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An increasing number of national, state, and local programs have offered grants or other monetary incentives to encourage homeowners to retrofit their homes to reduce damage from natural hazard events. Despite this fact, little is known about how these offerings influence a homeowner’s decision to carry out such structural retrofits. This paper studies the impact that different grant program designs in particular have on the decision to undertake different types of retrofits to mitigate against hurricane damage. Using data from a survey of homeowners in the eastern half of North Carolina, we implement a mixed logit model that allows for the combination of both revealed-preference and stated-preference data available from the survey. Our findings show that offering a grant results in households being, on average, 3 times as likely to retrofit as when a grant is not offered. In addition, both the percentage of retrofit cost and the maximum dollar amount covered by the grant have a substantial impact on the probability that households choose to retrofit. Living closer to the coastline also has a significant impact on the probability that households will choose to retrofit. Counter to some previous research, we find that households who have experienced two or more hurricanes are less likely to choose to retrofit their homes. From our research, we find that the percentage of retrofit cost covered by the grant and the total cost are both important factors when deciding on the best grant program configuration.

Regional hurricane risk is often assessed assuming a static housing inventory, yet a region's housing inventory changes continually. Failing to include changes in the built environment in hurricane risk modeling can substantially underestimate expected losses. This study uses publicly available data and a long short-term memory (LSTM) neural network model to forecast the annual number of housing units for each of 1000 individual counties in the southeastern United States over the next 20 years. When evaluated using testing data, the estimated number of housing units was almost always (97.3 % of the time), no more than 1 percentage point different than the observed number, predictive errors that are acceptable for most practical purposes. Comparisons suggest the LSTM outperforms the autoregressive integrated moving average (ARIMA) and simpler linear trend models. The housing unit projections can help facilitate a quantification of changes in future expected losses and other impacts caused by hurricanes. For example, this study finds that if a hurricane with characteristics similar to Hurricane Harvey were to impact southeastern Texas in 20 years, the residential property and flood losses would be nearly USD 4 billion (38 %) greater due to the expected increase of 1.3 million new housing units (41 %) in the region.

This paper introduces a computational framework that can be used to identify hurricane risk management solutions based on the operation of the system as a whole. The framework represents in...

Property acquisition programs are an important part of government mitigation efforts. Since they are voluntary, understanding how property owners make the decision to accept an offer is critical in determining how best to design such programs and assess their likely success. This paper uses logistic regression analysis of stated preference data collected through a phone survey of residents of Eastern North Carolina to identify the attributes of the property, the homeowner, and the homeowner's attachment to the community that influence the property acquisition decision. The results provide evidence that increased probability of acquisition offer acceptance is associated with location in a floodplain, shorter expected future tenure in the home, having experienced more past hurricanes, less feeling of control, and being White. Additional research is needed to better predict which homeowners will accept a specified offer and overall program participation rates.

Even as new incentive programs emerge to encourage homeowners to strengthen their homes so as to reduce the risk of damage in extreme events, little is known about how homeowners make such ...

This note surveys some behavioral models of preference relations applicable for analysis of decisions in the face of catastrophic risk. Catastrophic risk is characterized, and the implications of various representations explored in simple, illustrative examples. An argument is presented for the more general applicability of “variational preferences” for the analysis of behavior and decision making in the face of catastrophic risk.