Residential mobility of low-income, subsidized households: a synthesis of explanatory frameworks

Abstract

Mobility, residential quality, and life outcomes are linked in the literature and these relationships have influenced low-income housing policy. This research investigates the determinants of mobility for households with a federal housing subsidy. Combining unique data from a survey of Housing Choice Voucher households, client program files, and secondary data, this study uses logistic regression to test several explanations for mobility, including the life cycle, housing market perceptions, and perceived and actual neighborhood conditions. The results indicate that a synthesis of explanations produce the best model to predict residential mobility. Neighborhood quality, perceived and actual, variables most strongly influence mobility, but life cycle factors and perception of the local housing market also impact moving choices. At the same time, the results suggest a gap in our understanding of the relationship among individuals' environmental perceptions, formation of feelings of neighborhood satisfaction, and actual neighborhood conditions. The article concludes with a discussion of the research and policy implications from this study.

Sensitive Periods of Moving on Mental Health and Academic Performance Among University Students

“…Some previous studies suggested that the close association between exposure to childhood residential mobility and subsequent adverse outcomes could be restricted to households with lower family income, where a lot of psychosocial difficulties emerge (Webb et al, 2016). First, a weaker relation between residential mobility and adverse outcomes in households with higher family income may reflect aspirational residential movements for better employment, housing, and educational opportunities (Webb et al, 2016; Basolo and Yerena, 2017). In addition, family poverty has been identified to be a risk factor for a wide range of mental health and behavior issues among the youth (Letourneau et al, 2013; Reiss, 2013; Coley et al, 2018).…”

“…In addition, family poverty has been identified to be a risk factor for a wide range of mental health and behavior issues among the youth (Letourneau et al, 2013; Reiss, 2013; Coley et al, 2018). In families with fewer economic resources, parents may not be able to provide sufficient emotional support to their children to cope with different challenges (Albrecht and Teachman, 2003), such as moving, which may eventually link to an increased risk for poor mental health and poor academic performance (Basolo and Yerena, 2017; Mollborn et al, 2018).…”

Trapped in One’s Own Housing: The Limitations of Housing Choices in Segregated Roma Neighborhoods = Az én házam az én börtönöm: A lakásdöntések korlátai a szegregátumokban

“…Recent qualitative analysis reinforces this finding elsewhere in Hungary (Tóth et al, 2017), and previous European urban renewal projects found similar outcomes among first-generation movers (Bolt et al, 2010;Smets and Salman, 2008). This conclusion is also largely in line with the findings of the previous section, and confirms what Banerjee and Duflo (2011) found in relation to the poverty trap: it is insufficient to help to take the first steps to take sacrifice (that is, making trade-offs within the life-course-based analytical model), because it does not help balance the risks households face when losing their reciprocal networks of daily coping strategies and the capacity to continually recreate their ties.…”

“…Interviews during field work reveal that "household finance" and strategic household financial planning virtually does not exist in extremely poor neighborhoods. For outsiders, it may seem as if cash is perceived as a windfall gain, and is spent on "tasty" calories instead of "useful" ones (Banerjee and Duflo, 2011).…”

Residential Mobility and Quality of Life between Metropolitan Areas: The Case of South Korea

“…The first, from the urban informatics field, involves the use of data to measure effects on residential mobility or outcomes. This includes using objective indicators such as population, economics, and transportation (e.g., travel time and distance to work) [31]; land use and transportation accessibility [32]; the distance between residential areas and infrastructure [33]; health status [34]; population and population aging; poverty [35]; and income level [36]. Studies using these indicators have the advantage of explaining population movement between cities by way of long-term case studies, and such research has been conducted in multiple cities.…”