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Published in final edited form as: Soc Sci Med. 2021 Jun 5;282:114100. doi: 10.1016/j.socscimed.2021.114100

Does Rental Assistance Improve Mental Health? Insights from a Longitudinal Cohort Study

Whitney Denary a, Andrew Fenelon b, Penelope Schlesinger a, Jonathan Purtle c, Kim M Blankenship d, Danya E Keene a
PMCID: PMC8299474  NIHMSID: NIHMS1711851  PMID: 34144434

Abstract

Almost half of renters in the United States are rent-burdened, meaning that they pay more than 30% of their income toward housing costs. Rental assistance through programs administered by the U.S. Department of Housing and Urban Development, alleviates these financial strains for around 5 million households. However, due to budgetary constraints, fewer than one in four eligible households actually receive this assistance and waitlists average two years nationally. Using longitudinal data from a cohort of 400 low-income adults living in New Haven, CT, this paper investigates how access to rental assistance affects mental health through two analytical methods that address selection into rental assistance. First, we performed a cross-sectional analysis to identify how psychological distress differs among those receiving and those on a waitlist for rental assistance. Second, we used a within-person fixed-effects analysis to compare changes in individuals following entry into rental assistance. We find that those receiving rental assistance report significantly less psychological distress than those on waiting lists and that transitions into rental assistance are associated with statistically non-significant decreases in psychological distress. Our findings suggest that expanding rental assistance may be one potential step toward improving the mental health of low-income individuals in the United States.

Keywords: Rental Assistance, Psychological Distress, Mental Health, Housing Affordability, Housing Policy

Introduction

The U.S. Department of Housing and Urban Development (HUD) considers a household that pays rent more than 30% of their income to be rent-burdened and those that pay more than 50% to be severely rent-burdened (Center on Budget and Policy Priorities, 2019). Rent burden is prevalent in the United States due to rising costs of housing relative to income (Joint Center for Housing Studies, 2018). Nearly half (46%) of all renting households are rent-burdened and of these about half are severely rent-burdened (24% of all renting households). The COVID-19 pandemic is only expected to worsen rent burden (Benfer et al., 2021). The Census Bureau’s Household Pulse Survey showed that in 2020, over half of renter households lost at least some employment income and one in four households making less than $25,000 reported being behind on rent (Joint Center for Housing Studies, 2020). A growing body of research suggests that the affordable rental crisis and resultant rent-burdens have significant implications for individual and population health (Bovell-Ammon et al., 2021; Pollack et al., 2010; Sandel & Desmond, 2017; Swope & Hernandez, 2019).

This lack of access to affordable rental housing is likely to have particular implications for mental health. Individuals with high rent burdens are more likely to exhibit poor general mental health and demonstrate higher rates of depression and anxiety (Baker et al., 2020; Bentley et al., 2019; Mason et al., 2013). In cost-burdened households, tenants often make difficult tradeoffs between basic necessities and a roof over their heads (Desmond, 2018; Kushel et al., 2006; Martin et al., 2019). These tradeoffs may cause renters to forgo medications, treatments, and other health resources that are important to their mental health (Meltzer & Schwartz, 2016; Stahre et al., 2015). Furthermore, qualitative work documents how rental cost-burdens contribute to psychosocial stress and sleep disruptions that can adversely affect mental health (Keene et al., 2018a). Additionally, households facing limited affordable housing options may be forced to settle for poor housing conditions. Research indicates that poor quality housing can affect mental health through stress pathways (Evans et al., 2003; Jones-Rounds et al., 2014). Finally, high cost-burdens often lead to eviction and other forced moves (Desmond, 2018). This instability and displacement are associated with stress, depression, and anxiety (Desmond & Kimbro, 2015) and these moves can disrupt social relationships that are important to psychological well-being (Keene & Geronimus, 2011). Furthermore, during the COVID 19 pandemic, lack of stable and affordable housing may have an exacerbated stressors associated with the virus by increasing risk of exposure (Rosenberg et al 2021).

