Rental Housing: As More Households Rent, the Poorest Face Affordability and Housing Quality Challenges

Since the 2007–2009 financial crisis, growth in the share of renter households has reversed a decades-long trend toward homeownership. This change has underscored concerns about the availability, affordability, and condition of rental housing, especially for low-income households. The federal government subsidizes rents for around 4.4 million households per year, but more households qualify for assistance than receive it.

GAO was asked to provide a comprehensive assessment of the housing market. This report examines trends in the housing market prior to the COVID-19 pandemic and does not account for the profound impact it will likely have on renter households. This report, one of several GAO plans to issue, focuses on rental housing from 2001 through 2017 and analyzes (1) the share of households that rent, (2) the affordability of rental housing, and (3) rental housing conditions. GAO analyzed American Community Survey and American Housing Survey data from 2001 through 2017 (the most recent data available at the time of this review) at the national level and for different types of localities. GAO also reviewed recent reports by the Department of Housing and Urban Development (HUD), research organizations, and academic researchers on rental housing and obtained views from a variety of stakeholders selected for their knowledge of these issues, including federal agency officials, academic experts, research organizations, and industry groups.

In 2017, almost 7 million more households rented their homes than in 2001, which brought the share of households that rent from an estimated 34 percent to 36 percent. Renting became more common after the 2007–2009 financial crisis as foreclosures and changes in household characteristics reduced the proportion of homeowners. Renting was more prevalent across most age and race/ethnicity groups in 2017 than in 2001, with notable increases among higher-income households.

Rental affordability declined from 2001 to 2017. In 2017, 48 percent of renter households were rent burdened—that is, they paid over 30 percent of income for rent—which is 6 percentage points higher than in 2001. Rent burden was most common and most severe among lower-income households (80 percent or less than area median income), with almost three-quarters of extremely low-income households (30 percent or less than area median income) paying over half of their income in rent (see figure). Affordability declined because of a range of factors, including more households competing for rental units and the supply of low-cost rental units not keeping up with demand.

Note: Estimates in this figure have a margin of error of ±2 percentage points or fewer, at the 95 percent confidence level.

An estimated 15 percent of rental units in 2017—more than 5 million—had substantial quality issues (such as cracked walls and the presence of rodents) or lacked essential components of a dwelling (such as heating equipment or hot and cold running water), according to GAO's analysis of American Housing Survey data. The share of units with deficiencies was relatively stable from 2001 to 2017. Serious deficiencies more often affected households with extremely low incomes or rent burdens. In addition, lower-income households rented approximately two-thirds of the units with substantial quality issues and nearly 80 percent of units lacking essential components.

Since the 2007–2009 financial crisis, growth in rentership—that is, the  share of households that rent—has reversed a decades-long trend toward  homeownership. Although a majority of U.S. households still own their  homes, the larger number of renters has led to increased competition for  rental housing. These changes have given rise to concerns about the  availability and affordability of rental housing, especially for households  with low incomes. While the federal government subsidizes rents for  around 4.4 million households per year, housing assistance is not an  entitlement, and more households qualify for assistance than receive it.

You requested that we conduct a comprehensive assessment of the U.S.  housing market, including whether the housing market is meeting the  needs of the American people. This report examines trends in the  housing market prior to the Coronavirus Disease 2019 pandemic and  does not account for the profound impact it will likely have on renter  households. We have ongoing work that will examine implementation of  foreclosure and eviction protections authorized in recent legislation. This  report, one of several we plan to issue, focuses on rental housing from  2001 through 2017, the most recent year for which data were available at  the time of our review. Specifically, this report analyzes trends in (1) the  share of households that rent and their characteristics, (2) the affordability  of rental housing, and (3) rental housing conditions.

To describe trends in the share of households that rent, their  characteristics, and the affordability of rental housing, we analyzed the  Census Bureau’s American Community Survey data from 2001 through  2017, reviewed recent reports on rental housing, and obtained views from  a variety of stakeholders, including federal agency officials, academic  experts, research organizations, and industry groups. For renter  household characteristics, we analyzed data on renter household age,  race/ethnicity, and income at the national level and by locality type (that  is, areas we grouped by population density and growth rate). For  affordability, we analyzed data on household rent costs and household  incomes. Appendix I provides more detail on the data used in our  analysis. Consistent with other research and Department of Housing and  Urban Development (HUD) policies, we defined rent burden as spending  more than 30 percent of household income on rent and severe rent  burden as spending more than 50 percent of household income on rent.

To describe trends in rental housing conditions, we analyzed American  Community Survey data and American Housing Survey data from HUD  and Census, reviewed recent reports by federal agencies and research  organizations on rental housing conditions, and obtained views from  federal agency officials, academic experts, and research organizations.  We created two indexes to analyze American Housing Survey variables  related to housing conditions and identified trends at the national level  and for different types of localities. HUD and others have analyzed  aspects of housing quality from American Housing Survey data. Their  research helped inform our methodology for developing two indexes to  more specifically define the range of rental housing conditions. Appendix  II provides more information about our indexes.

We assessed the reliability of the American Community Survey data and  American Housing Survey data by reviewing technical information for  each survey and interviewing HUD and Census Bureau officials. We  determined the data were sufficiently reliable for purposes of reporting at  the national level on some renter household characteristics and rental  housing conditions for 2001 through 2017, but we identified limitations to  reporting for smaller geographies. To address this limitation, we obtained  aggregated Census Bureau data from HUD’s Office of Policy  Development and Research. To assess the reliability of the data, we  analyzed the underlying programming code and related documentation  from agency officials and reviewed the data for missing elements,  outliers, and errors. We determined that the data were sufficiently reliable  for purposes of analyzing renter household characteristics, rent burden,  and rental housing conditions from 2001 through 2017 at the national  level and for different types of localities.

