American Housing Survey Research Articles

A review on American senior housing and financing decisions

Purpose The world population is getting older and housing decisions matter to the security of retirement life and well-being. However, senior housing and financing decisions are largely overlooked in the literature. This study aims to provide a comprehensive overview of the senior housing and financing decisions in the USA. Design/methodology/approach Using 2021 American Housing Survey data and ordinary least squares estimates, this paper studies senior housing and financing decisions in the 15 most populous US metropolitan areas. Multiple regressions are used to analyze the whole sample as well as various subsamples. Findings This paper finds strong evidence that seniors are more likely to own a house with a higher market value compared with nonseniors. More specifically, although race and gender do not matter in this relationship, it is less pronounced among those with higher education. Also, seniors, especially white senior homeowners, are more likely to have a lower mortgage debt burden due to a higher upfront down payment than nonseniors. Last, seniors are more likely to refinance their homes and receive cash than nonseniors. Research limitations/implications Constrained by the data limitations, this study specifically focuses on seniors’ housing and mortgage decisions. Future research could involve exploring seniors’ mortgage decisions by considering factors such as mortgage loan terms, borrower credentials and the evolving landscape of the senior housing market, among other aspects. Practical implications The findings would help policymakers and contractors in the construction industry better understand the uniqueness of challenges in the senior housing market and develop effective housing policies and programs that cater to the needs of the elderly. A comprehensive understanding of the senior housing market would also help seniors make informed purchase and financing decisions. Social implications The authors propose providing financial education to the elderly, particularly those of disadvantaged status, to improve their expertise and bargaining skills in the mortgage loan application process. Originality/value This paper contributes to understanding the dynamic senior housing market, which encounters challenges and opportunities associated with the aging population. The study fills the gap of limited research regarding seniors’ housing decision and their financing choices in the USA and would help policymakers and contractors in the construction industry develop effective housing policies and programs that cater to the needs of the elderly.

Disaster preparedness among Asian, Pacific Islander, and Desi American communities.

Disaster experiences and explorations of preparedness among Asian, Pacific Islander, and Desi Americans (APIDA) in the United States are often overlooked owing to their relatively smaller population share. APIDA are not homogenous, and their disaster experiences warrant further examination. This paper does so by investigating disaster preparedness using disaggregated information about APIDA. The study utilises nationally representative data from the 2017 American Housing Survey, analysing sociodemographic covariates. The disaster preparedness score among APIDA communities was approximately 4.81 on a zero to nine scale. APIDA renters and non-US citizens were less prepared than homeowners and US citizens. Among subgroups, Korean, Chinese, and Vietnamese respondents who were non-US citizens were less prepared than those who were US citizens. Marital status was significantly and positively associated with preparedness among Indians, Japanese, Vietnamese, and multiracial respondents. The findings underscore the importance of data disaggregation and tailored preparedness information and resources to address specific challenges APIDA communities face instead of a one-size-fits-all approach.

Analyzing racial disparities in the United States homeownership: A socio-demographic study using machine learning

While increasing homeownership has been a focal point for policymakers in the United States, the distribution of access to homeownership opportunities across all families within the country has not been equitable. The complex relation between social factors and economic conditions has significantly shaped the journey toward homeownership. Of particular concern is the persistent disparity in homeownership rates among Black households in the United States. Effectively addressing these disparities within urban communities necessitates comprehensive policy interventions and social initiatives. However, the success of such endeavors hinges upon a comprehensive grasp of the underlying causes of these inequalities. Drawing upon machine learning techniques, our analysis of national American Housing Survey data reveals clear evidence for the multifaceted and socio-demographic nature of racial disparities in homeownership within the United States. Our findings provide evidence for two previously obscured patterns. First, we observe that race-related risk factors, separate from household characteristics, are heterogeneous, not uniformly affecting all households. Specifically, our analysis highlights: (1) there is a geographical variations in how race-related risk factors contribute to racial inequality, and (2) households with lower educational attainment are potentially at increased risk of discrimination. Second, our findings underscore the potential of policies and social programs aimed at enhancing educational attainment to bridge the racial gap through two mechanisms: (i) mitigating vulnerability by alleviating race-related risk factors, and (ii) suppressing the compounding effects of racial disparities.

