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

灾后成本的大幅上升往往会减缓修复过程，扩大不平等，并放大保险不足问题。这项研究将揭示区域住房建设市场的脆弱性特征，并确定有效的灾害相关政策，以减轻这些脆弱性。这将有助于确定自然灾害后导致建筑成本上升的建筑能力差距。这一发现对于提高认识、发展更大的建设能力、制定有效的重建目标、启动风险缓解和资源配置战略以及执行有效的法规和政策至关重要。通过有效的预先规划，可以减少住房恢复的时间和成本。该项目还旨在解决土木工程学科中能够领导灾后重建的人才严重短缺的问题。西班牙裔和女性研究生和本科生在西班牙裔服务的得克萨斯大学在阿灵顿（UTA）将参与这一项目的每一步。该项目将帮助学生与城市规划者合作，引导利益相关者参与弱势社区。该项目将通过以下方式解决现有需求激增模型的基本局限性：1）为住房建设成本变化创建无危害的计量经济学基线，2）创建量化灾后建筑成本上升的计量经济学测量方法，3）建立时空计量经济学模型，以反映住房重建需求的激增，并评估社区的住房重建脆弱性，以及4）量化灾害相关政策对住房重建的影响。该项目将在三个关键学科的联系中产生新的知识：住房建设，经济学和建筑环境复原力。该研究将通过建立灾前建筑市场状况与灾后建筑成本上升之间的联系，改变现有的建筑需求激增模型。为此，时空计量经济学模型，如空间面板数据模型，将使用过去十年中受大规模自然灾害影响的600多个美国县的数据，以及邻近县的数据。这些模型将根据一系列非危险到极端危险的条件考虑不同的情况。差异中的差异面板数据模型将估计与灾害有关的政策（从地方到联邦）的影响。该研究小组将与联邦、州和地方政府合作，确定和评估各种与灾害有关的政策。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

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.