Landlords’ rental businesses before and after the COVID-19 pandemic: Evidence from a National Cross-Site Survey

Elijah A de la Campa; Vincent J Reina

doi:10.1016/j.jhe.2022.101904

. 2022 Dec 14;59:101904. doi: 10.1016/j.jhe.2022.101904

Landlords’ rental businesses before and after the COVID-19 pandemic: Evidence from a National Cross-Site Survey^✰

Elijah A de la Campa ^a,^⁎, Vincent J Reina ^b,¹

PMCID: PMC9749398 PMID: 36530594

Abstract

This paper uses a survey of over 2500 rental property owners in ten cities across the United States to determine the impact of the COVID-19 pandemic on landlords’ rent collection and business behavior. Our findings show that yearly rent collection was down significantly in 2020 relative to 2019—both within and across rental markets—and that an increasing number of owners have a large share of their portfolio behind on rent. Small owners and owners of color faced the highest exposure to deep tenant arrears in 2020, challenges they were also more likely to face prior to pandemic. Our findings show that owner business practices changed dramatically in 2020, with a higher share of landlords granting tenants rent extensions or forgiving back rent during the pandemic relative to prior. However, many owners also disinvested in their rental properties through deferred maintenance, missed mortgage payments, and property sale listings. Landlords of color pursued disinvestment strategies during the pandemic at an elevated rate compared to white landlords. Owners of properties in neighborhoods with more non-white residents were both more likely to experience decreased rent collection and more likely to pursue evictions and rental late fees holding constant rental payment rates, implying the pandemic has disproportionately affected renters in communities of color. Overall, our findings highlight the strain the pandemic has placed on the housing stock, which has implications for the long-term viability and affordability of many of these units. More concerningly, our results show that households of color—which have been disproportionately affected by the pandemic in other domains—were more likely to face punitive measures from landlords in both 2019 and 2020, suggesting the pandemic has exacerbated existing racial inequality in housing markets.

Keywords: Landlords, Renters, Housing affordability, Racial discrimination, Covid-19

1. Introduction

The COVID-19 pandemic has had a profound impact on the ability of US renters to make rent. By December 2020, it was estimated that nearly one in five renter households were behind on monthly payments (Airgood-Obrycki et al., 2021), fueling a rent arrears crisis estimated to be as high as $57 billion (Parrot and Zandi 2021). While numerous studies have explored the consequences of this crisis for tenants, comparatively less is known about how rental property owners have absorbed and responded to this financial strain.2

This paper aims to fill a critical gap in our understanding of property owners’ businesses and behaviors during the pandemic, and in doing so, offers new insights into these topics under more typical circumstances. To accomplish this, we report results from an original and comprehensive survey of 2500 owners across ten US cities. From February to April 2021, we asked these owners to assess the financial health of their pre- and post-COVID rental business.3 We also asked about the tools they have relied on to manage their rental properties and collected information on their demographic, business, and property characteristics. Using these detailed data, we explore heterogeneity in landlords’ rent collection and business responses, both before and after the pandemic, by race and portfolio size, rental market, and property-level neighborhood composition. Overall, we find that landlords’ rental properties generated a significantly lower share of their potential rental revenue in 2020 relative to 2019; landlords modified their business practices accordingly; and punitive actions, such as evictions and rental late fees, were more likely to be reported at properties located in neighborhoods with more non-white residents.

Our work offers three key contributions. First, we add to an emerging though somewhat disjointed literature that explores COVID's impact on renters, property owners, and markets. Leveraging data gathered from property owners across ten cities, we corroborate evidence from renters that shows rental payments were down considerably during the first year of the pandemic (e.g., Engelhardt and Eriksen 2021). However, because our survey also asks about owners’ pre-pandemic rent collection, we extend this analysis to show that the pandemic is associated with proportionate year-over-year declines in payment across markets.4 Additionally, our survey asks landlords about their business responses to the crisis, building off several notable studies that have focused on single cities to provide important context to owners’ exposure to losses and practices throughout the pandemic (Reina et al., 2020a; Reina and Goldstein 2021). Critically, we put these disparate strands of information together to show that changes in landlords’ pandemic-era business practices cannot be explained by revenue loss alone, and that this holds true both within and across markets.

Second, this paper provides rigorous insight into the conditions under which various segments of the landlord population operate. For example, it is well-documented that there are differences in the business practices and characteristics of small vs. large landlords (e.g., Immergluck and Law 2014; Decker 2021a) and owners of color vs. white owners (e.g., Choi and Young 2020).5 At the same time, few studies even prior to the pandemic have been able to examine the relative differences in the business practices of these populations in a single, unified context.6 Research during the pandemic has been similarly disjointed, with data from the National Multifamily Housing Council (NMHC) (2020) suggesting that rental payments have been down only slightly for large, institutional investors, and national survey data focused on smaller-scale landlords suggesting this population has struggled significantly with rent collection (Decker 2021b; Choi and Goodman 2020). Our survey, offered to a diverse group of thousands of landlords, unifies these disparate strands of research and highlights small landlords and landlords of color as populations that were more likely to struggle with rental collection prior to and during the pandemic relative to their peers. We also find that these populations heavily disinvested in their properties during the pandemic, conditional on rent collection, which likely has the unintended consequence of perpetuating financial and housing instability for vulnerable tenants and property owners alike.

Finally, this paper provides additional evidence of the pandemic's outsized impact on Americans of color. We show that, during the pandemic, rental properties located in neighborhoods with a higher share of residents of color were significantly less likely to have tenants experiencing rental forgiveness and significantly more likely to have tenants facing rental late fees or eviction. These results hold after controlling for differences in rental collection; cannot be fully explained by landlord sorting on demographic and business characteristics, nor by residential sorting on economic characteristics; and were mostly observed in these communities prior to the pandemic Overall, we conclude that the disproportionate financial strain experienced by renters in communities of color has likely been exacerbated by landlords’ tendency to pursue business practices in these communities that increase housing instability, and that racialized business practices have persisted and in some cases taken on new forms during the pandemic. These findings add to a literature that documents a long history of discrimination in the rental housing market for Black and Hispanic Americans (Hanson and Hawley 2011; Reina, Pritchett, and Wachter 2020b; Hepburn et al., 2020), and highlight the need for current and future housing responses to be centered around and actively promote racial equity (Ellen et al., 2021).

The remainder of this paper proceeds as follows. Section 2 describes the survey implementation and methodology, Section 3 reviews key findings for landlords’ rent collection, Section 4 reviews findings for landlords’ business practices, and Section 5 concludes.

2. Survey implementation

2.1. Design and setting

The COVID-19 Landlord Survey is an extension of two prior survey efforts designed by members of the research team: one targeted owners of three or fewer rental properties in Albany and Rochester, New York and was distributed in June and October 2020 (de la Campa 2021), while the other was offered to landlords in Philadelphia (September 2020) and Los Angeles (December 2020) who had at least one tenant apply for pandemic-related emergency rental assistance (Reina et al., 2020a; Reina and Goldstein 2021). Both efforts offered insight into the pandemic's impact on landlords’ rental business, but they were also limited in scope. Accordingly, in December 2020, the research team began reaching out to cities and counties across the US to participate in a larger survey designed to explore the pandemic's impact across different types of rental markets, landlords, and properties.

The survey was designed to collect information at two levels: for the landlord's entire city-specific portfolio, and for an individual property representative of the landlord's portfolio.7 For each level, we asked landlords about their pre- and post-COVID rental income, as well as the various actions they have taken to manage their rental business. The survey also asked for basic demographics on the landlord, including race, age, and gender. We also asked landlords general questions about their rental business, such as whether they rely on a property manager or have tenants who use Housing Choice Vouchers (HCVs).8

Municipalities were recruited through the Bloomberg Harvard City Leadership Initiative network, as well as through ongoing rent-relief evaluations being conducted by the Housing Initiative at Penn, and were asked to partner with the research team by sharing landlords’ contact information and facilitating outreach. Conversations with municipalities that maintained significant contact information for landlords—specifically, mobile phone number or email—were prioritized. Overall, the research team had conversations with nearly forty US cities and counties and partnered with ten cities to implement the COVID-19 Landlord Survey: Akron, Ohio; Albany and Rochester, New York; Indianapolis, Indiana; Los Angeles, California; Minneapolis, Minnesota; Philadelphia, Pennsylvania; Racine, Wisconsin; San Jose, California; and Trenton, New Jersey.9

While these municipalities were chosen with an eye towards achieving geographic spread, we caution that our sample is not necessarily representative of all cities in the US.10 Nonetheless, our sample of survey cities resembles the universe of US cities along a few dimensions. Table 1 reports descriptive statistics for residents and renter households of the pooled survey sample cities as well as the population of all US cities.11 Data come from the 2018 ACS 5-year sample, with means and medians calculated from pooled population totals (across all cities within each sample).

Table 1.

COVID-19 landlord survey cities in comparison with US Cities.

	Survey cities		US Metro and Micropolitan principal cities
	Mean	SE	Mean	SE
Panel A: Resident Characteristics
White	34.7	0.05	48.2	0.02
Black	18.9	0.05	18.1	0.01
Hispanic	31.3	0.05	22.7	0.02
Asian	11.9	0.04	7.5	0.01
Other race	3.1	0.91	3.5	0.02
Median age (y)	34.0	0.39	36.1	1.4
N Residents	8500,786		115,799,553
Panel B: Renter Household Characteristics
Renter-occupied (among all households)	55.0	0.10	49.9	0.03
Reside in 1-unit property	27.7	0.16	27.9	0.04
Reside in 2–4 unit property	16.7	0.12	19.0	0.04
Reside in 5–9 unit property	11.6	0.11	12.5	0.03
Reside in 10–19 unit property	11.3	0.10	12.2	0.03
Reside in 20+ unit property	32.3	0.12	27.2	0.04
Median income ($)	34,967	1195	34,041	3913
Cost-burdened	53.8	0.20	48.7	0.06
Median gross rent ($)	1057	12.6	908	41
Median age of housing structure (y)	65	1.6	54	3.4
N Renter Households	1688,205		21,916,506

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Notes: This table reports descriptive characteristics of residents and renter households for the ten COVID-19 Landlord Survey cities (pooled) as well as for the universe of all US metro and micropolitan principal cities (N = 1253). Data come from the 2018 ACS 5-year sample. Medians calculated as weighted averages of city-specific median estimates. Standard errors calculated using the margin of error provided by the Census Bureau for the American Community Survey. Unless otherwise indicated, the means and standard errors above are expressed as percentages. Categorical variables may not sum to 100 due to rounding. Cost-burdened renters are defined as those who spend 30 percent or more of their yearly income on yearly rent.

The median age of residents across the cities in our sample is identical to that of residents in US cities as a whole (34.9). Just over half of all households in both survey and US cities are renter-occupied. The distribution of rental properties is also similar across the two groups, though survey cities have a higher share of large apartment buildings (32.3 percent of rental units are located in 20+ unit buildings in survey cities compared to 27.2 percent in U.S. cities as whole). The median income of renter households is also similar across the two groups ($38,577 vs. $36,691).

There are also some key differences. Relative to US cities, survey cities are, on average, less white (34.7 vs. 48.1 percent) and more Hispanic (31.9 vs. 23.2 percent). The rental housing stock in survey cities is slightly older than that of US cities overall (built 65 vs. 54 years ago), and median rents are slightly higher ($1186 vs. $1027). Along these lines, the share of cost-burdened renters, defined as those who spend 30 percent or more of their income on rent, is slightly higher in survey cities compared to U.S. cities as a whole (53.8 vs. 48.7 percent). Overall, 1.7 million of the nation's 21.8 million city-based rental units are located in the ten cities in our survey sample.

2.2. Outreach and response

We distributed the COVID-19 Landlord Survey on a rolling basis from early February through mid-April 2021. In each city, every landlord for which contact information was obtained was invited—either via email or text message—to participate in the online survey.12 Table 2 shows response rates for each city. Overall, we sent out nearly 58,000 survey invites and received 2850 partial or complete usable responses, for an overall response rate of 4.9 percent.13 This response rate ranged from a low of 1.3 percent in Los Angeles to a high of 8.8 percent in San Jose. In nearly all analyses, we focus on the sample of 2547 landlords who reported owning at least one overlapping rental property (in their respective city) in 2019 and 2020.

Table 2.

COVID-19 landlord survey response rates.

	Overall	Akron	Albany	Indianapolis	Los Angeles	Minneapolis	Philadelphia	Racine	Rochester	San Jose	Trenton
N Survey Invites	57,994	3440	1971	7615	18,810	10,540	6156	2294	2190	3476	1502
N Usable Responses	2850	258	114	449	248	676	312	172	178	307	136
Response Rate	4.9	7.5	5.8	5.9	1.3	6.4	5.1	7.5	8.1	8.8	9.1
N Analysis Sample	2547	236	107	413	219	593	277	158	163	267	114

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Notes: This table reports, both overall and separately for each participating city, the number of survey invites (less outbound bounces), number of usable survey responses, survey response rate, and analysis sample size for the COVID-19 Landlord Survey. An additional 80 property managers responded to the survey but were routed to the end of the survey when they indicated they were not specifically property owners; these individuals are not included among the number of usable survey responses. Respondents were not asked questions about their rental business profitability and management if they did not report owning at least one overlapping rental property in 2019; the analysis sample excludes these individuals and is comprised solely of respondents who owned at least one overlapping rental property in 2019 and 2020. In Albany, Indianapolis, Los Angeles, Minneapolis, Racine, Philadelphia, San Jose, and Trenton, participants were invited to participate in the survey via email. In Akron and Rochester, participants were invited via text message (SMS). Data come from the COVID-19 landlord survey.

In eight of the ten sample cities, we obtained landlord contact information from rental dwelling registries. In general, these registries exist to ensure safe living conditions for renters, and they typically require owners of residential properties with rental dwelling units to obtain a permit and pass an interior inspection before units can be legally leased to tenants.14 In San Jose, only owners of properties built before 1979 that contain three or more rental units are required to register.15 These older and larger rental buildings tend to be located in lower-income areas of the city, leading to a San Jose sample that has a disproportionate number of landlords who operate at the lower end of the rental market (though these landlords may also own properties in higher-income areas of the City). Compliance rates on rental registries vary from a low of around 10 percent in Indianapolis, to upwards of 70 percent in Trenton, to nearly 95 percent for San Jose's more limited registry.16

Landlord contact information for the remaining two cities—Los Angeles and Philadelphia—was obtained from emergency rental assistance (ERA) applications. In each city, it was incumbent upon tenants to apply for ERA, meaning the owners represented in this sample did not actively select into the process for receiving funds. Previous research finds that these properties include many landlords who are not traditionally engaged in ownership or trade organizations and/or any federal or local housing assistance programs (Reina and Goldstein 2021; Reina et al., 2020a).

Our sample of landlords, therefore, is selected in various ways. First, they are landlords in core US urban centers. Second, they are either landlords who have registered with their cities’ rental registry, or landlords with tenants who applied for emergency rental assistance.17 And third, they are landlords who chose to answer our survey. Consequently, there are many reasons to be cautious when extrapolating to the entire universe of US landlords. We discuss the representativeness of this sample, based on observable characteristics, in the following section.

2.3. Respondent characteristics and representativeness

Table 3 presents descriptive statistics for survey respondents and explores the representativeness of our sample using two additional data sources from the US Census. The first is the 2020 Current Population Survey Annual Social and Economic Supplement (CPS-ASEC). The CPS is the primary source for the nation's employment statistics and is offered monthly to a random sample of nearly 60,000 households in which there is at least one individual of working age (15 or older) who is not in the Armed Forces. The CPS-ASEC is the annual supplement to this survey effort that provides further detail on work activity and income sources, among other topics. We use the CPS-ASEC to identify all individuals who reported negative or positive rental income in 2019 (i.e., the year prior to the pandemic) and restrict our analysis to this subset.

Table 3.

Descriptive statistics of survey respondents and representativeness of sample.

