Keeping Shelters in Place: Understanding the Impacts of Residential Landlord Decision-Making on Post-Disaster Housing Stability

这项研究是为了应对新冠肺炎疫情期间出现的对当地租赁住房安全的威胁。租赁住房占美国大都市地区住房存量的很大一部分，但研究人员对持有这些财产所有权的机构和非机构实体的具体特征知之甚少，这些实体决定了住房供应、租金以及建筑物和单元的状况。让这种情况进一步复杂化的是，监管环境和租赁住房市场动态在不同空间存在很大差异，无论是在大都市地区内部还是在大都市地区之间。恢复力，即承受灾难或冲击并从冲击中恢复的能力，取决于当地房地产市场的状况和相关的监管环境。然而，它也是由在那个环境中运作的房东人口的行为决定的。在缺乏关于房东特征、行为和需求的现有知识的情况下，城市和应对灾害的政策制定者只能猜测如何稳定租赁市场，让租户有房住，为业主提供有意义的帮助，并为有效的灾后恢复做好规划。这项研究通过调查租赁物业所有者的特征并确定与灾难和灾后决策相关的有意义的租赁所有者类别，为科学进步做出了贡献。它通过将这一知识与特定地区与灾害有关的租赁住房成果联系起来，为国家健康、繁荣和福利做出贡献。新冠肺炎疫情表明，租赁住房市场的安全与房东应对财务挑战的能力息息相关。房东在灾难中做出的决定不仅影响租户继续居住的能力，也影响城市对灾难的反应和恢复以及确保未来住房稳定的能力。本研究的中心假设是，在应对灾难时，非机构租赁物业所有者做出的财产和投资决策加速了所有权巩固，并降低了社区内灾后住房的安全性。这项研究是一项纵向研究，采用了一种创新、融合的方法，将社会科学和数据科学结合在一起，以创建新的数据集和数据分析工具。它填补了现有知识的一个主要空白，调查了房东在灾害管理周期各个阶段的决策，并根据房东的特点确定了有意义的非机构租赁房产所有者类别。这项研究不仅要回答房东是谁，而且要回答他们如何应对灾害，以及灾害引发的房东人口变化在未来可能如何继续影响城市和社区的建成环境。这项提议包括两项研究工作：在大流行病后恢复和未来可能发生的冲击或灾害的背景下，了解房东的特征和决策；通过数据科学方法，查明和确定房东人口的特征以及更好地利用数据对促进当地从灾害相关的冲击和压力中恢复期间的租赁住房保障的潜在价值。在当地灾后恢复工作中改善住房成果的总体项目目标来自包括规划、社会学、经济学和金融学在内的社会科学领域。该项目的目标是创建工具，提高当地机构识别房东和与房东沟通的能力，这取决于数据科学研究领域。该项目的综合方法论加强了每个领域的能力，产生了一种创新的方法，其中社会科学研究能够解决房东看不见的持久问题，数据科学技术通过将其应用于现实世界的问题而得到改进。这项研究的分析单位是美国9个中等城市的租赁物业所有者，特别是非机构投资者。这些城市虽然规模相似，但拥有不同的住房存量、社会经济特征和政治取向。他们还为COVID住房危机提供了独特的州和地方应对措施。其中五个城市位于墨西哥湾沿岸地区的各州。所有这些人要么最近受到了与灾害有关的灾害的影响，要么面临着遭遇灾难的高风险。这一灾难经历和恢复的时间范围将允许在整个灾难管理周期内跟踪房东的观点。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

This research is responding to the local threats to rental housing security that emerged during the COVID-19 pandemic. Rental housing occupies a significant portion of the housing stock in US metropolitan areas, yet researchers know very little about the specific characteristics of the institutional and non-institutional entities that hold titles to those properties and determine housing supply, rents, and the conditions of both buildings and units. To further complicate this scenario, regulatory environments and rental housing market dynamics vary greatly across space, both within and between metropolitan regions. Resiliency, the ability to withstand and recover from a disaster or a shock, is shaped by the conditions of the local housing market and the associated regulatory environment. However, it is also shaped by the behaviors of the landlord population operating within that milieu. In the absence of existing knowledge about landlord characteristics, behaviors, and needs, cities and policy makers responding to disasters are left guessing how to stabilize their rental markets, keep renters housed, deliver meaningful assistance to property owners, and plan for an effective post-disaster recovery. This study contributes to the progress of science by investigating rental property owner characteristics and identifying meaningful rental owner categories as they relate to disaster and post-disaster decision-making. It contributes to the national health, prosperity and welfare by linking that knowledge to disaster-related rental housing outcomes in specific places. The COVID-19 pandemic has demonstrated that the security of the rental housing market is intertwined with a landlord’s ability to tackle financial challenges. The decisions that landlords make in the midst of a disaster affect not only their tenants’ ability to remain housed, but the ability of the city to respond to and recover from the event and ensure future housing stability.The central hypothesis of this study is that when responding to disasters, non-institutional rental property owners make property and investment decisions that accelerate ownership consolidation and reduce post-disaster housing security within communities. The research is structured as a longitudinal study using an innovative, convergent approach that brings together social science and data science in order to create new datasets and tools for data analysis. It fills a major gap in existing knowledge by investigating landlord decision-making across the stages of the disaster management cycle and identifying meaningful categories of non-institutional rental property owners based on landlord characteristics. This study sets out to answer not just who landlords are, but how they respond to disasters and how disaster-induced changes in the landlord population might continue to affect the built environment of cities and communities into the future. There are two nested research efforts within this proposal: to understand landlord characteristics and decision-making within the context of the post-pandemic recovery and potential future shocks or disasters; and, through data science approaches, to identify and characterize the landlord population and the potential value of better data utilization for promoting rental housing security during local recovery from hazard-related shocks and stresses. The overall project goals to improve housing outcomes within local disaster recovery efforts draw from the domain of social science including planning, sociology, economics, and finance. The project goals to create tools that improve the local institutional capacity for identifying and communicating with landlords rely on the domain of data science research. This project’s integrative methodology strengthens the capacity of each domain, generating an innovative approach where social science research is able to resolve the enduring problem of landlord invisibility and the data science techniques are refined through their application to real world problems. The unit of analysis for this study is the rental property owner, specifically non-institutional investors, in nine mid-sized US cities. These cites, though of similar size, have varied housing stocks, socioeconomic characteristics, and political orientations. They also provided unique state and local responses to the COVID housing crisis. Five of the cities are located in states that are part of the Gulf Coast region. All have either been recently affected by hazard-related disasters or are at high risk of experiencing a disaster. This chronological range of disaster experience and recovery will allow the tracking of landlord perspectives across the disaster management cycle.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.