American Housing Survey Research Articles

Predicting Mobility: Who Is Forced to Move?

We use the 2013 American Housing Survey to examine which households are more likely to experience a forced move compared to a voluntary move. We examine how household vulnerability varies by racial and socioeconomic stratification, as well as other household demographics among homeowners and renters. We analyze household-level predictors of experiencing an inclusively defined forced move, including moves caused by disasters, private and government displacement, and eviction (for renters) or foreclosure (for homeowners). Comparing an inclusive definition of displacement to voluntary mobility, we find that lower levels of education, income, and the presence of a disabled household member increase the likelihood of displacement for homeowners. Among renters, the presence of children, older households, and being native-born increase the odds of displacement, but a female-headed household reduces them. When examining type-specific displacement, we find variation in who is most susceptible to experience a forced move.

Hedonic Models and Market Segmentation

This paper explores the pricing of heterogeneous goods in the presence of market segmentation. We use housing as an example. We extend the theoretical hedonic model of Rosen (1974) and show that, in the presence of market segmentation, the hedonic price line is no longer continuous or unique. Using American Housing Survey data for the Miami and Louisville metropolitan areas and a finite mixture estimation approach, we find distinct market segments based on ethnicity, race, and income.

Are the Outcomes of Low-Income Housing Programs Consistent with their Rationales?

Traditional rationales for low-income housing assistance imply that a housing program is not successful unless it induces recipients to occupy better housing than they would choose if given equally costly cash grants. The general assumptions of economic theory do not imply that current housing programs would lead to that outcome. The few existing studies of this matter are dated and ignore the cost-ineffectiveness of project-based housing assistance. The purpose of this paper is to determine the extent to which different housing programs are consistent with traditional rationales. Based on data from the 2013 American Housing Survey and evidence on the cost-effectiveness of different types of housing assistance, the results indicate that public housing tenants had lower aggregate housing consumption than they would have chosen with equally costly cash grants and most public housing tenants and a substantial minority of recipients of each other type of housing assistance occupied worse housing than they would have chosen with this alternative policy. Indeed, about half of public housing tenants, a third of occupants of HUD-subsidized privately owned projects, and more than a fifth of recipients of the hodgepodge of other programs of project-based housing assistance occupied worse housing and consumed less of other goods and services with their housing assistance than with equally costly cash grants. Of the programs considered, tenant-based housing vouchers induced a larger fraction of recipients to occupy better housing relative to an equally costly cash transfer and provided the greatest increase in the market value of total consumption relative to taxpayer costs than any of the forms of project-based housing assistance. In fact, vouchers outperform project-based assistance across all metrics used in this study.

Can the Nation’s Housing Support a Population Seeking to Age in Place?

While surveys report that most older adults wish to “age in place,” the nation’s current housing and neighborhoods fall short on several dimensions needed to support independence and health in later life. Drawing from national data (including the American Housing Survey, American Community Survey, Health and Retirement Survey, and Survey of Consumer Finances), we describe the current housing and living situations of older adults and key challenges they face in securing affordable, accessible housing while also securing supportive services. We identify three challenges: the unaffordability of housing, which causes budgetary tradeoffs in healthcare spending; a lack of accessibility features in homes and neighborhoods, which can limit independence and safety, and the low-density location of much of the US housing stock (including that inhabited by older adults), where service delivery is difficult and the potential for isolation is high. We conclude with an overview of the policy implications of these challenges.

Small and medium multifamily housing: affordability and availability

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

Local economic conditions and the nature of new housing supply

Abstract We explore the effects of local economic conditions on the type and size of newly constructed housing units in a city. Exploiting the 1984–2004 metro area samples of the American Housing Survey and US Census building permit data from 1980 to 2018, we find that positive local income shocks (i) increase a city’s share of multi-family housing in new construction and (ii) trigger the construction of smaller units. These responses are driven by migration. Our findings are consistent with a modified open monocentric city model that more realistically assumes land is available for conversion into new housing throughout the city.