	Survey			CPS-ASEC			RHFS
	N	Mean	SD	N	Mean	SD	N	Mean	SD
Male	2255	61.4	48.7	6254	53.5	50.0
Missing gender	2850	20.9	40.7	6254	0	0.0
White	2338	66.3	47.3	6254	75.5	41.6
Black	2338	11.5	31.9	6254	6.3	24.4
Hispanic	2338	6.3	24.3	6254	8.9	25.5
Asian	2338	8.6	28.0	6254	7.6	26.6
Missing race	2850	18.0	38.4	6254	0	0
20–29 years old	2380	0	14.9	6254	3.1	16.8
30–39 years old	2380	14.7	35.5	6254	12.5	31.0
40–49 years old	2380	17.8	38.3	6254	18.8	36.0
50–59 years old	2380	25.6	43.6	6254	21.3	40.5
60+ years old	2380	39.6	48.9	6254	44.1	50.0
Missing age	2850	16.5	37.1	6254	0	0
Individual owner	2255	87.6	32.9				4028	77.9	41.5
Missing ownership structure	2850	20.9	40.7				4330	3.5	18.3
Self-manages rental units	2703	72.3	44.8				4267	72.8	44.5
Missing property manager	2850	5.2	22.1				4330	1.7	12.9
Accepts HCVs	2709	20.8	40.6				3945	5.8	23.3
Missing HCV	2850	4.9	21.7				4330	6.1	23.8
Owns single-family rental(s) (SFRs)	2536	50.7	36.3
Missing home type	2850	1.3	31.2
Small landlord	2803	65.6	47.5
Mid-sized landlord	2803	17.2	37.7
Missing portfolio size	2850	1.6	12.7

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Notes: This table reports descriptive statistics for the COVID-19 Landlord Survey respondents, for all individuals in the CPS-ASEC who reported rental income in 2019, and for all U.S. rental properties in 2018. The variables above are expressed as percentages. The omitted category for race is “Other Race.” The omitted category for age is “Under 20 years old.” No respondents in the survey reported being under 20. “Individual owner” indicates an owner who is not incorporated as an LLC or LLP. “Single-family rental” indicates a one- to four-unit rental property. “Small landlord” indicates a landlord who owns between 1 and 5 rental units. “Mid-sized landlord” indicates a landlord who owns between 6 and 19 rental units. The omitted category for landlord size is “large” (owns 20+ rental units). Categorical variables may not sum to 100 due to rounding. Survey data come from city administrative records and the COVID-19 landlord survey. CPS-ASEC data come from the 2020 Current Population Survey Additional. RFHS data come from the 2018 Rental Housing Finance Survey.

The second is the 2018 Rental Housing Finance Survey (RHFS). The RHFS is offered on a triennial basis to a randomly drawn subset of the owners and/or managers of all US properties with at least one unit that is rented or vacant for-rent (as determined by the American Housing Survey). The RHFS provides insight into the financial, managerial, and physical characteristics of US rental properties over the prior 12-month period. Though the unit of observation for these data is the rental property, with a dearth of information on landlords, we use these data to roughly approximate the business characteristics of our sample.

About 60 percent of survey respondents are male, compared to 53.5 percent of all landlords in the CPS-ASEC (53.5 percent). Two-thirds of survey respondents are white, 11.5 percent are Black, 6.3 percent are Hispanic, and 8.6 percent are Asian. A larger share of landlords are white (75.5 percent) and Hispanic (8.9 percent) in the CPS-ASEC, while a lower share are Black (6.3 percent). The over-representation of Black landlords in our sample likely reflects the fact that our survey was offered solely in large urban centers. Nearly 40 percent of our respondents are over the age of 60, the most common age range represented in the survey and a near exact match to landlords in the CPS-ASEC.

Nearly 88 percent of owners are individual investors as opposed to owners incorporated as an LLC or LLP. Though an imperfect comparison, a similar share (77.9 percent) of all rental properties in the RHFS are owned by individual investors. The share of landlords who report managing their properties themselves (as opposed to through a manager) is nearly identical to the share of rental properties in the RHFS managed by the owner (72.3 vs. 72.8 percent). Around 20 percent of survey respondents accept HCVs, which is substantially larger than the share of properties in the RHFS that have at least one tenant using HCVs (5.8 percent).18 Just over 50 percent of landlords in our sample own at least one rental property with 1–4 units—commonly referred to as single-family rentals (SFRs)—which are most likely to be owned by small, individual investors (Freddie Mac 2018). Accordingly, nearly two-thirds of landlord respondents own a total of 1–5 rental units. Equal shares of the remainder own 6–19 or 20+ units.19

Overall, while our sample of survey respondents may be an imperfect snapshot for the universe of US landlords, it allows for important insight into a variety of landlords with a variety of property holdings.20

3. Landlords’ rental collection before and after the pandemic

3.1. Landlords’ rent collection decreased significantly in 2020

Fig. 1 reports the landlords’ rental collection rates before (2019) and after (2020) the pandemic. Rent collected is expressed as a percentage of total rent charged across the portfolio and separated into four categories: 100, 90 to 99, 50 to 89, and less than 50 percent of yearly rent received.

In 2019, the vast majority (88.9 percent) of landlords reported collecting 90 percent or more of their charged yearly rent. In 2020, this share fell by nearly a third, to just over 60 percent, while the share reporting collection of 50 to 89 percent of rent rose from 8.2 percent in 2019 to 28.6 percent in 2020. We also see a substantial share of landlords experiencing serious financial strain during the pandemic, with the share of landlords collecting less than 50 percent of charged rent by year's end increasing from 2.9 percent in 2019 to 9.1 percent in 2020.

A lack of data on landlords’ pre-pandemic rental collection makes it difficult to contextualize our 2019 baseline rates. However, our results generally align with those from two national rent tracking systems. First, data from the NMHC (2020) show that, in the two months prior to the pandemic, around 95 percent of units owned by large, professionally managed landlord organizations paid rent in full by the end of the month. Data from a partnership between Avail and the Urban Institute (2021) report a corresponding figure of 87 percent for small, mom-and-pop landlords. Additionally, the pre-pandemic share of landlords reporting 90 percent of more of rent received in our study is nearly identical to that reported by a large survey of landlords in Los Angeles (Reina and Goldstein 2021).

There is comparatively more research that seeks to understand the extent of rental delinquency during the pandemic. For example, both the NMHC (2020) and Urban Institute (2021) data show modest, single-digit percentage point declines in the share of units paying rent in full during the pandemic. In a national survey of landlords, Decker (2021b) reports nearly a third of owners collected less than 90 percent of their 2019 rent in 2020. And finally, using consumer banking data from core US cities, Grieg, Chen, and Lefevre (2021) show that landlords’ year-over-year rental revenues fell by as much as 20 percent during the early months of the pandemic. In each case, there are important differences in the instruments, methods, and populations studied,21 but given the lack of data on this topic, these results provide important context for our findings.

Given that the Los Angeles and Philadelphia survey participants had at least one tenant who applied for local ERA, we may be concerned that this selection is mechanically biasing downwards our results for rent collection (in 2020 in particular). We offer several pieces of evidence to suggest this is not the case. First, Appendix Figure 1 presents a version of Fig. 1 that excludes both Los Angeles and Philadelphia from the sample; rental payment rates for both 2019 and 2020 are nearly identical when including or excluding these cities from the analysis. Second, despite higher rates of tenant ERA participation in these cities (roughly 60 percent), nearly one-quarter of landlords in the other cities sampled based on rental registries also indicated they had tenants who participated in ERA during the pandemic.22 Finally, in Appendix Figure 2, we present rental collection results solely among landlords with at least one tenant participating in emergency rental assistance, separately for the ERA cities of Los Angeles and Philadelphia (Panel A) and the rental registry cities (Panel B). While we observe modest pre-pandemic variation in the share of landlords collecting 100 vs. 90–99 percent of rental revenue, findings are qualitatively similar across the two samples. Moreover, in 2020, landlords’ mean collection rates by rental revenue category are virtually identical in ERA and rental registry cities. Thus, we conclude that differences among the ERA and rental registry samples are not substantially biasing our results for landlords’ rental collection.

3.2. Non-white and small landlords collect less in rent relative to their white and larger landlord counterparts while HCVs generally insulate landlords against rental non-payment

It is widely understood that different types of landlords own different portions of the rental housing stock. For example, one- to four-unit rental properties (SFRs), which tend to have lower median rents relative to multifamily properties, are owned primarily by small, mom-and-pop landlords (Freddie Mac 2018). Institutional investors, on the other hand, own most of the nation's multifamily apartment buildings (DeSilver 2021). With the newer, higher-end units of multifamily buildings naturally attracting higher-income individuals—who are disproportionately white—this implies that different types of landlords cater to different segments of the rental market.

What is less understood is the degree to which these landlord characteristics (and others) are correlated not only with rent charged, but also with rent collected. To shed light on this issue, we estimate the following OLS regression, separately for both 2019 and 2020:

R e n t_{i c}^{r, y} = β_{1} D e m o s_{i c} + β_{2} B u s i n e s s_{i c} + β_{3} P o r t f o l i o_{i c} + γ_{c} + ε_{i c} .

(1)

$R e n t_{i c}^{r, y}$ is a binary indicator for whether landlord $i$ in city $c$ collected less than $r$ percent of their charged yearly rent in year $y$ , where $r \in {90, 50}$ . We run separate regressions for each combination of $r$ and $y$ . $D e m o s_{i c}$ is a vector of baseline demographic characteristics, $B u s i n e s s_{i c}$ is a vector baseline business characteristics, and $P o r t f o l i o_{i c}$ is a vector of mutually exclusive and exhaustive indicators for landlord portfolio size. We also include missing indicators for landlord characteristics and city fixed effects $(γ_{c})$ to control for the time-invariant characteristics of sample cities.

We report coefficients from (1) in Fig. 2 . Panel A presents coefficients when the dependent variable is either $R e n t^{90, 2019}$ (dark gray) or $R e n t^{90, 2020}$ (light gray) and describes the relationship between landlord characteristics and their exposure to general rental non-payment, defined as collecting less than 90 percent of their charged yearly rent.23 Panel B instead explores the relationship between these characteristics and landlords’ exposure to severe rental non-payment, defined as collecting less than 50 percent of their charged yearly rent.24

Prior to the pandemic, male landlords were no more or less likely than female landlords to collect less than 90 percent of charged rent (conditional on all other covariates). However, landlords over the age of 60 and non-white landlords were around 3 and 7 percentage points more likely, respectively, to face exposure to non-payment compared to younger and white landlords.25 These findings—which represent 27 and 64 percent increases from the unconditional $R e n t^{90, 2019}$ mean, respectively—are consistent with the notion that a disproportionate number of older landlords and landlords of color house the nation's most economically vulnerable renters (e.g., Choi and Young 2020).

Along these lines, Panel B shows that landlords of color were also 5 percentage points more likely to collect less than 50 percent of charged yearly rent prior to the pandemic—nearly double the unconditional $R e n t^{50, 2019}$ mean, and the only significant predictor of severe rental non-payment among the group of demographic variables. In 2020, the relationships between landlords’ baseline demographics and rent collection were largely maintained. Notably, landlords of color continued to experience both general and severe rental non-payment compared to their white counterparts.

Interestingly, we find no evidence that landlords who manage their properties themselves or own SFRs—two typical markers of mom-and-pop landlords—had different pre- or post-pandemic rental collection rates relative to landlords who rely on property managers or own multifamily buildings after conditioning on additional demographic and business characteristics. This was not the case for individual owners, though, who faced higher pre-pandemic exposure to severe rental non-payment, in particular, relative to institutional ones. Further, the relationship between individual ownership and general rental non-payment was greatly attenuated in 2020, with individual landlords 13 percentage points less likely to experience rental non-payment relative to institutional ones.

Next, we explore the relationship between landlords’ acceptance of housing choice vouchers and yearly rent collection. An estimated 2.3 million low-income, primarily person-of-color-headed US renter households rely on HCVs each month to make rent (Reina et al., 2021). On the one hand, vouchers guarantee that landlords will receive at least a portion of their monthly rental revenue. On the other, the economic vulnerability of voucher users makes them particularly susceptible to economic downturns, which may hinder their ability to cover the remainder of rent. We find no evidence that landlords who accept HCVs were more likely to face pre-pandemic exposure to general or severe rental non-payment relative to non-voucher landlords. In 2020, however, HCV landlords were 8 percentage points more likely to report collecting less than 90 percent of charged rent, which is perhaps not surprising given the pandemic's disproportionate impact on individuals with similar characteristics to voucher users (Greene and McCargo 2020). At the same time, Panel B shows that these landlords were no more likely to have tenants fall into deep arrears in 2020. Thus, we find that HCVs not only appear to stave off non-payment of rent under typical conditions, they also appear to have insulated landlords from significant arrearage during the COVID crisis.26

Finally, we explore the relationship between landlords’ portfolio size and rental collection. As mentioned above, a potential driver of observed differences in pandemic rental collection rates across studies is the populations studied. The NMHC (2020) data, which show modest declines in landlords’ pandemic-era rental revenue, cover large, professionally managed organizations. Others who have studied exclusively smaller, mom-and-pop landlords (e.g., de la Campa 2021) have reported much larger declines relative to these data. Though two notable studies have focused on individual markets to provide important context to owners’ exposure to losses (Reina and Goldstein 2021; Reina et al., 2020a), there are no studies we are aware of that have deployed a common survey instrument across multiple cities and multiple types of property owners to understand the issue of rental non-payment.

Prior to the pandemic, small (1–5 units owned) and mid-sized (6–19 units owned) landlords were about 5 percentage points more likely to collect less than 90 percent of yearly rent compared to large ones (20+ units owned). Smaller landlords were also significantly more likely (3 percentage points) to face exposure to severe rental non-payment prior to the pandemic. As mentioned above, these pre-pandemic differences in rental collection rates likely reflect the different segments of the market these landlords serve.

In contrast, small landlords were significantly less likely to face exposure to general rental delinquency in 2020 relative to large ones (10 percentage points). A relatively larger share of mid-sized landlords also reported collecting less than 90 percent of rent during the pandemic, but this proportion is not significantly different from that of small or large owners. These findings likely reflect the fact that, as the number of rental units in one's portfolio increases, so too does the chance of at least one unit falling behind on rent. At the same time, Panel B shows that larger landlords were the group least likely to struggle with severe rental non-payment during the pandemic. The finding for small landlords is particularly striking, as the 7 percentage point increase in the share of landlords collecting less than 50 percent of yearly rent represents a nearly 80 percent increase of the unconditional mean.

3.3. Landlords’ collection rates were down across rental markets

While no region of the US has been spared by the COVID-19 pandemic, there has been significant variation in the timing and intensity of the crisis (e.g., Shrawder and Aguilar 2020). Accordingly, in Fig. 3 , we present the share of landlords with tenants in rent arrears separately for each city in our study, for both 2019 and 2020.

Fig 3 — Landlords’ rental collection, by city. *Notes*: This figure plots the raw share of landlords reporting less than 90 percent (panel A) and 50 percent (panel B) of total rent received in 2019 and 2020, by city. Rent received is expressed as a percentage of total rent charged, in a given year, for a landlord's rental portfolio. 10.5 percent of respondents are from San Jose, 8.6 from Los Angeles, 23.3 from Minneapolis, 6.2 from Racine, 16.2 from Indianapolis, 9.3 from Akron, 6.4 from Rochester, 4.2 from Albany, 10.9 from Philadelphia, and 4.5 from Trenton. The total number of survey respondents in the sample is 2548. Data come from the COVID-19 landlord survey.

Panel A shows considerable heterogeneity across cities in the share of landlords who were owed 10 percent or more of charged rent by the end of 2019—from a low of 6 percent of Minneapolis to a high of 18 percent in Rochester and Trenton. In general, we find that landlords in the Upper Midwestern cities of Minneapolis and Racine collected the most rent pre-COVID; those in the Industrial Midwestern cities of Indianapolis and Akron as well as the West Coast cities of San Jose and Los Angeles collected slightly less; and those in the East Coast cities of Rochester, Albany, Philadelphia, and Trenton collected the least. In each city, however, we observe a consistent three- to fourfold increase from 2019 to 2020 in the share of landlords owed 10 percent or more of charged rent. These findings support the notion that the pandemic has had a significant impact on the rental business of landlords across a variety of rental markets and political contexts and underscore the importance of looking at relative changes when examining the impact of COVID-19 on rental markets.27

Results are generally consistent when examining year-over-year changes in the percent of landlords reporting less than 50 percent of rental revenue received (Panel B). Once again, a higher share of landlords in the coastal cities of our study reported facing financial difficulty with their rental properties prior to the pandemic, and the share of landlords collecting less than 50 percent of charged rent in 2020 was up significantly across all rental markets. In contrast to Panel A, however, the year-over-year increase in severe rental non-payment was steeper in the coastal cities. Two potential explanations for this finding may be because pandemic unemployment rates were higher in the coastal cities of our sample relative to the Midwestern ones (Chetty et al., 2020a), and renters were more likely to be cost-burdened in these regions prior to the pandemic (JCHS 2019).

3.4. Renters in socially and economically vulnerable communities are further behind on rent

Emerging research has shown that, across a variety of domains, Black, Hispanic, and low-income Americans have disproportionately borne the impact of the COVID-19 pandemic. This has been true not only in terms of exposure to the virus (Reitsma et al. 2021; Zelner et al., 2021) and job loss Lee, Park, and Shin (2021), but also in other less obvious contexts, such as access to remote education Bacher-Hicks, Goodman, and Mulhern (2021). Studies have also found that these more socially and economically vulnerable groups were further behind on rent in 2020 compared to higher-income and white Americans (Airgood-Obrycki et al., 2021).

