Background Federal law requires executive agencies to: maintain adequate inventory controls and accountability systems for property under their control; continuously survey property under their control to identify excess; and promptly report excess property to GSA and generally dispose of it in accordance with GSA regulations so that it can be made available to other federal agencies and stakeholders for reuse. In addition, the FPPMA requires agencies to assess accountable property within their control in accordance with guidance from GSA. Property can be accountable or non-accountable. Accountable property is property with a useful life of at least 2 years that an agency determines should be tracked in its property records. Each agency determines what constitutes accountable property for that agency, and for our selected agencies, consideration is given to an item’s acquisition cost and other factors, such as ease of theft and sensitivity. For our three selected agencies, these acquisition cost thresholds ranged from $5,000 to $10,000 in fiscal year 2018. Non-accountable property is property that does not meet the agency’s definition of accountable property and may include items such as office furniture and printers. Agencies typically do not track non-accountable property unless they need to for specific purposes, such as managing inventory levels. Some agency property is located in warehouses. For the purpose of this report, we used the definition of “warehouse” in FRPP guidance: “buildings used for storage, such as ammunition storage, covered sheds, and buildings primarily used for storage of vehicles or materials.” This term encompasses a broad array of property that agencies may classify differently for internal purposes but classify as warehouses for FRPP reporting in the absence of more precise categories. For example, some buildings that DOE reports as warehouses in FRPP are specialized facilities for storing nuclear and nuclear-contaminated material. In the absence of an FRPP category for nuclear storage, these buildings are classified in FRPP as warehouses. In a similar manner, FAA classified as warehouses in FRPP buildings used to house air traffic support systems, such as approach lighting systems, because no other category in FRPP was a better fit. GSA’s role in agencies’ management of property they have acquired, whether in warehouses or elsewhere, is limited until an agency declares that property as excess. Once property is declared excess, it can be transferred to another agency or certain non-federal recipients, donated, sold, abandoned, or destroyed. GSA has issued regulations that govern agencies’ actions in the property disposal process, and it administers a web-based system that facilitates property disposal. However, prior to an agencies’ identifying property as excess, GSA’s authority to issue regulations or formal guidance regarding agencies’ management of property is limited to topics that have been specifically assigned to the GSA Administrator, according to GSA officials. GSA distinguishes between formal guidance and informal guidance, and GSA officials explained this distinction as follows. Formal guidance, such as a Federal Management Regulation bulletin, must be reviewed by GSA management and general counsel officials. For example, in 2017 GSA issued a Federal Management Regulation bulletin on warehousing that summarized industry perspectives from two voluntary consensus standards, which were published by ASTM International’s asset management committee and which GSA had participated in developing. One standard addressed storage of property and the other addressed strategic warehousing. GSA officials stated they were authorized to include content on property management in the bulletin under the authority of GSA’s real-property policy program because the content supported the real property goal of reducing the federal government’s real property footprint. In contrast, informal guidance does not require review by GSA management or general counsel officials but may be published on GSA’s website or disseminated at GSA trainings. Whether formal or informal, agencies are not required to adhere to such guidance. Typically, within agencies, responsibility for managing property is generally shared between property officials and property custodians. Property officials’ primary responsibilities relate to property management. For example, they may be responsible for updating property data systems, providing property lists and instructions for property inventories, resolving issues that arise with property management, and managing the disposal process after a property custodian has determined that an item is no longer needed. Property custodians are generally program managers who are assigned responsibility for specific property items associated with the program they manage as an ancillary duty. For example, property custodians may be required to conduct physical inventories of property assigned to their program and work with a property official to resolve any issues arising during the inventory. Depending on how an agency uses its warehouses and property, property custodians may be responsible for property in a single warehouse, in multiple warehouses, or in a variety of locations. Moreover, multiple property custodians may be responsible for property in a single warehouse, as depicted in figure 1. Selected Agencies Stored Various Types of Property in Warehouses but Had Limited Information about Storage Costs Selected Agencies Stored a Variety of Property in Warehouses The three selected agencies had a total of 1,221 warehouses, with over 6.4-million square feet, that contained a broad array of property. Although comprehensive data on property in these agencies’ warehouses were unavailable, interviews, site visits, and agencies’ data on warehouses themselves provided some information on the types of property in them. We found that the agencies had some commonalities in the contents of their warehouses. For example, all