Dynamic Adverse Selection in Flood Insurance

Abstract

The National Flood Insurance Program (NFIP) has been criticized for setting rates that inaccurately reflect flood risk, while on the other side of the market, studies have shown that homeowners typically hold inaccurate beliefs about their own risk. Despite cataloguing these sources of incomplete information, no research to date has studied asymmetric information in flood insurance. In this paper, I find evidence of adverse selection that emerges through dynamic changes in demand. I first show that experienced flood policy holders have higher claims conditional on rating factors relative to new purchasers. Initial buyers who drop their coverage have lower average costs than those that keep it. I show how this phenomenon can be understood as a form of dynamic adverse selection. To explore the possible sources and implications of dynamic adverse selection, I incorporate my empirical estimates into a model of insurance with learning to illustrate how much of flood insurance demand is driven by consumer-side risk type uncertainty. My findings suggest that 80% of initial take-up and two-thirds of consumer surplus stem from buyers’ uncertainty over their own risk relative to their knowledge after maintaining coverage for up to 15 years. In addition to their implications for flood insurance, my methods can be applied to understanding other markets that feature learning and dynamic demand processes.