Over 2.4 million properties in the United States, both residential and commercial, could face chronic flooding by the century’s end. Despite FEMA’s role in defining Special Flood Hazard Areas (SFHAs), regulating new developments, and mandat- ing flood insurance for federally backed mortgages, the efficacy of these policies in reducing population risk remains uncertain. This article relies on advancements in urban analytics and causal inference to scrutinize the accuracy of SFHAs in identifying flood-prone structures and evaluates the quality and condition of buildings within and outside these areas. Our study, based on a new database covering over 100,000 flood-prone buildings in Houston, Texas, reveals that 56% of at-risk structures lie outside SFHAs, with a disproportionate concentration in Black and Hispanic neighborhoods. We demonstrate that buildings outside SFHAs are more likely to be of substandard quality, poorly maintained, and uninsured. While SFHA-related policies may mitigate population vulnerability, limitations in flood mapping could also exacerbate disparities across neighborhoods, particularly affecting minority communities. Addressing existing knowledge gaps concerning interactions across different forms of vulnerability, our findings indicate that at-risk socially vulnerable communities also contend with greater structural and financial vulnerability. We conclude that advancements in urban population analytics can enable better planning for future flood events.

This study has shown how physically possible but statistically rare flood events—the “worst credible cases”—can expose deeper racial, infrastructural, and insurance disparities than conventional probabilistic planning often reveals. Current flood risk planning in the United States relies heavily on probabilistic modeling, which can underestimate the severe consequences of rare rainfall events that exceed statistical expectations. This constraint can obscure the potential for catastrophic inundation in densely populated areas and place extreme but plausible events outside the realm of responsible planning. Using Houston, Texas, as a case study, we examined how a “worst credible case” rainfall scenario would affect two urban watersheds within the same flood control district but with very different histories of development, drainage infrastructure, and flood protection. The study used an integrated possibilistic flood modeling framework that combined high-resolution hydrologic simulations, block group-level sociodemographic data, and National Flood Insurance Program coverage rates. Results showed that extreme but plausible rainfall could dramatically increase flood depths and amplify racial disparities compared with historical flood events. The analysis also revealed the systemic vulnerability of older conveyance-only drainage networks and identified a structural flood insurance gap shaped by watershed-level histories of infrastructural neglect and exclusionary development. These findings suggest that responsible flood planning should account not only for what is statistically likely, but also for what is physically possible and socially consequential.