Housing Reconstruction Demand Surge: Measurement, Modeling, And Vulnerability Assessment

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Significant post-disaster cost escalations often slow down repair process, magnify inequality, and amplify underinsurance problem. This research will reveal vulnerable characteristics in regional housing construction markets and identify effective disaster-related policies to alleviate these vulnerabilities. It will help identify the construction capacity gaps that cause the construction cost escalation following natural disasters. The discovery is critical for raising awareness, developing a greater construction capacity, setting effective reconstruction goals, initiating risk mitigation and resourcing strategies, and enforcing effective regulations and policies. Cutting the housing recovery time and cost through effective pre-planning could be realized. This project also aims to address the critical shortage of talents capable of leading post-disaster reconstruction in the civil engineering discipline. Hispanics and women graduate and undergraduate students at the Hispanic-serving University of Texas at Arlington (UTA) will participate in every step of this project. This project will enable students to work with city planners to lead stakeholder involvement in disadvantaged communities.This project will address fundamental limitations of existing demand surge models by 1) creating non-hazard econometric baselines for housing construction cost variations, 2) creating an econometric measurement method for quantifying post-disaster construction cost escalations, 3) creating spatiotemporal econometric models to represent the housing reconstruction demand surge, and assessing the housing reconstruction vulnerability of communities, and 4) quantifying the impacts of disaster-related policies on housing reconstruction. This project will generate new knowledge at the nexus of three critical disciplines: Housing Construction, Economics, and Built Environment Resilience. The research will transform existing construction demand surge models by establishing links between pre-disaster construction market conditions and post-disaster construction cost escalations. To that end, spatiotemporal econometric models, such as spatial panel data models, will use data from more than 600 U.S. counties affected by large-scale natural disasters over the past ten years, as well as data from their neighboring counties. These models will consider different circumstances based on a range of non-hazard to extreme hazard conditions. Difference-in-difference panel data models will estimate the effect of disaster-related policies (ranging from local to federal). The research team will engage federal, state, and local governments to identify and evaluate various disaster-related policies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.