Rental assistance is an important source of affordable housing for low-income renters that can alleviate rent-burdens and other housing challenges. The US Department of Housing and Urban Development (HUD) provides assistance to approximately 5 million families in the form of rental vouchers and project-based housing (Center on Budget and Policy Priorities, 2019). Other forms of rental assistance are provided through state programs. Rental assistance reduces rent burden by adjusting a household’s rent to fit the recommended 30% of a tenant’s income. By making housing affordable, these programs can protect against eviction and forced moves and increase housing stability and can provide access to better quality housing than recipients would otherwise be able to afford (Ahrens et al., 2016; Kim et al., 2017; Lundberg et al., 2020; Schapiro et al., 2021). Despite the potential benefits of rental assistance for low-income renters, due to supply constraints, fewer than one in four eligible households actually receives this resource and waitlists average two years nationally (Center on Budget and Policy Priorities, 2019). This unmet need for rental assistance creates a unique opportunity to study the effects of affordable housing on mental health by comparing those who receive this resource to similar individuals who are waiting for assistance.

Indeed, some recent studies have examined how rental assistance may alleviate unmet housing needs to improve mental health (Fenelon et al., 2017; Keene et al., 2020; Simon et al., 2017; Slopen et al., 2018). For example, some recent research combines health data from the National Health Interview Survey and HUD administrative data to compare individuals who receive rental assistance to those who go on to receive assistance within two years of when the health data are collected. By comparing rent-assisted households to those that ultimately receive this resource, these studies help to address selection into rental assistance that may confound the relationship with health. This research finds that individuals currently living in public housing had a reduced likelihood of psychological distress compared to those who obtained public housing assistance within the next two years (Fenelon et al., 2017). Studies using the same design find that children who live in public housing experience fewer mental health symptoms than those waiting to enter public housing (Fenelon et al., 2021; Fenelon et al., 2018). Addressing this unmet need for rental assistance may improve population mental health.

This study draws on a longitudinal cohort study to further examine the relationship between rental assistance and mental health. In this work, we use two approaches to address potential selection into rental assistance. First, we compare rent-assisted individuals to individuals who are on rental assistance waiting lists. Second, we apply a within-person fixed-effects analysis to longitudinal data to examine how psychological distress changes as individuals move off the waitlist and begin receiving assistance.

Methods

Our analyses utilize data from he Justice, Housing, and Health Study (JustHouHS) which is located in New Haven, CT. JustHouHS is a survey of low-income residents designed to examine the intersections of the criminal justice system, housing policies, and housing stability with health. Participants were recruited using a combination of flyers posted throughout the community, outreach from service providers, and snow-ball sampling. To be eligible for the study, all participants had to meet the following criteria: be over 18 years of age, a resident of New Haven, and have no household members already enrolled in the study. To obtain a low-income sample, participants at the time of their screening must also have either 1) identified as homeless, 2) resided in a low-income census tract (where more than 20% of residents live below the federal poverty level), 3) received housing or food assistance within the past year, or 4) received Medicaid. Given the study’s interest in the intersection of mass incarceration and health, the sample was stratified to include 200 individuals released from prison or jail in the last year and 200 individuals who were not recently released from prison or jail. To reach a sample size of 200 for each arm of the study, eligible participants were enrolled until their arm of the study was filled. Enrollment took place between September 2017 and March 2018. Longitudinal data collection occurred every six months for two years. The study retention rate was 78% across the four follow up surveys. All data collection and recruitment processes were approved by the Yale Institutional Review Board.

Data collection and sample

Participants (N=400) completed a Qualtrics survey in the study office once every six months. The analyses presented in this paper rely on survey data from the baseline and four follow up waves of the study, collected between October 2017 and April 2020. This includes 1,434 unique observation, or person-waves, of data. The longitudinal component of this analysis only included participants who gained rental assistance during the course of the study (N=84).

Measures

Rental Assistance.

The main independent variable consisted of three mutually exclusive categories regarding current rental assistance status: currently receiving, on a waitlist and not receiving, and neither receiving nor on a waitlist. Previous work suggests that self-report of rental assistance can be unreliable in part because of the multiple and inconsistent terms individuals use to denote participation in assistance programs (Boudreaux et al., 2018). We improve on prior surveys by asking participants about each specific form of rental assistance that is available in New Haven and surrounding communities and by collecting longitudinal information on the process of entering rental assistance.

Psychological Distress.

We used The Kessler 10-item Psychological Distress Scale (K- 10) to measure non-specific psychological distress over the past 30 days (Kessler et al., 2003). The K-10 has been validated against clinical diagnoses of depressive episodes and generalized anxiety disorder (Andrews & Slade, 2001; Furukawa et al., 2003) and has high within person reliability for our sample (alpha = 0.955). Participants were asked to rate how often they had feelings of nervousness, hopelessness, restlessness, worthlessness, and depression. Response options included a 5-level Likert scale, from 1 (none of the time) to 5 (all of the time), which was summed for a total score, ranging from 10 to 50. Because we were particularly interested in how rental assistance might influence severe psychological distress among individuals we dichotomized the K-10 score, with scores of 30 or above indicating a respondent’s likelihood to have a severe mental disorder (Kessler et al., 2003).