Assessment of sewer connectivity in the United States and its implications for equity in wastewater-based epidemiology.

Wastewater-based epidemiology is a promising public health tool that can yield a more representative view of the population than case reporting. However, only about 80% of the U.S. population is connected to public sewers, and the characteristics of populations missed by wastewater-based epidemiology are unclear. To address this gap, we used publicly available datasets to assess sewer connectivity in the U.S. by location, demographic groups, and economic groups. Data from the U.S. Census' American Housing Survey revealed that sewer connectivity was lower than average when the head of household was American Indian and Alaskan Native, White, non-Hispanic, older, and for larger households and those with higher income, but smaller geographic scales revealed local variations from this national connectivity pattern. For example, data from the U.S. Environmental Protection Agency showed that sewer connectivity was positively correlated with income in Minnesota, Florida, and California. Data from the U.S. Census' American Community Survey and Environmental Protection Agency also revealed geographic areas with low sewer connectivity, such as Alaska, the Navajo Nation, Minnesota, Michigan, and Florida. However, with the exception of the U.S. Census data, there were inconsistencies across datasets. Using mathematical modeling to assess the impact of wastewater sampling inequities on inferences about epidemic trajectory at a local scale, we found that in some situations, even weak connections between communities may allow wastewater monitoring in one community to serve as a reliable proxy for an interacting community with no wastewater monitoring, when cases are widespread. A systematic, rigorous assessment of sewer connectivity will be important for ensuring an equitable and informed implementation of wastewater-based epidemiology as a public health monitoring system.

Analyzing energy performance in American low income households: A DataDriven approach with machine learning insights

The energy burden within low-income households presents a significant societal challenge that necessitates a thorough understanding for developing effective and sustainable policies. However, very few studies analyze and model energy-related factors among low-income individuals. Thus, this study critically investigates the relationship between various buildings' characteristics and four distinct energy-related outputs (i.e., energy burden, annual energy cost, annual energy cost per square footage, and annual energy cost per square footage per person) for both low-income and non-low-income households across the United States, using six machine learning algorithms. Leveraging the American Housing Survey dataset, this study identifies factors impacting energy burden and consumption and highlights variables contributing to the energy burden disparity among these two household groups, compromising census division, number of floors within the unit, household income, federal poverty level threshold, and number of rooms in the unit for non-low-income households, and the number of persons aged 18 and over, census division, household income, number of rooms in a unit, and unit size in square feet for low-income households. Additionally, it introduces 48 models to predict energy-related outputs and evaluates the accuracy of models. To evaluate the accuracy of these models, two key metrics are employed: R2 (coefficient of determination) and RMSE (root mean square error), which provide insights into the models’ predictive accuracy and error rates, respectively. The study reveals that Gradient Boosting Regression and XGBoost predict outcomes for low-income and non-low-income households with higher accuracy and with fewer inputs, making them cost-effective and efficient models. The findings enhance our understanding of energy equity issues and equip policymakers with actionable insights to alleviate energy burden disparities and promote energy equity across diverse socioeconomic landscapes.

Racial/Ethnic Disparities in Renters’ Experiences with Maintenance Delays in the United States

Structural racism and individual discrimination contribute to racial inequalities in poor housing conditions in the United States. Less is known about whether and how structural racism and individual discrimination shape a parallel, but distinct, process that is also consequential for family wellbeing: experiencing housing unit maintenance delays. Maintenance delays transform acute problems into chronic stressors and increase exposure to physical hazards over time. Using the 2013 American Housing Survey, I examine racial/ethnic disparities in maintenance delays across non-Hispanic White, Black, Hispanic, Asian, and American Indian/Alaska Native renters. Given that 2.3 million low-income households rent using Housing Choice Vouchers (HCVs), a federal housing assistance program with requirements around repair timing, I also examine how renting with a voucher shapes maintenance delays. There are three principal findings. First, White renters are more likely to report timely repairs than either Black or Hispanic renters. Second, for Black renters, both structural racism experienced in rental markets and individual discrimination drive this disparity, whereas Hispanic renters’ diverging maintenance experiences are largely explained by pathways impacted by structural racism. Third, renting with an HCV is not associated with repair timeliness for any racial/ethnic group. Taken together, the findings suggest that racial/ethnic disparities in substandard housing emerge not only through unequal exposure to housing quality problems but also through unequal responses to these issues.

