US exposed to $375 billion-$1 trillion in aggregated uninsured flood losses from a range of extreme events

US residential flood exposure poses a growing credit risk, given large insurance protection gap.

Understanding flood risk concentration under different severity scenarios at the US county level

Flood risk is a growing credit challenge for US state and local governments, given increased frequency and severity of flooding events, residential development in flood zones, and limited insurance coverage. This Moody’s report quantifies potential insurance protection gaps ⁱ at the county level under different scenarios to provide a forward-looking view of risk exposure.

Per Moody’s Ratings, residential flood exposure poses significant credit risk to US state and local governments, including through rising property insurance costs, declining property values and the need for extensive investment in climate-resilient infrastructure.

Moody’s has conducted a nationwide analysis of residential flood risk in the US using the Moody’s RMS US Inland Flood HD model, illustrating different scenarios of potential uninsured flood losses at the county level as the flood footprint expands. The scenarios include (i) a 1-in-100-year flood (the Federal Emergency Management Agency [FEMA]’s threshold for federally regulated flood insurance); (ii) a more extreme 1-in-500-year flood; and (iii) a 1-in-100-year flood in an intermediate-emissions scenario by 2050. ⁱⁱ 

All scenarios assume insurance coverage remains static with no additional investment in flood defenses, indicating current structural exposure rather than a forecast of realized losses.

The Moody’s flood analysis compares the full stock of residential properties that could be insured, based on their replacement value and exposure to flooding, with homes that currently carry National Flood Insurance Program (NFIP) coverage. This framework quantifies the insurance protection gap across fluvial, pluvial and coastal sources of flooding. It identifies areas of concentrated high uninsured flood risk and highlights where insurance protection gaps are most pronounced under the different scenarios.

This report highlights a structural mismatch between the broadening of US flood risk exposure and insurance protection. Uninsured losses arise not from isolated outliers, but from persistent gaps between expanding flood hazards – particularly beyond regulatory flood maps that dictate mortgage requirements, as well as rarer, high-severity events – and insurance take-up.

As the flood footprint expands across the different scenarios, the concentration of higher potential uninsured loss spreads beyond counties in coastal states with moderate-to-high credit exposure to physical climate risk to additional inland states, including ones with low physical climate risk exposure. All references to credit exposure to physical risk throughout this report are per Moody's Ratings. ⁱⁱⁱ 

While a relatively small number of counties account for a disproportionate share of potentially high uninsured losses, most counties face some degree of flood exposure and high insurance protection gaps, which essentially transfer recovery costs and increase reliance on federal relief aid, households, and state and local government support.

Key findings

• US counties face high flood insurance protection gaps in 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. ^iv  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. 
• The concentration of risk is clear when considering Moody’s analysis that although 90% of counties are exposed to some level of flood risk and generally have high insurance protection gaps, the magnitude of their potential uninsured loss exposure is relatively small at $150 million or less per county. This can reflect lower flood hazard in the region, lower residential exposure, or both. 
• As the flood footprint expands in rarer 1-in-500-year events, nationwide uninsured loss exposure could triple to over $1 trillion, with a more than 70% protection gap. Counties with potential uninsured losses above $5 billion extend to 11 states beyond the Gulf and Atlantic coasts, including some with low exposure to physical climate risk. 
• Under an intermediate-emissions scenario, uninsured loss exposure could increase by about 25% on average by 2050, to around $472 billion. This is not a forecast, but a stress scenario analysis to illustrate how flood risk and insurance gaps might evolve. Counties with potential uninsured losses above $5 billion would expand to one more state beyond the 1-in-100-year flood scenario, in New Jersey, but remain within states with moderate-to-high credit exposure to physical climate risk.
• The catastrophic flooding associated with Hurricane Helene in Asheville, North Carolina, in September 2024 illustrates how extreme precipitation can significantly exceed historical levels, resulting in uninsured losses for households and businesses under various severity scenarios. Recent events in other regions have also illustrated risk exposure from record rainfall in short time periods that can breach even rarer 1-in-1,000-year rainfall return periods. 
• High insurance protection gaps in percentage terms can signal pockets of high risk exposure, but 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 determine credit impact. 
• Uninsured loss exposure ratios (potential uninsured loss to replacement cost values) provide an added lens to analyze risk exposure. Counties with the largest potential uninsured losses do not always have the highest uninsured loss exposure ratios. This means that even in counties where the magnitude of losses may not be as high, they may still be significant for that particular county; conversely, for some counties, high uninsured loss exposure in absolute terms may reflect a relatively small share of the county’s overall residential base.

From nationwide impact to local burden

Flood risk is present nationwide in the US. Insurance take-up rates are low, even in areas of high risk, indicating credit risk transfer to households and local governments, and reliance on relief aid.

Although most counties are likely to experience relatively low losses from flooding, they are also significantly uninsured. As extreme precipitation risk expands the flood footprint, the number of exposed households will rise and so will their level of potential uninsured losses, absent a mitigating uptake in insurance or investment in flood protection strategies.

As the frequency and intensity of severe weather events increase, quantifying exposure, or tail risk, using different scenarios offers information for stress testing at the local-government level, which increases risk transparency.

Per Moody’s Ratings, effective governance and risk management often correlate with lower physical climate risk vulnerability in regions of high exposure, supported by preparedness measures (including alert systems and adaptation steps), dedicated reserves, relief funds and infrastructure maintenance, as well as zoning, regulation and building codes. Investment in adaptation and resilience can reduce the protection gap by lowering risk exposure as well as contributing to insurance market stability.

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Flood footprint expansion across different scenarios shows higher overall losses and protection gaps, as well as flood risk exposure reaching states with high, moderate and low physical climate risk exposure

Estimates in the table below reflect aggregated potential nationwide loss exposure across US counties, rather than losses from a single nationwide flood event

These figures represent aggregate loss exposures from every county, rather than a single nationwide flood event.

^{Source: Moody's}

Footnotes

• ^{i . Uninsured losses as a share of total economic losses}
• ^{ii . See Analytical Approach section where this is explained in greater detail.}
• ^{iii . https://www.moodys.com/web/en/us/insights/methodologies-and-models.html} 
• ^{iv . A 1‑in‑100‑year flood is a flood event with a 1% chance of occurring in any given year. It does not mean the event happens only once every 100 years, but reflects an ongoing annual probability.} 
• ^{v . TEV is derived by Moody’s from replacement cost values rather than market values, and therefore excludes land value.}