A health impact assessment of lack of air conditioning during extreme heat in a temperate climate

Introduction: Air conditioning (AC) ownership and use can mitigate the effects of summer heat on health. With increasing temperatures, increasing economic disparities, and the unequal distribution of electricity production harms (e.g., air pollution) and benefits (e.g., AC), heat-related health disparities are of increasing concern. We reviewed the literature on the protective effects of AC and performed a quantitative health impact assessment of AC in Wayne County, Michigan, USA.Methods: We used: 1) a model of central AC prevalence based on American Housing Survey and tax assessor’s data; 2) heat-mortality and -hospitalization association estimates; 3) estimates of the AC relative risk ratio (RRR), or relative risk of the health event during vs. not during extreme heat among those without AC vs. the relative risk among those with AC; and 4) mortality, hospitalization, and population estimates to calculate and map extreme-heat attributable health burden and costs (2020 $USD) by census tract (500-5,000 persons).Results: RRRs ranged widely (1.02-11.5) depending on extreme-heat definitions, health outcomes (e.g., all-cause mortality or heatstroke), and AC geographic resolution (community or individual). Ten-year extreme-heat (defined at the 99th percentile of temperature) associated health rates ranged widely across census tracts (0.6-5.6 deaths/100,000 persons and 2.0-26 hospitalizations/100,000 persons), as did monetized health costs ($11,000-$1,640,000 per census tract).Conclusions: Health impacts of lack of AC vary spatially. In future work, we will consider the added effects of low usage among those with AC, the cumulative burden of exposure to power plant emissions and lack of AC, and the impacts of heat during an extended power outage. Quantifying and mapping the cumulative health impacts of energy production can inform neighborhoods of air- and climate-associated risk disparities and inform energy mix and utility rate regulatory decisions.

Asset maintenance as hidden investment among the poor and rich: Application to housing

Substituting maintenance of existing fixed assets for investment in new assets, and thus affecting their depreciation, can play a major role in the economic decisions of low-income agents. We develop a theoretical framework that shows that poorer households, which generally face relatively higher adjustment (moving) costs and more credit constraints, will adjust their home maintenance decisions to a higher degree than richer households after an income change. We test our predictions using a panel of homeowners from the American Housing Survey for 1997-2011. We find that the income elasticity of maintenance is higher among low- versus high-income households and that the adjustment differences are more acute for permanent than for transitory income changes. For every dollar increase in the estimated permanent and transitory income, home maintenance expenses will increase by 5.2 and 2.9 cents in the lower income tertile and by zero and 0.5 cents in the upper tertile.

Social capital as a predictor of neighborhood satisfaction: an analysis of American Housing Survey national data

ABSTRACT Social capital plays a critical role in neighborhood satisfaction, which in turn contributes to healthy and vibrant communities. However, scholars in housing and community development often leave social capital variables out of analyses. This study uses the American Housing Survey (AHS) 2013 national data, the only AHS data with social capital variables, to measure whether and how social capital dimensions relate to perceived livability of neighborhoods. The research controls for household characteristics, actual and perceived neighborhood and housing attributes, and locational variables. The study uses perceived livability as a proxy to measure the satisfaction levels of residents with their neighborhoods. Overall, citizen participation is weak, while social trust, cohesion, and ties within neighborhoods are strong among the survey respondents. Most of the social capital dimensions are significantly related to perceived neighborhood livability in a positive direction. The results indicate that stronger social capital contributes to higher perceived neighborhood livability. However, the effect of social capital is different among homeowners, renter, urban, and suburban/rural households. Therefore, the important roles of social capital in promoting neighborhood satisfaction and livability require policymakers to pay more attention to social capital factors in neighborhood needs assessment and community development practice.