Multidimensional well-being of US households at a fine spatial scale using fused household surveys

Social science often relies on surveys of households and individuals. Dozens of such surveys are regularly administered by the U.S. government. However, they field independent, unconnected samples with specialized questions, limiting research questions to those that can be answered by a single survey. The presented data comprise the fusion onto the American Community Survey (ACS) microdata of select donor variables from the Residential Energy Consumption Survey (RECS) of 2015, the National Household Travel Survey (NHTS) of 2017, the American Housing Survey (AHS) of 2019, and the Consumer Expenditure Survey - Interview (CEI) for the years 2015–2019. This results in an integrated microdataset of household attributes and well-being dimensions that can be analyzed to address research questions in ways that are not currently possible. The underlying statistical techniques, designed under the fusionACS project, are included in an open-source R package, fusionModel, that provides generic tools for the creation, analysis, and validation of fused microdata.

Has Housing Filtering Stalled? Heterogeneous Outcomes in the American Housing Survey, 1985–2021

Filtering of housing units—the process through which housing units over time serve occupants with lower or higher incomes—is a primary source of low-cost housing supply in the United States. However, the extent of filtering can vary widely in response to local housing market conditions, and this variation carries implications for the affordable housing strategies used in different areas. This paper uses the American Housing Survey (AHS) panels for 1985–2013 and 2015–2021 to construct repeat income measures of filtering. The analyses then describe the extent of variation in filtering outcomes across time periods, price points, and metropolitan areas. The results show significant variation across all three domains. Temporal analyses document significant changes in the estimated filtering outcomes across multiple time periods. In particular, the estimates for 2015–2021 suggest that downward filtering of housing units stalled or reversed as housing markets tightened in recent years. The direction and extent of filtering is also shown to vary significantly across metropolitan areas with higher and lower home price appreciation. These findings highlight the importance of heterogeneity in filtering outcomes to the conclusions drawn for policy. They also provide insight into the potential limitations of filtering as a source of affordable housing supply.

Climate change and cooling equity: Spatial dynamics of vulnerable populations

AbstractClimate change disproportionately affects low‐income and minority populations. This study identified vulnerable populations to extreme heat, focusing on home air conditioning. State and municipal laws and regulations usually consider home air conditioning an amenity rather than a requirement for habitability such as heat, water, and electricity. Using the historical census data and the American Housing Survey data, this study identified the vulnerable populations to extreme heat and their spatial dynamic in Los Angeles County, CA. This study found that low‐income minority populations are more likely to live without home air conditioning, and they are more likely to be exposed to extreme heat in the coming years if their residential location patterns continue. Changing spatial patterns of low‐income and minority populations need to be incorporated into urban and regional planning for climate change. State regulations and municipal codes should require air conditioning as a habitability requirement for cooling equity. Also, cooling stations that provide immediate relief for those without home air conditioning need strategic placements based on the locational concentration of the vulnerable populations.

