Rental Property Owner Stress During the COVID-19 Pandemic: Results from a Minneapolis, MN Survey

ABSTRACT

The COVID-19 pandemic has placed a unique strain on the US housing system. Unprecedented job losses combined with a public health imperative to keep people housed pushed policymakers to issue a series of orders pausing residential evictions. These moratoria kept people in their homes but did little to address the underlying housing stresses. In this paper, we document the early impact of the pandemic on private rental housing owners with the results of a new survey. Between December 2020 and January 2021, we surveyed rental property owners in Minneapolis, Minnesota, asking general questions about their businesses and specific questions about how the pandemic has affected their ability to operate rental properties. In this paper, we present a descriptive analysis of the responses. Nearly half of the respondents to our survey reported that the pandemic affected their business in some way. In addition, we find associations between property owner stress and rents, portfolio size, property location, and owning physically-deficient properties. The results of our analysis will be useful for policymakers as they continue to confront the housing challenges brought on by the COVID-19 pandemic.

KEYWORDS:

• Housing
• landlords
• COVID-19
• rental property

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. https://www.mprnews.org/story/2021/03/06/timeline-covid-19-minnesota

2. https://www.leg.mn.gov/archive/execorders/20–14.pdf

3. https://www.kare11.com/article/news/state/some-minnesota-renters-protected-federal-eviction-moratorium-ends/89-de1915cc-6993-458c-9b54-bc3625b71fb7

4. See https://www2.minneapolismn.gov/business-services/licenses-permits/rental-licenses/ for information on the rental licensure process.

5. We considered weighting our survey to make it more representative of the population of Minneapolis owners. Unfortunately, we do not have any data on the population in question. We could, of course, create weights based on the characteristics of the rental housing stock, but our unit of analysis is of rental owners, not rental units.

6. We use the “GLM” command in the “stats” package in R, with a logit link. We report our models with robust standard errors, which we estimate using the “summ” command in the “jtools” package (Long, 2019).

7. This question is somewhat different from the rest of our outcomes, as we ask respondents to anticipate the outcome, rather than assess the impact to-date. We do so out of necessity, since we administer our survey in December 2020, within both the calendar and financial year of the start of the pandemic. We considered asking about cash flow impacts in the current month, but we were worried that we would not capture the full extent of the pandemic, since some of the initial pandemic impacts may have improved by December 2020.

8. In the survey, we ask about the respondents’ total income in buckets (e.g., under $30,000, $30,000–$49,999, etc.). For our analysis, we re-coded these responses into high- (over $125,000) and low-income (under $50,000) dummy variables. Changing these cut-points does not dramatically alter our results.

9. For both the rent and cash flow, in our survey we ask these questions in buckets (e.g., how many units do you own where you charge between $750 and $1,000 in rent, etc.). To create the portfolio-wide averages, we take the midpoint of each bucket and multiply by the number of units they report owning in that bucket. Using instead percentage measures of low/high rent/cash flow properties yields similar results.

Additional information

Funding

The work was supported by the National Science Foundation

Notes on contributors

Daniel Kuhlmann

Daniel Kuhlmannis an Assistant Professor of Planning and Real Estate in the School of Landscape Architecture and Planning at the University of Arizona. In his research, he examines how planning and public policy affect peoples’ housing choices and housing behavior. He received his Ph.D. and MA in Urban Planning from Cornell University and his BA from Carleton College. Before returning for his graduate studies, Professor Kuhlmann worked professionally in the real estate industry at a development advisory firm, in commercial real estate, and at a mortgage servicing company.

Jane Rongerude

Jane Rongerudeis an associate professor in the Department of Community and Regional Planning at Iowa State University where she teaches classes in planning theory, housing, community planning, and planning and social justice. Her research investigates how housing policy and planning practices shape and maintain poverty places and contribute to or inhibit opportunities for community transformation. Within this frame, her work addresses a range of topics including rental housing instability, housing and disasters, the redevelopment of public housing, and community engagement practices. Her research has been published in the Journal of Community Practice, Planning Theory and Practice, Urban Affairs Review, and the Journal of the American Planning Association. She earned her MCP and PhD in city and regional planning from the University of California at Berkeley.

Biswa Das

Biswa Dasis a faculty member in the department of community and regional planning at Iowa State University (ISU), and an extension specialist with ISU Extension and Outreach. Besides teaching courses at ISU, as part of ISU Extension, Dr. Das is the program leader of a public finance outreach initiative (Iowa Government Finance Initiative) targeted at Iowa cities and counties. He also delivers outreach programs relating to the community and economic development of Iowa communities. His main areas of research interest are in applied public finance, economic development, and natural resource and environmental economics. He received his Ph.D. in agricultural and applied economics from Texas Tech University.

Lily Wang

Lily Wangis a Professor in the Department of Statistics at George Mason University. Dr. Wang’s primary areas of research include developing cutting-edge statistical methods and data science tools, statistical (machine) learning of data objects with complex features, survey sampling, and the application of statistics to problems in economics, neuroimaging, epidemiology, engineering, environmental studies, official statistics, and biomedical science.