Participant Characteristics

We include demographic variables that are associated with rental assistance receipt and may confound the relationship with mental health. In particular, age and female gender are associated with greater access to rental assistance. We also include race and ethnicity. Owing to an ongoing history of racialized housing policy, race is associated with access to affordable housing access and with receipt of rental assistance (Swope & Hernandez, 2019). Race is also associated with mental health (Bratter & Eschbach, 2005; Kessler & Neighbors, 1986). Age was included as a continuous variable; race was categorized as black, white, or other; ethnicity as Hispanic or Latino, regardless of race identification; and gender as either male or not male (including female or nonbinary).

Factors Related to Rental Assistance.

Our analyses also include factors that may affect access to rental assistance, including age, disability status, having children under18 in the household, history of incarceration, and current employment. Some housing is specifically designated for individuals who are older adults, seniors, or are living with a disability (Helms et al., 2017; Hudson, 2010). We assessed disability status via the question “have you ever received social security disability benefits?” Additionally, households with children are sometimes given preferential access to rental assistance (Kathleen Moore, 2016). At each study wave, participants were asked how many of their children under the age of 18 lived with them. This variable was dichotomized to indicate a participant either had no children or at least one living with them at the time of the survey. Having a history of arrest and or incarceration acts as a barrier to receiving rental assistance (Purtle, 2013). To measure this effect, participants were asked at baseline when they had been most recently released from jail or prison and categorized as “never incarcerated,” released “within one year,” “over one year but less than two years ago,” or “over two years ago.” Finally, employment was measured as a dichotomous variable of any versus no employment in the past six months.

Analyses

First, ANOVA tests were conducted to compare characteristics of the rent-assisted, waitlisted, and neither rent-assisted nor waitlisted groups. These characteristics included race, ethnicity, gender, and employment, as well as the factors noted above (previous incarceration, documented disability, and having children at home) which have been indicated as barriers or facilitators to receiving rental assistance.

Second, a generalized estimating equation model (GEE) was used to examine the cross-sectional relationship between rental assistance and K10 across the study waves. We performed this analysis in three models. Model 1 examined psychological distress as a function of rental assistance status. Model 2 investigated this relationship adjusting for demographic characteristics. And model 3 included the additional factors associated with access to rental assistance (disability status, having children in the household, incarceration history, and current employment). We controlled for study wave in all models.

Additionally, we conducted three sensitivity analyses to investigate whether these relationships change after excluding certain housing conditions. First, because supportive housing arrangements may provide unique benefits beyond regular rental assistance, we exclude observations (N=94 person-waves) where a participant receives supportive housing. Second, in order to determine whether any association between rental assistance and mental health was driven solely by homelessness, we conducted analyses excluding observations (N= 211 person-waves) of homeless individuals. While preventing homelessness is one mechanism through which rental assistance may improve mental health, we wanted to consider additional benefits. Furthermore, we wanted to address the possibility that mental health problems may contribute to homelessness (Chamberlain & Johnson, 2013). Finally, we run analyses excluding observations where an (N=138 person-waves) where an individual is staying with some who receives rental assistance. These individuals may receive some benefit of rental assistance, despite not receiving it themselves.

Third, a fixed effects regression model was used to investigate the change in psychological distress for all participants (N=84) who began to receive rental assistance during the study period. The continuous K10 scores from the wave immediately before and immediately after the participants obtained rental assistance were compared in this analysis.

The first two analyses were conducted using SAS software (version 9.2) and the final analysis utilized Stata version 13 (StataCorp LP, College Station, TX).

Results

The study sample included 400 participants at baseline, of which 81 received rental assistance, 100 were waitlisted, and 219 were neither waitlisted nor receiving rental assistance. The rental assistance and waitlist groups were similar in race, ethnicity, disability and children at home status but the rental assistance group had fewer men and recently incarcerated individuals and were more frequently employed, compared to the waitlist group. At baseline, 30% of the total sample was considered to have severe psychological distress (mean K10 = 22.21, SD = 10.51), with 25% in the rent-assisted group (mean K10 = 21.69, SD = 9.17), 34% in the waitlisted group (mean K10 = 24.33, SD = 11.22), and 21.5% in the neither group (mean K10 = 21.43, SD = 10.55). Type of rental assistance included section 8 vouchers (35%), public housing (21%), supportive housing (20%) rental assistance programs (10%), and other housing programs, including Access to Recovery, Ryan White, and Housing Opportunities for Persons with AIDs (HOPWA) (14%).

