Moody’s warns that US could face $375bn uninsured flood losses from 1-in-100 year event

A new assessment by Moody’s shows that the United States faces a widening gap between growing flood risk and the take-up of flood insurance, potentially exposing the country to more than $375 billion in aggregated uninsured flood losses from a 1-in-100-year event.

A recent report published by the firm, authored by Jennifer Chang, Senior Vice-President for Sustainable Finance Credit at Moody’s Ratings, and Firas Saleh, Director of North American Flood Models at Moody’s, evaluates residential flood exposure across the US, and finds significant gaps between insured and uninsured losses at county level under different flooding scenarios.

The agency argues that flood exposure is becoming an increasing credit concern for US state and local governments, driven by more frequent and severe weather events, residential development in flood zones, and limited levels of insurance coverage.

By using the Moody’s RMS US Inland Flood HD model, the agency managed to assess potential uninsured losses under a 1-in-100-year flood scenario, a 1-in-500-year event, and a 1-in-100-year flood scenario under an intermediate-emissions pathway scenario by 2050.

Amongst the key findings of the report, the authors highlighted that US counties face high flood insurance protection gaps within a 1-in-100-year flood scenario with nationwide aggregate uninsured loss exposure of $375 billion and a 65% protection gap.

“Estimates reflect aggregated potential loss exposure across US counties, rather than losses from a single nationwide flood event. Counties with the largest potential uninsured losses – greater than $5 billion – are concentrated in Florida, Louisiana, South Carolina and Texas, all states with moderate-to-high credit exposure to physical climate risk,” Moody’s added.

Although most US counties show some exposure, the agency explained that the majority face relatively modest potential uninsured losses of $150 million or less, while higher concentrations above $5 billion tend to be located in Florida, Louisiana, South Carolina and Texas.

Roughly 90% of counties across the United States are exposed to flood risk to some degree, but the scale of losses varies.

In regard to a 1-in-500-year scenario, Moody’s estimates that uninsured losses could potentially rise to more than $1 trillion, with the protection gap exceeding 70% and high-loss counties extending across 11 additional states beyond traditional coastal hotspots.

Under the intermediate-emissions scenario by 2050, uninsured loss exposure could increase by roughly 25% to approximately $472 billion. Moody’s also noted that the geographic spread of high exposure could widen further, including into New Jersey, but remain within states with moderate-to-high credit exposure to physical climate risk.

Moody’s also observed that high insurance protection gaps in percentage terms can signal pockets of high risk exposure, but stated, that it is ultimately the magnitude of such uninsured losses and a county’s ability to absorb such shocks through federal disaster aid, state and local resources, liquidity and revenue, insured loss proceeds, and the strength of governance frameworks, that will ultimately determine credit impact.

As well as this, Moody’s also observes that severe rainfall events in recent years have demonstrated how realised flooding can exceed historical expectations, creating uninsured losses in areas not usually associated with high flood risk.

While protection gap percentages signal vulnerability, Moody’s report emphasises that the financial impacts are ultimately determined by the total magnitude of losses and the ability of counties and states to withstand shocks via federal assistance, reserves, insurance payouts and governance frameworks.

It’s also important to remember that with flood risk models improving all the time across the industry, and more private market capital venturing towards underwriting flood risk too, this indicates that there is a chance that the gap will narrow in time.