To describe common trends across similar types of localities for all of our  objectives, we developed metro area groupings based on population  change over our review period and population density, as of 2017. We  identified three growth categories (high, moderate, and negative) and  further categorized the moderate growth group by density (high and  moderate). We also identified a group of nonmetro areas consisting of all  counties in selected states that are outside the boundaries of any metro  area. See table 1 for a summary of the different types of areas we used  for our analysis. Appendix I contains more information on our approach  for grouping metro areas.

We conducted this performance audit from February 2018 to May 2020 in  accordance with generally accepted government auditing standards.  Those standards require that we plan and perform the audit to obtain  sufficient, appropriate evidence to provide a reasonable basis for our  findings and conclusions based on our audit objectives. We believe that  the evidence obtained provides a reasonable basis for our findings and  conclusions based on our audit objectives.

Federal policy for rental housing has traditionally focused on assisting  low-income households through rental assistance and incentives for the  development of housing with below-market rents. In fiscal year 2020,  Congress appropriated about $43.9 billion for HUD’s three largest federal  rental assistance programs: public housing, Housing Choice Vouchers,  and Project-Based Rental Assistance. These programs make rents  affordable to eligible households, generally by paying the difference  between the unit’s rent and 30 percent of a household’s adjusted income.

Unlike certain other means-tested programs, federal rental assistance  programs are not entitlements. The number of households that the  programs can assist is limited by the amount of budget authority that HUD  requests and Congress provides through the annual appropriations  process. Historically, appropriations for rental assistance programs have  not been sufficient to assist all households that HUD has identified as  having worst case housing needs—that is, renter households that (1)  have very low incomes; (2) do not receive housing assistance; and (3)  use more than one-half of their income to pay for rent, live in severely  inadequate conditions, or both. In 2017, HUD reported that 8.3 million  households had worst case needs in 2015, an increase from 7.7 million in  2013. HUD reported that among very low-income renters in 2015, 25  percent of them received rental assistance, and an additional 43 percent  had worst case needs.

To determine program eligibility and identify populations in need of  assistance, many federal rental assistance programs have specific  income eligibility requirements. HUD sets income limits that determine  eligibility for its assistance programs based on median family income and  market rent estimates. These income limits can vary across different  types of localities.

Renting Became More Common after the 2007–2009 Financial Crisis but Varied by Demographic Group and Location

Renting Expanded after the Financial Crisis

The national rentership rate increased from 2001 through 2017 (see fig.  1). In 2004, the estimated rentership rate fell below 33 percent, the lowest  in U.S. history, then climbed to 37 percent in 2013, a rate not seen since  the 1960s. By 2017, almost 7 million more households rented their homes  than in 2001, which brought the rentership rate to an estimated 36  percent.

This increase of 7 million households reflects both overall growth in the  population as well as the net shift from owning to renting. Many  households experienced lasting financial effects of the financial crisis,  such as impaired credit or loss of income, which hampered their ability to  enter into or transition back into homeownership. Although the national  foreclosure rate has slowed significantly in recent years, past research  has shown that most households struggle to return to homeownership  after foreclosure. Further, median home prices have risen faster than  median incomes nationally, which makes achieving homeownership more  challenging. Specifically, the gap between rising home prices and wage  growth has likely contributed to increases in rentership in many metro  areas.

Renting Became More Prevalent among Most Age Groups, with Notable Increases among Middle- Aged Households

Nationally, the rentership rate increased from 2001 through 2017 across  all age categories we analyzed, except for older households (65 years or  older), as shown in figure 2. The greatest increase was among early  middle-aged households (35–49 years old), an estimated increase of  nearly 8 percentage points. In addition, rentership for late middle-aged  (50–64 years old) and younger (20–34 years old) households increased  by 5 percentage points.

Renters are, on average, older than they previously were. The late  middle-aged group (50–64 years) experienced the largest estimated  increase in the number of renter households—an increase of 4 million  households—and accounted for more than half of the total increase in  renter households from 2001 through 2017 (see fig. 3). Many of these  households have not recovered from the financial crisis, and this group  has lower incomes and higher rentership rates than in previous  generations, Harvard’s Joint Center for Housing Studies has reported. We  previously reported that the homeownership rate for the poorest older  households was significantly lower after the financial crisis than before  it.

Renting Became More Common among Black Households and Declined for Hispanic and Asian Households

Black households had higher estimated rentership rates than White,  Hispanic, and Asian households, and rentership among Black households  increased from 54 percent in 2001 to 58 percent in 2017 (fig. 4). In  contrast, rentership among White households was lowest among the  race/ethnicity groups and remained generally stable during our analysis  period (ranging from 26 to 29 percent from 2001 through 2017). While  rentership among Hispanic and Asian households increased slightly in the  aftermath of the financial crisis, as of 2017, their rentership rates had  returned to levels below those of 2001, although these rates were still  higher than those of White households.

As of 2017, high-growth and moderate-growth/high-density metro areas  we analyzed tended to have more racially diverse renter populations than  other areas, and renters in these metro areas were mostly from minority  groups. For example, in Dallas, Texas, which is high-growth, an  estimated 59 percent of renter households were minority households, and  in Miami, Florida, which is moderate-growth/high-density, an estimated 75  percent of renter households were minority households.

Higher-Income Renter Households Increased Substantially after 2010

The most significant change in rentership from 2001 through 2017 by  income group was for higher-income households (more than 120 percent  of area median income), with the greatest change between 2010 and  2017. Nationally, higher-income households were the second smallest  renter group in 2001, with an estimated 6.6 million households, or 17  percent of renter households. In 2017, higher-income households were  the second largest renter group, with approximately 10.3 million  households, approximately 20 percent of renter households (see fig. 5).