Table 2 shows findings from cross-sectional models that estimate relationships between rental assistance and mental health throughout the survey. In the unadjusted model (model 1), individuals who were waitlisted were more likely to experience severe psychological distress compared to those receiving rental assistance (OR = 1.59 [1.03, 2.45]). When adjusting for demographics (model 2), the waitlisted group continued to have increased likelihood of severe psychological distress (aOR = 1.67 [1.08, 2.59]). The third model demonstrated similar findings regarding rental assistance (aOR = 1.69 [1.10, 2.62]). No significant difference in psychological distress was found related to the categories of age, gender, ethnicity, disability status, having children under 18 at home, or incarceration history. Psychological distress did differ by race with black individuals less likely to report distress (aOR=0.60 [0.41–0.88]. Employed participants were also less likely to report distress (aOR = 0.37 [0.28, 0.56]). Of note, controlling for demographic characteristics and other factors did not change the difference in odds of psychological distress between the rent assisted and waitlisted groups, lending support to the quasi-experimental nature of this analytic approach.

Table 2.

Rental Assistance and Psychological Distress

Model 1 Model 2 Model 3
OR (95% CI) OR (95% CI) OR (95% CI)
Rental Assistance
 Waitlist 1.59 (1.03, 2.45) 1.67 (1.08, 2.59) 1.64 (1.04, 2.58)
 Neither 0.76 (0.51, 1.13) 0.74 (0.48, 1.14) 0.77 (0.49, 1.21)
 Assisted Reference Reference Reference
Age (years) 0.99 (0.97, 1.00) 0.99 (0.97, 1.00)
Gender
 Male Reference Reference
 Not Male 0.74 (0.50, 1.11) 0.68 (0.44, 1.03)
Race
 Black 0.60 (0.41, 0.88) 0.61 (0.41, 0.89)
 Other 0.67 (0.33, 1.34) 0.07 (0.33, 1.36)
 White Reference Reference
Hispanic 1.44 (0.89, 2.33) 1.52 (0.93, 1.50)
Receiving disability 1.05 (0.65, 1.77)
Having child(ren) under 18 at home 0.77 (0.50, 1.18)
Employed 0.37 (0.28, 0.56)
Time since release from incarceration 1.09 (0.72, 1.66)
 Within the past year 1.22 (0.75, 2.01)
 Over one but less than two years ago 1.38 (0.76, 2.49)
 Over two years ago 1.04 (0.61, 1.77)
 Never Incarcerated Reference
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Note: N = 1434 across all 3 models, all models controlled for study wave

The sensitivity analyses for the cross-sectional investigation of rental assistance and psychological distress across all waves are shown in Table 3. While no longer significant, waitlisted individuals have increased psychological distress compared to rent assisted individuals when the sample excludes individuals in supportive housing (aOR = 1.624 [0.976, 2.702]), individuals who are homeless (aOR = 1.504 [0.930, 2.431]), or individuals who live with somebody who is rent-assisted (aOR = 1.491 [0.913, 2.435]).

Table 3.

Sensitivity Analyses

Excluding participants living in supportive housing (n=1340) Excluding homeless participants (n=1223) Excluding participants living with someone in rent-assisted housing (n=1296)
OR (95% CI) P OR (95% CI) P OR (95% CI) P
Waitlist 1.62 (0.98, 2.70) 0.062 1.49 (0.91, 2.44) 0.110 1.50 (0.93, 2.43) 0.096
Neither 0.77 (0.46, 1.29) 0.315 0.72 (0.45, 1.15) 0.172 0.75 (0.47, 1.20) 0.232
Assisted Reference Reference Reference
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Note: Models adjusted for all covariates included in Table 2, Model 3.

The third analysis examines how psychological distress changes over time for individuals as they enter rental assistance. Eighty-four participants began to receive rental assistance during the study period, including supportive housing (30%), section 8 vouchers (23%), public housing (23%), rental assistance programs (9%), and other housing programs, including Access to Recovery, Ryan White, and HOPWA (15%). This model predicts the K-10 score as a function of rental assistance status including individual fixed effects. Although the relationship was nonsignificant for this sample, the coefficient indicates decreased psychological distress scores among individuals after obtaining rental assistance (β = 0.301 [0.031, 2.901]). This finding is consistent with the cross-sectional analysis above.

