Measuring Price Effects from Disasters using Public Data: A Case Study of Hurricane Ian

Natural disasters can disrupt housing markets, causing destruction to communities and distress to economic activity. To estimate the effects of disasters on home prices, publicly-available data on property damages are often used to classify "treated" properties. However, by design these data lack precise geospatial information, leading to measurement error in the treatment variable as aggregate measures must be used. We leverage leading difference-in-differences and synthetic control methodologies across various treatments and levels of geography to measure price effects with such data following Hurricane Ian's unexpected landfall in southwest Florida during September 2022, coinciding with the state's initial recovery from the COVID-19 pandemic. Empirical results suggest positive, time-varying price effects, though we place caveats on these results as there may be many mechanisms underway; our results should be interpreted as descriptive correlations and not causal effects for various reasons. Our main contribution is methodological, highlighting the importance of robustness checks, functional form, statistical techniques, and testing across different samples. Additionally, quicker access to high quality public data could enhance quantitatively-informed conversations on natural disaster effects.