Real-time Hurricane Damage Nowcasts

This paper uses an empirical model that incorporates multiple hazards and vulnerabilities to nowcast direct hurricane damages immediately following landfall on the continental United States over the last quarter century using real-time information. I evaluate the performance of the model by constructing a novel database of real-time damage predictions from commercial catastrophe models. I also analyze how official estimates of damage are revised. I find that my empirical model is substantially more accurate than simpler models that only incorporate wind speed and income. While commercial nowcasts are generally accurate, especially when averaging across multiple models, my empirical model is performs best immediately after landfall and when there is a large proportion of uninsured and flood losses. The improved nowcasts are beneficial to many stakeholders including policymakers, insurers, and financial markets.