flood, only low-income borrowers faced credit tightening, but in high-resilience areas they experienced smaller rate hikes and maintained access to credit. Resilience also predicts repayment after disasters, yet banks ignore it in normal times. This state-contingent reliance shows that community resilience enters credit pricing only in crises, when its information content beyond standard borrower characteristics is valuable enough to justify adoption.

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  • Beyene, Winta, 2025. "Disasters and lending signals: From borrower information to community characteristics," SAFE Working Paper Series 455, Leibniz Institute for Financial Research SAFE.
    • Handle: RePEc:zbw:safewp:325484
    DOI: 10.2139/ssrn.5435455
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