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2019冠状病毒病大流行期间出现的当地租赁住房安全威胁。在美国大都市地区，租赁住房占据了住房存量的很大一部分，然而，研究人员对持有这些房产所有权并决定住房供应、租金以及建筑物和单元条件的机构和非机构实体的具体特征知之甚少。更复杂的是，监管环境和租赁住房市场动态在不同的空间，无论是在大都市地区内部还是在大都市地区之间，都有很大的不同。弹性，即抵御灾难或冲击并从中恢复的能力，是由当地房地产市场的条件和相关的监管环境决定的。然而，它也受到在该环境中运作的地主人口行为的影响。在缺乏对房东特征、行为和需求的现有知识的情况下，城市和政策制定者在应对灾害时只能猜测如何稳定租赁市场，为租房者提供住房，为业主提供有意义的援助，并制定有效的灾后恢复计划。本研究通过调查租赁业主的特征，并确定与灾害和灾后决策相关的有意义的租赁业主类别，有助于科学的进步。它通过将这些知识与特定地区与灾害有关的租赁住房成果联系起来，有助于国家的健康、繁荣和福利。2019冠状病毒病大流行表明，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会影响租户的住房能力，还会影响城市应对和从事件中恢复的能力，并确保未来的住房稳定。本研究的中心假设是，在应对灾害时，非机构租赁业主做出的财产和投资决策加速了所有权巩固，降低了灾后社区内的住房安全。该研究采用了一种创新的、融合的方法，将社会科学和数据科学结合起来，以创建新的数据集和数据分析工具，这是一项纵向研究。它通过调查灾害管理周期各阶段的房东决策，并根据房东特征确定非机构租赁物业所有者的有意义类别，填补了现有知识的主要空白。这项研究不仅要回答房东是谁，还要回答他们如何应对灾难，以及房东人口中由灾害引起的变化如何继续影响未来城市和社区的建筑环境。在本提案中有两个嵌套的研究工作：在大流行后的复苏和潜在的未来冲击或灾难的背景下，了解房东的特征和决策；并且，通过数据科学方法，识别和描述房东人口以及在当地从灾害相关的冲击和压力中恢复期间更好地利用数据以促进租赁住房安全的潜在价值。整个项目的目标是从规划、社会学、经济学和金融学等社会科学领域出发，在当地灾后恢复工作中改善住房状况。该项目的目标是创建工具，提高当地机构在数据科学研究领域识别和与房东沟通的能力。该项目的综合方法加强了每个领域的能力，产生了一种创新的方法，使社会科学研究能够解决房东不可见的长期问题，并通过将数据科学技术应用于现实世界的问题而得到改进。本研究的分析单位是美国九个中等城市的出租物业业主，特别是非机构投资者。这些城市虽然规模相似，但住房存量、社会经济特征和政治取向各不相同。他们还为新冠肺炎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州。所有这些国家要么最近受到与灾害有关的灾害的影响，要么面临遭受灾害的高风险。这种灾难经验和恢复的时间顺序将允许房东在整个灾难管理周期中跟踪他们的观点。该奖项反映了美国国家科学基金会的法定使命，并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，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.