Social protection and absorptive capacity: Disaster preparedness and social welfare policy in the United States

Climate change exacerbates drivers of impoverishment, necessitating a more robust evidence base to inform policy and action. Recent studies have examined how social protection programs tie into climate action and disaster risk reduction globally; however, this remains underexamined in high-income country contexts. Our study aims to address this gap by investigating the association between social protection and disaster preparedness in the United States. We hypothesize that reducing household vulnerability through welfare recipience builds long-term resilience and capacity for better risk-mitigation decisions. We examined how households prepare for a disaster and conceptualized disaster preparedness as a capability at the household level using nationally representative data from the American Housing Surveys 2017. The study employed multiple regression analyses and the Karlson/Holm/Breen method to decompose the total, direct, and indirect effects of welfare recipience on the association between household social vulnerabilities and disaster preparedness. On average, households scored 5.01 on a nine-item preparedness scale; higher preparedness was positively associated with higher income and education, a male household head, the presence of an older adult, and non-welfare recipience. Welfare recipience mediated the association of disaster preparedness with the presence of a person with a disability at home, socioeconomic status, and race. This analysis strengthens the hypothesis that welfare recipience could potentially mediate disaster preparedness capabilities among households, pointing to a probable shock absorber effect that can offset vulnerabilities experienced by communities.

Exploring Differences in Home Modification Strategies According to Household Location and Occupant Disability Status: 2019 American Housing Survey Analysis.

Home modification is essential in helping older adults age in place safely and independently. The purpose of this study was to examine the impact of housing location and disability status on home modification projects among older adults using the 2019 American Housing Survey Data. The study found that recent home modification strategies were significantly different according to older adults' housing locations and health status. Older adults in non-metropolitan areas and with disability were less likely to make home modifications, and when they did so, they spent less money on the modifications. The findings highlight the importance of providing adequate home modification programs and resources for home modifications, as well as support and education from community organizations, particularly for older adults in non-metropolitan areas. The results of this study can assist in developing housing policies and guidelines to address these current and future challenges in home modification implementation for the aging population.

Social and contextual determinants of heat pump adoption in the US: Implications for subsidy policy design

Building electrification with heat pumps (HPs) is an important pillar of decarbonization. The US Inflation Reduction Act (IRA) of 2022 provides unprecedented cash incentives to households. However, little is understood of peoples’ preferences or constraints, and hence of the efficacy of these incentives. We develop a residential heating appliance choice model that identifies socio-demographic, contextual and economic determinants of adoption using structural estimation methods with data from the American Housing Survey (AHS). We find that households in warmer, urban areas, with younger heads of household, in larger homes, and with better insulation are more likely to adopt HPs. Using New York state as a case, we predict willingness to pay (WTP) for and adopt HPs under the IRA incentives. We estimate that half of New York households would be willing to adopt HPs by 2030, setting aside other market barriers that could prevent actual adoption. The total subsides would amount to almost four times the IRA’s initial allocation of 9 billion for home improvement for the entire US. The subsidy cost of replacing 70 percent of the heating appliance stock by 2030 could be a third higher. However, this cost could be reduced by 15 percent if subsidies were weighted more towards higher income households, implying a modest trade-off between equity and cost efficiency. These findings underscore the importance of better understanding the heterogeneous home and household conditions that influence HP adoption. Funding for building electrification would have to be increased substantially to achieve high adoption rates.

Household level wastewater management and disposal data collection in the U.S.: the history, shortcomings, and future policy implications

Abstract Country-level sanitation access is monitored globally by the Joint Monitoring Programme (JMP). However, recent reports on sanitation access in high-income countries indicate that the JMP data may underestimate the prevalence of unsafely managed sanitation in these settings. This study explains the surveys that collect household-level wastewater management data in the U.S. and analyzes the accuracy and reliability of these data sets. From 1940 to 1990, sewage disposal data were collected comprehensively through the U.S. Decennial Census. These data are currently collected through the American Housing Survey (AHS) which appears to greatly underestimate the usage of onsite wastewater treatment systems (OWTS). In addition to these surveys, we highlight current efforts to introduce a sewage disposal question to the American Community Survey (ACS), localized efforts to collect wastewater data, and the Point-in-Time count of people experiencing homelessness. Using estimates of OWTS usage in new housing, this study provides the first defensible national estimate of OWTS usage since 1990. We estimate that 25.03% of U.S. households use OWTS which exceeds the AHS estimate (15.7%) by over 12 million households. This study discusses the potential for better wastewater data collection to inform future wastewater policy and improve the quality of life for U.S. residents.

