Cumulative housing cost burden exposures and disadvantages to children's well-being and health.

Housing affordability is a growing challenge for households in the United States and other developed countries. Prolonged exposure to housing cost burden can have damaging effects on households, and, in particular, children. These burdens can exacerbate parental stress, reduce investments in children and expose households to greater neighborhood disadvantage. In this study, we use national survey data to assess whether cumulative housing cost burden exposure is associated with disadvantages to children's well-being and health. We observe that long-term exposures are linked to lower achievement in math and reading standardized test scores, as well as higher levels of behavior problems. Moreover, we identify that three mechanisms--caregiver distress, economic strain, and neighborhood disadvantage--operate as mediating pathways for these disadvantages to different degrees between these three outcomes. Overall, our study highlights how the dimension of time is increasingly important to our understanding of the challenges that families face related to housing affordability.

Move-In Fees as a Residential Sorting Mechanism Within Online Rental Markets.

An increasing number of American renters within major metropolitan housing markets rely on online platforms such as Craigslist to find rental units. Landlords that advertise rentals on these websites have been found to tailor the language used in their listings in reference to surrounding neighborhood demographics to influence prospective tenants' rental searches. This work investigates the underexplored subject of move-in fees, referring to upfront costs to secure a lease, such as security deposits, application charges, and advanced rent payments that can affect whether a prospective renter can afford an advertised unit. This study advances a framework for how housing researchers can assess variations in landlord discourse within online housing marketplaces using text analysis methods and web scraping. It then illustrates how the resulting measures about move-in fees have distinct variations in prevalence along sociodemographic, spatial, and policy measures through a series of descriptive analyses, with subsequent conclusions toward policy implications designed to assist low-income renters with overcoming financial barriers in securing rental housing.

Segmented information, segregated outcomes: Housing affordability and neighborhood representation on a voucher-focused online housing platform and three mainstream alternatives.

Online platforms have become an integral component of the housing search process in the United States and other developed contexts, but recent studies have demonstrated that these platforms offer uneven representation of different neighborhoods. In this study, we use listings covering the largest 50 U.S. metropolitan areas to assess how GoSection8, a platform uniquely focused on affordable housing and voucher-assisted households, compares with "mainstream" alternatives of Craigslist, Apartments.com and Zillow. Through descriptive and regression analyses of the housing and neighborhoods represented on these websites and a new way of measuring the distribution of rental housing opportunities, we advance a multisource perspective on the role of online information exchanges in housing search processes. Specifically, we find that GoSection8 and mainstream alternatives capture spatially-segmented information about housing markets, with GoSection8 ads representing units that are more affordable but also more constrained to higher-poverty neighborhoods where assisted households are already concentrated. The findings suggest that disadvantaged households are potentially funneled toward high-poverty, isolated neighborhoods by the operation of stratified information systems available for online housing searches.

Racial disparity in exposure to housing cost burden in the United States: 1980-2017.

This paper uses the Panel Study of Income Dynamics to analyze Black-White differences in housing cost burden exposure among renter households in the United States from 1980 to 2017, expanding understanding of this phenomenon in two respects. Specifically, we document how much this racial disparity changed among renters over almost four decades and identify how much factors associated with income or housing costs explain Black-White inequality in exposure to housing cost burden. For White households, the net contribution of household, neighborhood, and metropolitan covariates accounts for much of the change in the probability of housing cost burden over time. For Black households, however, the probability of experiencing housing cost burden continued to rise throughout the period of this study, even after controlling for household, neighborhood, and metropolitan covariates. This suggests that unobserved variables like racial discrimination, social networks or employment quality might explain the increasing disparity in cost burden among for Black and White households in the U.S.

Racialized Discourse in Seattle Rental Ad Texts.

Racial discrimination has been a central driver of residential segregation for many decades, in the Seattle area as well as in the United States as a whole. In addition to redlining and restrictive housing covenants, housing advertisements included explicit racial language until 1968. Since then, housing patterns have remained racialized, despite overt forms of racial language and discrimination becoming less prevalent. In this paper, we use Structural Topic Models (STM) and qualitative analysis to investigate how contemporary rental listings from the Seattle-Tacoma Craigslist page differ in their description based on neighborhood racial composition. Results show that listings from White neighborhoods emphasize trust and connections to neighborhood history and culture, while listings from non-White neighborhoods offer more incentives and focus on transportation and development features, sundering these units from their surroundings. Without explicitly mentioning race, these listings display racialized neighborhood discourse that might impact neighborhood decision-making in ways that contribute to the perpetuation of housing segregation.