three had warehouses that contained material-handling equipment, such as fork lifts, as well as excess property being processed for disposal. However, much of the property in agencies’ warehouses was specific to their missions, according to agency officials and our observations. Table 1 includes information about the agencies’ warehouses and examples of the types of agency-specific property in them. We also visited warehouses at each of the selected agencies to obtain additional information about and view the types of property stored within them, as described below. FAA. FAA had warehouses at four main sites that contained property specific to the sites’ missions, and most of the remaining warehouses were buildings that contained equipment, tools, or materials to maintain aviation support systems or housed support systems, such as approach lighting systems, according to our analysis of FAA warehouse data, FAA officials, and sites we visited. For example, we visited FAA’s warehouses at the Mike Monroney Aeronautical Center, including the Logistics Support Facility, FAA’s largest warehouse and central location for maintaining and repairing aviation support systems deployed throughout the national airspace system. Most items in the warehouse were spare parts, materials, and systems or system components that had been sent to the facility for repair. We also visited FAA’s Staging Area, which supports FAA’s manufacture and assembly of new systems to be deployed throughout the country. Accordingly, much of the property at the two warehouses that comprise this facility was equipment, parts, and material, along with the machines and tools to manufacture and assemble the material. For example, we viewed components of a wind shear alert system that were being prepared to be shipped. FAA’s Mobile Asset Deployment Center stored and maintained FAA’s mobile assets, such as air traffic control towers and housing units that FAA deploys to maintain service during disruptions such as natural disasters. Finally, we visited a 96-square-foot shack—identified in FRPP as a warehouse and pictured below—that housed an approach lighting system. (See fig. 2 for examples of FAA warehouses and property.) Office of Science. Most Office of Science warehouses were located at Office of Science national laboratories. Warehouses at the two national laboratories we visited—Argonne National Laboratory and Fermi National Accelerator Laboratory—contained a broad variety of equipment, including equipment being staged for near-term use and equipment in longer-term storage specifically designated for future projects. For example, one warehouse at Fermi National Accelerator Laboratory contained a cryogenic system acquired by CERN, the European Organization for Nuclear Research, as its contribution to a planned experiment. This cryogenic system will be used for cooling purposes. A warehouse at the same site also contained some decades-old items kept as replacements for items still in use. According to officials, many of these older items would be difficult to obtain in a reasonable time frame for a reasonable price if a replacement were needed. In addition, at Fermi National Accelerator Laboratory, we saw a large, out-of-use calorimeter— a device commonly used in physics experiments—that was being stored for eventual use in an educational display. Warehouses also contained parts, materials, and supplies for laboratory use. (See fig. 3 for examples of Office of Science warehouses and property.) BOP. Most of the BOP warehouses were located at correctional institutions throughout the country, served similar functions, and contained similar types of property for inmate use, according to BOP headquarters officials and our review of BOP real property data. The two correctional institutions we visited each had a warehouse that served as a distribution center, where items arriving at the institution were received, processed, and sent to the appropriate personnel within the institution, and a food service warehouse, where food items used to feed the inmate population were stored. At one institution, non-perishable items for inmate use, such as uniforms, mattresses, soap, and toilet paper were stored at the distribution center, while the other institution we visited stored less property at the distribution center and expedited delivery to the relevant division. Additionally, one institution used a warehouse to store dairy equipment in support of an inmate-run dairy. (See fig. 4 for examples of BOP warehouses and property.) Selected Agencies Tracked Warehouse Costs but Lacked Information on Property Storage Costs All three selected agencies tracked certain direct costs for owned and leased warehouses, including operations and maintenance costs for owned warehouses and some leased warehouses, and the rental cost for leased warehouses (see table 2). Although the agencies had this cost information, they did not use it to systematically determine how much it costs to store their property in warehouses, whether at an aggregate or per-item level. Two features of how these agencies track property and warehouse costs would make it difficult to do so. First, as mentioned above, selected agencies did not have comprehensive information on items in warehouses, information that would be needed to determine per-item storage costs. Second, selected agencies generally incurred direct costs—rent, operations costs, and maintenance costs—at a warehouse level. However, because a warehouse may have had some of its square footage dedicated to other uses, such as office or laboratory space, it would be difficult to ascertain what percentage of costs would be allocated to storage versus these other uses. Moreover, in some cases, operations costs, such as utilities, were incurred at a multi-building level, making it difficult to determine what portion of the bill is attributable to a single warehouse. Finally, selected agencies generally did not track indirect costs, such as personnel costs for conducting regular