While our survey did not ask landlords to report the race of their tenants, because we asked landlords about their experiences with a single property in their portfolio, we can explore heterogeneity in property-level collection rates according to the neighborhood demographics associated with a property's address. Accordingly, in the following section, we change the unit of analysis from the landlord to the landlord-owned rental property and explore whether certain types of properties and communities, if any, were more likely to fall behind on rent.28

In Fig. 4 , we explore yearly changes in property-level rent collection rates separately for properties in neighborhoods with a majority of non-white residents with those where white residents are the majority.29 To construct this figure, we first demean the rental payment and majority non-white-neighborhood indicators by city, and then add back the mean of each variable to its demeaned value to aid in interpretability. In so doing, we control for inter-city differences in rental payment and neighborhood racial composition which might affect the pooled analysis of the relationship between these two variables.

In both neighborhoods with a majority of non-white residents and those with mostly white residents, the share of rental properties behind 10 percent or more on rent roughly tripled from 2019 to 2020 (Panel A). However, the proportion of properties behind on rent in 2020 was significantly larger in neighborhoods of color neighborhoods compared to those with relatively more white residents (38 vs. 28 percent). Correspondingly, Panel B shows that these properties were also more likely to be behind 50 percent or more on rent by the end of 2020 compared to properties in communities with a majority of white residents (14 vs. 8 percent). This basic pattern is also observed when examining changes in rental payment rates according to median neighborhood income (Appendix Figure 4).30

4. Landlords’ business practices before and after the pandemic

4.1. Landlords have changed their business practices during the pandemic

In Fig. 5 , we explore year-over-year changes in landlords’ portfolio-level rent collection, tenant, and ownership policies. Landlord actions are shown on the x-axis, while the percent of landlords who reported taking these actions is displayed on the y-axis. Results will not sum to 1 because landlords could report taking multiple steps to manage their rental property portfolio.

Overall, the pandemic is associated with a sharp increase in certain types of actions and a decrease in others.31 For example, 15 percent of landlords reported granting rental extensions to at least one of their tenants in 2019 compared to nearly half the following year. These findings are particularly noteworthy for two reasons. First, while previous qualitative work has indicated that, under typical conditions, landlords rely on rental payment plans to avoid costly evictions (Balzarini and Melody, 2021; Decker 2021a), our study is the first of which we are aware to quantify landlords’ pursuance of this strategy. Second, the marked increase in this tactic in 2020 aligns with numerous anecdotal accounts of landlords—particularly small, mom-and-pop owners—working with tenants to find solutions to rental non-payment during the pandemic (e.g., Arnold 2020).

Though charging tenants late rent fees and increasing rents were the two most common actions reported by landlords prior to the pandemic, in 2020, the prevalence of these actions fell by 12 and 9 percentage points, respectively. While these statistics are striking given the lower amount of rent, on average, landlords collected during the pandemic, part of these declines are likely due to pandemic-induced, intermittent prohibitions on these practices in our study cities (Raifman et al., 2020).

Perhaps more salient to landlords than the above restrictions were the eviction moratoria put in place at the local, state, and federal level. Indeed, recent research has estimated 1.5 million evictions were prevented during the pandemic due to eviction bans (Hepburn et al., 2021). Nonetheless, the share of landlords who brought eviction proceedings against at least one tenant is nearly identical for both 2019 and 2020 (15 percent). While this implies that the eviction rate conditional on not receiving rent in full was lower in 2020 than in 2019 (23.2 versus 29.4 percent), it may still be surprising that an equivalent share of landlords in 2019 and 2020 indicated that they had brought eviction proceedings against at least one tenant. However, we offer two potential reasons this may be the case. First, our survey asked landlords about the initiation of eviction proceedings rather than their conclusion. Second, despite the aforementioned reduction in evictions, an estimated 1.1 million tenants were evicted in 2020, and it may be the case that landlords who moved forward with evictions during the pandemic—which were relatively more difficult to execute—are those more familiar with the eviction system (e.g., Rutan and Desmond 2021). While our study cannot speak to this phenomenon more broadly, 45 percent of the landlords in our study who brought eviction proceedings against at least one tenant in 2020 did so in 2019 as well.32 On the one hand, this reinforces the growing body of literature which shows that a large share of evictions are often concentrated among a small pool of owners (e.g., McCabe et al., 2020), but it also demonstrates how the pandemic may have temporarily expanded the pool of owners looking to use this practice.

We also find that some landlord practices that were relatively uncommon prior to the pandemic became widespread in 2020. Around one-fifth of landlords reported forgiving outstanding rent; decreasing rents; and missing at least one mortgage, utility, and/or property tax payment in 2020—in each case, a roughly 15 percentage point increase from the prior year. The share of landlords missing mortgage payments in 2020—nearly one in ten (not shown)—is particularly troubling as it calls into question the future financial viability of these properties.33 Adding additional strain, the share of landlords who reported delaying repairs increased from 5 percent in 2019 to 31 percent in 2020. Ultimately, 13 percent of landlords took steps to sell one or more rental properties in 2020 compared to only 3 percent the prior year.

The steps landlords have taken to disinvest from their properties—via delayed payments, deferred maintenance, and property listings—in many ways mirror the actions of other small business owners during the pandemic. Bartik et al. (2020) found that three-quarters of small business owners surveyed at the beginning of 2020 had two months or fewer of cash on hand, and as a result, reported pursuing cost-cutting measures such as reducing staffing and temporarily closing. Along these lines, despite the gradual reopening of the economy in the second half of 2020, Crane et al. (2021) report a 3 percentage point year-over-year increase in the small businesses closure rate for retail and service establishments.

While this latter finding is markedly lower than the 10 percentage point year-over-year increase in property listing rates observed in our study, there are crucial differences between these findings. First, we asked landlords solely whether any properties had been listed for sale, not necessarily sold. Second, property sales may generate a financial return, which likely makes them a more attractive option relative to small business closures. However, we note that the high rates of deferred maintenance and missed mortgage payments in our sample likely affects the viability of these potential sales, and in either scenario, there are direct and indirect implications for the renters in and future affordability of those properties.

4.2. Decreased rent collection cannot fully explain landlords’ changing rental business practices

It may be the case that changing rental business practices during the pandemic are a reflection of landlords’ decreased rental collection, as observed in Fig. 1. To further explore this possibility, we estimate the following OLS regression:

P r a c t i c e_{i c y}^{p} = β_{1} R e n t L T 90_{i c y} + β_{2} 2020_{y} + β_{3} R e n t L T 90_{i c y} * 2020_{y} + γ_{c} + ε_{i c y} .

(2)

$P r a c t i c e_{i c y}^{p}$ is an indicator for whether landlord $i$ in city $c$ and year $y$ implemented rental business practice $p$ . We estimate Eq. (2) separately for each of the nine rental business practices $p$ reported in Fig. 5. $R e n t L T 90_{i c y}$ indicates whether landlord $i$ in city $c$ collected at most 90 percent of their rental revenue in year $y$ , and $2020_{y}$ is an indicator for the 2020 (i.e., post-COVID) time period. As in (1), we include city fixed effects ( $γ_{c}$ ) to control for the time-invariant characteristics of the cities in our sample.

Table 4 presents results from Eq. (2), with heteroskedastic-robust standard errors reported in parentheses. Coefficient $β_{1}$ captures the relationship, in 2019, between rental non-payment and business practice $p$ . Apart from listing one's properties for sale, prior to the pandemic, the intensity with which landlords pursued their rental business practices was highly correlated with yearly rental collection. For example, column (4) shows that collecting at most 90 percent of 2019 rent was associated with a 12.3 percentage point decrease in the share of landlords’ increasing tenants’ rents (in that year). Conversely, relative to collecting 90 percent or more of yearly rental revenue, partial collection is associated with a 13.7 percentage point increase in landlords’ eviction initiation rate (column 6).

Table 4.

Relationship between rental collection and business practices.

	Grant rent ext.	Forgive rent	Charge rent fee	Inc. rents	Dec. rents	Evict tenants	Miss payments	Defer maint.	List props. for sale
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
< 90% Rent Received	0.083***	0.037**	0.064**	−0.123***	0.052***	0.137***	0.066***	0.088***	0.019
	(0.028)	(0.016)	(0.030)	(0.023)	(0.015)	(0.028)	(0.020)	(0.022)	(0.014)
2020	0.226***	0.118***	−0.144***	−0.215***	0.115***	−0.065***	0.053***	0.164***	0.058***
	(0.014)	(0.010)	(0.011)	(0.011)	(0.009)	(0.009)	(0.008)	(0.011)	(0.008)
< 90% Rent Received*2020	0.234***	0.138***	0.029	0.102***	0.057**	0.099***	0.209***	0.181***	0.106***
	(0.034)	(0.023)	(0.033)	(0.025)	(0.022)	(0.032)	(0.026)	(0.029)	(0.021)
N Landlord-Years	4808	4808	4808	4808	4808	4808	4808	4808	4808

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Notes: This table reports OLS estimates of the relationship between landlords’ business practices and rental collection, prior to and during the pandemic. Each column presents results from a separate OLS regression, where the indicated business practice is the dependent variable. “Grant Rent Ext.” indicates rental extensions and/or putting tenants on repayment plans. “Forgive Rent” indicates rental forgiveness (either in full or a portion). “Charge Rent Fee” indicates charging fees for late rent. “Inc. Rents” indicates increases to monthly rents. “Dec. Rents” indicates decreases to monthly rents. “Evict Tenants” indicates the commencement of eviction procedures (and potentially, the conclusion). “Miss Payments” indicates missed mortgage, property tax, and/or utility payments. “Defer Maint.” indicates delayed property repairs or maintenance. “List Props. For Sale” indicates one or more properties were listed for sale. Landlords could choose multiple actions. Models include city fixed effects. Heteroskedastic-robust standard errors are reported in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1. Data come from the COVID-19 landlord survey.

The coefficient $β_{2}$ instead reports the effect of the pandemic on landlords’ rental business practices solely among those who received 90 percent or more of their rental revenue. Even for this group of landlords, the pandemic has had an impact on nearly every rental business practice, with particularly large increases (relative to 2019) in the share of landlords granting rent extensions (22.6 percentage points), forgiving rent (11.8 percentage points), and deferring property maintenance (16.4 percentage points). There have also been steep decreases in the share of landlords charging late rent fees (14.4 percentage points), increasing rents (21.5 percentage points), and evicting tenants (6.5 percentage points). Taken together, these findings imply that the observed changes from 2019 to 2020 in landlords’ business practices were not driven exclusively by decreased rent collection from the pandemic and likely reflect a variety of other factors including local policies and restrictions (e.g., eviction moratoria), weakened demand in the rental market, COVID-related limitations on building access, and supply-side challenges for maintenance and repair.

Finally, the coefficient on the interaction term ( $β_{3}$ ) sheds light on how the relationship between rental payment and business practices has changed, if at all, in 2020 (post-COVID) compared to 2019 (pre-COVID). In addition to shifting the levels of nearly all business practices in 2020, the pandemic has also intensified the rate at which landlords have taken certain actions conditional on partial rent payment. This is particularly evident for the implementation of rental payment plans. In 2019, collecting at most 90 percent of rental revenue was associated with an 8.3 percentage point increase in landlords’ implementation of rental payment plans; during the pandemic, the strength of that relationship roughly tripled, such that partial payment was associated with a 31.7 percentage point increase in this business practice ( $β_{1} + β_{3}$ ). The amplification of this relationship may be a result of the restrictions placed on landlords’ traditional responses to rental non-payment—such as late fees and evictions—during the pandemic (e.g., Raifman et al., 2020).34 Indeed, in 2020, there was no significant relationship between partial rental payment and the implementation of late rent fees, and that between rental payment and evictions was significantly weakened.

Other actions that have been significantly altered during the pandemic are those related to property ownership, such as missing financial payment obligations, deferring property maintenance, and listing one's properties for sale. For example, while there was no statistically significant relationship between rental non-payment and property listings in 2019, collecting at most 90 percent of rental revenue in 2020 was associated with a 12.5 percentage point increase in the probability of trying to sell one's property. Combined, the results in column (9) show that, while all landlords became interested in selling their rental properties during the pandemic, this was particularly true for those who collected at most 90 percent of their rental revenue.35

4.3. Landlords’ business practices vary according to their portfolio and demographic characteristics even after accounting for group-level differences in rent collection

The results in Table 4 suggest that landlords’ business practices are highly correlated with rent collection, both prior to and during the pandemic. At the same time, Fig. 2 shows that landlords’ rental collection rates vary according to salient business and demographic characteristics. Accordingly, in Table 5 we examine yearly variation in six key business practices according to the size of landlords’ rental property portfolios (Panel A) and self-reported race (Panel B), conditional on rent collection. Specifically, we present results from OLS regressions of landlords’ business practices on indicators for landlord characteristics, the interaction of these characteristics with the 2020 indicator, and a categorical variable of rental payment. For reference, in each panel, we report the unconditional mean for the omitted group.36

Table 5.

Relationship between landlord characteristics and six key business practices.

	Grant rent ext.	Charge rent fee	Evict tenants	Miss payments	Defer maint.	List props. for sale
	(1)	(2)	(3)	(4)	(5)	(6)
Panel A: Portfolio Size
Small Landlord	−0.104***	−0.298***	−0.325***	−0.001	−0.004	−0.051***
	(0.028)	(0.033)	(0.031)	(0.013)	(0.014)	(0.016)
Mid-Sized Landlord	−0.068**	−0.136***	−0.166***	−0.007	0.009	−0.047***
	(0.032)	(0.038)	(0.035)	(0.015)	(0.018)	(0.017)
2020	0.462***	−0.311***	−0.096**	0.119***	0.290***	0.173***
	(0.037)	(0.040)	(0.041)	(0.027)	(0.033)	(0.031)
Small*2020	−0.241***	0.223***	0.071*	−0.042	−0.120***	−0.111***
	(0.039)	(0.041)	(0.042)	(0.028)	(0.035)	(0.032)
Mid-Sized*2020	−0.097**	0.092*	0.014	0.026	−0.032	−0.078**
	(0.046)	(0.048)	(0.048)	(0.034)	(0.041)	(0.036)
50–89% Rent Received	0.249***	0.089***	0.182***	0.161***	0.198***	0.084***
	(0.018)	(0.016)	(0.016)	(0.015)	(0.018)	(0.013)
<50% Rent Received	0.156***	0.077***	0.245***	0.371***	0.244***	0.092***
	(0.029)	(0.024)	(0.028)	(0.030)	(0.029)	(0.022)
Omitted Group Mean	0.24	0.46	0.38	0.03	0.05	0.07
N Landlord-Years	4764	4764	4764	4764	4764	4764
Panel B: Race
Landlord of Color	−0.018	0.027	−0.004	0.013	−0.010	−0.030***
	(0.018)	(0.021)	(0.018)	(0.011)	(0.012)	(0.008)
2020	0.223***	−0.152***	−0.067***	0.056***	0.166***	0.075***
	(0.016)	(0.014)	(0.012)	(0.009)	(0.013)	(0.010)
Landlord of Color*2020	0.068**	−0.033	0.011	0.084***	0.059**	0.001
	(0.028)	(0.025)	(0.023)	(0.021)	(0.024)	(0.017)
50–89% Rent Received	0.292***	0.101***	0.200***	0.171***	0.211***	0.108***
	(0.020)	(0.017)	(0.017)	(0.016)	(0.019)	(0.015)
<50% Rent Received	0.141***	0.072***	0.229***	0.371***	0.240***	0.102***
	(0.033)	(0.027)	(0.031)	(0.033)	(0.032)	(0.025)
Omitted Group Mean	0.15	0.24	0.14	0.03	0.05	0.04
N Landlord-Years	4180	4180	4180	4180	4180	4180

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Notes: This table reports OLS estimates of the relationship between landlords’ business practices and demographic/business characteristics, prior to and during the pandemic. Panel A presents results according to landlords’ portfolio size, while Panel B presents results according to landlords’ race/ethnicity. Each column presents results from a separate OLS regression, where the indicated business practice is the dependent variable. “Grant Rent Ext.” indicates rental extensions and/or putting tenants on repayment plans. “Charge Rent Fee” indicates charging fees for late rent. “Evict Tenants” indicates the commencement of eviction procedures (and potentially, the conclusion). “Miss Payments” indicates missed mortgage, property tax, and/or utility payments. “Defer Maint.” indicates delayed property repairs or maintenance. “List Props. For Sale” indicates one or more properties were listed for sale. Landlords could choose multiple actions. 70.6 percent of respondents are small landlords, and 18.3 percent are mid-sized landlords. 32.3 percent of respondents are landlords of color. Models additionally control for city fixed effects. Heteroskedastic-robust standard errors are reported in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1. Data come from the COVID-19 landlord survey.