Consistent with national trends, in all locality types—that is, those with  higher and lower population density or rates of growth—the estimated  number and proportion of higher-income renter households increased  from 2001 through 2017 (see fig. 6). The greatest increase occurred in  high- and moderate-growth metro areas. This trend could reflect (1) a  change in income, (2) relocation from moderate-growth/high-density  metro areas, and (3) consolidation of households—such as having  multiple roommates, extended families occupying one housing unit, or  households doubling up with relatives or others to make ends meet.  There were modest changes in the number and proportion of low-income  households during the same period. Rural areas and metro areas with  shrinking populations had the highest proportion of renter households  with low incomes as of 2017—for example, an estimated 63 percent of  renters in negative-growth metro areas had low to extremely low incomes.

Population growth and two other factors appear to have contributed to the  growth in higher-income renter households. First, many homeowners who  experienced foreclosure during the financial crisis became renters.  Second, with rising housing costs, there has been a trend toward  consolidated households. The share of households with three or more  adults was higher in 2017 than in 2001. Some of these households may  have chosen to combine as an alternative to eviction or homelessness,  and they may have overcrowded or unstable living arrangements.

Rent as Share of Income Increased from 2001 through 2017, with Serious Consequences for the Poorest

The Percentage of Rent- Burdened Households Increased from 2001 through 2017

Most renter households paid a larger share of their income in rent in 2017  than in 2001. Federal housing policy generally considers rents at or below  30 percent of household income to be affordable, and households that  pay more than 30 percent of income in rent are considered to be rent  burdened. We found that by 2017, an estimated 48 percent of renter  households were rent burdened, 6 percentage points higher than in 2001.  Severe rent burden, where more than 50 percent of household income is  paid in rent, also became more common. Of the households that were rent burdened in 2017, about half were severely rent burdened. These  households represented 24 percent of all renter households—an increase  of 4 percentage points from 2001 (see fig. 7).

The rising rent burden is part of a long-term trend in rental unaffordability,  as supply has not kept pace with demand for rental units. With fewer  affordable apartments available, rent burdens increased among lower- income households, who were forced to spend a greater proportion of  income on rent. Government, academic, and industry research has  identified several factors that contribute to this trend:

Local regulation and geography have long constrained where and  how much rental housing can be built. Cities have adopted zoning and  land use regulations that can prohibit or increase costs for new rental  units. Metro areas, particularly those in coastal or mountainous  regions, have limited available land for new housing.

Construction of new rental units has been limited since the 2007– 2009 financial crisis, in part because developers struggled to rebuild  workforce capacity after layoffs of skilled construction workers. As a  result, since 2009, the construction industry has focused on building  luxury apartments, which have higher profit margins, and produce few  units affordable to lower-income households. Conversion of lower-rent  units to higher-rent units through renovation also reduced the number  and share of rental units affordable to lower-income households.

Demographic changes, particularly the aging of the millennial and  baby boomer generations, have increased demand for rental units. As  previously discussed, we found that renters were, on average, older in  2017 than in 2001. In addition, Harvard’s Joint Center for Housing  Studies has reported that late middle-age renters (50–64 years) have  lower incomes and higher rentership rates than previous generations.  Populations with higher rentership rates—including minority  households—are forecasted to continue growing through 2030.

The spike in foreclosures during the financial crisis resulted in millions  of households entering the rental market, increasing competition for  available units. Tighter credit standards after the financial crisis have  kept many of those who lost their home due to foreclosure from  qualifying for a new mortgage.

In the United States, rent burden has been most common among  minorities and older adults and in dense metropolitan areas (see fig. 8):

Rent burden was about 10 percentage points more common among  Black and Hispanic households than White households in 2017. This  disparity was due to sizable differences in median income. In 2017,  estimated median income was $63,704 for White households,  $49,793 for Hispanic households, and $40,232 for Black households.

Rent burden was more than 10 percentage points more common  among older adult (65 and over) households than working-age (20– 64) households in 2017. This disparity was also due to sizable  differences in median income, as older adults were less likely to be in  the workforce. In 2017, median income was $69,459 for households  age 25–64 and $43,735 for households age 65 and over.

Rent burden was nearly 10 percentage points more common among  renters in high-density metro areas than in nonmetro areas in 2017.  According to the Urban Institute, the shortage of affordable rental  housing was more acute in urban areas than rural areas in 2014.

See appendixes III and IV for more detailed information on rent burden by  age, race/ethnicity, and locality type.

Lower-Income Households Commonly Experienced High Rent Burdens from 2001 through 2017

In 2017, moderate and severe rent burdens were common among low- to  extremely-low income households and relatively rare among moderate- to  higher-income households (see fig. 9).

From 2001 through 2017, the estimated number of renters with moderate  or severe rent burdens increased across all income levels, but the  increase was more pronounced among lower-income groups (see fig. 10).  Specifically, we found the following:

The estimated number of higher-income renters increased by more  than 3.6 million households from 2001 through 2017, but relatively few  of these households experienced rent burden. In contrast, the  numbers of low-income, very low-income, and extremely-low income  renters also increased over this period, and these groups saw  significant increases in rent burden.

In more recent years, the estimated number of extremely low-income  renter households with severe burden actually decreased—from 7.4  million in 2011 to 6.6 million in 2017. This decrease, however, does  not necessarily indicate improved conditions for these households  because it was not accompanied by a corresponding increase in  either (1) the number of extremely low-income households that were  less burdened or (2) the number of very low-income households (the  next highest income group). An increase in either of these groups  could indicate that the poorest, most burdened households  experienced either an increase in income or a decrease in rent  burden. However, because these other groups did not increase, it is  possible that some of these extremely low-income, severely burdened  households moved in with other households or experienced some  other form of homelessness.

