Vulnerability and opportunity: making sense of the rise in single-family rentals in US neighbourhoods

Abstract

The growth of single-family rentals (SFRs) in the wake of the US foreclosure crisis has recently begun attracting overdue scholarly attention. The transformation of millions of single-family homes from owner- to renter- occupied over the past decade raises numerous important questions about the vulnerabilities and opportunities created by this historic tenure shift for both households and neighbourhoods. This research reports on the demographics and housing conditions of single-family renters and the characteristics and trajectories of high SFR growth neighbourhoods over the recent housing market cycle. We show that SFRs are distinguished by their high prevalence of children, particularly those living in poverty, and conspicuous lack of tenant protections. Further, SFR growth is most intense in racially diverse yet economically segregated neighbourhoods. Overall, these findings suggest the need for urgent policy responses to reduce vulnerabilities.

Keywords:

• Housing tenure
• neighbourhoods
• private rented sector

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 The number of dwellings with four or more bedrooms in buildings of 20 or more units is not published, likely due to large margins of error, but also is likely very small.

2 We randomly sampled 44 properties from the database, which was the threshold needed for our estimates to be within a 10 percent margin of error for the real number of LIHTC units that make up the three categories. We determined the property type by entering the address into Google Maps and using the Street View function to assess the property features. Properties with units of a mix of housing types were coded based on their majority unit property type. Single-family attached homes were differentiated from two-to-four-unit properties based on their frontages. Single-family attached homes had at least two separate adjoining structures and two doors visible on the first floor from the street and two-to-four-unit properties had one structure with at least two doors visible from any floor from the street.

3 High margins of error for single-family attached homes prevented inclusion of this housing type in our analysis.

4 We calculated the coefficient of variation (CV) to assess the reliability of the estimates for each neighbourhood (National Research Council, 2007). The formula for the CV is: CV = ((Margin of Error/1.645)/Estimate)*100. Estimates with a CV of less than 12 are generally deemed highly reliable (the sampling error accounts for less than 12 percent of the estimate), while those with a CV of greater than 40 are generally deemed unreliable (the sampling error accounts for more than 40 percent of the estimate). We do not analyze variables that had over 40 percent of tracts with a coefficient of variation (CV) of greater than 40. However, some of the variables analyzed had between 10 and 40 percent of tracts with a CV of greater than 40. These were: population by age, Latinxs, Asians and Pacific Islanders, and households by income. Estimates derived from these variables should be interpreted with caution.

5 Sunbelt states include: Alabama, Arizona, Arkansas, California, Florida, Georgia, Kansas, Louisiana, Mississippi, Nevada, New Mexico, North Carolina, Oklahoma, South Carolina, Texas, and Tennessee.

6 OLS regression was an appropriate specification for our analysis, given the relatively normal distribution of the main outcome variables, the change in racial/ethnic and class integration, and the relatively consistent linear relationships between these variables and changes in SFR growth and our continuous control variables.

7 The margins of errors for estimates of African Americans in SFH neighbourhoods were too high to account for this racial/ethnic group separately; hence, African Americans are included in the Other category. Whites are non-Hispanic. The Asian category includes Pacific Islanders. A household’s race was determined by the race of the householder.

8 The formula for the entropy score is as follows: $$E_i={\sum }_{g=1}^g\left({\prod }_{gi}\right)\ln [{1/\prod }_{gi}]$$ where $E_{i\mathrm{}}$ is the entropy score for neighbourhood i, and ${\prod }_{gi}$ represents a particular group’s population proportion in neighbourhood i. The formula for the entropy index is as follows: $$H={\sum }_{i=1}^n\left[\frac{p_i(E-E_i)}{EP}\right]$$ where $p_i$ is the total population of the neighbourhood, $P$ is the total population of the region, $n$ is the number of neighbourhoods, and $E_i$ and $E$ are the neighbourhood and regional entropy scores respectively. See Theil (1972) and Iceland (2004) for additional details.

9 According to data from the 2015 AHS, about 10 percent of rental units in the Los Angeles MSA and 12 percent in the New York MSA were protected by some form of rent regulation.

10 We tested whether there were differences based on location in the Sunbelt or the Rustbelt. See Note 5 above for the definition of the Sunbelt. Rustbelt regions are based on Stone (2018)’s analysis and include those at least partly in Illinois, Indiana, Iowa, Michigan, Minnesota, Missouri, Ohio, Pennsylvania, West Virginia, and Wisconsin, as well as non-coastal regions in New York. We found no meaningful differences based on Sunbelt or Rustbelt location.

11 In 2017, Fannie Mae underwrote a ten-year, almost $1 billion-dollar securitization of SFRs by Invitation Homes (Goodman and Kaul, 2017).

Additional information

Notes on contributors

Deirdre Pfeiffer

Deirdre Pfeiffer is an associate professor in the School of Geographical Sciences and Urban Planning at Arizona State University and a member of the American Institute of Certified Planners. She is a housing planning scholar, with expertise on housing as a cause and effect of growing social inequality and the role of housing planning in meeting the needs of diverse social groups. She holds a MA and PhD in Urban Planning from the University of California, Los Angeles.

Alex Schafran

Alex Schafran, PhD, is a lecturer in urban geography at the University of Leeds. A planner, geographer, and urbanist, his research focuses on the contemporary restructuring and retrofitting of urban regions, with a particular emphasis on the changing dynamics of race, class, and segregation across space and place. He spent a decade as an immigrant rights and housing activist in California and New York before becoming an academic.

Jake Wegmann

Jake Wegmann is an assistant professor in the Community and Regional Planning program in the School of Architecture at the University of Texas at Austin. His research is at the intersection of housing affordability, real estate development, and land-use regulation. He received his doctorate from University of California at Berkeley in 2014. Prior to academia, he worked in for-profit and nonprofit affordable housing development in Denver and San Francisco.