Homeowner purchase of insurance for hurricane-induced wind and flood damage

Dong Wang, Rachel A. Davidson (), Joseph E. Trainor, Linda K. Nozick and Jamie Kruse
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Dong Wang: University of Delaware
Rachel A. Davidson: University of Delaware
Joseph E. Trainor: University of Delaware
Linda K. Nozick: Cornell University
Jamie Kruse: East Carolina University

Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, 2017, vol. 88, issue 1, No 11, 245 pages

Abstract: Abstract Understanding the circumstances under which homeowners will purchase insurance is critical to creating an effective insurance market for hurricane wind and flood loss. This paper contributes to the empirical literature on the subject through an analysis of survey data for homeowners in North Carolina. We develop separate mixed logit models for flood insurance and wind insurance purchasing decisions. The analysis uses stated preference data on the influence of premium and deductible to address some limitations of revealed preference data in which it is difficult to fully decouple effects of premium, deductible, risk, and coverage limit, and mandatory purchase requirements. The results for flood insurance and wind insurance are similar. We find evidence that the following are all significant and associated with higher probability of purchasing insurance—lower premium, lower deductible, more recent previous hurricane experience, location in a floodplain or closer to the coast, higher income, and younger homeowners. However, demand is relatively inelastic with respect to premium and deductible, and the willingness to pay for a $1 reduction in deductible varies throughout the population with some willing to pay more than $1, a behavioral anomaly. The recency of the last hurricane experience is more influential for homeowners who experienced damage than for homeowners who did not. Results suggest that insurance purchase and home retrofits are complements, not substitutes. Finally, the paper presents statistical models that can be used to predict insurance penetration rates for a region under different premium levels.

Keywords: Insurance; Wind; Flood; Mixed logit; Stated preference; Homeowner (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s11069-017-2863-x

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