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.

Flooding is a multidimensional hazard causing beyond physical damage to long term socio-economic consequences. The measurement of hazard losses has been facing persistent challenges in capturing both direct costs, such as property damage, and indirect impacts, including displacement and economic stagnation. This dynamic places historically underrepresented communities at a disadvantage, with fewer resources for preparation and recovery. Wealth inequality heightens financial vulnerability, limiting access to protective measures, insurance, and capital for rebuilding efforts. These disparities are historically tied to racial inequalities, creating barriers for marginalized populations, who often reside in neighborhoods with limited economic resources and fewer planning options. This study integrates the Geographic Wealth Inequality Database (GEOWEALTH-US) and Spatial Hazard Events and Losses Database (SHELDUS) within a multi-scale analysis to assess flood-related losses at finer geographic scales, illustrating disparity patterns both between and within regions. This multi-scale spatial approach underscores the uneven distribution of flood hazards across Harris County in Texas, highlighting the importance of localized planning to address site-specific vulnerabilities. By examining hazard data and wealth indicators, the analysis shows how socio-economic disadvantages, infrastructure deficits, and environmental risk historically reinforced flood damage. Refining flood assessments with comprehensive datasets can better inform policy interventions, reducing cumulative flood losses and advancing environmental justice in vulnerable communities.