Impacts of ENERGY STAR Appliances on U.S. Multifamily Rents

Implementation of ENERGY STAR appliances can be a significant source of energy efficiency within the multifamily rental industry. While there are many positive environmental outcomes by reducing energy consumption, there are also potential positive financial outcomes including lower operating costs, increased asset valuation, and higher rent potential. This study, through a rental hedonic estimation using the American Housing Survey (AHS) dataset, examines the rent impacts of ENERGY STAR appliances in multifamily rental units in the United States. The results suggest that there is a 1.6% average rent premium per ENERGY STAR product included in a rental unit. Knowing that these types of ENERGY STAR appliance changes may actually warrant rent increases could help improve the chances of changes taking place, but more research is needed with other datasets to further account for other rental unit features and location.

The effects of unplanned moves on post-crisis housing situations

ABSTRACT Currently, there is not much research on subsequent housing decisions for households that are displaced due to a crisis event. Crisis events include disasters, but can also include unplanned moves related to eviction and foreclosure as well. The American Housing Survey provides the research community the opportunity to examine such situations through a special module related to recent movers introduced in 2013. Using measures of well-being related to residential satisfaction and housing cost burden, we analyze the differences in these measures for both regular movers and those who have moved due to circumstances of crisis. Initial analysis shows that there are highly significant differences between the two populations, with regular movers having higher housing unit satisfaction and neighborhood satisfaction, and lower housing cost ratios, when compared against households which moved under crisis situations. Similar findings are made when comparing against households’ previous housing situations as well. After controlling for demographic and other independent variables, however, the effect of crisis events is nullified for residential satisfaction. Housing cost ratios take divergent paths according to tenure, with renters having heavy cost burdens upon relocation and homebuyers actually reducing their ratios significantly.

Identifying Jurisdictions at Risk of Containing Housing Units With Deteriorated Paint: Results and Targeting Implications for the US Department of Housing and Urban Development.

The US Department of Housing and Urban Development provides millions in annual funding to make low-income housing lead safe, but funds are limited relative to need. To adequately target efforts, local program administrators must identify neighborhoods that are the most "at risk" of residential lead exposure; however, no federal agency currently provides a public data set for this use. To examine pre-1980 households with large areas of deteriorated paint, a significant and common predictor of lead dust, and identify high-risk jurisdictions. To highlight the potential use of a newly available data set for strategic lead poisoning prevention and targeting. Microdata from the 2011 American Housing Survey and the 2009-2013 American Community Survey were used to develop a household-level predicted risk metric that identifies housing units at risk of containing large areas of deteriorated paint. Predicted risk, defined as the mean predicted percentage of occupied housing units at risk of containing deteriorated paint within a given jurisdiction, was summarized by state, county, and tract. National, all occupied housing units. Occupied housing units summarized by household (n = 9 363 000), census tract (n = 72 235), county (n = 3143), and state (n = 51). Housing units built prior to 1980 with a large area of deteriorated paint. New York, Rhode Island, New Jersey, Massachusetts, and Pennsylvania had the highest predicted percentage of at-risk households (range: 2.52%-2.90%). County-level and tract-level estimates are the most useful when examining a predefined jurisdiction; New York state was presented as a case study. County-level quartile risk scores revealed Albany as an at-risk jurisdiction. Tract-level quartile risk scores further identified at-risk neighborhoods in northeastern Albany. Findings can help housing and health policy makers identify and target geographic areas with a high probability of households at risk of potential exposure to deteriorated lead-based paint.

Residential Building Lifespan and Community Turnover

Environmental impact studies within the built environment rely on predicting building lifespan to describe the period of occupation and operation. Most life cycle assessments (LCAs) are based on arbitrary lifespan values, omitting the uncertainties of assessing service life. This research models the lifespan of American residential housing stock as a probabilistic survival distribution based on available data from the American Housing Survey (AHS). The log-normal, gamma, and Weibull distributions were fit to demolition data from 1985 to 2009 and these three models were compared with one another using the Bayesian information criterion. Analysis revealed that the estimated average housing lifespan in the United States is 130 years given model assumptions, although a probabilistic approach to lifespan can yield higher accuracy on a case-by-case basis. Parameters for modeling housing lifespan as log-normal, gamma, and Weibull survival functions are published with the intent of further application in LCA. The application of probabilistic housing lifespan models to community-wide turnover and integration with existing simulations of natural disaster are proposed as potential ways to achieve community sustainability and resilience goals.