Rent burdens affect households differently depending on their income.  Households with lower incomes may pay the same percentage of income  in rent as moderate- or higher-income households but have less income  left over for other necessities. Even relatively inexpensive units may not  leave enough money for lower-income households to cover other  necessities like food, clothing, transportation, or medical care. These  households may also be sensitive to shocks, such as job loss and health  emergencies, and may be at heightened risk of eviction and  homelessness.

Challenges that lower-income households face can vary across cities and  regions due to differences in local market rents and incomes. For  example, as figure 11 shows, in the San Francisco area in 2017 a very  low-income family of four would experience a severe rent burden if it paid  the fair market rent for a two-bedroom apartment ($3,018 per month).  Such a family would struggle to pay the rent and afford other necessities  even with two or three full-time minimum wage jobs. In contrast, a very  low-income family of four in the St. Louis area in 2017 would experience a  moderate or no rent burden if it paid the fair market rent for a two- bedroom apartment ($896). Such a family with at least two full-time  minimum wage jobs would have relatively more money left over for other  necessities. See appendixes III and IV for more detailed information on  rent burden by household income.

For moderate-income households, the consequences of rent burden are  less dire than for lower-income households, but they are still significant.  For example, a family of four earning the median income in San  Francisco that paid fair market rent for a two-bedroom apartment would  be rent burdened. A housing unit that would be considered affordable to  them would cost at least $135 per month below fair market rent (or  approximately $2,882 or less). Money that a family could save on a unit  below fair market rate could help reduce household debt, add to  retirement savings, or pay for necessities like child care. Rent burden  among moderate-income households tends to be more common in large  cities with strong economies and significant geographic and regulatory  constraints on new housing, such as San Francisco and New York.

The lowest-income households face challenges securing affordable rental  units. There are not enough rental units that are affordable to the lowest- income households without rental assistance. Specifically, according to  HUD, lower-income households face competition from moderate- or high- income households to rent affordable units. HUD’s analysis showed that  although there were enough affordable units nationwide to house 66  percent of extremely-low income renters in 2015, 43 percent of those  units were occupied by renters with higher incomes.

We also found that for all income groups, rents rose faster than incomes  and therefore became less affordable to varying degrees. Specifically,  estimated median rent-to-income ratios, which indicate the median  proportion of income devoted to rent, generally increased from 2001  through 2017, according to our analysis (see fig. 12). For the lowest- income households, even small declines in affordability have a big impact  because these households face the highest rent burdens and have the  fewest options in the housing market. See appendix III for more detailed  information on rent-to-income ratios.

About 15 Percent of Rental Units Had Serious Deficiencies in 2017

Based on two indexes we created to analyze rental housing conditions  using American Housing Survey data, we found that an estimated 15  percent of renter households—more than 5 million—lived in units with  serious deficiencies in 2017. Specifically, an estimated 12 percent of  renter households (more than 4 million households) lived in units with  substantial quality issues. These units typically had a combination of  issues, such as cracked walls and the presence of rodents, or multiple  heating problems and the presence of rodents. An additional 3 percent  of renter households (more than 1 million households) lived in incomplete  units—that is, units lacking essential components of a dwelling (such as  heating equipment or hot and cold running water). Further, an estimated  28 percent of households—nearly 10 million—rented units with less  substantial quality issues. Table 2 presents these findings and how our  indexes described different types of rental housing conditions.

The proportion of rental units with the three types of deficiencies— substantial quality issues, less substantial quality issues, and absence of  essential components of a dwelling—generally remained stable from  2001 through 2017 (see fig. 13). The proportion of rental units that had at  least one of these deficiencies ranged from an estimated 39 to 47 percent  from 2001 through 2017.

Serious Deficiencies More Often Affected Lower- Income and Rent- Burdened Households

We analyzed rental housing conditions by renter household and rental  unit characteristics. Households with low incomes (those with low, very  low, or extremely low incomes) or with rent burdens comprised half or  more of renters living in units with substantial quality issues and  incomplete housing units (those lacking essential components of a  dwelling). Although incomplete housing units represented a small  percentage of rental units overall (about 3 percent), there were more than  an estimated 1 million such units in 2017.

Low-income renters have fewer affordable options and, as a result, may  end up in units with deficiencies out of necessity. Households with low,  very low, or extremely low incomes represented an estimated 62 percent  of renters overall in 2017. These households occupied an estimated 67  percent of units that had substantial quality issues and nearly 80 percent  of incomplete units. Similarly, rent-burdened households represented an  estimated 50 percent of renters overall in 2017 and occupied an  estimated 53 percent of units with substantial quality issues and 60  percent of incomplete units.

There were some notable differences in housing conditions by age and  race/ethnicity. Older households (65 and older) were the most likely age  group to live in rental units with no deficiencies in 2017. About half of  renting households were White in 2017, and White households comprised  the largest share of renters in each quality or completeness category we  analyzed. The proportions of Hispanic and Asian households that rented  incomplete units (estimated at 31 percent and 11 percent, respectively)  were higher than the overall proportions of Hispanic and Asian renter  households (estimated at 20 percent and 6 percent, respectively). In  addition, the proportion of Black households that rented units with  substantial quality issues (estimated at 24 percent) was slightly higher  than the overall proportion of Black renter households (estimated at 21  percent).

Older and Single-Family Rental Units Were More Likely to Have Deficiencies

Rental housing conditions by unit age or type were generally consistent  from 2001 through 2017. As expected, units built after 2000 had fewer  deficiencies than those built before. Older rental housing—units built prior  to 1980—were more likely to have substantial quality issues than those  built after. An estimated 63 percent of units in high-growth metro areas  had no quality issues as of 2017, compared to an estimated 55 to 57  percent of units in other types of localities. There was little other variation  in housing conditions by locality type.