In Panel A, the coefficients on the Small Landlord and Mid-Sized Landlord variables report the relationship between landlord size and business practices for those who collected 90 percent or more of their 2019 yearly rent (relative to larger landlords with comparable rental collection rates). In general, larger landlords, conditional on rental payment, were more likely to report pursuing each of the listed business practices compared to small and mid-sized ones. For example, landlords who collected 90 percent or more of their yearly rent and who have five or fewer units in their portfolio were 29.8 percentage points less likely to charge late rent fees, 32.5 percentage points less likely to evict tenants, and 5.1 percentage points less likely to list a property for sale compared to landlords with the same rental collection rates and 20 or more units in their portfolio.

The coefficient on the 2020 variable captures the level shift across years in large landlords’ business practices, conditional on rent collection. Large landlords exhibited considerable adaptability of their business practices—both upwards and downwards—during the pandemic. In fact, when compared to the impact of rent collection on 2019 business practices, large landlords’ behavioral change during the pandemic was significant: the 46.2 percentage point increase in rental extension rate associated with the pandemic was nearly triple that associated with collecting less than 50 percent of yearly rent (for the same business practice). Large landlords were also about twice as likely to list properties for sale in 2020 relative to pursuing this action in response to rental non-payment prior to the pandemic, which may imply that the pandemic has sent landlords’ negative signals about the rental market above and beyond those sent by rental non-payment.

The coefficients on the interaction terms show that small and mid-sized landlords demonstrated relatively less behavioral change relative to large ones, though on balance, these groups still adjusted their business practices during the pandemic. In certain instances, these behavioral changes are even larger than large landlords’ pre-pandemic responses to rental non-payment—a finding that is notable given the generally more active role large landlords take in managing their rental properties. For example, column (5) shows that property sale listings increased by 17.3 percentage points in 2020 for large landlords, leading to net increases of 6.1 and 9.5 percentage points for small and mid-sized landlords, respectively—the latter being larger than the coefficients on both 50–89% Rent Received and <50% Rent Received.

Panel B explores the relationship between landlord race, business practices, and rental collection. Apart from property listings, there were few meaningful differences in rental practices between landlords of color and white landlords prior to the pandemic. However, the fact that landlords of color were significantly more likely than white landlords to hold onto their rental properties is notable in light of emerging research documenting discrimination faced by Black and Latino sellers in the home sale market (Freddie Mac 2021).

Once again, we observe a sharp change in business practices across years for all landlords. Contrary to our findings in Panel A, however, the change in business practices associated with the pandemic are generally of a smaller magnitude than the changes associated with rental non-payment prior to the pandemic for white and non-white landlords alike. However, landlords of color were significantly more likely than their white counterparts to grant rental extensions (6.8 percentage points), defer maintenance (5.9 percentage points), and miss property payments (8.4 percentage points) during the pandemic, holding constant rental collection. While there are various potential explanations for these findings, the latter, in particular, may reflect the fact that Black and Hispanic owners were significantly less likely than their white counterparts to request mortgage forbearance during the pandemic (Gerardi et al., 2021).

In sum, we find notable differences in the pre- and post-pandemic business practices of small vs. large and non-white vs. white landlords conditional on rent collection differences across groups. Both small and non-white landlords took significant steps to disinvest from their properties during the pandemic—in the case of non-white landlords, disproportionately so relative to their white counterparts—by deferring maintenance and listing rental properties for sale. Given the critical role both groups of landlords play in housing lower-income renters and renters of color, their tendency to pursue these practices likely has the unintended consequence of perpetuating financial and housing instability for vulnerable tenants and property owners alike.

4.4. Regional variation in pandemic policies contributed to observed cross-city variation in landlord practices

Table 6 explores cross-city, cross-year heterogeneity among the six key landlord business practices. Each cell of this table reports the share of landlords in city $c$ (row) pursuing business practice $p$ (column), for year $y$ (sub-column). Column (1) shows that, prior to the pandemic, a substantial share of landlords in each city reported granting rental extensions—from a low of around 10 percent in Los Angeles to a high of nearly 20 percent in Albany. In 2020, these proportions increased by 20 to 50 percentage points (column 2), with the most significant increases concentrated among cities where landlords collected less rent during the pandemic.37 Columns (3) and (4) show an opposite trend for rental fees: though they were common in all cities prior to the pandemic, landlords in each city reported significantly lower rates of this rental business practice in 2020.

Table 6.

Changes in six key landlord rental business practices, by city.

	Grant rent extensions		Charge rent fees		Evict tenants		Miss payments		Defer maintenance		List props. for sale
	2019	2020	2019	2020	2019	2020	2019	2020	2019	2020	2019	2020
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)
Akron	17.5	49.3	24.5	20.1	19.2	17.9	7.4	21	3.9	29.7	2.2	16.6
Albany	19.6	45.1	27.5	9.8	17.6	21.6	3.9	26.5	9.8	46.1	3.9	22.5
Indianapolis	18.1	53.1	26.0	17.9	17.3	19.1	1.8	9.7	3.6	25.8	4.1	15.3
Los Angeles	9.5	59.5	26.5	4.5	8.5	6.5	2.5	20.5	6.5	35.0	1.0	11.5
Minneapolis	10.2	29.2	14.5	6.6	5.5	3.0	1.6	10.6	2.5	24.2	2.7	8.2
Philadelphia	19.0	68.6	29.1	15.9	17.8	25.6	5.8	32.2	5.4	35.7	3.5	20.9
Racine	13.9	43.8	20.1	9.0	11.1	9.7	2.8	17.4	9.7	24.3	2.1	5.6
Rochester	14.6	53.2	22.8	12.7	25.3	31.6	6.3	33.5	5.1	43.0	3.8	17.7
San Jose	11.4	50.4	26.0	3.5	11.4	5.5	3.1	12.2	5.9	32.7	1.2	6.7
Trenton	22.2	57.4	24.1	17.6	20.4	38.9	5.6	38	9.3	36.1	2.8	15.7

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Notes: This table reports the share of landlords’ pursuing five key rental business practices in 2019 and 2020, for each city in the study. “Grant Rent Extensions” indicates rental extensions and/or putting tenants on repayment plans. “Charge Rent Fees” indicates charging fees for late rent. “Evict Tenants” indicates the commencement of eviction procedures (and potentially, the conclusion). “Miss Payments” indicates missed mortgage, property tax, and/or utility payments. “Defer Maintenance” indicates delayed property repairs or maintenance. “List Props. For Sale” indicates one or more properties were listed for sale. Responses do not sum to 100 (within a city-year) because landlords could choose multiple actions. 10.5 percent of respondents are from San Jose, 8.6 from Los Angeles, 23.3 from Minneapolis, 6.2 from Racine, 16.2 from Indianapolis, 9.3 from Akron, 6.4 from Rochester, 4.2 from Albany, 10.9 from Philadelphia, and 4.5 from Trenton. The total number of survey respondents in the sample is 2525. Data come from the COVID-19 Landlord Survey.

Results are more mixed for evictions (columns 5 and 6). The proportion of landlords initiating eviction proceedings fell by several percentage points for half the cities in our sample—specifically, the West Coast cities of Los Angeles and San Jose and the Midwestern cities of Minneapolis, Racine, and Akron. At the same time, evictions were up slightly in all East Coast cities and the Midwestern city of Indianapolis. While this finding, like that for the granting of rental extensions, may in part be explained by cross-city variation in landlords’ rental collection rates, it may also be a function of the differing intensities and duration of renter protections for the cities in our sample.38

Fewer than 10 percent of landlords in any of our sample's cities reported missing mortgage, utility, and/or property tax payments (column 7) or deferring maintenance (column 9) at one or more of their rental properties prior to the pandemic. The share of landlords pursuing each of these actions increased dramatically in 2020. In each city, at least 10 percent of landlords reported missing financial obligations in 2020, with rates particularly high in the East Coast cities of Rochester, Philadelphia, and Trenton (column 8). Further, roughly one-quarter of Midwestern, one-third of West Coast, and two-fifths of East Coast landlords indicated they had delayed necessary property upkeep for at least one of their rental properties (column 10). Property sales were even less common prior to the pandemic (column 11), but this action increased dramatically in 2020 (column 12). During the pandemic, over 15 percent of landlords in Akron, Albany, Indianapolis, Philadelphia, Rochester, and Trenton reported listing at least one rental property for sale.

As mentioned above, several of the year-over-year changes to landlords’ business practices are most striking in cities where declines to rent collection were most severe. At the same time, variation in local rules, regulations, and politicians’ response to the pandemic (e.g., Raifman et al., 2020) might lead to an independent impact on landlords’ rental businesses, irrespective of rental payment. To better explore this issue, in Table 7, we present weighted OLS estimates from a version of Eq. (1) collapsed to the city-year level.39 Estimates from this regression shed light on: 1) the average, pre-pandemic relationship between city-level rent collection and business practices ( $β_{1}$ ), 2) the average impact of the pandemic on landlords’ rental business practices, conditional on city-level collection rates ( $β_{2}$ ), and 3) whether the pandemic has, on average, altered the relationship between rental collection and business practice implementation across cities ( $β_{3}$ ).

Table 7.

Relationship between city-level rental collection and business practices.

	Grant rent ext.	Charge rent fees	Evict tenants	Miss payments	Defer maint.	List props. for sale
	(1)	(2)	(3)	(4)	(5)	(6)
Share < 90% Rent Received	0.583**	0.756*	1.173***	0.421***	0.236	0.041
	(0.208)	(0.395)	(0.204)	(0.065)	(0.152)	(0.051)
2020	0.072	−0.108*	−0.126**	−0.037	0.117***	−0.004
	(0.068)	(0.059)	(0.045)	(0.058)	(0.037)	(0.024)
Share < 90% Rent Received*2020	0.280	−0.561	−0.483*	0.191	0.198	0.253***
	(0.272)	(0.400)	(0.228)	(0.150)	(0.174)	(0.084)
N City-Years	20	20	20	20	20	20

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Notes: This table reports weighted OLS estimates of the relationship between landlords’ rental collection and business practice implementation rates, at the city-level, prior to and during the pandemic. To generate these estimates, data are first collapsed on means to the city-year level, and all regressions are weighted by the number of survey respondents within the city. “Grant Rent Ext.” indicates rental extensions and/or putting tenants on repayment plans. “Charge Rent Fees” indicates charging fees for late rent. “Evict Tenants” indicates the commencement of eviction procedures (and potentially, the conclusion). “Miss Payments” indicates missed mortgage, property tax, and/or utility payments. “Defer Maint.” indicates delayed property repairs or maintenance. “List Props. For Sale” indicates one or more properties were listed for sale. Landlords could choose multiple actions. Heteroskedastic-robust standard errors are reported in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1. Data come from the COVID-19 landlord survey.

Column (1) of Table 7 shows that, in 2019, a 1-unit increase in the share of landlords collecting at most 90 percent of rental revenue was associated with a statistically significant 0.58 unit increase, on average, in the share of landlords granting rental extensions. If we assume effects are linear throughout the distribution, this implies a 5.8 percentage point increase in the city-level rental extension rate for a 10 percentage point increase in city-level partial rental revenue collection rate. With coefficients on the pandemic (i.e., 2020) indicator and interaction term statistically indistinguishable from 0, we thus conclude that the primary driver of cross-city differences in landlords’ rental extension rates is indeed cross-city variation in their rental collection rates.

This is not the case when examining the relationship between rent collection and the incidence of late rent fees (column 2) and tenant evictions (column 3). While we once again observe a strong, positive relationship in 2019 between a city's share of landlords who collected at most 90 percent of their rental revenue and pursued tenant late rent fees and/or evictions, these proportions fell by 10.8 and 12.6 percentage points, respectively, during the pandemic (holding constant rental collection). We also find suggestive evidence that the pandemic has attenuated the relationship between rent collection and the pursuance of evictions. Taken together, these results imply that cross-city variation beyond that observed in rental collection rates—perhaps arising from different pandemic rules, regulations, and responses across cities, among other factors—contributed to the observed variation in the issuance of rental fees and tenant evictions in 2020. This result is notable in that it indicates local policies and regulations directed towards landlords are likely able to affect behavioral change among this population.

Finally, in columns (5) and (6) we see that the pandemic has altered city-level rates of deferred property maintenance and rental property sale listings, albeit in slightly different ways. Prior to the pandemic, there was no robust relationship between landlords’ rental collection and deferred maintenance rates. Though the share of landlords reporting the latter practice was up significantly during the pandemic, there remains no significant relationship between citywide rent collection and deferred maintenance in 2020. For property sales, listings were up more dramatically in 2020 in cities with lower rental collection rates, mirroring heterogeneity in small business closures across regions (Bartik et al., 2020). These responses in particular raise concern about the potential impact of the pandemic on both long-term housing stock quality and affordability.

4.5. Landlords’ responses to the pandemic may be increasing housing instability in communities of color

Prior to the pandemic, Black and Hispanic Americans have faced discrimination in the rental housing market in numerous ways—from housing search (Hanson and Hawley 2011; Fang, Guess, and Humphreys (2019)), to securing affordable housing via Section 8 (Cunningham et al., 2018), to evictions (Hepburn et al., 2020). Given this history, a natural question to pose is: how have landlords managed their rental properties during the pandemic in communities of color, particularly given the relatively higher rates of rental non-payment observed in these communities?

In Fig. 6 , we again switch our unit of analysis to the individual rental property and explore variation in landlords’ 2020 rental business practices according to a neighborhood's racial composition. Specifically, we present nine binned scatter plots of landlords’ rental property business practices (y-axis) versus the neighborhood share of non-white residents (x-axis). To construct these plots, we first demean both landlords’ rental business practices and the neighborhood share of non-white residents by city, 2020 rent collection, and neighborhood population. We then divide the observations into 20 equal-sized groups (vigintiles) based on the racial distribution and plot the share of landlords pursuing the indicated rental practice within each bin. The solid lines show the best linear fit estimated on the underlying micro data using OLS regression, and Panel A of Table 8 presents these regression estimates.

Table 8.

Relationship between landlords’ 2020 property-level rental business practices and neighborhood share of non-white residents.

	Grant rent ext.	Forgive rent	Charge rent fee	Inc. rents	Dec. rents	Evict tenants	Miss payments	Defer maint.	List prop. for sale
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
Unconditional 2020 Mean	0.368	0.148	0.083	0.037	0.123	0.085	0.137	0.257	0.069
Panel A: Baseline Model
Share Non-White Residents	0.069**	−0.083***	0.057***	−0.003	−0.090***	0.076***	0.104***	0.022	−0.005
	(0.035)	(0.028)	(0.021)	(0.013)	(0.026)	(0.020)	(0.026)	(0.033)	(0.021)
Panel B: Landlord Controls
Share Non-White Residents	0.061*	−0.064**	0.063***	0.009	−0.063**	0.076***	0.077***	0.021	0.000
	(0.036)	(0.029)	(0.023)	(0.013)	(0.027)	(0.021)	(0.026)	(0.034)	(0.023)
Panel C: Neighborhood Controls
Share Non-White Residents	0.073*	−0.127***	0.076***	0.003	−0.103***	0.094***	0.097***	−0.002	−0.023
	(0.043)	(0.035)	(0.028)	(0.017)	(0.031)	(0.026)	(0.032)	(0.041)	(0.029)
Panel D: Landlord and Neighborhood Controls
Share Non-White Residents	0.070	−0.109***	0.088***	0.015	−0.076**	0.099***	0.072**	0.007	−0.019
	(0.044)	(0.036)	(0.030)	(0.018)	(0.032)	(0.026)	(0.033)	(0.042)	(0.030)
N Rental Properties	2402	2402	2402	2402	2402	2402	2402	2402	2402

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Notes: This table reports OLS regression estimates of the relationship between landlords’ 2020 property-level rental business practices and the neighborhood share of non-white residents. Each column presents results from a separate OLS regression of a residualized version of the indicated business practice on a residualized version of the neighborhood share of non-white residents. In Panel A, we residualize on 2020 rent collection, neighborhood population, and city fixed effects. In Panel B, we residualize on the controls from Panel A, as well as the set of indicators for landlords’ demographics, business details, and portfolio size reported in Fig. 2. In Panel C, we residualize on the controls from Panel A, as well as the neighborhood median income and age distribution. In Panel D, we residualize on all controls from Panels A, B, and C. A neighborhood's share of non-white residents is defined as the sum of individuals who identify as Black, Hispanic, Asian, Native American, multiracial and/or other races. “Grant Rent Ext.” indicates rental extensions and/or putting tenants on repayment plans. “Forgive Rent” indicates rental forgiveness (either in full or a portion). “Charge Rent Fee” indicates charging fees for late rent. “Inc. Rents” indicates increases to monthly rents. “Dec. Rents” indicates decreases to monthly rents. “Evict Tenants” indicates the commencement of eviction procedures (and potentially, the conclusion). “Miss Payments” indicates missed mortgage, property tax, and/or utility payments. “Defer Maint.” indicates delayed property repairs or maintenance. “List Prop. For Sale” indicates the property was listed for sale. Landlords could choose multiple actions. Heteroskedastic-robust standard errors are reported in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1. Data come from the COVID-19 landlord survey and 2018 ACS.