Self-help housing and DIY home improvements: evidence from the American Housing Survey

Self-help housing has been widely studied across the developing world. Little is known, however, about the contemporary use of self-help housing in the U.S. In this study we examine self-help housing in the U.S. and its potential links to two related concepts: incremental development and informal housing. We use data from the American Housing Survey from 1997 to 2011 to examine the prevalence, location, and the development process of self-help housing in comparison with that of conventional housing. Our analysis shows that self-help housing is common in the United States, comprising approximately 10% of new owner-occupied single-family housing units nationwide and more than double that in rural areas across the country. We analyse home improvement activity for self-help and conventional homeowners, illustrating the incremental process by which self-builders improve their homes, widespread reliance on do-it-yourself (DIY) methods, and the impact of these methods on housing affordability. We conclude by discussing the implications of our findings for housing research and policy.

Association of US Households’ Disaster Preparedness With Socioeconomic Characteristics, Composition, and Region

Since the terrorist attacks on September 11, 2001, the US government has promoted household disaster preparedness, but preparedness remains low. To identify disparities in disaster preparedness among US households. This cross-sectional study used data from a nationally representative sample of US households from the 2017 American Housing Survey's topical section on preparedness to assess associations of disaster preparedness with households' socioeconomic characteristics, composition, and region. Logistic regressions were used to assess associations of household characteristics with overall preparedness, resource- and action-based preparedness, and specific preparedness items. Data analyses were completed on March 27, 2020. Combined household income, head of household's education level, race/ethnicity, marital status of head of household, head of household aged 65 years or older, presence of children or a household member with a disability, and region. Nine actionable preparedness items, such as having an emergency carry-on kit (resource), food and water stockpiles (resource), and alternative communication plans and meeting locations (action). Items were summed for the measures of overall, resource-based, and action-based preparedness, with preparedness defined as meeting at least half of the criteria. Among 16 725 included households, 9103 household heads were men (54.4%), 11 687 were married (69.9%), and 10 749 (66.1%) had some college education or higher. In all, 1969 household heads (11.8%) were black, while 2696 were Hispanic/Latino (16.1%); 3579 household heads (21.4%) were 65 years or older. A total of 7163 households (42.8%) included children, and 3533 households (21.2%) included a person with a disability. Households were more likely to fulfill at least half of the criteria for resource-based preparedness (10 950 households [65.5%]) than for action-based preparedness (6876 households [41.1%]). Wealthy households and those with household heads aged 65 years or older were more likely to fulfill at least half of resource-based items (wealthy households: adjusted odds ratio [aOR] by logged income, 1.18 [95% CI, 1.13-1.22]; household heads age ≥65 years: aOR, 1.42 [95% CI, 1.29-1.55) but less likely to fulfill action-based items (wealthy households: aOR: 0.96 [95% CI, 0.93-0.99]; household heads age ≥65 years: aOR, 0.92 [95% CI, 0.84-0.99]). Households with black household heads were more likely to fulfill items directly related to emergencies (carry-on emergency kit: aOR, 1.26 [95% CI, 1.14-1.39]; alternative communication plan: aOR, 1.55 [95% CI, 1.39-1.72]; alternative meeting location: aOR, 1.18 [95% CI, 1.07-1.31]) but less likely to fulfill resource-based items (at least half of resource items: aOR, 0.89 [95% CI, 0.80-0.99]). These findings suggest that types of preparedness vary by household characteristics. Targeted strategies are needed to promote preparedness across communities.