inventories and other administrative costs associated with storing property in their warehouses, according to agency officials. While none of the selected agencies systematically tracked property storage costs, we did identify one Office of Science site, one Department of Transportation site, and one Department of Justice site that analyzed the use of specific portions of warehouses for cost allocation purposes. Officials at these agencies said that this approach may create incentives for property custodians to identify excess property in a timelier manner. Argonne National Laboratory, within the DOE’s Office of Science, annually analyzes direct costs for each building, including warehouses, and charges each division within the laboratory for the space it occupies. A report assessing contractor performance at DOE’s Fermi National Accelerator Laboratory noted that implementation of such a system could be an effective way to hold divisions accountable for the number of items they have in storage. The Department of Transportation and the Department of Justice each manage a warehouse near their respective headquarters that they use to store property for various divisions within each department. The departments charge users for the portions of the warehouses they occupy. While these approaches may create incentives to identify unneeded property in a timely manner, they may not be applicable for all circumstances. For example, staff at Fermi National Accelerator Laboratory stated that they explored the cost and benefits of analyzing space use to allocate costs by user but had not found it to be cost- effective or feasible. In addition, allocating costs based on warehouse usage would be challenging if users’ space usage changes regularly. Without Guidance, Selected Agencies Did Not Systematically Assess the Ongoing Need for Property in Their Warehouses Two of the Three Agencies Specified When to Identify Some Types of Unneeded Property, but None Speciied How to Assess Most Items for Ongoing Need Two of the three agencies we reviewed had policies in place explaining the frequency in which property custodians should assess property for ongoing need. Specifically, the Office of Science and BOP had policies that called for identifying unneeded property beyond the statutory requirement to continually survey property to identify excess. For example, DOE regulations, which cover the Office of Science, require managers to perform walkthroughs at least every 2 years to identify unneeded property. According to officials, these walkthroughs are conducted by contractors that manage national laboratories. Similarly, BOP policy requires that property custodians conduct an annual site inspection to identify unneeded property prior to the annual inventory, and, according to officials, this process is overseen by the institution’s associate warden. In contrast, FAA policy does not set any timeframe for property custodians to identify unneeded property. However, according to one FAA headquarters official, assessing property for ongoing need is inherent to the inventory process, which, according to FAA policy, should occur at least every 3 years for accountable property. In addition, only DOE had specific requirements to determine if property is needed. Specifically, DOE regulation requires written justification for retention of property classified as equipment held for future projects. If equipment is retained for longer than a year, the justification is to be reviewed by a higher level of authority, and retention of such equipment for longer than 3 years requires approval by the head of the DOE field organization. The Office of Science Organizational Property Management Officer—who is responsible for reviewing contractors that manage Office of Science sites—reviewed sites’ adherence to this requirement using metrics, such as acquisition date and time in storage, according to officials. Beyond this particular requirement for DOE, none of the agencies had a systematic way to identify property that may be unneeded. Instead, they primarily relied on professional judgment to determine the ongoing need for property in warehouses in the absence of guidance on how to determine whether property is still needed. For example, FAA officials confirmed that they do not have guidance or metrics on how to identify unneeded property and typically rely on property custodians’ professional judgment. According to officials at one FAA site we visited, property custodians do not use specific criteria for identifying unneeded property because it is obvious when items are no longer needed. Similarly, at the BOP institutions we visited, officials confirmed that they rely on property custodians’ professional judgment, along with the judgment of associate wardens, to identify unneeded property during the annual site inspections, but acknowledged that this has led to different outcomes. For example, at one site we visited, site officials stated that some associate wardens are more inclined than others to require property custodians to identify property as unneeded. While officials at all of the selected agencies said they believed property custodians were able to identify unneeded property in a timely manner using their professional judgement, we identified instances, through our interviews and agency assessments, where agencies had retained unneeded property in storage. While the agencies identified and in most cases addressed these instances, these situations demonstrate the challenges associated with agencies’ existing approaches. Specifically: A 2016 report from DOT’s inspector general found that FAA property custodians allowed obsolete computers to remain on the property records, including computer systems manufactured in 2006 or earlier that were likely no longer in use because of their 3- to 4-year lifecycles. In 2018, a review found that Fermi National Accelerator Laboratory’s contractor was storing IT equipment, which had not been classified as equipment held for future projects, dating