Nearly all of landlords’ practices are statistically significantly related to the neighborhood racial and ethnic composition of their rental properties. Typically, properties in communities with more residents of color are more susceptible to business actions that likely contribute to housing instability. For example, a 1-unit increase in a neighborhood's share of residents of color (i.e., moving from a neighborhood with no residents of color to one with exclusively residents of color) was associated with an 8.3 percentage point reduction in the share of landlords offering rental forgiveness, 9.0 percentage point reduction in the share decreasing monthly rents, and a 5.7 percentage point increase in the share charging late rent fees. Put differently, moving from a neighborhood at the 25th percentile of the city-demeaned resident of color distribution to one at the 75th percentile is associated with a 3.4 percentage point decrease in landlords’ rent forgiveness rate, 3.7 percentage point decrease in their monthly rent decrease rate, and 2.3 percentage point increase in their late fee implementation rate.40 These findings, which indicate relatively greater landlord-induced financial strain for renters of color, are particularly relevant given the pandemic's outsized financial impact in these communities (e.g., Lee et al. 2021).

We also observe a disproportionate share of landlords reporting tenant evictions at properties in communities of color. In this case, moving from the 25th to 75th percentile of the city-demeaned racial distribution is associated with a 3.1 percentage point increase in the tenant eviction rate. For context, this is over 35 percent of 2020 unconditional mean eviction rate (8.5 percent). The higher rate of displacement in these communities aligns with emerging research on the unequal rate at which Black and Hispanic renters have been evicted during the pandemic (Hepburn et al., 2021; Stein et al., 2021). While landlords have been more likely to miss financial payments in neighborhoods with more residents of color, there once again is no meaningful relationship between neighborhood racial composition and deferred maintenance or property sales.

There are many potential explanations for these results. One is that landlord sorting across neighborhoods according to demographic or business characteristics that are known to be correlated with management tactics—such as landlord race—may be driving observed differences.41 Accordingly, in Appendix Figure 5, we explore the degree to which cross-neighborhood differences in landlords’ characteristics may be contributing to landlords’ business practices in communities of color. Even conditional on the full set of landlord characteristics presented in Fig. 2, the relationships between landlords’ 2020 business practices and neighborhood racial composition are qualitatively similar to those reported in Fig. 6 . More specifically, Panel B of Table 8 shows that the coefficients on the Share Non-White Residents variable are remarkably consistent, albeit slightly attenuated, relative to the baseline model. Thus, we conclude that differences in landlord characteristics across neighborhoods alone cannot explain landlords’ tendency to disproportionately pursue punitive actions in communities of color. Together with the results from Table 5, this implies that, while landlords modified their business practices during the pandemic according to their own demographic and business characteristics, the strength of those relationships did not additionally vary according to a neighborhood's racial composition.42

We also investigate whether residential sorting across neighborhoods, particularly on economic characteristics, may influence landlords’ business practices (Appendix Figure 8). This could occur, for example, if landlords’ expectations about tenants’ ability to pay rent in the future vary according to characteristics that are correlated with tenants’ race. To test this hypothesis, we add controls for neighborhood median income and age to our baseline model.43 We choose these controls for two primary reasons: 1) non-white Americans are younger and have lower median incomes relative to white Americans (Schaeffer 2019; Chetty et al., 2020b), and 2) emerging research has shown a disproportionate economic impact of the pandemic on these groups (e.g., Lee et al. 2021). Panel C of Table 8 shows that, conditional on rental collection and neighborhood characteristics that could be correlated with perceived economic ability, landlords were still significantly more likely to pursue actions such as evictions and significantly less likely to offer rent decreases in communities with a higher share of non-white residents. Including landlord controls in this model (Panel D) again does not meaningfully alter our interpretation of the results. Indeed, the qualitative conclusions across all four models are identical: landlords were more likely to report taking punitive actions and less likely to report offering concessions in communities of color during the pandemic. While we cannot identify the precise cause for these differential behaviors, our robustness checks allow us to rule out the possibility that they are fully explained by the sorting of landlords and/or tenants across neighborhoods.44

Appendix Fig 8 — Landlords’ 2020 property-level business practices (y-axis) and neighborhood share of non-white residents (x-axis), conditional on additional neighborhood attributes. *Notes:* This figure presents binned scatter plots of landlords’ 2020 property-level rental business practices versus the neighborhood share of non-white residents (for the property), conditional on the neighborhood median income and age distribution. For each plot, the share of landlords reporting the practice is reported on the y-axis while the neighborhood share of non-white residents is presented on the x-axis. A neighborhood's share of non-white residents is defined as the sum of individuals who identify as Black, Hispanic, Asian, Native American, multiracial and/or other races. To construct these plots, we first demean both landlords’ rental business practices and neighborhood share of non-white residents by city, 2020 rent collection, neighborhood population, neighborhood median income, the share of residents under the age of 25, and the share of residents aged 25 – 54. We then divide the observations into 20 equal-sized groups (vigintiles) based on neighborhood racial composition and plot the share of landlords pursuing the indicated rental practice within each bin. The solid lines show the best linear fit estimated on the underlying micro data using OLS regression, and Table 8 presents these regression estimates. “Grant rent exten.” indicates rental extensions and/or putting tenants on repayment plans. “Forgive rent” indicates rental forgiveness (either in full or a portion). “Charge rent fees” indicates charging fees for late rent. “Increase rents” indicates increases to monthly rents. “Decrease rents” indicates decreases to monthly rents. “Evict tenants” indicates the commencement of eviction procedures (and potentially, the conclusion). “Miss payments” indicates missed mortgage, property tax, and/or utility payments. “Defer maintenance” indicates delayed property repairs or maintenance. “List prop. for sale” indicates the property was listed for sale. The number of rental properties for each plot is 2402. Data come from the COVID-19 landlord survey and 2018 ACS.

Finally, to better understand the degree to which the observed relationships between business practices and neighborhood racial composition are unique to the pandemic, Fig. 7 presents binned scatter plots of landlords’ 2019 rental property business practices against neighborhood share of non-white residents. Even prior to the pandemic, landlords’ pursual of punitive business practices was increasing in a neighborhood's share of non-white residents. Moreover, the strength of these relationships was stronger in 2019 relative to 2020: moving from a neighborhood at the 25th percentile of the city-demeaned resident of color distribution to one at the 75th percentile was associated with a 3.9 and 5.1 percentage point increase in rental fees and evictions, respectively (compared to 2.3 and 3.1 percentage points in 2020). Conversely, we find no relationship in 2019 between neighborhood racial composition and concessionary business practices, such as the granting of rental extensions, which were decreasing in the share of non-white residents during the pandemic. Thus, while the relationship between punitive actions and neighborhood racial composition was attenuated in 2020—perhaps due to intermittent prohibitions put on these practices (Raifman et al., 2020)—the racialized nature of landlords’ business practices persisted and even took on new forms during the pandemic. We conclude that landlords’ behavioral change in 2020 was moderate compared to 2019, but crucially, their business responses were racialized in both years.45

Fig 7 — Landlords’ 2019 property-level business practices (y-axis) and neighborhood share of non-white residents (x-axis). *Notes:* This figure presents binned scatter plots of landlords’ 2019 property-level rental business practices versus the neighborhood share of non-white residents (for the property). For each plot, the share of landlords reporting the practice is reported on the y-axis while the neighborhood share of non-white residents is presented on the x-axis. A neighborhood's share of non-white residents is defined as the sum of individuals who identify as Black, Hispanic, Asian, Native American, multiracial and/or other races. To construct these plots, we first demean both landlords’ rental business practices and neighborhood share of non-white residents by city, 2019 rent collection, and neighborhood population. We then divide the observations into 20 equal-sized groups (vigintiles) based on neighborhood racial composition and plot the share of landlords pursuing the indicated rental practice within each bin. The solid lines show the best linear fit estimated on the underlying micro data using OLS regression, and Appendix Table 9 presents these regression estimates. “Grant rent exten.” indicates rental extensions and/or putting tenants on repayment plans. “Forgive Rent” indicates rental forgiveness (either in full or a portion). “Charge rent fees” indicates charging fees for late rent. “Increase rents” indicates increases to monthly rents. “Decrease rents” indicates decreases to monthly rents. “Evict tenants” indicates the commencement of eviction procedures (and potentially, the conclusion). “Miss payments” indicates missed mortgage, property tax, and/or utility payments. “Defer maintenance” indicates delayed property repairs or maintenance. “List prop. for sale” indicates the property was listed for sale. The number of rental properties for each plot is 2402. Data come from the COVID-19 landlord survey and 2018 ACS.

In sum, landlords’ tendency to pursue business practices differentially according to neighborhood racial composition—even conditional on rental collection rates, landlord characteristics, and other neighborhood characteristics—has resulted in tenants in more marginalized communities disproportionately bearing the consequences of pandemic-induced rental non-payment. While these racialized business responses are not new, they likely have increased housing instability in communities in which tenants were more already likely to be adversely affected by the pandemic in other ways (Bacher-Hicks et al. 2021; Bambra et al., 2020; Lee et al. 2021). We conclude that landlords’ pre- and post-pandemic business practices ultimately serve to exacerbate and reinforce the many historical rental market discriminations facing renters of color (Hanson and Hawley 2011; Cunningham et al., 2018; Hepburn et al., 2020).

5. Conclusion

In this paper, we explore the impact of the COVID-19 pandemic on the rental business of landlords in ten cities across the US. We find that landlords’ rental properties generated a significantly lower share of their potential rental revenue in 2020 relative to 2019. We observe proportionate three- to fourfold increases in rental non-payment during the pandemic for all cities in our sample with 9 percent of landlords receiving less than half of their yearly rent in 2020. Small owners and landlords of color faced high exposure to rental non-payment prior to the pandemic and continued to struggle with rent collection in 2020. While changes to landlord business practices—such as the granting of rental extensions—were strongly correlated with rental revenue decreases, business impacts alone cannot explain landlords’ behavioral responses.

The pandemic also amplified the relationship between rental collection and actions such as rent forgiveness and deferred property maintenance, perhaps due to pandemic-induced constraints on landlords’ traditional responses to rental non-payment. This suggests that many owners modified their practices to recover funds and attempted to cut costs by reducing investments in their properties, much like small business owners more generally (e.g., Bartik et al., 2020). This was particularly the case for landlords of color, who delayed property payments and maintenance during the pandemic at a significantly higher rate than white landlords.

There are two distinct negative effects of landlords’ rental property disinvestment. First, in the short term, it may imply that renters are residing in units of substandard quality, thus affecting their health and well-being. Second, it may indicate that many properties need further investment post-pandemic to remain viable. This latter implication is particularly worrisome as small and mid-sized typically struggle to access capital to invest in their properties (Local Housing Solutions 2021). Absent concerted efforts to bridge these credit gaps, property owners will have difficulty restructuring their financing to ensure their properties are viable. This may result in rental units leaving the housing stock earlier than they previously would have, thus exacerbating preexisting issues around housing affordability.

The rental business challenges associated with the pandemic are clearly affecting owner behavior, with city-level rental non-payment rates positively correlated with property sale listings. Such sales could place further strain on the overall stock of affordable housing, although they also present an opportunity for localities to actively broker the sale and purchase of these properties to ensure their long-term viability. This approach could also serve as an opportunity for localities to provide subsidy support, with coterminous affordability restrictions, to increase the affordability of these units. Cities may be well positioned to pursue such a strategy given the unprecedented level of federal funds currently being deployed to stabilize local housing markets.

Among the many concerning findings in our study is the disproportionate impact of the pandemic on the renters in and housing stock of communities of color. By showing that owners are more likely to exercise punitive actions on renters in markets with a majority of residents of color—both prior to and during the pandemic—we demonstrate that the ways in which owners are engaging with challenges around rental collection are racialized. Numerous notable works have documented persistent and pernicious racial discrimination in rental housing markets and investments (e.g., Taylor 2019; Reina et al. 2020), and our findings suggest that these discriminatory actions have persisted and even taken on new forms during the pandemic. As localities continue to build out and sustain renter assistance programs, they may want to offer additional supports and protections for tenants in these communities.

One limitation of the study is its relatively limited sample size and owner response rate. It is also worth cautioning that, due to the population surveyed, our results likely reflect the experiences of those landlords who serve at least a portion of lower-income tenants. However, a dearth of information on property owners and their tenants at both the national and local level makes it difficult to assess this claim for certain. Of the pandemic's many important lessons, one is that we still know little about who owns rental properties and how these owners behave. The results of our report are critical to filling this gap, though we encourage readers to consider our findings in concert with other local and national efforts to understand this population.

Funding

Funding for this work was provided by Bloomberg Philanthropies through the Bloomberg Harvard City Leadership; the Housing Crisis Research Collaborative, supported by JPMorgan Chase & Co. and the Wells Fargo Foundation, managed by the Urban Institute, and made available to the authors through the Joint Center for Housing Studies of Harvard University; and the Annie E. Casey Foundation through the Housing Initiative at Penn. We thank all sources for their generous support and acknowledge that the findings and conclusions presented in this report are those of the authors alone.

Declaration of Competing Interest

None.

Footnotes

^✰

We are grateful to our partners in the City of Akron Mayor's Office and Office of Integrated Development; City of Albany Department of Buildings and Regulatory Compliance; City of Indianapolis Mayor's Office; City of Los Angeles Mayor's Office and Department of Housing and Community Investment Department; City of Minneapolis Department of Regulatory Services; City of Philadelphia Department of Planning and Development; City of Racine Mayor's Office and Department of Management Information Systems; City of Rochester Department of Neighborhood and Business Development; and City of Trenton Mayor's Office and Department of Housing and Economic Development for their support and promotion of this work. We are also grateful to Kate Bischoff, Andrew Kieve, Bolek Kurowski, and Glen Nuckols from Tolemi for providing feedback on early iterations of the survey, assisting with data access, and assisting with survey coding. Jessica Creighton, Tanya Dall, and Sydney Goldstein provided excellent project management. Eleanor Dickens, Raheem Hanifa, and Ashley Marcoux provided exceptional research assistance. Chris Herbert provided invaluable support and feedback throughout all phases of this work. Andrew Bacher-Hicks, Jorrit de Jong, Nat Decker, Ingrid Gould Ellen, Emma Foley, Austin Harrison, Reed Jordan, Monique King-Viehland, Elizabeth Kneebone, David Luberoff, Alan Mallach, Kathy O'Regan, Snapper Poche, Rebecca Yae and two anonymous reviewers provided many useful comments, as did members of the Rental Research Community of Practice.

For an excellent overview of the renter-focused research, see Airgood-Obryicki et al. (2021). See Hepburn et al. (2021) for a discussion of tenant evictions during the pandemic.

Throughout this paper, we will use the term “property owner” and “landlord” interchangeably. We will also use the term “pre-COVID” to refer to the 2019 calendar year, while “post-COVID” will refer to the 2020 calendar year. Similarly, “pre-pandemic” will refer to 2019, while “during the pandemic” will refer to 2020.

⁴

Other studies have more explicitly estimated the value of tenant and landlord rental assistance need in specific markets, but these studies have relied on secondary data sources to approximate these findings (Kneebone & Murray 2020; Kneebone & Reid 2020).

⁵

Most notably, small landlords and landlords of color are typically seen as the primary providers of naturally occurring (i.e., market-provided) affordable housing, and thus, house relatively more financially vulnerable tenants.

⁶

A notable exception is the work of Raymond et al. (2017) in Fulton County, Georgia. Using parcel-level eviction records, the authors show that corporate landlords are more likely than small landlords to file for tenant eviction, conditional on property and neighborhood characteristics.

⁷

Specifically, landlords were instructed to choose a property whose profitability prior to the pandemic was typical of their portfolio's pre-pandemic profitability. Asking questions at the rental property level allows us to explore variation according to property and neighborhood characteristics.