We also found that detached single-family homes and mobile homes  were somewhat more likely to have serious deficiencies than multifamily  units. The proportion of units with these deficiencies remained relatively  steady from 2001 through 2017. One reason for this is single-family units  lack on-site building managers and other benefits of shared maintenance  that multifamily units may provide. Some researchers and industry  participants have noted possible maintenance challenges for a growing  number of investor-owners of single-family rentals that manage  thousands of properties of varying size, age, and condition. From 2001  through 2017, the proportion of single-family units with serious  deficiencies (rental units lacking essential components of a dwelling or  units with substantial quality issues) ranged from around 13 to 20 percent  (see fig. 14). During the same period, the proportion of single-family  units with less substantial quality issues ranged from an estimated 28 to  34 percent.

We also analyzed household crowding trends based on American  Community Survey data. We defined crowded households as those  having more than two people per bedroom. From 2001 through 2017,  the incidence of renter household crowding decreased, with the greatest  percentage point declines for Hispanic households prior to the housing  crisis. Generally, households that were younger, Hispanic or Asian, or  had lower incomes were more likely to experience crowding. In addition,  crowded households were more common in high-density and high-growth  metro areas. Appendix VI includes information on household crowding by  race/ethnicity, age, household income, and locality type.

We provided a draft of this report to HUD for review and comment. HUD  officials told us that they had no comments on the draft report.

As agreed with your offices, unless you publicly announce the contents of  this report earlier, we plan no further distribution until 30 days from the  report date. At that time, we will send copies of this report to the  appropriate congressional committees, the Secretary of Housing and  Urban Development, and other interested parties. In addition, the report  will be available at no charge on the GAO website at http://www.gao.gov.

If you or your staff have any questions about this report, please contact  me at (202) 512-4529 or garciadiazd@gao.gov. Contact points for our  Offices of Congressional Relations and Public Affairs may be found on  the last page of this report. GAO staff who made key contributions to this  report are listed in appendix VII.

Appendix I: Objectives, Scope, and Methodology

The objectives of this report were to analyze trends in (1) the share of  households that rent and their characteristics, (2) the affordability of rental  housing, and (3) rental housing conditions.

We analyzed 2001–2017 data from the American Community Survey and  American Housing Survey to describe renter household characteristics,  rent affordability, rental housing conditions, and trends at the national  level and across different types of localities.

The American Community Survey is an ongoing survey administered  by the Census Bureau of around 3.5 million households across the  United States; the data we used in our analysis were current as of  2017, the most recently available data at the time of our review.

The survey collects data on the economic, social, housing, and  demographic characteristics of communities at various geographic  levels, including metropolitan areas, states, and counties.

The American Housing Survey is a biennial survey sponsored by the  Department of Housing and Urban Development (HUD) and  administered by the Census Bureau that collects a range of housing  information, including the size and composition of the U.S. housing  inventory, physical condition of housing units, characteristics of  occupants, and other information.

Findings from each survey are subject to sampling errors. To assess the  reliability of the data, we reviewed technical information for each survey.  In addition, we interviewed HUD and Census Bureau officials to identify  differences across survey years and understand geographic limitations of  publicly available data. We determined that the surveys were sufficiently  reliable for purposes of reporting at the national level on renter household  characteristics. However, we determined that additional, nonpublic data  were needed from each survey to analyze renter household  characteristics, rent burden, and rental housing conditions for smaller  geographic units.

To address this limitation, staff from HUD’s Office of Policy Development  and Research provided us with aggregated Census Bureau data. To  assess the reliability of these data, we analyzed the underlying  programming code and related documentation from agency officials and  reviewed for missing data, outliers, and errors. We determined that the  data were sufficiently reliable for purposes of analyzing renter household  characteristics, rent burden, and rental housing conditions from 2001  through 2017 at the national level and for different types of localities.

For all objectives, to describe common trends and differences across  localities—that is, localities with different population growth rates and  densities—we developed metro area groupings. The groupings provide a  general framework for describing metro areas that experienced varying  degrees of population growth from 2000 through 2017 and how trends in  renter household characteristics, rent affordability, and rental housing  conditions compared to trends in other types of areas.

To identify the locality types, we analyzed core-based statistical areas by  population growth from 2000 through 2017 and population density as of  2017. We identified three growth categories (high, moderate, and  negative) and further categorized the moderate growth group by density  (high and moderate). We also identified a group of nonmetro areas  consisting of all counties in each state that are outside the boundaries of  any metro area. These areas included micropolitan areas, small towns,  and low-density rural areas. The five locality types were high-growth  metro areas, moderate-growth/high-density metro areas, moderate- growth/moderate-density metro areas, negative-growth metro areas, and  nonmetro areas.

To describe trends in the share of households that rent and their  characteristics, we analyzed American Community Survey data from  2001 through 2017 at the national level and across different types of  localities, with a focus on renter household age, race/ethnicity, and  income.

We defined four head-of-household age categories: younger (20–34  years old), early middle age (35–49 years old), late middle age (50–64  years old), and older (65 years and older). We reported on five  race/ethnicity categories, combining some Census categories for our  analysis: White, Black, Hispanic (an ethnicity that applies to individuals of  any racial background), Asian (includes Asian, Native Hawaiian, and  Other Pacific Islander), and Other (includes American Indian, Alaska  Native, two or more races, and some other race). We defined five  income categories based on income ranges that HUD uses for  determining rental assistance eligibility or reporting to Congress on worst  case needs: extremely low income (up to 30 percent of HUD area median  family income (HAMFI)); very low income (more than 30, up to 50 percent  of HAMFI); low income (more than 50, up to 80 percent of HAMFI);  moderate income (more than 80, up to 120 percent of HAMFI) and higher  income (greater than 120 percent of HAMFI).

