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Impacts of extreme weather events on mortgage risks and their evolution under climate change: A case study on Florida

Author

Listed:
  • Raffaella Calabrese

    (Edin. - University of Edinburgh)

  • Timothy Dombrowski

    (University of Missouri [St. Louis] - University of Missouri System)

  • Antoine Mandel

    (PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)

  • R. Kelley Pace

    (LSU - Louisiana State University)

  • Luca Zanin

    (Prometeia)

Abstract

We develop an additive Cox proportional hazard model with time-varying covariates, including spatio-temporal characteristics of weather events, to study the impact of weather extremes (heavy rains and tropical cyclones) on the probability of mortgage default and prepayment. We compare the survival model with a flexible logistic model and an extreme gradient boosting algorithm. We estimate the models on a portfolio of mortgages in Florida, consisting of 69,046 loans and 3,707,831 loan-month observations with localization data at the five-digit ZIP code level. We find a statistically significant and non-linear impact of tropical cyclone intensity on default as well as a significant impact of heavy rains in areas with large exposure to flood risks. These findings confirm existing results in the literature and also provide estimates of the impact of the extreme event characteristics on mortgage risk, e.g. the impact of tropical cyclones on default more than doubles in magnitude when moving from a hurricane of category two to a hurricane of category three or more. We build on the identified effect of exposure to flood risk (in interaction with heavy rainfall) on mortgage default to perform a scenario analysis of the future impacts of climate change using the First Street flood model, which provides projections of exposure to floods in 2050 under RCP 4.5. We find a systematic increase in risk under climate change that can vary based on the scenario of extreme events considered. Climate-adjusted credit risk allows risk managers to better evaluate the impact of climate-related risks on mortgage portfolios.

Suggested Citation

  • Raffaella Calabrese & Timothy Dombrowski & Antoine Mandel & R. Kelley Pace & Luca Zanin, 2024. "Impacts of extreme weather events on mortgage risks and their evolution under climate change: A case study on Florida," PSE-Ecole d'économie de Paris (Postprint) halshs-04409393, HAL.
    • Handle: RePEc:hal:pseptp:halshs-04409393
    DOI: 10.1016/j.ejor.2023.11.022
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    Cited by:

    1. Pascal Kundig & Fabio Sigrist, 2024. "A Spatio-Temporal Machine Learning Model for Mortgage Credit Risk: Default Probabilities and Loan Portfolios," Papers 2410.02846, arXiv.org.
    2. Si-yao Wei & Wei-xing Zhou, 2024. "The resilience of China's financial markets: With a focus on the impact of its climate policy uncertainty," Papers 2409.18422, arXiv.org.
    3. Xu, Xin & An, Haizhong & Huang, Shupei & Jia, Nanfei & Qi, Yajie, 2024. "Measurement of daily climate physical risks and climate transition risks faced by China's energy sector stocks," International Review of Economics & Finance, Elsevier, vol. 93(PB), pages 625-640.

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