⁸

A potential concern with our survey methodology—particularly with respect to landlords’ pre-pandemic business performance and management—is recall bias (e.g., Sudman & Bradburn 1974). However, our survey was designed to minimize this bias in several ways. First, the survey was distributed close to tax season with the expectation that yearly profitability and business management would be top of mind. Second, we asked about business practices that were hypothesized to occur relatively infrequently, but which also have economic and social consequences—two factors that are likely to affect an event's salience and thus respondents’ recall decay (Bradburn, Sudman, & Wansink 2004). Along these lines, we asked respondents to recall simply whether they ever took the indicated action rather than how frequently as an additional strategy for minimizing this bias (Biemer et al. 2013).

⁹

The two most common reasons cities did not participate are that they did not maintain sufficient landlord contact data and/or did not have internal capacity to collaborate.

¹⁰

Notably, we were not able to secure the participation of any Southern US cities.

¹¹

Appendix Table 1 presents these descriptive statistics separately for each city in the survey sample.

¹²

See Appendix A for the Call to Participate. Interested parties may contact the authors directly for the survey instrument itself.

¹³

There were technically 2,930 respondents to the COVID-19 Landlord Survey. We exclude 80 individuals who reported managing as opposed to owning property from our usable response sample, as these individuals were automatically routed to the end of the survey.

¹⁴

Typically, owners must pay a small fee to register their rental properties with their city, which covers the cost of a housing habitability inspection. For example, the rental inspection fee in Albany, New York is $50 per rental property unit. Examples of common inspection criteria include working smoke and carbon monoxide detectors; open means of egress; clean, running water; and basic unit security. Owners who fail their initial inspection must remedy any habitability issues and then pass a re-inspection. In most municipalities, though owners are subject to monetary penalties for lapsed rental registrations, they are often given the opportunity to rectify the situation prior to the issuance of fees.

¹⁵

These types of properties represent 8.2 percent of all San Jose rental properties, while the units therein represent 35.4 percent of all San Jose rental units.

¹⁶

While we cannot adequately explore landlord characteristics for registry compliers and non-compliers, we can explore the characteristics of rental properties by registry compliance status (Appendix Table 2). Properties in compliance with the rental registry tend to be older, have more units, and are more likely to be owned by a landlord registered as a limited liability corporation or partnership. They also tend to have lower per-unit property values and have slightly less residential area. Rental registry properties tend to be located in neighborhoods with a higher share of residents of color, though we do not observe any meaningful differences across compliance status in neighborhood median household income or gross rent. Note that landlords with properties in compliance with their city's rental registry may also own properties not in compliance. Unfortunately, our survey is not equipped to explore this issue.

¹⁷

And as discussed above, rental registry requirements may also vary across cities.

¹⁸

This discrepancy is perhaps not surprising as voucher use tends to be concentrated in the urban core (McClure, Schwartz, & Taghavi 2015), and past research has shown non-white landlords—which are over-represented in our sample—are more likely than white ones to accept HCVs (Choi & Goodman 2021).

¹⁹

A lack of data on landlords’ portfolios makes it difficult to contextualize these figures. 10.3 million individual investors own an average of 1.7 rental properties, roughly 99 percent of which are SFRs. An estimated 1 million institutional landlords own around 45 percent of all rental units (DeSilver 2021). Thus, while most US rental property owners are small- to mid-sized landlords who own at least one SFR, a small share of owners manage nearly half of the rental stock.

²⁰

Appendix Table 3 shows there is considerable variation in these demographics across cities, though most of the landlords that responded to our survey tend to be male, over the age of 50, disproportionately white compared to the racial composition of their city, and own fewer than 20 rental units. In each city, with the exception of Los Angeles and San Jose, over 60 percent of landlords own five or fewer rental units.

²¹

For example, Decker (2021b) obtained owner information from a third-party company and recruited study participants through mailed letters. This mode of outreach limited the response rate, ultimately affecting the observed and unobserved composition of those owners that responded.

²²

Note that even though all Los Angeles and Philadelphia landlords had at least one tenant apply for local ERA, this need not imply that the tenant participated in the program and/or received funds.

²³

We treat collecting 90 percent or less of charged yearly rent as exposure to general rental non-payment for two related reasons: 1) existing data show that, even under typical conditions and among the highest-end units, landlords do not necessarily collect rent in full each month (NMHC 2020), and 2) the best way to approximate this fact given our rental collection categories is to aggregate the 50 to 89 and less than 50 percent of yearly rent received buckets.

²⁴

In this case, the dependent variables are $R e n t^{90, 2020}$ and $R e n t^{50, 2020}$ .

²⁵

We focus on landlords over the age of 60 because this is the oldest age grouping in our survey, and thus, the grouping of landlords closest to retirement age. Non-white landlords are defined as those who identify as Black, Hispanic, Asian/Pacific Islander, Native American, or other races.

²⁶

In both cases, our results are supported by prior research on HCVs. Lundberg et al. (2020) show that vouchers significantly reduce non-payment of rent. Kneebone et al. (2021) show that voucher and non-voucher users in California and New York City both struggled to make rent during the pandemic, but subsidized households accrued lower levels of rent arrears.

²⁷

For instance, Parrot and Zandi (2021) use the Census Bureau Household Pulse Survey to demonstrate that renters are further behind on rent in West and East Coast urban areas; our results show that, proportionally, renters are behind on rent at relatively consistent rates across the geographic regions in our study.

²⁸

Recall, landlords were instructed to choose a property whose profitability prior to the pandemic was typical of their portfolio's pre-pandemic profitability. Accordingly, Appendix Figure 3 presents landlords’ 2019 and 2020 rental collection rates at the individual rental property they reported on in the survey. While there is some variation in the magnitude of certain rental payment categories, the conclusions from this figure are qualitatively similar to those of Figure 1 for landlords’ portfolio-level rental collection.

²⁹

A neighborhood's share of residents of color is defined as the sum of individuals who identify as Black, Hispanic, Asian, Native American, multiracial and/or other races. In practice, due to the cities in our study, communities of color are primarily comprised of Black and Hispanic residents. To construct neighborhood racial and ethnic composition classifications, we first match each property in our rental property sample to its census block group (CBG). We then use the 2018 ACS to obtain the mean share of residents of color for that CBG and classify the CBG according to whether this share is above or below 50 percent. We perform this exercise separately for the 10 cities in our sample.

³⁰

To construct neighborhood income classifications, we first match each property in our rental property sample to its census block group (CBG). We then use the 2018 ACS to obtain the median household income for that CBG and classify the CBG according to whether its median household income falls above or below the citywide median. We perform this exercise separately for the 10 cities in our sample.

³¹

Of course, these actions may be changing precisely because rental collection was down in 2020 relative to 2019. We explore this possibility in further detail in Table 4 below.

³²

Compared to the overall pool of landlords, landlords who reported evicting tenants in both 2019 and 2020 are disproportionately male (70.6 percent), Black (17.4 percent), younger (72.5 percent under the age of 60), non-individual owners (32.8 percent structured as an LLC or LLP), accept HCVs (39.2 percent), and own 6+ properties (67.3 percent). Appendix Table 4 provides more detail on this population, as well as those who never reported evicting tenants, those who reported evicting tenants only in 2019, and those who reported evicting tenants only in 2020.

³³

Note that our survey specifically asked landlords if they had “missed mortgage payments” at any point during the aforementioned time periods. Though not intended to be inclusive of missed payments due to forbearance, some landlords may have interpreted the payment deferrals offered by these programs to be “missed payments.” Estimates of forbearance enrollment during the pandemic range from around 10 percent (e.g., Grief et al. 2021) to 20 percent (Engelhardt & Eriksen 2021). Our estimate for missed mortgage payments is nearly identical to the rates of non-forbearance-induced missed payments reported in the aforementioned studies.

³⁴

Of course, there may be other reasons this is the case. For example, the pandemic may have caused landlords to develop an increased desire to assist tenants through their financial hardships, thus making rental payment plans a preferred response to rental non-payment.

³⁵

As was the case with rental collection, we may be concerned that landlords in Los Angeles and Philadelphia were predisposed to pursuing certain types of management practices during the pandemic—such as granting rental extensions—since they, by definition, had at least one tenant behind on rent. Accordingly, Appendix Table 5 reproduces the results from Table 4 excluding these cities from the sample. Results are nearly identical across the two tables, indicating that differences among the ERA and rental registry samples are not substantially biasing our results for landlords’ business practices.

³⁶

For Panel A, this is the share of large landlords who collected 90 percent or more of their 2019 rent. For Panel B, this is the share of white landlords who collected 90 percent or more of their 2019 rent.

³⁷

Indeed, it may be the case that decreased rent collection within each city is driving the changes to landlords’ rental extension rates. We explore this possibility in further detail in Table 7 below.

³⁸

In general, the cities of our study with stronger eviction moratoria experienced greater reductions in late fees and landlord eviction filing rates, the latter of which aligns with the findings of Hepburn et al. (2021). For example, in Minneapolis, a ban on all phases of the eviction process has been in place since March 2020, whereas in Rochester and Albany, landlords could serve tenants eviction notices from July through December 2020 (Raifman et al. 2020).

³⁹

Specifically, we estimate $\bar{P r a c t i c} e_{c y}^{p}$ = $β_{0} + β_{1} \bar{R e n t L T 90_{c y}} + β_{2} 2020_{y} + β_{3} R \bar{e n t L T 90_{c y}} * 2020_{y} + ε_{c y}$ for the five key business practices $p$ reported in Table 5. $\bar{P r a c t i c} e_{c y}^{p}$ represents the mean share of landlords pursuing business practice $p$ in city $c$ in year $y$ . $\bar{R e n t L T 90_{c y}}$ represents the mean share of landlords collecting at most 90 percent of rental revenue in city $c$ in year $y$ . $2020_{y}$ is a binary indicator for the post-COVID time period. Results are weighted by the number of respondents in each city.

⁴⁰

Moving from the 25^th to 75^th percentile of the city-demeaned resident of color distribution is associated with a 41percentage point change in a neighborhood's share of residents of color.

⁴¹

Appendix Table 6 provides insight into the different demographics of landlords who own at least one property in a majority non-white neighborhood versus those who do not. In general, the landlords in our sample who own in communities of color are more likely to be people of color themselves (45.2 vs. 16.2 percent), are more likely to accept HCVs (29.4 vs. 12.5 percent), and are less likely to be small landlords (70.6 vs. 80.1 percent).

⁴²

Said differently, the slopes of the relationship between business practices and neighborhood racial composition are roughly constant for landlords with different demographic and business characteristics (conditional on 2020 rent collection, city fixed effects, neighborhood population, and the remaining set of demographic covariates) despite differing intercepts. Appendix Figure 6 shows this visually for non-white versus white landlords: for all business practices apart from the granting of rental extensions and missed payments, the best-fit regression lines are similarly sloped across the two groups. While there is some variation among large vs. small- and mid-sized landlords’ business practice intensity in communities of color, Appendix Figure 7 shows best-fit regression lines also tend to be similarly sloped across these three groups.

⁴³

We control for the neighborhood residential age distribution through two variables: 1) share of residents under the age of 25, and 2) share of residents aged 25 to 54. Data on age come from the 2018 ACS.

⁴⁴

We also directly explore the relationship between landlords’ business practices and neighborhoods’ economic characteristics. Appendix Figure 9 presents results according to neighborhood median income. We generally observe no meaningful relationship between landlords’ business practices and the neighborhood median income of their rental properties; the one exception is for the rate at which landlords have missed at least one mortgage, property tax, and/or utility payment, which is strongly and statistically significantly decreasing in neighborhood median income. Appendix Table 7 shows that these relationships are slightly more sensitive to the inclusion of controls, though the qualitative conclusion of no real economically meaningful relationship between landlords’ 2020 business practices and neighborhood median income is maintained. Appendix Figure 10 presents results according to neighborhood rent appreciation, which we define as the three-year average (from 2015 to 2018) in the year-over-year change in median gross rent (measured in $100 s, data from ACS).We observe no statistically significant or economically meaningful relationship between any of landlords’ business practices and neighborhood rent appreciation, and these results are robust to all specifications (Appendix Table 8).

⁴⁵

As was the case in 2020, these results largely hold after accounting for basic landlord demographics and additional neighborhood characteristics, indicating that landlord and/or residential sorting also cannot fully explain the 2019 results (Appendix Table 9).

Appendix

A. Call to Participate

Dear {CITY} rental property owner,

We would like to invite you to participate in a brief online survey about how the COVID-19 pandemic is impacting your rental business. We want to hear your voice!

When you are ready, please proceed with the following link:

{SURVEY_LINK}

The survey should take 10–15 min to complete. Participation is entirely voluntary and your individual responses will never be shared with the City of {CITY}. Choosing not to participate will in no way affect your relationship with the City, and City officials will not know which owners participated or those who opted not to. For more information about the study, please visit ash.harvard.edu/covid-19-landlord-survey.

After you finish, you will be able to enter for a chance to win a $100 Amazon gift card. Multiple winners will be chosen!

We hope you will participate! It won't take long and it will help the City better serve you and your tenants.

Sincerely,

Dr. Elijah de la Campa, Harvard Kennedy School Bloomberg Harvard City Leadership Initiative on behalf of

The City of {CITY}

Follow the link to opt out of future emails:

${OPT_OUT_LINK}

B. Tables and Figures

Appendix Figs. 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10

Appendix Fig 1 — Landlords’ rental collection prior to and during the pandemic, excluding Los Angeles and Philadelphia landlords. *Notes:* This figure plots landlords’ rental collection rates in 2019 and 2020, excluding Los Angeles and Philadelphia landlords. Rental payment is expressed as a percentage of total rent charged, in a given year, for a landlord's rental portfolio. The number of survey respondents in the sample is 2051. Data come from the COVID-19 landlord survey.

Appendix Fig 2 — Landlords’ rental collection prior to and during the pandemic, among landlords with at least one tenant participating in ERA. *Notes:* This figure plots landlords’ rental collection rates in 2019 and 2020 among the sample of landlords who had at least one tenant receiving emergency rental assistance (ERA) in 2020. Panel A presents results for Los Angeles and Philadelphia landlords (N = 299). Panel B presents results for Akron, Albany, Indianapolis, Minneapolis, Racine, Rochester, San Jose, and Trenton landlords (N = 479). Rental payment is expressed as a percentage of total rent charged, in a given year, for a landlord's rental portfolio. Data come from the COVID-19 landlord survey.

Appendix Fig 3 — Landlords’ rental collection prior to and during the pandemic, for landlords’ individual rental properties. *Notes:* This figure plots landlords’ rental collection rates in 2019 and 2020 at the individual rental properties reported in the survey. Rental payment is expressed as a percentage of total rent charged at that property in a given year. The number of rental properties in the sample is 2513. Data come from the COVID-19 landlord survey.

Appendix Fig 4 — Landlords’ property-level rental collection rates, by neighborhood median income. *Notes:* This figure plots, for 2019 and 2020, the share of landlords reporting less than 90 percent of total rent received at an individual rental property (Panel A) and less than 50 percent of total rent received at an individual rental property (Panel B), according to the neighborhood median income for that property. Properties are classified as “Below median” if they are located in a neighborhood whose median income falls below the median for their city. Neighborhoods are classified according to census block groups (CBGs). 46.5 percent of properties are located in a neighborhood with an above-median household income. See Appendix Table 1 for each city's median household income. Models include city fixed effects. The number of rental properties in the sample is 2428. Heteroskedastic-robust confidence intervals are reported. Data come from the COVID-19 landlord survey and 2018 ACS.

Appendix Fig 5 — Landlords’ 2020 property-level business practices (y-axis) and neighborhood share of non-white residents (x-axis), conditional on landlords’ demographic and business characteristics. *Notes:* This figure presents binned scatter plots of landlords’ 2020 property-level rental business practices versus the neighborhood share of non-white residents (for the property), conditional on landlords’ demographics and business characteristics. For each plot, the share of landlords reporting the practice is reported on the y-axis while the neighborhood share of non-white residents is presented on the x-axis. A neighborhood's share of non-white residents is defined as the sum of individuals who identify as Black, Hispanic, Asian, Native American, multiracial and/or other races. To construct these plots, we first demean both landlords’ rental business practices and neighborhood share of non-white residents by city; 2020 rent collection; and the set of indicators for landlords’ demographics, business details, and portfolio size reported in Fig. 2. We then divide the observations into 20 equal-sized groups (vigintiles) based on neighborhood racial composition and plot the share of landlords pursuing the indicated rental practice within each bin. The solid lines show the best linear fit estimated on the underlying micro data using OLS regression, and Table 8 presents these regression estimates. “Grant rent exten.” indicates rental extensions and/or putting tenants on repayment plans. “Forgive rent” indicates rental forgiveness (either in full or a portion). “Charge rent fees” indicates charging fees for late rent. “Increase rents” indicates increases to monthly rents. “Decrease rents” indicates decreases to monthly rents. “Evict tenants” indicates the commencement of eviction procedures (and potentially, the conclusion). “Miss payments” indicates missed mortgage, property tax, and/or utility payments. “Defer maintenance” indicates delayed property repairs or maintenance. “List prop. for sale” indicates the property was listed for sale. The number of rental properties for each plot is 2402. Data come from the COVID-19 landlord survey and 2018 ACS.