To describe trends in the affordability of rental housing, we analyzed  American Community Survey data on gross rent as a percentage of  household income from 2001 through 2017 at the national level and  across different types of localities. Consistent with other housing research  and HUD policies, we defined rent burden as spending more than 30  percent of household income on rent, moderate rent burden as spending  more than 30 and up to 50 percent of household income on rent, and  severe rent burden as spending more than 50 percent of household  income on rent. Further, as described in appendix IV, we developed a  supplementary analysis of rental housing affordability for rural areas by  state.

To describe trends in rental housing conditions, we analyzed data from  the American Community Survey and American Housing Survey from  2001 through 2017 at the national level and across different types of  localities. HUD designed the American Housing Survey to include  indicators of housing quality. HUD analyzes and reports periodically on a  housing adequacy measure as part of its worst case housing needs  assessments for Congress. HUD’s adequacy measure and related  research informed our methodology for developing two indexes to  analyze rental housing conditions. We developed the indexes to more  specifically define the range of housing conditions. The two indexes  include 13 quality-related variables and nine variables we identified as  essential components of a dwelling from the American Housing Survey,  described in table 3. Appendix II includes more detailed information  about our methodology. Appendix V includes information on the  similarities and differences between HUD’s adequacy index and the  indexes we developed for this report.

With our indexes, we analyzed trends in rental housing conditions by   renter household characteristics and rental unit characteristics. The renter  household characteristics we analyzed included household income and  affordability, race/ethnicity, and age. The rental unit characteristics we  analyzed included location, age, and structure type. In addition, from  American Community Survey data, we analyzed household crowding as  another aspect of housing conditions. Further, we reviewed reports and  studies on housing conditions and interviewed stakeholders including  federal agency officials, academic experts, and research organizations.

To further describe trends in renter household characteristics, rent  affordability, and rental housing conditions during our review period, we  reviewed reports and studies by federal agencies and research  organizations and interviewed a variety of stakeholders selected for their  knowledge of these issues, including federal agency officials from HUD,  the Census Bureau, Congressional Research Service, the Department of  Agriculture, the Federal Housing Finance Agency, and the Department of  the Treasury; academic experts, including researchers from Harvard’s  Joint Center for Housing Studies and others; research organizations that  included the Bipartisan Policy Center, various researchers associated  with the Board of Governors of the Federal Reserve System, Brookings  Institute, Center on Budget and Policy Priorities, Housing Assistance  Council, National League of Cities, National Rural Housing Coalition,  Urban Land Institute, and Urban Institute; and industry groups that  included the National Association of Home Builders, National Association  of Realtors, and the National Housing Conference.

We conducted this performance audit from February 2018 to May 2020 in  accordance with generally accepted government auditing standards.  Those standards require that we plan and perform the audit to obtain  sufficient, appropriate evidence to provide a reasonable basis for our  findings and conclusions based on our audit objectives. We believe that  the evidence obtained provides a reasonable basis for our findings and  conclusions based on our audit objective.

Appendix II: Statistical Analysis of Rental Housing Conditions

This appendix provides additional details on our analysis of the conditions  of the national rental housing stock between 2001 and 2017.

To assess rental housing conditions, we used data from the national  American Housing Survey (AHS), which is administered by the Census  Bureau and conducted every odd year. Specifically, we considered two  concepts, unit completeness and unit quality, and relied on questions  which were consistently asked over the 2001 to 2017 period to define  nine completeness and 13 quality variables.

The survey questions underlying uncomfortably cold periods and heating  equipment breakdowns were only asked of respondents who occupied  their unit in the winter prior to the survey year, so our main analysis of  rental unit quality only considered cash-rent housing units occupied by  households since the prior winter, while the analysis of unit completeness  considered all cash-rent units. See table 5 for the distributions of the  completeness and quality variables in 2017.

We used each set of variables to construct two indexes, one of unit  completeness (an indicator) and one of unit quality (a continuous  measure). We collapsed the continuous quality index into three  categories (no quality issues, less substantial quality issues, and  substantial quality issues) to facilitate a summary of rental housing quality  trends.

To determine rental unit completeness, we first summed the number of  missing components contained in the nine completeness components for  each rental unit in the surveys. We obtained an estimate of Cronbach’s  alpha associated with this sum of 0.40, which was low enough to suggest  that a simple indicator would be a more appropriate summary measure.  We therefore determined that each of the nine completeness components  was essential for us to consider a unit livable, and assigned a  completeness score to each rental unit based on the absence of any of  them. The resulting index therefore measured incompleteness, where a  score of 1 indicated that a unit was missing one or more of the essential  components. See table 6 for the distribution of the completeness index in  the survey years between 2001 and 2017.

for inference that is robust to non-normal distributions of the latent  continuous variables. Finally, we obtained quality score estimates by  empirical Bayes, which selects the mode of the posterior distribution  𝑝𝑝(𝜂𝜂̂|𝑦𝑦𝑖𝑖) evaluated at the estimates of the model’s parameters. Note that  because all quality variables increased in the presence or number of  issues, the quality index correspondingly increased in poor quality.

We first estimated two variants of the factor model, one accounting for the  sample weights assigned to units in each survey (our preferred  specification), and one that did not account for these weights. The robust  root mean square error of approximation from the latter of 0.020  suggested that our single factor model provided an appropriate  representation of the AHS data. Estimates of the polychoric correlation  matrix and of the factor loadings are reported in tables 7 and 8  respectively. We assessed stability by estimating the model on each AHS  year separately and broadly found that factor loadings estimates varied  little over time. Given the estimates from our preferred specification,  which accounts for the survey design, we then relied on empirical Bayes  estimation to assign a quality score to each unit for which responses to all  13 quality variables were observed. The full distributions of the resulting  quality index in the survey years between 2001 and 2017 are reported in  figure 15.

