Department of Systems Design Engineering, University of Waterloo, Waterloo, Ontario, Canada.
Department of Civil and Environmental Engineering, Stanford University, Stanford, California, U.S.A.
Risk Anal. 2024 Apr;44(4):850-867. doi: 10.1111/risa.14206. Epub 2023 Aug 12.
In the United States, assistance from the Department of Housing and Urban Development (HUD) plays an essential role in supporting the postdisaster recovery of states with unmet housing needs. HUD requires data on unmet needs to appropriate recovery funds. Ground truth data are not available for months after a disaster, however, so HUD uses a simplified approach to estimate unmet housing needs. State authorities argue that HUD's simplified approach underestimates the state's needs. This article presents a methodology to estimate postdisaster unmet housing needs that is accurate and relies only on data obtained shortly after a disaster. Data on the number of damaged buildings are combined with models for expected repair costs. Statistical models for aid distributed by the Federal Emergency Management Agency (FEMA) and the Small Business Administration (SBA) are then developed and used to forecast funding provided by those agencies. With these forecasts, the unmet need to be funded by HUD is estimated. The approach can be used for multiple states and hazard types. As validation, the proposed methodology is used to estimate the unmet housing needs following disasters that struck California in 2017. California authorities suggest that HUD's methodology underestimated the state's needs by a factor of 20. Conversely, the proposed methodology can replicate the estimates by the state authorities and provide accounts of losses, the amount of funding from FEMA and SBA, and the total unmet housing needs without requiring data unavailable shortly after a disaster. Thus, the proposed methodology can help improve HUD's funding appropriation without delays.
在美国，住房和城市发展部 (HUD) 的援助在支持有未满足住房需求的州进行灾后恢复方面发挥着重要作用。HUD 需要关于未满足需求的数据来适当分配恢复资金。然而，灾难发生后数月内都无法获得真实数据，因此 HUD 采用简化方法来估计未满足的住房需求。州当局认为 HUD 的简化方法低估了该州的需求。本文提出了一种准确的、仅依赖于灾难发生后不久获得的数据来估计灾后未满足住房需求的方法。受损建筑物数量的数据与预期修复成本模型相结合。然后，为联邦紧急事务管理局 (FEMA) 和小企业管理局 (SBA) 分配的援助开发并使用统计模型，以预测这些机构提供的资金。根据这些预测，估算由 HUD 资助的未满足需求。该方法可用于多个州和多种灾害类型。作为验证，本文提出的方法用于估算 2017 年加利福尼亚州发生灾害后的未满足住房需求。加利福尼亚州当局表示，HUD 的方法低估了该州需求的 20 倍。相反，该方法可以复制州当局的估计，并提供损失、FEMA 和 SBA 的资金数额以及无需在灾难发生后不久获得数据的情况下的总未满足住房需求的说明。因此，该方法可以帮助改善 HUD 的资金拨款，而不会造成延误。