Appendix Fig 6 — Landlords’ 2020 property-level business practices (y-axis) and neighborhood share of non-white residents (x-axis), by landlord race. *Notes:* This figure presents binned scatter plots of landlords’ 2020 property-level rental business practices versus the neighborhood share of non-white residents (for the property), separately for white (blue) and non-white (red) landlords. For each plot, the share of landlords reporting the practice is reported on the y-axis while the neighborhood share of non-white residents is presented on the x-axis. A neighborhood's share of non-white residents is defined as the sum of individuals who identify as Black, Hispanic, Asian, Native American, multiracial and/or other races. To construct these plots, we first demean both white and non-white landlords’ rental business practices and neighborhood share of non-white residents by city; 2020 rent collection; and the set of indicators for landlords’ demographics, business details, and portfolio size reported in Fig. 2 (with the exception of landlord race), separately for each race. We then divide the observations into 20 equal-sized groups (vigintiles) based on neighborhood racial composition and plot the share of landlords pursuing the indicated rental practice within each bin. The solid lines show the best linear fit estimated on the underlying micro data using OLS regression. “Grant rent exten.” indicates rental extensions and/or putting tenants on repayment plans. “Forgive rent” indicates rental forgiveness (either in full or a portion). “Charge rent fees” indicates charging fees for late rent. “Increase rents” indicates increases to monthly rents. “Decrease rents” indicates decreases to monthly rents. “Evict tenants” indicates the commencement of eviction procedures (and potentially, the conclusion). “Miss payments” indicates missed mortgage, property tax, and/or utility payments. “Defer maintenance” indicates delayed property repairs or maintenance. “List prop. for sale” indicates the property was listed for sale. The number of rental properties for each plot is 2402. Data come from the COVID-19 landlord survey and 2018 ACS.

Appendix Fig 7 — Landlords’ 2020 property-level business practices (y-axis) and neighborhood share of non-white residents (x-axis), by landlord portfolio size. *Notes:* This figure presents binned scatter plots of landlords’ 2020 property-level rental business practices versus the neighborhood share of non-white residents (for the property), separately for small (blue), mid-sized (red), and large (orange) landlords. For each plot, the share of landlords reporting the practice is reported on the y-axis while the neighborhood share of non-white residents is presented on the x-axis. A neighborhood's share of non-white residents is defined as the sum of individuals who identify as Black, Hispanic, Asian, Native American, multiracial and/or other races. To construct these plots, we first demean both small, mid-sized, and large landlords’ rental business practices and neighborhood share of non-white residents by city; 2020 rent collection; and the set of indicators for landlords’ demographics and business details reported in Fig. 2 (with the exception of portfolio size), separately for each portfolio size grouping. We then divide the observations into 20 equal-sized groups (vigintiles) based on neighborhood racial composition and plot the share of landlords pursuing the indicated rental practice within each bin. The solid lines show the best linear fit estimated on the underlying micro data using OLS regression. “Grant rent exten.” indicates rental extensions and/or putting tenants on repayment plans. “Forgive rent” indicates rental forgiveness (either in full or a portion). “Charge rent fees” indicates charging fees for late rent. “Increase rents” indicates increases to monthly rents. “Decrease rents” indicates decreases to monthly rents. “Evict tenants” indicates the commencement of eviction procedures (and potentially, the conclusion). “Miss payments” indicates missed mortgage, property tax, and/or utility payments. “Defer maintenance” indicates delayed property repairs or maintenance. “List prop. for sale” indicates the property was listed for sale. The number of rental properties for each plot is 2380. Data come from the COVID-19 landlord survey and 2018 ACS.

Appendix Fig 9 — Landlords’ 2020 property-level business practices (y-axis) and neighborhood income (x-axis). *Notes:* This figure presents nine binned scatter plots of landlords’ 2020 property-level rental business practices versus the neighborhood median income (for the property). For each plot, the share of landlords reporting the practice is reported on the y-axis while the natural log of neighborhood median income is presented on the x-axis. To construct these plots, we first demean both landlords’ rental business practices and neighborhood median income by city, average rent collection, and neighborhood population. We then divide the observations into 20 equal-sized groups (vigintiles) based on the natural log of neighborhood median income and plot the share of landlords pursuing the indicated rental practice within each bin. The solid lines show the best linear fit estimated on the underlying micro data using OLS regression, and Appendix Table 7 presents these regression estimates. “Grant rent exten.” indicates rental extensions and/or putting tenants on repayment plans. “Forgive rent” indicates rental forgiveness (either in full or a portion). “Charge rent fees” indicates charging fees for late rent. “Increase rents” indicates increases to monthly rents. “Decrease rents” indicates decreases to monthly rents. “Evict tenants” indicates the commencement of eviction procedures (and potentially, the conclusion). “Miss payments” indicates missed mortgage, property tax, and/or utility payments. “Defer maintenance” indicates delayed property repairs or maintenance. “List prop. for sale” indicates the property was listed for sale. The number of rental properties for each plot is 2322. Data come from the COVID-19 landlord survey and 2018 ACS.

Appendix Fig 10 — Landlords’ 2020 property-level business practices (y-axis) and yearly rent appreciation (x-axis). *Notes:* This figure presents nine binned scatter plots of landlords’ 2020 property-level rental business practices versus the three-year average, from 2015 to 2018, in the year-over-year change in neighborhood median gross rent (for the property). For each plot, the share of landlords reporting the practice is reported on the y-axis while the neighborhood average gross rent appreciation, measured in $100s, is presented on the x-axis. To construct these plots, we first demean both landlords’ rental business practices and neighborhood average gross rent appreciation by city, average rent collection, and neighborhood population. We then divide the observations into 20 equal-sized groups (vigintiles) based on the average gross rent appreciation and plot the share of landlords pursuing the indicated rental practice within each bin. The solid lines show the best linear fit estimated on the underlying micro data using OLS regression, and Appendix Table 8 presents these regression estimates. “Grant rent exten.” indicates rental extensions and/or putting tenants on repayment plans. “Forgive rent” indicates rental forgiveness (either in full or a portion). “Charge rent fees” indicates charging fees for late rent. “Increase rents” indicates increases to monthly rents. “Decrease Rents” indicates decreases to monthly rents. “Evict tenants” indicates the commencement of eviction procedures (and potentially, the conclusion). “Miss payments” indicates missed mortgage, property tax, and/or utility payments. “Defer maintenance” indicates delayed property repairs or maintenance. “List prop. for sale” indicates the property was listed for sale. The number of rental properties for each plot is 2322. Data come from the COVID-19 landlord survey and 2015–2018 ACS.

Appendix Tables 1 , 2 , 3 , 4 , 5 , 6, 7 , 8 , 9 .

Appendix Table 1.

Descriptive statistics of residents and renter households in survey cities.

	Akron	Albany	Indianapolis	Los Angeles	Minneapolis	Philadelphia	Racine	Rochester	San Jose	Trenton
Panel A: Resident Characteristics
White	58.5	49.9	55.2	28.5	59.8	34.6	49.9	36.6	26	12.9
Black	29.9	27.9	28.1	8.6	19.1	41.0	22.2	38.2	2.8	48.4
Hispanic	2.5	10.2	10.2	48.6	9.6	14.5	23.1	18.3	32	36.4
Asian	4.6	6.9	3.2	11.7	6.1	7.1	0.9	3.2	35.6	1.1
Other race	4.5	5.0	3.3	2.8	5.5	2.8	3.9	3.8	3.6	1.2
Median age (y)	36.7	31.2	34.2	35.4	32.1	34.3	34	31.9	36.5	33.9
N Residents (100,000 s)	2.0	1.0	8.6	39.7	4.2	15.8	0.8	2.1	10.3	0.8
Panel B: Renter Household Characteristics
Renter-occupied (among all households)	49.4	63.2	46.7	63.2	52.7	47.0	49.0	63.7	42.8	63.4
Reside in 1-unit property	44.7	9.0	38.2	21.1	15.3	40.8	33.6	30.2	32.9	45.8
Reside in 2–4 unit property	20.5	57	13.6	12	18.1	25	33.1	34.6	13.3	20.6
Reside in 5–9 unit property	8.9	12.3	19.0	12.9	6.3	7.4	7.9	9.2	9.3	7.2
Reside in 10–19 unit property	8.6	6.1	12.8	14.2	12.8	4.5	7.2	5.0	10.1	4.5
Reside in 20+ unit property	17.0	15.5	15.4	39.4	47.3	21.9	17.7	20.6	33.4	22
Median income ($)	25,598	30,972	31,299	43,015	37,155	31,508	28,900	24,043	72,825	24,355
Cost-burdened	47.7	53.5	49.0	57.3	46.3	50.2	50.8	57.0	50.2	56.3
Median gross rent ($)	735	951	865	1376	985	1007	824	831	1970	1029
Median age of housing structure (y)	63	80	47	55	59	70	68	77	43	–
N Renter Households (10,000 s)	4.2	2.6	15.6	86.8	9.2	28.0	1.5	5.5	13.8	1.7

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Notes: This table reports descriptive characteristics of residents and renter households separately for the ten COVID-19 landlord survey cities. Data come from the ACS 2018 five-year sample. Unless otherwise indicated, the variables above are expressed as percentages. Categorical variables may not sum to 100 due to rounding. Cost-burdened renters are defined as those who spend 30 percent or more of their yearly income on yearly rent.

Appendix Table 2.

Descriptive statistics of survey city rental properties, by rental registry compliance.

	Not on registry	On registry
Panel A: Property Characteristics
Property units (n)	1.5	2.8
Property age (y)	78.2	92.9
Missing property age	2.7	1.4
LLC or LLP/LP owner	24.8	30.6
Per-unit assessed property value ($)	119,301	97,402
Missing per-unit assessed property value	1.0	0.4
Per-unit residential area (sq. ft.)	1614	1366
Missing per-unit residential area	14.0	28.8
Panel B: Neighborhood Characteristics
Residents of color	48.1	46.7
Median household income ($)	47,541	44,0230
Median gross rent ($)	870	926

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Notes: This table reports descriptive means for all rental registry eligible rental properties in Akron, Albany, Indianapolis, Minneapolis, Racine, Rochester, San Jose, and Trenton. In Rochester, owner-occupied two-family rental properties are exempt from the rental registry, though because we cannot identify these properties, they are included in the eligible sample. In San Jose, only properties built before 1979 with three or more rental units are required to register with the city and thus included in the eligible sample. Unless stated otherwise, the variables above are expressed as percentages. Data on property characteristics come from city administrative records. Data on neighborhood characteristics come from the ACS 2018 five-year sample.

Appendix Table 3.

Descriptive statistics of survey respondents, by city.

	Akron	Albany	Indianapolis	Los Angeles	Minneapolis	Philadelphia	Racine	Rochester	San Jose	Trenton
Male	63.9	75.8	65.5	51.6	60.4	56.7	54.3	63.8	61.5	68.7
Missing gender	12.0	16.7	23.2	25.8	16.4	23.1	18.6	14.6	32.2	27.2
White	78.4	76.8	74.9	50.8	80.2	42.9	83.4	57.4	47.3	39.6
Black	12.5	8.1	9.8	14.1	3.7	27.2	8.3	20.6	0.9	31.1
Hispanic	2.2	5.1	3.1	18.3	3.1	11.0	3.4	4.5	11.5	6.6
Asian	1.3	5.1	4.7	8.9	6.5	8.7	2.1	6.5	31.4	15.1
Missing race	10.1	13.2	20.3	23.0	15.4	18.6	15.7	12.9	26.4	22.1
20–29 Years Old	1.3	2.0	3.3	0.0	3.7	2.7	1.4	1.3	0.9	1.9
30–39 Years Old	11.5	22.0	15.2	7.2	20.4	18.4	11.6	14.5	3.9	15.7
40–49 Years Old	12.8	18.0	19.8	12.4	19.8	24.2	14.3	18.9	11.2	23.1
50–59 Years Old	34.6	19.0	24.8	23.2	21.6	25.4	32.7	25.2	23.3	37.0
60+ Years Old	39.7	39.0	36.9	57.2	34.4	29.3	40.1	40.3	60.8	22.2
Missing age	9.3	12.3	19.2	21.8	13.2	17.9	14.5	10.7	24.4	20.6
Individual owner	80.2	79.8	92.5	–	95.9	–	85.8	76.8	88.2	66.2
Missing ownership structure	0.4	0.0	1.6	100.0	2.1	100.0	1.7	0.6	1.0	4.4
Self-manages rental units	76.5	74.3	60.8	63.9	75.7	77.6	83.5	83.2	63.4	77.5
Missing management structure	4.3	4.4	5.1	6.0	5.2	5.4	8.1	2.8	4.9	5.1
Accepts HCVs	31.6	27.3	13.1	20.4	10.5	23.5	19.6	32.2	33.7	22.5
Missing HCV status	4.3	3.5	4.9	5.2	5.3	4.5	8.1	2.2	5.2	5.1
Owns single-family rentals(s) (SFRs)	96.7	92.5	93.8	53.3	82.8	89.8	95.4	95.2	63.8	92.1
Missing home type	7.4	7.0	10.7	9.3	13.2	9.0	11.6	7.3	13.7	16.2
Small landlord	72.3	67.5	72.2	40.7	73.3	60.9	78.9	68.0	45.7	69.4
Mid-sized landlord	14.5	20.2	12.0	31.1	12.3	17.9	9.4	14.0	34.1	11.2
Missing portfolio size	0.8	0.0	1.6	2.8	1.3	1.6	0.6	0.0	4.6	1.5
N Respondents	258	114	449	248	676	312	172	178	307	136

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Notes: This table reports descriptive statistics for the COVID-19 Landlord Survey respondents, by city. The variables above are expressed as percentages. The omitted category for race is “Other Race.” The omitted category for age is “Under 20 years old.” No respondents in the survey reported being under 20. “Individual owner” indicates an owner who is not incorporated as an LLC or LLP. “Single-family rental” indicates a one- to four-unit rental property. “Small landlord” indicates a landlord who owns between 1 and 5 rental units. “Mid-sized landlord” indicates a landlord who owns between 6 and 19 rental units. The omitted category for landlord size is “large” (owns 20+ rental units). Categorical variables may not sum to 100 due to rounding. Data come from city administrative records and the COVID-19 landlord survey.

Appendix Table 4.

Descriptive statistics of survey respondents, by eviction history.

	No past evictions	2019 evictions only	2020 evictions only	Evictions 2019 and 2020
Male	58.3	72.6	73.9	70.6
Missing gender	16.1	9.2	27.6	9.5
White	69.7	64.8	54.4	61.7
Black	9.9	7.9	19.7	17.4
Hispanic	5.8	6.7	4.1	4.7
Asian	8.1	8.5	11.6	7.4
Missing race	13.2	4.6	25.0	5.7
20–29 years old	1.8	1.2	0.7	0.7
30–39 years old	13.3	12.4	22.1	17.0
40–49 years old	18.4	14.8	18.8	20.9
50–59 years old	24.7	33.7	26.2	34.0
60+ years old	41.7	37.9	32.2	27.5
Missing age	11.8	2.3	24.0	3.2
Individual owner	90.7	80.4	76.4	67.2
Missing ownership structure	19.9	17.3	24.5	22.8
Self-manages rental units	72.9	59.3	58.5	63.1
Missing property manager	0.8	0.6	0.5	0.6
Accepts HCVs	17.4	42.2	35.9	39.2
Missing HCV	0.6	0.0	0.5	0.0
Owns single-family rental(s) (SFRs)	84.0	80.2	87.6	91.4
Missing home type	3.1	3.5	1.0	4.4
Small landlord	77.1	47.7	59.2	32.7
Mid-sized landlord	16.3	27.9	20.9	28.8
Missing portfolio size	1.0	0.6	0.0	1.3
N Respondents	1877	173	196	158

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Notes: This table reports descriptive statistics for the COVID-19 landlord survey respondents according to the number of years in which landlords have reported evicting one or more tenants. The variables above are expressed as percentages. The omitted category for race is “Other Race.” The omitted category for age is “Under 20 years old.” No respondents in the survey reported being under 20. “Individual owner” indicates an owner who is not incorporated as an LLC or LLP. “Single-family rental” indicates a one- to four-unit rental property. “Small landlord” indicates a landlord who owns between 1 and 5 rental units. “Mid-sized landlord” indicates a landlord who owns between 6 and 19 rental units. The omitted category for landlord size is “large” (owns 20+ rental units). Categorical variables may not sum to 100 due to rounding. Survey data come from city administrative records and the COVID-19 landlord survey.

