This study estimates the effects of flood hazard on residential property values using a hedonic property price method. We utilize digital flood maps coupled with extensive residential property sales records between September 2000 and September 2004 from Carteret County, North Carolina. Carteret County is a coastal county that contains numerous floodplains including areas subject to wave action. Our results indicate that on average property values are 5–10% lower if located within a flood zone that is not subject to wave action. However, location within a flood zone that is vulnerable to wave action is associated with higher property values. We find that, in most inland areas, price differentials for flood risk are roughly equivalent to the capitalized value of flood insurance premiums. However, the risk of coastal flooding seems inconsequential in determining property values due to the substantial premiums that appear to be associated with proximity to coastal water.

The retrofit of wood-frame residential buildings is a relatively effective strategy to mitigate damage caused by windstorms. However, little is known about the effect of modifying building performance for intense events such as a tornado and the subsequent social and economic impacts that result at the community level following an event. This paper presents a method that enables a community to select residential building performance levels representative of either retrofitting or adopting a new design code that computes target community metrics for the effects on the economy and population. Although not a full risk analysis, a series of generic tornado scenarios for different Enhanced Fujita (EF) ratings are simulated, and five resilience metrics are assigned to represent community goals based on economic and population stability. To accomplish this, the functionality of the buildings following the simulated tornado is used as input to a computable general equilibrium (CGE) economics model that predicts household income, employment, and domestic supply at the community level. Population dislocation as a function of building damage and detailed sociodemographic US census-based data is also predicted and serves as a core community resilience metric. Finally, this proposed methodology demonstrates how the metrics can help meet community-level resilience objectives for decision support based on a level of design code improvement or retrofit level. The method is demonstrated for Joplin, Missouri. All analyses and data have been developed and made available on the open-source IN-CORE modeling environment. The proposed multidisciplinary methodology requires continued research to characterize the uncertainty in the decision support results.