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

被引:0
|
作者
Dong Wang
Rachel A. Davidson
Joseph E. Trainor
Linda K. Nozick
Jamie Kruse
机构
[1] University of Delaware,Department of Civil and Environmental Engineering
[2] University of Delaware,School of Public Policy and Administration
[3] Cornell University,School of Civil and Environmental Engineering
[4] East Carolina University,Department of Economics
来源
Natural Hazards | 2017年 / 88卷
关键词
Insurance; Wind; Flood; Mixed logit; Stated preference; Homeowner;
D O I
暂无
中图分类号
学科分类号
摘要
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.
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页码:221 / 245
页数:24
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