back to 1998. The report recommended that the contractor review all IT equipment for continued need and that certain items be removed from the active inventory in their asset management system. BOP headquarters officials told us that, when assisting regional office personnel in training a new property official at an institution, they noticed the institution was storing inmate clothing that exceeded the institution’s needs. According to the officials, they worked with the new property official to transfer the clothing from the institution to another BOP institution that needed it. Stakeholders Identified Systematic Methods to Assess Property for Ongoing Need Selected agencies’ limited guidance on how to identify unneeded property and reliance on professional judgment were not unique to the agencies in our review. For example, in a previous review that examined five agencies—Environmental Protection Agency, Forest Service, GSA, Department of Housing and Urban Development, and Internal Revenue Service—we found that selected agencies did not have policies and processes for identifying unneeded property on a proactive basis and relied on “triggering events,” such as an office move to make excess property decisions. Moreover, the industry and standards-setting groups we interviewed for this review indicated that these approaches were common across the federal government. However, the industry stakeholders and federal agencies that participated in ICPM that we interviewed identified more systematic ways to identify unneeded property. For example: Periodic justification for continued storage. One agency implemented a policy in 2013 requiring written justification to retain certain accountable property for certain time periods, with the time period varying for different types of property. After the initial storage time period, written justification for continued storage must be reviewed and approved by an official who is above the property custodian. According to property officials, this policy has contributed to an estimated 35 to 40 percent reduction of property held in storage. Data analytics. Officials from another agency stated that they use a logistics management application to track and analyze information, such as property age, amount, rate of usage, and warehouse space availability. As a result, the agency has identified and disposed of excess property at various warehouses that otherwise would likely have been retained. For example, according to officials, analysis conducted using this application on idle property in one warehouse informed the decision to identify as unneeded a significant amount of furniture. A previous manager had acquired the furniture for use in staff housing, but the items were not well-suited to available housing in the area. Utilization reviews. Industry groups we interviewed advocated for increased use of data to assess utilization to inform decisions on whether to retain stored property, such as utilization reviews that systematically assess property utilization and continued need. For example, when conducting a utilization review, one stakeholder recommended a process that begins with pinpointing where the inactive population of property items reside. Upon locating anything that has been inactive for a certain period of time, within a certain storage area, those items are identified as candidates for disposal. After the results come in, the property custodians can recommend that a certain amount of items on the overall list are marked for disposal. Limited Guidance Exists for Property Management While some stakeholders identified systematic ways to identify unneeded property in certain circumstances, limited government-wide guidance exists for agencies to use to determine whether property in warehouses is still needed and being used. Specifically, there are two sources for guidance related to assessing property in warehouses for ongoing need: ASTM’s standards for strategic warehousing and storage of property. The standard for strategic warehousing notes that entities often continue a warehouse activity largely because it is easier than going through the effort of dismantling it. It urges that entities consider whether warehousing is needed. Furthermore, the standard asserts that a sound business case should be in place to support storage of property, including a decision of whether the items need to be warehoused. The standard for storage of property notes that entities should deploy an inventory management system to track incoming and outgoing assets; such a system can help in developing performance metrics for stored items. GSA’s federal warehousing bulletin. This bulletin references the two ASTM standards identified above and discusses the importance of critically assessing the need for items in storage, but provides limited information on how to make such assessments. According to GSA officials, the use of voluntary consensus standards, such as ASTM standards, can assist agencies with property management. However, only one agency official we interviewed stated that voluntary consensus standards informed the agency’s policy; the others we interviewed were either unaware of the standards or said the standards were not relevant to agency policy or practice. The FPPMA requires agencies, in accordance with GSA guidance, to inventory and assess property. As part of such assessments, it calls for evaluations of the age and condition of the property and the extent to which the agency uses it. According to officials at the selected agencies, they are waiting for guidance from GSA before taking steps to implement FPPMA. According to GSA officials, they are in the process of developing informal guidance on minimizing and identifying excess property to meet this requirement because FPPMA did not provide GSA additional authority to issue regulations or formal guidance. In particular, GSA developed draft guidance, which incorporated principles from a new ASTM standard on identifying and reducing excess property that