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A Comparison of Hurricane Loss Models


  • Cassandra R. Cole
  • David A. Macpherson
  • Kathleen A. McCullough


Hurricane models are a significant tool used in estimating loss costs in catastrophe-prone areas. While the major hurricane loss cost models consider a consistent set of factors, there are variations in how the factors are treated in the models. This can lead to considerable variation in the modeled average annual losses (AALs), even at the exposure level, depending on the catastrophe model used. Therefore, the model selected could have a dramatic impact on price. Given that in some states, such as Florida, insurers are only allowed to use a single model in rating, an understanding of what drives the differences in AALs is critical. This paper uses a large dataset of wind-only policies in order to analyze the impact of housing, insurance, and mitigation characteristics on AALs for four hurricane loss models. We find that while there is some correlation among the modeled loss costs, the extent of the correlation does vary overall and with respect to housing, insurance, and mitigation characteristics. In addition, our results indicate that there are significant differences in the direction and magnitude of the relation of AAL and housing, insurance, and mitigation characteristics across the models. These results are of interest to insurers, consumers, and regulators as they indicate that the insurer’s selection and use of a particular model is likely to affect the cost of coverage.

Suggested Citation

  • Cassandra R. Cole & David A. Macpherson & Kathleen A. McCullough, 2010. "A Comparison of Hurricane Loss Models," Journal of Insurance Issues, Western Risk and Insurance Association, vol. 33(1), pages 31-53.
  • Handle: RePEc:wri:journl:v:33:y:2010:i:1:p:31-53

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    Cited by:

    1. Matthew Ranson & Lisa Tarquinio & Audrey Lew, 2016. "Modeling the Impact of Climate Change on Extreme Weather Losses," NCEE Working Paper Series 201602, National Center for Environmental Economics, U.S. Environmental Protection Agency, revised May 2016.
    2. Lorilee A. Medders & Charles M. Nyce & J. Bradley Karl, 2014. "Market Implications of Public Policy Interventions: The Case of Florida's Property Insurance Market," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 17(2), pages 183-214, September.
    3. Yagci Sokat, Kezban & Dolinskaya, Irina S. & Smilowitz, Karen & Bank, Ryan, 2018. "Incomplete information imputation in limited data environments with application to disaster response," European Journal of Operational Research, Elsevier, vol. 269(2), pages 466-485.
    4. Ron Cheung & Cassandra R. Cole & David A. Macpherson & Kathleen A. McCullough & Charles Nyce, 2015. "Demographic Factors and Price Distortions in Insurance," Risk Management and Insurance Review, American Risk and Insurance Association, vol. 18(1), pages 1-28, March.
    5. Erdem Karaca & Hesaam Aslani, 2016. "Review of two Japan Typhoon catastrophe models for commercial and industrial properties," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 83(1), pages 19-40, August.

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