Discussion

We examined the association between rental assistance and psychological distress among 400 low-income individuals living in New Haven, CT. We found that those receiving rental assistance were less likely to report severe psychological distress compared to those on rental assistance waiting lists. These relationships remained robust when analyses controlled for age, gender, race, ethnicity, disability status, having children under 18 in the home, or incarceration history. This finding is consistent with prior literature that finds associations between receipt of rental assistance and improved mental health (Fenelon et al., 2017) and with literature that documents mental health costs of rental burdens and other housing challenges (Baker et al., 2020; Bentley et al., 2019; Mason et al., 2013). The greater burden of psychological distress observed among those waiting for rental assistance likely results from the many housing challenges associated with limited access to affordable housing and the subsequent stressors associated with these challenges. Individuals waiting for housing assistance are more likely than rent-assisted individuals to experience cost-burdens, instability, poor housing conditions and homelessness (Jones-Rounds et al., 2014; Kim et al., 2017; Schapiro et al., 2021).

In our study, the observed relationships between mental health and rental assistance were attenuated and no longer statistically significant in more conservative sensitivity analyses that excluded different groups from our models. In the first analysis, we excluded those living in supportive housing from our sample given that this group receives case management resources in addition to reduced rent. While it is possible that these case-management resources contributed to the observed effect for the whole sample, it is also possible that the observed attenuation is due to reduced power. In a second analysis, we exclude those who are homeless. This conservative model seeks to examine the benefit of rental assistance above and beyond its role in addressing homelessness. Our findings suggest that addressing homelessness may be an important pathway through which rental assistance reduces psychological distress. In a third sensitivity analysis, we exclude participants who are on a waitlist but report living with someone else who has rental assistance given that these individuals may partially benefit from rental assistance, leading to an attenuation of group differences. Contrary to our expectations, removing this group decreased the observed association between rental assistance and psychological distress. It is possible that individuals who are doubled up in rent-assisted housing face stressors related to shared housing (Harvey, 2017) or related to housing authority rules that prohibit guests who are not on the lease (Keene et al., 2018b). Further, these individuals may not experience the same stability or financial benefits of rental assistance because they are not really living in their own space. Rental assistance may contribute to improved mental health by addressing these stressors and thus removing these individuals from our sample yields a conservative estimate.

In our longitudinal analysis, we find that participants who recently received rental assistance during the course of the study showed a nonsignificant decrease in psychological distress following this receipt. There are a few possible reasons that a larger and significant effect was not observed. First, our sample was limited to 84 transitions into rental assistance and thus we likely had limited statistical power. Second, there may be differences based on type of rental assistance that we could not observe due to limited sample size. For example, research has demonstrated that public housing has a larger impact on health than housing choice vouchers (Boudreaux et al., 2020; Fenelon et al., 2017). Third, given stressors associated with moving, it is possible that mental health does not improve immediately following receipt of rental assistance and that we did not observe participants long enough to detect an effect. Beyond this, new recipients of voucher-based rental assistance may face stressors in finding a place to rent with their voucher. Individuals receiving vouchers only have 60 days to find a qualifying unit that accepts voucher payments, and passes HUD certification (Dastrup et al., 2018; Tighe et al., 2017). Even in areas with ample affordable rentals, individuals may face challenges identifying landlords that accept vouchers (Cunningham et al., 2018). One US city, Austin, Texas, reported that only 10% of its affordable units accepted vouchers and most of the rentals were located in the higher-poverty areas with underperforming schools and high crime rates (Austin Tenants Council, 2012). Only one in three voucher holders live in states or municipalities that legally protect tenants from discrimination against voucher holders (Bell et al., 2018).

Policy Implications

While rental assistance policies represent one of the largest and most important sources of affordable housing for families whose incomes fall below 50% of the area median, the current supply of assisted units falls well short of demand. Fewer than one in four qualifying families actually receives assistance, suggesting that as many as 15 million families would benefit from rental assistance but are not able to access it (HUD, 2020). Our findings suggest that investments in rental assistance are likely to have significant implications for population well-being. Indeed, recognizing these potential benefits, rental assistance programs have been identified as a strategy to improve population mental health and are recommended by the U.S. Community Preventive Services Taskforce (Community Preventative Services Task Force, 2020; Purtle et al., 2020a). Furthermore, our results suggest that the waiting period to obtain assistance can be harmful for low-income adults’ mental health. Thus, public housing agencies that are successful in shortening tenant wait times or increasing voucher success rates may observe significant improvements in tenant mental health upon entry into rental assistance.