Identifying demographic and home features influencing older adults’ home functioning by using machine learning models

This study aimed to identify demographic and housing features associated with functional difficulties experienced by older adults in their homes. Individuals aged ≥ 65 years who completed American Housing Survey (AHS) questionnaires. We selected one random person per household and excluded participants with missing data for the 12 AHS functional challenge items. Multiple machine learning models were compared to identify the best-performing model, which was then used to analyze the impact of demographic and housing features on older adults' functional difficulties at home. The random forest model was selected for its preferred predictive performance (accuracy: 85.8%, sensitivity: 94.4%, specificity: 60.2%, precision: 87.6%, and negative predictive value: 78.2%). The top five variables that significantly influenced the model were: 1) walking disability, 2) presence or use of a cane or walker, 3) presence or use of handrails or grab bars in the bathroom, 4) go-outside-home disability, and 5) self-care disability. These variables had a stronger impact on the model than the householder's health and age. Home modifications and environmental adaptations may be critical in enhancing functional abilities and independence among older adults. These findings could inform the development of interventions that promote safe and accessible living environments for older adults, thereby improving their quality of life.

Disaster Preparedness and Housing Tenure: How Do Subsidized Renters Fare?

Homeowners are significantly more prepared for disasters than renters. However, disaster preparedness among subsidized renters is an understudied topic despite their increased vulnerability to negative disaster outcomes. Previous research shows that one in three subsidized units is at risk for exposure to disasters, relative to one in four unsubsidized rental units and one in seven owner-occupied units. Subsidized housing residents often fall into many vulnerable statuses that would make them less prepared than renters and owners. Using 2017 American Housing Survey data, we examine differences in household disaster preparedness by housing tenure, with and without controls for such factors. Logistic regression analyses indicate that subsidized renters are significantly less prepared than unsubsidized renters, and both renter types are significantly less prepared than homeowners, controlling for demographic and neighborhood characteristics. The policy implications of this research are considered as they relate to the location and management of subsidized housing in an era of climate change.

Impact of ethnic and cultural diversity on millennial living preferences and homeownership

Purpose This study aims to examine living preference and tenure among millennials, with a particular focus on the impact of ethnic and cultural diversity on housing outcomes including observed homeownership inequalities. Design/methodology/approach Using the individual panel data from three waves in American Housing Survey, 2015–2019, this study compares the likelihood of co-residing among Asian and Hispanic millennials with non-Hispanic white millennial peers. Furthermore, this study estimates the effect of co-residence on homeownership across generational and ethnic backgrounds. Findings This study finds a preference for coresident adult familial households among foreign-born Asian and Hispanic millennials, and US-born Hispanic millennials when compared to their non-Hispanic white millennial peers. The results are robust after considering neighborhood selection bias, affordability and education. The effect of co-residence on ownership is significant and positive, suggesting this living arrangement contributes to homeownership across all generational and ethnic groups. Practical implications Housebuilders should be aware of Asian and Hispanic millennials’ increased appetite for extended family living arrangements and consider increasing the physical size of affordable or workforce-oriented rental housing and new single family construction to accommodate more adult co-living arrangements. Originality/value This study provides a more comprehensive understanding of the role ethnic and cultural diversity has on millennial adult living preferences and its generational differences, which is not just “boomeranging” as identified by previous literature, contributing to the growing interest in the housing research on the effect of ethnic diversity and culture on millennials’ homeownership rates.

Estimating the electric vehicle charging demand of multi-unit dwelling residents in the United States