GSA officials expect will be issued in early 2020, and provided it to ICPM participants for review and comment in September 2019. According to GSA officials, this informal guidance will be issued in December 2019. GSA officials plan to include the guidance on the GSA website and disseminate it to ICPM participants and may provide it in hard copy at relevant GSA events. The draft guidance we reviewed encourages agencies to designate an individual to manage an agency’s asset management program and use that system to capture and provide information on property age, condition, utilization, and mission dependency on a real-time basis, among other things. The draft guidance also included some criteria agencies could use to identify excess property. However, the guidance did not provide specific approaches or practices agencies could use to assess property utilization, including property stored in warehouses. The draft guidance and an accompanying strategy document indicate that GSA will collect best practices and incorporate them into the guidance, but GSA officials did not specify what types of best practices it plans to include or provide a timeline for doing so. Including additional information on approaches or practices agencies can use to assess property use and ongoing need—such as periodic property justifications, data analytics, and utilization reviews—could assist agencies in fulfilling their FPPMA requirements. Moreover, GSA officials did not provide a documented plan or time frame for communicating the guidance beyond publishing it on GSA’s website and disseminating it to ICPM members, an approach that can limit the reach and awareness of this information to agencies government-wide. As we have previously reported, work by others has shown that inaction on unneeded government property can limit its efficient use. Conclusions As agencies continue efforts to manage their warehouse space in accordance with government-wide initiatives, improvements to how agencies assess property utilization and identify unneeded property in warehouses could enhance these efforts. The agencies in our review did not systematically assess their property for ongoing need and in some cases, retained unneeded property. More broadly, agencies across the government are operating without the benefit of government-wide guidance that could help assess their property for ongoing need in a systematic manner. With the recent enactment of FPPMA, an opportunity exists for GSA to develop and communicate guidance to help agencies assess property utilization and identify unneeded property in warehouses more efficiently that includes practices GSA identifies as being useful. Such guidance could help agencies avoid retaining property that is no longer needed and, as a result, allow them to better manage the use of their warehouse space. Recommendation The Administrator of GSA should direct the Office of Government-wide Policy (1) to incorporate into its guidance approaches or practices that agencies could use to assess utilization of and the ongoing need for property—approaches such as recommendations for periodic justifications, data analytics, and utilization reviews—and (2) to develop a plan and timelines for communicating the guidance to agencies government-wide. (Recommendation 1) Agency Comments We provided a draft of this report to GSA, DOT, DOE, and DOJ for review and comment. GSA concurred with our recommendation and provided written comments, which are reprinted in appendix II and summarized below. DOT, DOE, and DOJ each stated in an email that they had no comments on the draft report. In its written comments, GSA agreed with our recommendation and stated that it is further developing its guidance as well as a plan and timeline for dissemination of that guidance to executive agencies. 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 GSA Administrator, the Secretary of Energy, the Attorney General, and the Secretary of Transportation, 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 members have any questions regarding this report, please contact Lori Rectanus at (202) 512-2834 or rectanusl@gao.gov. Contact points for our Offices of Congressional Relations and Public Affairs may be found on the last page of this report. Key contributors to this report are listed in appendix III. Appendix I: Objectives, Scope, and Methodology This report addresses: (1) what is known about property in selected federal agencies’ warehouses and how much they spend to store this property and (2) the extent to which selected agencies assess the ongoing need for property stored in warehouses. To address both objectives, we selected three agencies for analysis—the Federal Aviation Administration (FAA) within the Department of Transportation; the Office of Science within the Department of Energy (DOE); and the Bureau of Prisons (BOP) within the Department of Justice. We limited our scope to civilian agencies because we have already done extensive work on property management within Department of Defense. At the department level, we used Reduce the Footprint data from fiscal year 2017 because they were the most current data available when we conducted the analysis to identify the top 10 departments in terms of warehouse square footage. To obtain variation among these agencies, we categorized these departments as large, medium, or small in terms of warehouse square footage and selected one from each category based on changes in square footage between fiscal years 2015 and 2017 using fiscal year 2017 Reduce the Footprint data and on the proportion of leased warehouse space to owned warehouse space using fiscal year 2017 Federal Real Property Profile (FRPP) data. Because none of the selected agencies manages property at the department level, we then selected a component within each department. For the Department of Transportation and the Department of Justice, we selected the components with the most warehouse square footage according to fiscal year 2017 FRPP data—FAA and