COVID Specific Implications

Findings of the current study underscore the importance of policy interventions that promote housing security, such as expanding rental assistance, as strategies to address mental health inequities that have resulted from the COVID-19 pandemic (Condon et al., 2020; Donnelly & Farina, 2021; Purtle, 2020; Shim, 2020). The populations for whom housing security has been negatively impacted by the pandemic overlap with the populations that have experienced disproportionately adverse mental health impacts. For example, a spring 2020 survey of U.S. adults found that rates of severe psychological distress were substantially higher among respondents in the lowest versus highest income tertile (33% vs 17%) (Keeter, 2020). A summer 2020 survey found that, compared to non-Hispanic Whites, rates of trauma- and stressor-related disorders specifically related to COVID-19 were significantly and 50% higher among Hispanics and 30% higher among non-Hispanic Blacks (Czeisler et al., 2020). A follow up survey suggests that these disparities persisted through at least fall 2020 (Czeisler et al., 2021). The results of the current study—coupled with findings from the larger body of evidence about housing security, mental health, and the social and economic consequences of the COVID- 19 pandemic—indicate that rental assistance programs and other interventions that promote housing security should be included in recovery efforts that aim to ameliorate the mental health effects of the pandemic (Benfer et al., 2021; Purtle et al., 2020b).

Limitations

Our analysis examines mental health immediately before and after receiving rental assistance and does not investigate if or how psychological distress changes after individuals have lived in rental assisted housing for a longer period of time. Our study also does not distinguish between participants who have secured housing versus those that have secured rental assistance only, nor does it have enough participants to investigate how the relationship may differ based on the type of rental assistance. Further research is needed to determine how rental assistance impacts psychological distress over longer periods of time and if this relationship is dependent on the type of assistance.

Conclusion

Our study demonstrates that rent assisted individuals have decreased psychological distress compared to those on waiting lists. While improvement is seen for participants as they initially receive assistance, these findings were not significant, indicating that other factors may influence mental health upon recipient of rental assistance. Our findings emphasize the importance of rental assistance on adult mental health.

Table 1.

Characteristics at Baseline

Receive Rental Assistance (n = 81) On Waitlist and do not receive (n = 100) P Not on waitlist & do not receive (n = 219) P
Mean Age (years) 49.5 45.7 0.020 42.7 <0.001
K-10 Score (mean) 21.68 24.33 0.091 21.42 0.064
% with K-10 Score ≥ 30 20 (25%) 34 (34%) 0.190 47 (22%) 0.057
Race 0.359 0.018
 % Black 58 (72%) 68 (68%) 130 (59%)
 % Other 6 (7%) 14 (14%) 17 (8%)
 % White 17 (21%) 18 (18%) 72 (32%)
% Hispanic 14 (17%) 16 (16%) 0.817 33 (15%) 0.894
% Male 33 (41%) 61 (61%) 0.007 177 (80%) <0.001
% Receiving disability 23 (28%) 20 (20%) 0.187 22 (10%) <0.001
% With child(ren) under 18 at home 15 (19%) 14 (14%) 0.410 23 (11%) 0.176
% Employed 17 (21%) 10 (10%) 0.039 39 (18%) 0.104
Time since release from incarceration <0.001 <0.001
 % Within the past year 9 (5%) 39 (39%) 111 (51%)
 % Over one but less than two years ago 6 (3%) 17 (17%) 28 (7%)
 % Over two years ago 36 (44%) 18 (18%) 36 (16%)
 % Never Incarcerated 30 (37%) 26 (26%) 44 (20%)
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Table 4.

Individual fixed-effects model of psychological distress

Difference in K10 P
Receiving Rental Assistance (n = 84) −1.201 (−3.468, 1.065) 0.297
Not Receiving Rental Assistance Reference
Individual Fixed Effects Yes
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Note: Analysis compared psychological distress between the waves immediately before and after receiving rental assistance

Highlights.

  • Lack of affordable housing is associated with poor mental health

  • We find that rent assistance is associated with reduced psychological distress

  • Addressing unmet need for rental assistance may improve mental health

Acknowledgments

Funding for this study was provided by the National Institute of Mental Health and the National Institute of Allergy and Infectious Diseases (RO1MH110192 Kim M. Blankenship, Ph.D., Principal Investigator). Research reported in this publication was also supported by National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health (R01DK124500 Danya E. Keene, Ph.D., Principal Investigator). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Footnotes

Disclosure Statement: The authors have no conflicts of interest to disclose.

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