We then selected thresholds in the distribution of the continuous quality  index to distinguish between units without any quality issues, units with a  quality score indicating the presence of less substantial issues, and units  with a score denoting more substantial issues (those with either a  combination of some of the most severe issues as determined by the  model, or a large number of issues of varying severities).

The first threshold between units with no quality issues and units with at  least one issue occurred at a score of -0.2280. Units with no issues  represented between 54 and 62 percent of the rental units to which we  were able to assign quality scores. To further separate units experiencing  any issues into two groups, we inspected the quality score distribution for  local minima in its density to find a score around which small  perturbations in threshold choice would have little effect on the share of  units falling into each of the two groups. We examined all quality issue  profiles experienced in units with scores in the region around two  candidates where the density nearly reached 0, and selected a score of  0.5240 as the second threshold, immediately above which were units with  one or more holes in the floor large enough to catch someone’s foot. All  units with a quality score of 0.5240 or higher were therefore considered to  have substantial issues.

Table 9 reports the share of cash-rent, previous-winter-occupied units for  each quality level in the survey years between 2001 and 2017, and table  10 reports the most common quality issue profiles in 2017.

Our analysis is subject to several limitations. In determining both unit  completeness and quality, we were limited to the variables consistently  available across all survey years. Therefore, we could not include  features not observed in the AHS which could be deemed to be important  components of either unit completeness (such as a unit’s access to an  internet service provider) or quality (such as the presence of major  defects in the structure of the unit’s building).

In the quality factor model, we assumed that quality was uncorrelated with  the error term from each measurement equation and that the error terms  were uncorrelated with each other to obtain estimates of the model’s  parameters, and ultimately the quality scores. A violation of these  assumptions would bias the estimates. For example, if rental units located  in regions with harsh weather were of systematically worse quality than  units in fairer weather regions, the estimated effect of poor quality on a  variable like the number of outdoor leaks could be overstated, which  would in turn overweight the importance of outdoor leaks in the estimation  of the quality scores, resulting in overly poor quality score estimates for  units experiencing outdoor leaks. Conversely, if units in harsh weather  regions were of systematically better quality than those in fairer weather  regions (e.g. as a measure of resilience) the estimated effect of poor  quality on outdoor leaks would be understated, biasing down the  importance of outdoor leaks in the estimation of quality scores. In general,  any systematic linear relationship between latent quality (𝜂𝜂) and the  unobserved factors (𝜀𝜀𝑖𝑖) affecting one of the 13 unobserved latent  continuous variables, or between the unobserved factors themselves,  would be a violation of the model’s assumptions.

Since the two quality variables recording uncomfortably cold periods and  heating equipment breakdowns were only asked of respondents who  occupied their unit in the winter prior to the survey year, we could not  assign quality scores to the 10 to 25 percent of rental units across years  which were occupied by recent movers. To assess potential biases on the  quality distribution of the full cash-rent-occupied rental housing stock  introduced by excluding this group, we therefore compared both groups  along the remaining 11 dimensions  Of the 11 observable quality variables, three exhibited an incidence of  issues that differed meaningfully across the two groups. These  differences were persistent throughout survey years and consistent in  their direction: units whose respondents moved in later than the winter  prior to the interview were between 5 to 10 percentage points less likely  to experience any outside leaks, inside leaks, and to report evidence of  rodents. The differences were meaningful in that they corresponded to  over a halving of the incidence of the evidence of rodents, and up to a  halving of the incidence of both types of leaks in the recent-mover units  relative to the units for which all quality variables were available.

To evaluate the effect of these differences on the quality distribution of  the full universe of cash-rent units, we estimated a modified quality factor  model in which we dropped the uncomfortably cold periods and heating  equipment breakdowns variables. This allowed us to obtain quality scores  for both the units with the original scores and the recent-mover units. The  distributions of the modified quality indices in the two groups reached  their largest difference at the share of units without any of the set of 11  quality issues, and we estimated that across all survey years, 1.3 to 2.4  percentage points more units would likely have no measured quality  issues in the full cash-rent universe than we found in that which excludes  the recent movers.

Furthermore, the distributions of the modified indices truncated to exclude  the respective units without any of the 11 quality issues were largely  comparable. In the full universe of cash-rent units, we would therefore  expect decreases in each of the shares of less substantial issues and  substantial issues units proportional to their respective shares in the  partial universe, and in sum corresponding to the magnitude of the  increase in units with no issues each year. The alternative of including  recent movers in our main model at the expense of the uncomfortably  cold periods and heating equipment breakdowns variables would have  yielded a share of units without any other quality issues that we estimated  to be 3 to 4 percentage points higher than the share calculated using the  original index in the partial universe. Because we believed that these  variables should ultimately be included in the quality index, and because  we considered the biases we estimated to be relatively small, we retained  the original index.

Appendix III: Additional Information on Rentership and Affordability Trends

In this appendix we present our analysis of rentership and housing affordability by  age, race/ethnicity, locality type, and income from 2001 through 2017. The data on  renter households are from the American Community Survey’s 1-year estimates.

Appendix IV: Estimated Rent Burden in Statewide Rural Areas

In this appendix we present state-level analysis of housing affordability for  rural renter households. While rental affordability is a challenge in both  rural and urban areas, differences in demographics, economies, housing  stock, and federal rental assistance programs make rural rental  affordability issues unique   We defined rural areas using the U.S. Department of Agriculture’s 2010  rural-urban commuting area (RUCA) codes. The data on renter  households living in these areas are from the American Community  Survey’s 5-year estimates for 2013 through 2017.

While renter households lived in rural areas of all 50 states, generally the  most populous states had the largest populations of renter households in  rural areas (fig. 16). From 2013 through 2017, more than an estimated  2.2 million renter households lived in rural areas. The states with the  largest estimated populations of renter households in rural areas were  Texas (119,000), Missouri (96,000), Wisconsin (96,000), and Kentucky  (93,000).