The unequal housing and neighborhood outcomes of displaced movers

ABSTRACT Involuntary housing displacement is a stress-inducing life event that can cause and exacerbate both psychological and material hardship. Forced moves may invoke a disattainment process, whereby displaced movers move into lower quality housing and neighborhoods, placing them in a precarious housing position. Employing propensity score analyses, this study uses data from the recent mover module of the American Housing Survey to match recent movers whose moves were voluntary to recent movers whose moves were forced. Results show that moves caused by displacement compared to voluntary moves generally lead to worse housing and neighborhood outcomes. However, these results are dependent on the type of displacement experienced. Movers forced to leave their homes due to eviction move into worse housing and neighborhoods while forced moves caused by private action and foreclosure do not. Meanwhile, forced moves caused by natural hazards or government action result in worse housing, but not neighborhoods.

Modeling and comparing central and room air conditioning ownership and cold-season in-home thermal comfort using the American Housing Survey.

Household-level information on central (cenAC) and room (rmAC) air conditioning and cold-weather thermal comfort are often missing from publicly available housing databases hindering research and action on climate adaptation and air pollution exposure reduction. We modeled these using information from the American Housing Survey for 2003–2013 and 140 U.S. core-based statistical areas employing variables that would be present in publicly available parcel records. We present random-intercept logistic regression models with either cenAC, rmAC or “home was uncomfortably cold for 24 hours or more” (tooCold) as outcome variables and housing value, rented vs. owned, age, and multi- vs. single-family, each interacted with cooling- or heating-degree days as predictors. The out-of-sample predicted probabilities for years 2015–2017 were compared to corresponding American Housing Survey values (0 or 1). Using a 0.5 probability threshold, the model had 63% specificity (true negative rate), and 91% sensitivity (true positive rate) for cenAC, while specificity and sensitivity for rmAC were 94% and 34%, respectively. Area-specific sensitivities and specificities varied widely. For tooCold, the overall sensitivity was effectively 0%. Future epidemiologic studies, heat vulnerability maps, and intervention screenings may reliably use these or similar AC models with parcel-level data to improve understanding of health risk and the spatial patterning of homes without AC.

Housing accessibility for seniors with mobility and grasping disabilities: lessons from the American Housing Survey

As the aging population increasingly grows over the coming decades, the number of new constructions or modifications made to housing units that need basic accessibility features is projected to rise considerably in the United States. This study focuses on the living conditions of existing housing for stayers – those who have remained in their place of dwelling since reaching the retirement age of 65. In doing so, this study investigates how accessible stayer housing is to meet the daily needs for aging in place, particularly for seniors with mobility and grasping disabilities. The lack of housing stock designed to meet the needs of the aging population is a critical concern that housing policy must address. Using national American Housing Survey (AHS) data, this study provides critical insights on this issue.

Forced to move: Patterns and predictors of residential displacement during an era of housing insecurity

Recent work on residential displacement—being forced out of one’s home—hints that its nature and prevalence have changed during the early twenty-first century. We evaluate this supposition against the backdrop of past displacement research. Reason-for-move data from seven waves of the American Housing Survey (2001-2013) are used to construct displacement measures that range from narrow (limited to forced moves prompted by government or private action or disaster loss) to broad (also including eviction and foreclosure). Our analysis shows that, regardless of measure, no consistent upward trend over time is apparent in the small percentage of mobile households experiencing displacement, although as many as 3.6 million individuals may be affected biennially. We also find that longstanding socioeconomic, racial, and other disparities in displacement persist but tend to be of modest magnitude. Such patterns could contribute to a perception of displacement as socially unpredictable, further heightening public concern about the issue.

Rental Housing Spot Markets: How Online Information Exchanges Can Supplement Transacted-Rents Data.

Traditional U.S. rental housing data sources such as the American Community Survey and the American Housing Survey report on the transacted market-what existing renters pay each month. They do not explicitly tell us about the spot market-that is, the asking rents that current homeseekers must pay to acquire housing-though they are routinely used as a proxy. This study compares governmental data to millions of contemporaneous rental listings and finds that asking rents diverge substantially from these most recent estimates. Conventional housing data understate current market conditions and affordability challenges, especially in cities with tight and expensive rental markets.