Early battery electric vehicle (EV) adopters can access home chargers for reliable charging. As the EV market grows, residents of multi-unit dwellings (MUDs) may face barriers in owning EVs and charging them without garage or parking availability. To investigate the mechanisms that can bridge existing disparities in home charging and station deployment, we characterized the travel behavior of MUD residents and estimated their EV residential charging demand. This study classifies the travel patterns of MUD residents by fusing trip diary data from the National Household Travel Survey and housing features from the American Housing Survey. A hierarchical agglomerative clustering method was used to cluster apartment complex residents’ travel profiles, considering attributes such as dwell time, daily vehicle miles traveled (VMT), income, and their residences’ US census division. We propose a charging decision model to determine the charging station placement demand in MUDs and the charging energy volume expected to be consumed, assuming that MUD drivers universally operate EVs in urban communities. Numerical experiments were conducted to gain insight into the charging demand of MUD residents in the US. We found that charging availability is indispensable for households that set out to meet 80% state of charge by the end of the day. When maintaining a 20% comfortable state of charge the entire day, the higher the VMT are, the greater the share of charging demand and the greater the energy use in MUD chargers. The upper-income group requires a greater share of MUD charging and greater daily kWh charged because of more VMT.

The geography and socioeconomic characteristics of U.S. households reliant on private wells and septic systems

AbstractHouseholds reliant on unregulated, non‐grid water and sanitation infrastructure, like private wells and septic systems, face water quality and reliability deficiencies and associated negative impacts on human health at greater proportions than households reliant on publicly‐regulated, water and sewage systems. This study uses the 2019 American Housing Survey to produce the first joint, nationally‐representative analysis of household reliance on wells and septics in decades. We find that there are lower proportions of U.S. households off the regulated water grid than other contemporary estimates. We also find that while 9.1% of U.S. households rely on both private well and septics simultaneously, a slightly higher proportion of households rely on only one of these systems, with the companion infrastructure being publicly regulated. Our results show that both private well and septic reliant households are much more likely to be non‐Hispanic White, live in a single family home (a detached or attached one‐family dwelling) or mobile home (a portable habitable structure that was originally fitted with wheels to facilitate movement), and to live outside a metropolitan area than those reliant on publicly regulated service. Yet, surprisingly both private well and septic reliant households do not have lower average incomes than households reliant on regulated systems. These results suggest that federal, state and local financial assistance, technical assistance and educational programs can be better targeted to ensure that in‐need private well and septic reliant households can operate and maintain their essential water and sanitation infrastructure.

Differential Access in Mortgage Credit: The Role of Neighborhood Spatial and Racial Stratification

AbstractInequality in homeownership is a major component of ethno‐racial stratification. Previous studies demonstrate large ethno‐racial differences in access and outcomes throughout the home buying process at both the individual and neighborhood levels. An underlying assumption in these studies is that neighborhood ethno‐racial disparities in lending are similar across neighborhood spatial types. However, mortgage lending disparities are unclear when examining the neighborhood's racial composition across urban, suburban, and rural communities. This paper draws on annual data from the American Housing Survey and the Home Mortgage Disclosure Act (HMDA) from 2018 and 2019 to assess loan outcome disparities by neighborhood type and racial composition. I show that applicant disparities in loan outcomes vary when factoring the neighborhood's spatial type and racial composition. Borrowers seeking a mortgage in predominantly minority rural communities are more likely to be denied a mortgage than borrowers applying across all other neighborhood type and racial composition. The trends for urban communities are similar to suburban communities across adverse loan outcomes. When comparing the community's racial composition and neighborhood type, the observed lending pattern provides evidence of constrained mortgage access in rural areas, especially among minority rural communities.

Displaced and unsafe: The legacy of settler-colonial racial capitalism in the U.S. rental market

ABSTRACT Unsafe rental units are disproportionately located in communities of color, resulting in numerous detrimental effects for residents’ health and socioeconomic well-being. Yet, scholars disagree regarding the mechanisms driving this phenomenon. Exogenous capitalism theories emphasize socioeconomic factors while setter-colonial racial capitalism theories emphasize the racist policies and practices that incentivize unequal investment and maintenance. We empirically adjudicate between these mechanisms by merging restricted-access versions of the American Housing Survey, the Rental Housing Finance Survey, and the American Community Survey at a Census Restricted Data Center. Our findings demonstrate neighborhood White proportion is a key mechanism shaping the condition of rental units even when controlling for neighborhood socioeconomic status, property features, and renter demographics. We argue these results support settler-colonial racial capitalism theories and discuss the implications of these findings for future research and housing policy.