BOP, respectively. For DOE, we used the agency’s fiscal year 2017 real property data to identify the components with the most warehouse square footage because DOE reports most information to FRPP at the department level rather than for specific offices, such as the Office of Science. We then selected the Office of Science, which had third highest amount of warehouse square footage, because of security concerns with one of the components with more warehouse square footage and because the other component with more warehouse square footage used a greater proportion of warehouse space to store nuclear and nuclear-related material. To determine what is known about property in selected agencies’ warehouses, we interviewed headquarters-level officials regarding the agencies’ property data, conducted site visits to view and photograph property stored in warehouses, and gathered information in interviews with agency officials. In selecting sites, we selected at least one site per agency that was among the largest in terms of warehouse square feet for that agency and at least one other site that was near one of the large sites, as described below: FAA: Mike Monroney Aeronautical Center in Oklahoma City, Oklahoma; Staging Area and Mobile Asset Deployment Center in Independence, Missouri; and Charles B. Wheeler Downtown airport in Kansas City, Missouri. Office of Science: Argonne National Laboratory and Fermi National Accelerator Laboratory in the Chicago area. BOP: U.S. Penitentiary Leavenworth in Leavenworth, Kansas, and Federal Correctional Institute El Reno in El Reno, Oklahoma. For each agency, we also interviewed officials at the headquarters and regional levels. Information obtained from these sites and regional officials is not generalizable to the selected agencies, and information from these agencies is not generalizable to other agencies. To determine how much selected agencies spend to store property in warehouses, as well as the numbers and square footage of these warehouses, we analyzed FRPP data from fiscal year 2018 for FAA and BOP, and DOE fiscal year 2018 real property data for the Office of Science because DOE reported most data to FRPP at the department level; both sources included information about direct costs such as rent, operations, and maintenance costs. We used FRPP data from fiscal year 2018 because that was the most recent data available when we conducted our analysis and DOE data covering the same period to be consistent. We reviewed documentation related to these data sources, interviewed knowledgeable officials, and determined that these data were sufficiently reliable for providing information about warehouse numbers, square footage, and the costs listed above. To determine the extent to which selected agencies assess the ongoing need for property stored in warehouses, we reviewed statutes, regulations, GSA guidance, our prior work, reports by federal agencies’ Offices of Inspector General, and relevant industry standards related to property storage and warehousing practices. In addition, for selected agencies we analyzed property policies and procedures for identifying and disposing of unneeded property and interviewed headquarters, regional, and site officials. We also interviewed three industry stakeholders—two property-management and one standards-setting organization—to discuss property storage and warehousing processes, practices, and standards that agencies could use to assess the ongoing need for property. We selected these organizations based on their knowledge about property management practices. Furthermore, we interviewed officials from four agencies—Census Bureau, Department of State, Internal Revenue Service, and National Aeronautics and Space Administration—that participate in the Interagency Committee on Property Management (ICPM), a committee chaired by GSA that consists of executive agency representatives interested in federal property. We invited all ICPM participants to speak with us regarding their practices for identifying unneeded property and interviewed all participants who volunteered to participate to understand how other agencies assess property for ongoing need. Finally, we reviewed FPPMA’s requirements and interviewed GSA’s Office of Government-wide Policy officials about GSA’s role in assisting agencies in identifying unneeded federal property, how FPPMA could affect GSA’s roles and responsibilities going forward, and GSA’s progress in implementing FPPMA. We conducted this performance audit from October 2018 to December 2019 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. Appendix II: Comments from the General Services Administration Appendix III: GAO Contact and Staff Acknowledgments GAO Contact Staff Acknowledgments In addition to the individual named above, the following individuals made important contributions to this report: Nancy Lueke (Assistant Director), Rebecca Rygg (Analyst-in-Charge), Terence Lam, Malika Rice, Kelly Rubin, Patrick Tierney, Laurel Voloder, and Crystal Wesco.


Background 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. Agency Comments 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. Data Used in Our Analysis 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. Locality Types 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. Renter Household Characteristics 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). Rent Affordability 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. Rental Housing Conditions 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. Data 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. These are described in table 4. 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. Methodology and Results 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. Completeness 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. Quality 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. Limitations 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 GAO Contact 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.