Unrefined national building inventories can mislead risk assessments and decisions

Flood-risk assessments increasingly rely on large-scale building inventories that offer fine spatial detail but limited and uneven quality assurance. As a result, exposure is often treated as a static, “ready-to-use” input, even though small errors in where assets are located or how they are characterized can propagate into loss estimates. Despite the centrality of exposure for understanding changing risk under climate and socio-economic change, the implications of adopting exposure data without refinement remain poorly quantified. Here, we test how exposure data quality influences flood-loss estimates and decision-relevant metrics by comparing damages derived from a widely used national building inventory to estimates produced with high-quality, feature-rich local building data across an ensemble of flood scenarios. We find that adopting an unrefined building inventory can systematically distort decision-relevant damage metrics. For example, roughly one-fifth of areas are misclassified with respect to a funding priority status metric used in the U.S. Simple, transferable exposure refinements—particularly corrections to building locations—substantially reduce these errors, yielding near-complete agreement with rankings based on high-quality local data. Our findings demonstrate that credible assessments of flood risk require explicit attention to the spatio-temporal reliability of exposure inputs, not only improved hazard characterization or vulnerability functions. We provide actionable guidance for diagnosing exposure errors and implementing practical corrections.