Appendix Table 5.

Relationship between rental collection and business practices, excluding Los Angeles and Philadelphia landlords.

	Grant rent ext.	Forgive rent	Charge rent fee	Inc. rents	Dec. rents	Evict tenants	Miss payments	Defer maint.	List props. for sale
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
< 90% Rent Received	0.090***	0.030*	0.074**	−0.112***	0.058***	0.135***	0.071***	0.091***	0.025
	(0.032)	(0.017)	(0.034)	(0.026)	(0.018)	(0.032)	(0.023)	(0.025)	(0.017)
2020	0.190***	0.113***	−0.132***	−0.193***	0.103***	−0.071***	0.042***	0.154***	0.048***
	(0.015)	(0.010)	(0.012)	(0.013)	(0.009)	(0.010)	(0.008)	(0.012)	(0.008)
< 90% Rent Received*2020	0.245***	0.143***	0.030	0.093***	0.054**	0.113***	0.203***	0.198***	0.110***
	(0.038)	(0.026)	(0.038)	(0.029)	(0.025)	(0.037)	(0.030)	(0.033)	(0.024)
N Landlord-Years	3892	3892	3892	3892	3892	3892	3892	3892	3892

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Notes: This table reports OLS estimates of the relationship between landlords’ business practices and rental collection, prior to and during the pandemic, excluding Los Angeles and Philadelphia landlords, where respondents were invited to participate according to whether they had at least one tenant participate in emergency rental assistance (ERA). Each column presents results from a separate OLS regression, where the indicated business practice is the dependent variable. “Grant Rent Ext.” indicates rental extensions and/or putting tenants on repayment plans. “Forgive Rent” indicates rental forgiveness (either in full or a portion). “Charge Rent Fee” indicates charging fees for late rent. “Inc. Rents” indicates increases to monthly rents. “Dec. Rents” indicates decreases to monthly rents. “Evict Tenants” indicates the commencement of eviction procedures (and potentially, the conclusion). “Miss Payments” indicates missed mortgage, property tax, and/or utility payments. “Defer Maint.” indicates delayed property repairs or maintenance. “List Props. For Sale” indicates one or more properties were listed for sale. Landlords could choose multiple actions. Models include city fixed effects. Heteroskedastic-robust standard errors are reported in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1. Data come from the COVID-19 landlord survey.

Appendix Table 6.

Descriptive statistics of survey respondents according to whether they own at least one property in a majority non-white neighborhood.

	Owns at least one property in majority non-white N'hood		Does not own any properties in majority non-white N'hood
	N	Mean	N	Mean
Male	1190	59.9	1072	62.3
Missing gender	1331	10.6	1182	9.3
White	1222	54.8	1110	83.8
Black	1222	19.4	1110	2.6
Hispanic	1222	7.9	1110	3.1
Asian	1222	10.5	1110	4.7
Missing race	1331	8.2	1182	62.3
20–29 years old	1245	1.5	1127	1.5
30–39 years old	1245	14.1	1127	14.1
40–49 years old	1245	16.8	1127	18.6
50–59 years old	1245	25.7	1127	25.7
60+ years old	1245	41.9	1127	40.0
Missing age	1331	6.5	1182	4.7
Individual owner	965	80.2	1046	83.7
Missing ownership structure	1331	27.5	1182	11.5
Self-manages rental units	1302	69.7	1156	71.7
Missing property manager	1331	2.2	1182	2.2
Accepts HCVs	1309	29.4	1157	12.5
Missing HCV	1331	1.7	1182	2.1
Owns single-family rental(s) (SFRs)	1295	84.1	1150	85.6
Missing home type	1331	2.7	1182	2.7
Small landlord	1319	70.6	1170	80.1
Mid-sized landlord	1319	18.4	1170	12.7
Missing portfolio size	1331	0.9	1182	1.0

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Notes: This table reports descriptive statistics for the COVID-19 landlord survey respondents according to whether they own at least one property in a majority non-white neighborhood. A neighborhood's share of non-white residents is defined as the sum of individuals who identify as Black, Hispanic, Asian, Native American, multiracial and/or other races. The variables above are expressed as percentages. The omitted category for race is “Other Race.” The omitted category for age is “Under 20 years old.” No respondents in the survey reported being under 20. “Individual owner” indicates an owner who is not incorporated as an LLC or LLP. “Single-family rental” indicates a one- to four-unit rental property. “Small landlord” indicates a landlord who owns between 1 and 5 rental units. “Mid-sized landlord” indicates a landlord who owns between 6 and 19 rental units. The omitted category for landlord size is “large” (owns 20+ rental units). Categorical variables may not sum to 100 due to rounding. Survey data come from city administrative records and the COVID-19 landlord survey and 2018 ACS.

Appendix Table 7.

Relationship between landlords’ 2020 property-level rental business practices and neighborhood median income.

	Grant rent ext.	Forgive rent	Charge rent fee	Inc. rents	Dec. rents	Evict tenants	Miss payments	Defer maint.	List prop. for sale
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
Unconditional 2020 Mean	0.37	0.15	0.08	0.04	0.12	0.09	0.14	0.26	0.07
Panel A: Baseline Model
Log Median Income	−0.027	−0.010	−0.016	0.004	0.005	−0.014	−0.027**	−0.016	−0.004
	(0.020)	(0.015)	(0.011)	(0.008)	(0.015)	(0.010)	(0.013)	(0.019)	(0.011)
Panel B: Landlord Controls
Log Median Income	−0.019	−0.015	−0.013	0.001	0.001	−0.009	−0.019	−0.011	−0.006
	−0.02	−0.016	−0.011	−0.008	−0.015	−0.01	−0.013	−0.019	−0.011
Panel C: Neighborhood Controls
Log Median Income	−0.019	−0.051***	−0.004	0.005	−0.035**	0.01	0.003	−0.01	−0.005
	−0.023	−0.019	−0.014	−0.01	−0.017	−0.013	−0.017	−0.023	−0.014
Panel D: Landlord and Neighborhood Controls
Log Median Income	−0.009	−0.047**	0.001	0.006	−0.027	0.014	0.003	−0.005	−0.005
	−0.023	−0.019	−0.014	−0.01	−0.017	−0.013	−0.017	−0.023	−0.014
N Rental Properties	2322	2322	2322	2322	2322	2322	2322	2322	2322

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Notes: This table reports OLS regression estimates of the relationship between landlords’ 2020 property-level rental business practices and the neighborhood median income. Each column presents results from a separate OLS regression of a residualized version of the indicated business practice on a residualized version of neighborhood median income. In Panel A, we residualize on 2020 rent collection, neighborhood population, and city fixed effects. In Panel B, we residualize on the controls from Panel A, as well as the set of indicators for landlords’ demographics, business details, and portfolio size reported in Fig. 2. In Panel C, we residualize on the controls from Panel A, as well as the neighborhood share of non-white residents and age distribution. In Panel D, we residualize on all controls from Panels A, B, and C. “Grant Rent Ext.” indicates rental extensions and/or putting tenants on repayment plans. “Forgive Rent” indicates rental forgiveness (either in full or a portion). “Charge Rent Fee” indicates charging fees for late rent. “Inc. Rents” indicates increases to monthly rents. “Dec. Rents” indicates decreases to monthly rents. “Evict Tenants” indicates the commencement of eviction procedures (and potentially, the conclusion). “Miss Payments” indicates missed mortgage, property tax, and/or utility payments. “Defer Maint.” indicates delayed property repairs or maintenance. “List Prop. For Sale” indicates the property was listed for sale. Landlords could choose multiple actions. Heteroskedastic-robust standard errors are reported in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1. Data come from the COVID-19 landlord survey and 2018 ACS.

Appendix Table 8.

Relationship between landlords’ 2020 property-level rental business practices and yearly rent appreciation.

	Grant rent ext.	Forgive rent	Charge rent fee	Inc. rents	Dec. rents	Evict tenants	Miss payments	Defer maint.	List prop. for sale
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
Unconditional 2020 Mean	0.37	0.15	0.08	0.04	0.12	0.09	0.14	0.26	0.07
Panel A: Baseline Model
3-Yr Avg. Rent Appreciation	−0.006	0.002	−0.010	0.004	0.018	−0.002	−0.001	0.000	0.014
	(0.017)	(0.014)	(0.010)	(0.008)	(0.013)	(0.009)	(0.013)	(0.016)	(0.010)
Panel B: Landlord Controls
3-Yr Avg. Rent Appreciation	−0.009	−0.001	−0.010	0.003	0.015	−0.002	0.001	0.003	0.013
	(0.017)	(0.014)	(0.010)	(0.008)	(0.012)	(0.009)	(0.012)	(0.016)	(0.010)
Panel C: Neighborhood Controls
3-Yr Avg. Rent Appreciation	0.002	0.001	−0.009	0.005	0.015	−0.002	−0.002	−0.002	0.016*
	(0.017)	(0.014)	(0.010)	(0.008)	(0.013)	(0.010)	(0.013)	(0.017)	(0.010)
Panel D: Landlord and Neighborhood Controls
3-Yr Avg. Rent Appreciation	−0.002	−0.001	−0.008	0.005	0.013	−0.002	−0.000	0.001	0.016
	(0.017)	(0.014)	(0.010)	(0.008)	(0.012)	(0.010)	(0.013)	(0.016)	(0.010)
N Rental Properties	2203	2203	2203	2203	2203	2203	2203	2203	2203

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Notes: This table reports OLS regression estimates of the relationship between landlords’ 2020 property-level rental business practices and the three-year average, from 2015 to 2018, in the year-over-year change in neighborhood median gross rent (measured in $100s). Each column presents results from a separate OLS regression of a residualized version of the indicated business practice on a residualized version of median gross rent appreciation. In Panel A, we residualize on 2020 rent collection, neighborhood population, and city fixed effects. In Panel B, we residualize on the controls from Panel A, as well as the set of indicators for landlords’ demographics, business details, and portfolio size reported in Fig. 2. In Panel C, we residualize on the controls from Panel A, as well as the neighborhood median income and age distribution. In Panel D, we residualize on all controls from Panels A, B, and C. “Grant Rent Ext.” indicates rental extensions and/or putting tenants on repayment plans. “Forgive Rent” indicates rental forgiveness (either in full or a portion). “Charge Rent Fee” indicates charging fees for late rent. “Inc. Rents” indicates increases to monthly rents. “Dec. Rents” indicates decreases to monthly rents. “Evict Tenants” indicates the commencement of eviction procedures (and potentially, the conclusion). “Miss Payments” indicates missed mortgage, property tax, and/or utility payments. “Defer Maint.” indicates delayed property repairs or maintenance. “List Prop. For Sale” indicates the property was listed for sale. Landlords could choose multiple actions. Heteroskedastic-robust standard errors are reported in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1. Data come from the COVID-19 Landlord Survey and 2018 ACS.

Appendix Table 9.

Relationship between landlords’ 2019 property-level rental business practices and neighborhood share of non-white residents.

	Grant rent ext.	Forgive rent	Charge rent fee	Inc. rents	Dec. rents	Evict tenants	Miss payments	Defer maint.	List prop. for sale
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)
Unconditional 2019 Mean	0.166	0.032	0.247	0.286	0.022	0.145	0.040	0.054	0.030
Panel A: Baseline Model
Share Non-White Residents	0.059**	0.002	0.095***	−0.118***	−0.014	0.125***	0.044***	−0.008	−0.016
	(0.029)	(0.016)	(0.034)	(0.035)	(0.011)	(0.028)	(0.016)	(0.019)	(0.015)
Panel B: Landlord Controls
Share Non-White Residents	0.033	0.009	0.076**	−0.108***	−0.009	0.114***	0.033**	−0.001	−0.014
	(0.030)	(0.016)	(0.034)	(0.035)	(0.011)	(0.027)	(0.016)	(0.019)	(0.015)
Panel C: Neighborhood Controls
Share Non-White Residents	0.011	−0.014	0.097**	−0.096**	−0.014	0.103***	0.044**	−0.009	−0.010
	(0.038)	(0.021)	(0.042)	(0.043)	(0.014)	(0.036)	(0.020)	(0.023)	(0.021)
Panel D: Landlord and Neighborhood Controls
Share Non-White Residents	−0.007	−0.008	0.098**	−0.065	−0.009	0.113***	0.032*	−0.001	−0.008
	(0.038)	(0.021)	(0.042)	(0.042)	(0.013)	(0.034)	(0.019)	(0.023)	(0.021)
N Rental Properties	2402	2402	2402	2402	2402	2402	2402	2402	2402

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Notes: This table reports OLS regression estimates of the relationship between landlords’ 2019 property-level rental business practices and the neighborhood share of non-white residents. Each column presents results from a separate OLS regression of a residualized version of the indicated business practice on a residualized version of the neighborhood share of non-white residents. In Panel A, we residualize on 2019 rent collection, neighborhood population, and city fixed effects. In Panel B, we residualize on the controls from Panel A, as well as the set of indicators for landlords’ demographics, business details, and portfolio size reported in Fig. 2. In Panel C, we residualize on the controls from Panel A, as well as the neighborhood median income and age distribution. In Panel D, we residualize on all controls from Panels A, B, and C. A neighborhood's share of non-white residents is defined as the sum of individuals who identify as Black, Hispanic, Asian, Native American, multiracial and/or other races. “Grant Rent Ext.” indicates rental extensions and/or putting tenants on repayment plans. “Forgive Rent” indicates rental forgiveness (either in full or a portion). “Charge Rent Fee” indicates charging fees for late rent. “Inc. Rents” indicates increases to monthly rents. “Dec. Rents” indicates decreases to monthly rents. “Evict Tenants” indicates the commencement of eviction procedures (and potentially, the conclusion). “Miss Payments” indicates missed mortgage, property tax, and/or utility payments. “Defer Maint.” indicates delayed property repairs or maintenance. “List Prop. For Sale” indicates the property was listed for sale. Landlords could choose multiple actions. Heteroskedastic-robust standard errors are reported in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.1. Data come from the COVID-19 landlord survey and 2018 ACS.

Data Availability

The authors do not have permission to share data.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The authors do not have permission to share data.

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PERMALINK

Landlords’ rental businesses before and after the COVID-19 pandemic: Evidence from a National Cross-Site Survey✰

Elijah A de la Campa

Vincent J Reina

Abstract

1. Introduction

2. Survey implementation

2.1. Design and setting

Table 1.

2.2. Outreach and response

Table 2.

2.3. Respondent characteristics and representativeness

Table 3.

3. Landlords’ rental collection before and after the pandemic

3.1. Landlords’ rent collection decreased significantly in 2020

Fig. 1.

3.2. Non-white and small landlords collect less in rent relative to their white and larger landlord counterparts while HCVs generally insulate landlords against rental non-payment

Fig. 2.

3.3. Landlords’ collection rates were down across rental markets

Fig. 3.

3.4. Renters in socially and economically vulnerable communities are further behind on rent

Fig. 4.

4. Landlords’ business practices before and after the pandemic

4.1. Landlords have changed their business practices during the pandemic

Fig. 5.

4.2. Decreased rent collection cannot fully explain landlords’ changing rental business practices

Table 4.

4.3. Landlords’ business practices vary according to their portfolio and demographic characteristics even after accounting for group-level differences in rent collection

Table 5.

4.4. Regional variation in pandemic policies contributed to observed cross-city variation in landlord practices

Table 6.

Table 7.

4.5. Landlords’ responses to the pandemic may be increasing housing instability in communities of color

Fig. 6.

Table 8.

Appendix Fig. 8.

Fig. 7.

5. Conclusion

Funding

Declaration of Competing Interest

Footnotes

Appendix

A. Call to Participate

B. Tables and Figures

Appendix Fig. 1.

Appendix Fig. 2.

Appendix Fig. 3.

Appendix Fig. 4.

Appendix Fig. 5.

Appendix Fig. 6.

Appendix Fig. 7.

Appendix Fig. 9.

Appendix Fig. 10.

Appendix Table 1.

Appendix Table 2.

Appendix Table 3.

Appendix Table 4.

Appendix Table 5.

Appendix Table 6.

Appendix Table 7.

Appendix Table 8.

Appendix Table 9.

Data Availability

References

Associated Data

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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Landlords’ rental businesses before and after the COVID-19 pandemic: Evidence from a National Cross-Site Survey^✰