The prevalence of rural renter households varied significantly by state.  While only about 5 percent of renter households lived in rural areas from  2013 through 2017, some states had significantly larger proportions of  renters in rural areas. States with higher estimated proportions of rural  renter households generally had small populations and were in northern  New England or along the Missouri, Mississippi, or Ohio Rivers (fig. 17).  The states with the largest estimated proportions of renter households in  rural areas were Vermont (39 percent) and Montana (32 percent).

Renter households in rural areas generally had lower incomes than other  renter households. From 2013 through 2017, while the median income for  renter households overall was an estimated $36,653, nearly three in five  rural renter households had incomes lower than $35,000. For context, a  household with two full-time jobs earning the federal minimum wage in  2017 would earn approximately $30,160. In general, Southern states  had the highest estimated proportion of rural renter households with  incomes less than $35,000 (fig. 18). The states with the smallest  proportion of rural renter households with incomes lower than $35,000  were New Jersey (25 percent), Rhode Island (32 percent), Alaska (35  percent), Hawaii (39 percent), and Connecticut (39 percent).

Rent burden was common among renter households in rural areas, but  prevalence varied by state. Rent burden was slightly less common  among rural renter households from 2013 through 2017 (45 percent) than  renter households in general in 2017 (48 percent). In eight of 48 states, at  least 50 percent of rural renter households were rent burdened (fig. 19).  In general, rural rent burden was most common in the Northeast, South,  and West Coast, and least common in the U.S. interior. Louisiana had the  highest estimated rate of rent burden among rural renter households (55  percent) and Wyoming had the lowest (33 percent).

Rent burdens were more common among rural households with incomes  below $35,000. From 2013 through 2017, an estimated 70 percent of  these households were rent burdened, and in no individual state were  less than 50 percent of these households rent burdened (fig. 20). The five  states with the highest proportion of lower-income rural renter households  that were rent burdened were Alaska (81 percent), Massachusetts (83  percent), Hawaii (83 percent), California (83 percent), and Delaware (85  percent). As discussed previously in this report, lower-income households  with rent burdens may struggle to pay for essential needs like food,  transportation, health care, and clothing.

Rent burdens were uncommon among rural households with incomes of  $35,000 or greater. From 2013 through 2017, only an estimated 9 percent  of these households were rent burdened, and in no state were more than  30 percent of these households rent burdened (fig. 21). In 40 of 48 states,  less than an estimated 15 percent of rural renter households with  incomes of $35,000 or greater were rent burdened. The four states with  the highest proportion of rural renter households with income $35,000 or  greater that were rent burdened were Connecticut (28 percent), Hawaii  (26 percent), California (24 percent), and Massachusetts (22 percent).

Appendix V: Comparison of GAO Housing Conditions Indexes and HUD Adequacy Index

This appendix describes how the indexes we developed to analyze rental  housing conditions compare to an index the Department of Housing and  Urban Development (HUD) uses to measure housing adequacy. Although  our index uses many of the same American Housing Survey variables as  HUD’s adequacy index, differences in our analytic methods allowed us to  produce more detailed results on housing conditions.

HUD measures housing adequacy as part of its ongoing efforts to analyze  and report on worst case housing needs   The adequacy index is a measure that is based on 19 variables in the  American Housing Survey. It categorizes housing units as severely  inadequate, moderately inadequate, or adequate based on whether a  surveyed housing unit meets certain conditions or criteria. Severely  inadequate housing units represented 2 to 3 percent of all rental units  from 2001 through 2017.

We developed two indexes based on a factor analysis of 13 quality- related variables and nine variables we identified as essential  components of a dwelling. We determined that two indexes were needed  to describe rental housing unit conditions based on American Housing  Survey data, as relevant variables fell into two categories that required  different statistical treatment and interpretation. Figure 22 provides a  detailed comparison between the variables and scoring techniques of our  indexes and HUD’s adequacy index.

We compared HUD’s 2017 housing adequacy findings to the results of  our indexes and identified some notable differences. Among rental units  that HUD considered adequate in 2017, an estimated 8 percent had  substantial quality issues as measured by our quality index—affecting 2.7  million households. In addition, another estimated 9.7 million units had  less substantial quality issues. These units did not satisfy HUD’s scoring  criteria for inadequate or moderately inadequate units, but they had a  combination of issues that exceeded our statistical thresholds for  substantial and less substantial quality issues. Figure 23 provides a  detailed comparison of how our results compare to HUD’s.

Appendix VI: Additional Information on Rental Housing Conditions

This appendix provides additional information on rental housing conditions by household income, affordability (rent  burden), race/ethnicity, age, rental unit age, and structure type, based on two indexes we developed to analyze  American Housing Survey data   The appendix also includes information on household crowding based on our analysis of American Community  Survey data by household income, rent burden, race/ethnicity, and age.

Appendix VII: GAO Contact and Staff Acknowledgments

In addition to the contact named above, Heather Chartier (Analyst in  Charge), Jeremy Anthony, Daniel Benson, Abigail Brown, Stephen  Brown, Nina Daoud, Davis Judson, John McGrail, Yann Panassie, Dae  Park, Lena Richenberg, Paul Schmidt, Jennifer Schwartz, Jena Sinkfield,  Farrah Stone, and Jeff Tessin made key contributions to this report.

The 2007-09 financial crisis resulted in millions of foreclosures and demographic changes—making renter households more common. We looked at declining rent affordability since then, but before COVID-19.

Rent burden was most common and most severe among lower-income households, with most of the poorest households paying over half of their income to rent. Affordability has declined because the supply of low-cost rental units hasn’t kept up with demand.

With more competition for affordable rentals, low income and rent-burdened households in 2017 were more likely to have to rent units with issues like water leaks, rodents, or heating problems.