Philip mulder

We describe the initial impact of risk-based flood insurance pricing in the United States. More than two-thirds of National Flood Insurance Program policyholders are paying higher premiums under "Risk Rating 2.0" (RR2.0), and increases will continue in the coming years. Premium increases are largest for policyholders classified as low risk on the Federal Emergency Management Agency's (FEMA's) flood maps, and the average premium gap between those in and out of the FEMA-mapped floodplain is narrowing. FEMA's flood maps, which fail to account for the new risk information, are still relied on by homeowners and regulators, blunting the adaptation benefits of RR2.0. 

Crowdfunding is an increasingly popular way to raise emergency funding after disasters. However, for victims of a recent major Colorado wildfire, we find that crowdfunding raised more support for high income and prime credit beneficiaries rather than helping the most vulnerable. Specifically, beneficiaries with income above $100,000 receive 40% more social network support on GoFundMe than beneficiaries with income below $50,000. High income households are also 13 percentage points more likely to have a crowdfunding campaign at all. The regressive nature of disaster crowdfunding is not due to less severe losses by financially vulnerable consumers, nor is it fully explained by the degree of online campaign sharing. Our findings highlight substantial disparities in social network insurance, which, as we show, likely exacerbate income inequalities in the recovery process.

Rapidly improving data and models are giving homeowners more information about their disaster risk while also increasing insurance premiums for the highest risk homes. In this paper, I study the economic consequences of using better flood risk models to more accurately identify and price flood insurance for high-risk homes. I estimate my results with administrative flood insurance policy data and a novel survey measuring flood insurance demand, risk perceptions, and objective risk. To identify the effects of risk information, I use variation created by outdated elevation data and risk models that caused high-risk homes to be misclassified as low-risk.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 not only for insurers, but also for 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.

In this paper, we explore dynamic changes in the capitalization of sea level rise (SLR) risk in housing and mortgage markets. Our results suggest a disconnect in coastal Florida real estate: From 2013-2016, home sales volumes in the most-SLR-exposed communities declined 20% relative to less-SLR-exposed areas, even as their sale prices grew in lockstep. Only by 2019 did relative prices in these at-risk markets ultimately decline 5%. Over this period, home sellers accumulated an excess inventory of unsold properties as they maintained high list prices. Lender behavior cannot reconcile these patterns, as both all-cash and mortgage-financed purchases similarly contracted. We propose a demand-side explanation where previously neglected SLR risk became more salient in the home price expectations of prospective buyers than sellers. The lead-lag relationship between transaction volumes and prices in SLR-exposed markets is consistent with dynamics of prior real estate bubbles.

Millions of homeowners are exposed to increasing financial risk from natural disasters. Yet, many households are uninsured against the costliest disaster: flooding. We show that low home equity depresses flood insurance take-up. To isolate the causal effect of home equity on flood insurance demand, we exploit price changes over the housing boom and bust. Insurance take-up follows house price dynamics closely, with a home price elasticity around 0.3. Mechanism tests are consistent with a debt over-hang channel, whereby mortgage default limits the flood risk exposure of uninsured households. As a result, households do not fully internalize their disaster risk. 

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.