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

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.