Systemic Financial Risk Arising From Residential Flood Losses.

Direct damage from flooding at residential properties has typically been categorized as insured, with liabilities accruing to insurers, or uninsured, with costs accruing to property owners. However, residential flooding can also expose lenders and local governments to financial risk, though the distribution of this risk is not well understood. Flood losses are not limited to direct damages, but also include indirect effects such as decreases in property values, which can be substantial, though are rarely well quantified. The combination of direct damage and property value decrease influences rates of mortgage default and property abandonment in the wake of a flood, creating financial risk. In this research, property‐level data on sales, mortgages, and insurance claims are used in combination with machine learning techniques and geostatistical methods to provide estimates of flood losses that are then utilized to evaluate the risk of default and abandonment in eastern North Carolina following Hurricane Florence (2018). Within the study area, Hurricane Florence generated $366M in observed insured damages and an estimated $1.77B in combined uninsured damages and property value decreases. Property owners, lenders, and local governments were exposed to an additional $562M in potential losses due to increased rates of default and abandonment. Areas with lower pre‐flood property values were exposed to greater risk than areas with higher valued properties. Results suggest more highly resolved estimates of a flooding event's systemic financial risk may be useful in developing improved flood resilience strategies. Plain Language Summary: The financial impacts of flooding are complex and their distribution across different groups is difficult to quantify. Traditionally, the focus has been on estimating damages that directly impact insurers and property owners, but lenders and local governments can also be affected. Following a flood, uninsured damage and reductions in property value can combine to reduce a property owner's equity, hampering their ability to borrow money and recover from the flood. This can lead to mortgage default or even property abandonment, resulting in financial consequences for the property owner, the mortgage lender, and/or the local government. This research estimates uninsured damage and property value changes throughout eastern North Carolina following Hurricane Florence via a novel machine learning approach, using data on the physical characteristics of residential properties, insurance claims, property sales, and mortgages. Results indicate that uninsured damage and property value decreases combined to be substantial and this combination significantly increased risk of mortgage default and/or abandonment. Lower valued properties experienced higher rates of default and abandonment than high valued properties, with risk varying widely across communities. This type of analysis allows for property‐level assistance to be targeted toward the most vulnerable. Key Points: Estimates of uninsured flood damage and property value decrease can be used to predict rates of mortgage default and abandonmentFinancial risk, previously not well quantified, thus expands beyond property owners and insurers to include lenders and local governmentsA new analytical approach estimates $562M in financial risk from Hurricane Florence with disproportionate impact at low valued properties [ABSTRACT FROM AUTHOR]