The frequency of coastal floods around the United States has risen sharply over the last few decades, and rising seas point to further future acceleration. Residents of low-lying affordable housing, who tend to be low-income persons living in old and poor quality structures, are especially vulnerable. To elucidate the equity implications of sea level rise (SLR), we provide the first nationwide assessment of recent and future risks to affordable housing from SLR and coastal flooding in the United States. By using high-resolution building footprints and probability distributions for both local flood heights and SLR, we identify the coastal states and cities where affordable housing - both subsidized and market-driven - is most at risk of flooding. We provide estimates of both the expected number of affordable housing units exposed to extreme coastal water levels and of how often those units may be at risk of flooding. The number of affordable units exposed in the United States is projected to more than triple by 2050. New Jersey, New York, and Massachusetts have the largest number of units exposed to extreme water levels both in absolute terms and as a share of their affordable housing stock. Some top-ranked cities could experience numerous coastal floods reaching higher than affordable housing sites each year. As the top 20 cities account for 75% of overall exposure, limited, strategic and city-level efforts may be able to address most of the challenge of preserving coastal-area affordable housing stock.

Sea level rise (SLR) and heavy precipitation events are increasing the frequency and extent of coastal flooding, which can trigger releases of toxic chemicals from hazardous sites, many of which are in low-income communities of color. We used regression models to estimate the association between facility flood risk and social vulnerability indicators in low-lying block groups in California. We applied dasymetric mapping techniques to refine facility boundaries and population estimates and probabilistic SLR projections to estimate facilities' future flood risk. We estimate that 423 facilities are at risk of flooding in 2100 under a high emissions scenario (RCP 8.5). One unit standard deviation increases in nonvoters, poverty rate, renters, residents of color, and linguistically isolated households were associated with a 1.5-2.2 times higher odds of the presence of an at-risk site within 1 km (ORs [95% CIs]: 2.2 [1.8, 2.8], 1.9 [1.5, 2.3], 1.7 [1.4, 1.9], 1.5 [1.2, 1.9], and 1.5 [1.2, 1.9], respectively). Among block groups near at least one at-risk site, the number of sites increased with poverty, proportion of renters and residents of color, and lower voter turnout. These results underscore the need for further research and disaster planning that addresses the differential hazards and health risks of SLR.