Small and medium multifamily housing: affordability and availability

Abstract

Housing units in small and medium multifamily (SMMF) properties, defined as buildings with 2 to 49 units, comprise over 20% of the U.S. housing stock. Using the American Community Survey and American Housing Survey, this study fills an important gap in the literature by examining the affordability and availability of these housing units. This analysis reveals that SMMF units contain the largest percentage of the lowest-income households and the majority of rental units across the country. It employs models of filtering and quality-adjusted rents to decompose the factors that make these units accessible to such households. Even after controlling for these factors, their affordability persists, and their market share is declining. These findings raise concerns about the future availability of these affordable units. Policy-relevant conclusions are drawn about their role in the future of local, regional, and national economies.

Keywords:

• Small and medium multifamily
• building size
• affordability
• preservation

Acknowledgements

This research was funded in part by a generous grant from the JPMorgan Chase Foundation to Enterprise Community Partners, and we gratefully acknowledge their support, as well as the University of Southern California’s Bedrosian Center on Governance and the Public Enterprise. It is a final and much revised version of an earlier working paper, which was previously available online through the Social Science Research Network. We thank the anonymous referees, as well as participants at the California conference of the American Planning Association. All errors are ours alone.

Disclosure statement

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

Notes

1 This ratio—of a demographic measure such as school-age children per household—is commonly used in fiscal impact analysis for local planners and policymakers to forecast the effects of new development (Burchell & Listokin, 1978; Lamie et al., 2012; Wong et al., 2017).

2 We use the 5-year estimates because our goal is to examine the geographic distributions and population characteristics of SMMF properties in all 917 Core-Based Statistical Areas. The 5-year estimates are the most reliable information in ACS if the geographic unit is smaller than 20,000.

3 We follow the current OMB’s definition on Core-Based Statistical Areas as of February 2013. Initially, there were 929 CBSAs but we exclude those in Puerto Rico. This gives us 917 CBSAs.

4 Several variables exist to measure rent in the AHS. We use the variable ‘RENT’ as the fullest and closest representations of actual rents. It is important to note that, as in other variables, AHS respondents may have chosen to not answer rent or value-related questions for privacy, fear, lack of knowledge, or other reasons. Nevertheless, the AHS weighting methodology provides enough data that these omissions do not affect the results in a significant way.

5 However, the high number and concentration of subsidized units in 50+ unit structures may be driven by the large number of public housing units in New York City.

6 ‘Filtering’ can also be measured as a change in rent, rather than a change in tenant income. We focus on the latter because of data availability and because we have already controlled for changes in rent in the analyses above. While rent may not be the only reason for low-income tenants to occupy a unit, their high price sensitivity typically requires rent to be a primary concern.

7 Table 2 provides a summary of the filtering analysis. For fuller information, please see Table C1 in Appendix C, supplementary material.

8 This calculation is shown in more detail in the ‘Availability of SMMF units’ section below, particularly Figure 4.

9 ‘ZINC2’ in AHS.

10 These patterns continue to hold when we exclude subsidized units, as shown in Appendix A, supplementary material, where Tables 2–4 are replicated for this smaller population.

11 In both AHS and ACS, the measurement is units per building. Thus, we are counting the number of units by building type, not the number of buildings by their type. Throughout the paper, we use this measure consistently.

12 Two consistent categorizations within SMMF are used, depending on the source survey. For ACS, we use three categories: 2–4 units, 5–19 units, and 20–49 units. For AHS, we use 2 units, 3–4 units, 5–9 units, 10–19 units, 20–29 units, 30–39 units, and 40–49 units.

13 As defined by Census regions in the AHS.

14 We first calculate the population share for each housing unit category in this geography. In estimating the population share in each housing unit type, we use total population in occupied-housing units, not total CBSA population. The difference is due to population in group quarters and/or facilities.

15 The AHS designates housing units based on their siting within a metropolitan area. The past two decades of surveys divide units into five density typologies, three within a Metropolitan Statistical Area (MSA) (central city, suburban, and rural) and two outside of it (urban and rural).

16 The term ‘Missing Middle’ housing was coined by Dan Parolek of Opticos Design; for more, see http://missingmiddlehousing.com/.

Additional information

Notes on contributors

Brian Y. An

Brian Y. An is Assistant Professor of Public Finance in the School of Public Policy at the Georgia Institute of Technology. He holds a PhD in Public Policy and Management from the University of Southern California. His work has appeared in such journals as the Journal of Real Estate Finance and Economics, Urban Affairs Review, Public Administration Review, and American Politics Research.

Raphael W. Bostic

Raphael W. Bostic is President and Chief Executive Officer of the Federal Reserve Bank of Atlanta. Previously, he served as Judith and John Bedrosian Chair in Governance and the Public Enterprise at the Sol Price School of Public Policy at the University of Southern California, as well as Assistant Secretary for Policy Development and Research at the U.S. Department of Housing and Urban Development.

Andrew Jakabovics

Andrew Jakabovics is Vice President of Policy Development at Enterprise Community Partners. Previously, he served as Senior Policy Advisor to the Assistant Secretary for Policy Development and Research at the U.S. Department of Housing and Urban Development, as well as Associate Director for Housing and Economics at the Center for American Progress.

Anthony W. Orlando

Anthony W. Orlando is Assistant Professor of Finance, Real Estate, & Law in the College of Business Administration at California State Polytechnic University, Pomona. He also serves as Donor’s Scholar of Analytics in the College of Business Administration and Visiting Scholar at the Federal Reserve Bank of Atlanta. He holds a PhD in Public Policy and Management from the University of Southern California.

Seva Rodnyansky

Seva Rodnyansky is Assistant Professor of Urban and Environmental Policy at Occidental College. He holds a PhD in Urban Planning and Development from the University of Southern California. His work has appeared in the Journal of Real Estate Finance and Economics, Cityscape, and Transportation Research Part D: Transport and Environment.