Flood risk management in the U.S. has contributed to overdevelopment in at-risk areas, increases in flood losses over time, significant deficits in federal emergency programs, and inequitable outcomes to households and communities. Addressing these issues in a cost-effective and socially equitable manner relies on the ability of policy analysts to identify and understand complex interactions that characterize coupled natural-human systems, and tools for accurate estimates of the risks that arise from these interactions. This dissertation addresses this need by developing and investigating a flood risk analysis system that integrates data on property locations, assessments and transactions, high resolution flood hazard models, and flood risk policy and impacts across the coterminous United States. We focus on the degree to which markets accurately value their exposure to flooding and its impacts, and the accuracy of procedures and tools to estimate flood losses. In the first chapter, we identify heterogeneous valuation of storm risk in the Florida Keys that depends on the presence of structural defense and proximity to damaged homes after Hurricane Irma. This result suggests that stranded assets, properties with increasing vulnerability to storms but unable to rebuild structures and recover wealth, and overvalued assets at risk, which raise disaster costs, can occur simultaneously. This runs counter to the common framing of competing drivers of observed market valuation. In the second, we show that conventional methods employed in flood loss assessments to achieve large spatial scales introduce large aggregation bias by sacrificing spatial resolution in inputs. This investigation adds important context to published risk assessments and highlights opportunities to improve flood loss estimation uncertainty quantification which can support more cost effective and equitable management. In the final chapter, we conduct a nationwide study to contrast the predictive accuracy of predominantly used U.S. agency flood damage prediction models and empirical alternatives using data on 846 K observed flood losses to single-family homes from 446 flood events. We find that U.S. agency models mischaracterize the relationships of losses at the lowest low and high inundation depths, for high-valued structures, and structures with basements. Evaluated alternatives improve mean accuracy on these dimensions. In extrapolation to 72.4 M single-family homes in the U.S., these differences translate into markedly different predictions of U.S.-wide flood damages to single-family homes. The results from this dissertation provide an improved empirical foundation for flood risk management that relies on the valuation and estimation of flood risk from county to continental scales.