Abstract: Using fine-grained data on 465,000 Florida home sales over twelve years and semiparametric machine learning methods, we examine how home prices respond to weather-related risk factors. After controlling for geography, home characteristics, and transaction features, we find that home prices are negatively related to property-level expected weather losses derived from catastrophe models, insurance premiums, and exposure to recent hurricanes, but with notably different magnitudes. Expected weather losses show the strongest association with prices and are consistent with markets rationally capitalizing anticipated future losses into current property valuations. Insurance premiums have a statistically significant but economically more modest negative effect on home prices, suggesting that housing market participants may view premium differences across properties as more transitory. Recent hurricane exposure has only a marginal effect on house prices, suggesting that hurricane risk is well-understood in Florida so that realized weather events convey little new information about future losses.

https://www.federalreserve.gov/econres/feds/premiums-or-peril.htm