Mismeasuring Risk: The Welfare Effects of Flood Risk Information

Rapidly improving data and models giving homeowners more information about their disaster risk while also increasing insurance premiums for the highest risk homes led to my study of the economic consequences of using better flood risk models to identify and price flood insurance for high-risk homes more accurately. I drew my results by analyzing administrative flood insurance policy data and conducting a novel survey measuring flood insurance demand, risk perceptions, and objective risk. To identify the effects of risk information, I used variation created by outdated elevation data and risk models that caused high-risk homes to be misclassified as low-risk.

My findings show that flood risk classification provides valuable information for insurers and  homeowners. Misclassifying high-risk homes as low-risk causes owners to underestimate their current and future flood risk, invest less in risk-reducing adaptation, and buy less flood insurance despite substantially lower premiums. Embedding these estimates in a sufficient statistics model with dynamic risk and endogenous risk beliefs and adaptation, I find that identifying and pricing the estimated six million high-risk homes outside the floodplain would increase social welfare by $138 billion.

Philip Mulder received his PhD in Applied Economics from The Wharton School. He will work at the U.S. Treasury as a post-doctoral researcher studying climate risk for the next year and then join the University of Wisconsin Madison School of Business as an Assistant Professor of Risk and Insurance in 2023.