Toward a Cross-Platform Framework: Assessing the Comprehensiveness of Online Rental Listings.

Research on rental housing markets in the United States has traditionally relied on national or local housing surveys. Those sources lack temporal and spatial specificity, limiting their use for tracking short-term changes in local markets. As rental housing ads have transitioned to digital spaces, a growing body of literature has utilized web scraping to analyze listing practices and variations in rental market dynamics. Those studies have primarily relied on one platform, Craigslist, as a source of data. Despite Craigslist's popularity, the authors contend that rental listings from various websites, rather than from individual ones, provide a more comprehensive picture. Using a mixed-methods approach to study listings across various platforms in five metropolitan areas, this article demonstrates considerable variation in both the types of rental units advertised and the features provided across those platforms. The article begins with an account of the birth and consolidation of online rental platforms and emergent characteristics of several selected websites, including the criteria for posting, search parameters, search results priority, and first-page search results. Visualizations are used to compare features such as the 40th percentile of rent, rent distribution, and bedroom size based on scraped data from six online platforms (Padmapper, Forrent.com, Trulia, Zillow, Craigslist, and GoSection8), 2020 Fair Market Rents, and 2019 American Community Survey data. The analyses indicate that online listing platforms target different audiences and offer distinct information on units within those market segments, resulting in markedly different estimates of local rental costs and unit size distribution depending on the platform.

Is geography destiny? Disrupting the relationship between segregation and neighbohrood outcomes.

Considerable research has shown that, in the cross-section, segregation is associated with detrimental neighborhood outcomes for blacks and improved neighborhood outcomes for whites. However, it is unclear whether early-life experiences of segregation shape later-life neighborhood outcomes, whether this association persists for those who migrate out of the metropolitan areas in which they grew up, and how these relationships differ for blacks and whites. Using the Panel Study of Income Dynamics from 1979 to 2013, we find that the level of segregation experienced during adolescence is associated with significantly worse neighborhood outcomes in adulthood for blacks. However, migrating out of the metropolitan area an individual grew up in substantially moderates these relationships. In contrast, adolescent segregation is associated with improved, or not significantly different, neighborhood outcomes in adulthood for whites. These findings have important implications for theorizing about the mechanisms linking segregation and neighborhood outcomes and for considering potential means of assuaging racial disparities in harmful neighborhood exposures.

Does Hypersegregation Matter for Black-White Socioeconomic Disparities?

Massey and Denton's concept of hypersegregation describes how multiple and distinct forms of black-white segregation lead to high levels of black-white stratification. However, numerous studies assessing the association between segregation and racial stratification applied only one or two dimensions of segregation, neglecting how multiple forms of segregation combine to potentially exacerbate socioeconomic disparities between blacks and whites. We address this by using data from the U.S. Census from 1980 to 2010 and data from the American Community Survey from 2012 to 2016 to assess trajectories for black-white disparities in educational attainment, employment, and neighborhood poverty between metropolitan areas with hypersegregation and black-white segregation, as measured by the dissimilarity index. Using a time-varying measure of segregation types, our results indicate that in some cases, hypersegregated metropolitan areas have been associated with larger black-white socioeconomic disparities beyond those found in metropolitan areas that are highly segregated in terms of dissimilarity but are not hypersegregated. However, the contrasts in black-white socioeconomic inequality between hypersegregated metropolitan areas and those with high segregation largely diminish by the 2012 to 2016 observation.

A machine learning approach to automated structural network analysis: application to neonatal encephalopathy.

Neonatal encephalopathy represents a heterogeneous group of conditions associated with life-long developmental disabilities and neurological deficits. Clinical measures and current anatomic brain imaging remain inadequate predictors of outcome in children with neonatal encephalopathy. Some studies have suggested that brain development and, therefore, brain connectivity may be altered in the subgroup of patients who subsequently go on to develop clinically significant neurological abnormalities. Large-scale structural brain connectivity networks constructed using diffusion tractography have been posited to reflect organizational differences in white matter architecture at the mesoscale, and thus offer a unique tool for characterizing brain development in patients with neonatal encephalopathy. In this manuscript we use diffusion tractography to construct structural networks for a cohort of patients with neonatal encephalopathy. We systematically map these networks to a high-dimensional space and then apply standard machine learning algorithms to predict neurological outcome in the cohort. Using nested cross-validation we demonstrate high prediction accuracy that is both statistically significant and robust over a broad range of thresholds. Our algorithm offers a novel tool to evaluate neonates at risk for developing neurological deficit. The described approach can be applied to any brain pathology that affects structural connectivity.