Quantifying Wind Risk Reduction Potential Across Diverse Building Stocks: A Census Tract-Level Assessment for Coastal Louisiana

Accurate quantification of damage reduction potential from building code enhancements is essential for insurers, investors, and policymakers seeking to prioritize climate adaptation investments. This study develops a tract-level risk assessment framework to estimate the benefits of enhanced building code practices across 708 census tracts within Louisiana's Coastal Zone. Using the National Structure Inventory for exposure data, Hazus-derived damage functions, and ASCE 7-22 wind speed maps, the study calculates expected annual structural damage (EASD) and expected annual damage in dollars (EADD) under baseline and mitigated scenarios.

To identify drivers of spatial variation in mitigation effectiveness, the study links damage reduction estimates to sociodemographic data from the 2018–2022 American Community Survey and the 2020 Decennial Census Demographic and Housing Characteristics file. Spatial regression analysis examines associations between social vulnerability indicators and four damage reduction outcomes: absolute and percentage reductions in both EASD and EADD.

Results reveal substantial spatial variation in damage reduction potential, with tract-level EADD reductions averaging 77%. Key drivers of this variation include building stock characteristics, housing tenure patterns, and population density. Tracts with higher proportions of owner-occupied housing and urban development show larger absolute reductions, while rural tracts demonstrate lower mitigation benefits despite comparable hazard exposure. These patterns suggest that building age, construction quality, and existing code compliance—factors often correlated with sociodemographic characteristics—significantly influence where mitigation investments yield the greatest returns.

This framework provides actionable intelligence for risk-informed decision-making, enabling targeted identification of high-return mitigation zones for insurance loss reduction, public investment prioritization, and resilience planning. The methodology is transferable to other coastal regions facing wind hazards and offers a replicable approach for integrating physical risk modeling with socioeconomic exposure data to support climate adaptation strategies.