Abstract: Neighborhood Satisfaction Between Low- and Moderate-Income Homeowners and Renters: Multilevel Analysis (Society for Social Work and Research 14th Annual Conference: Social Work Research: A WORLD OF POSSIBILITIES)

13245 Neighborhood Satisfaction Between Low- and Moderate-Income Homeowners and Renters: Multilevel Analysis

Friday, January 15, 2010: 8:00 AM
Pacific Concourse L (Hyatt Regency)
* noted as presenting author
Yeong Hun Yeo, MSW , University of North Carolina at Chapel Hill, PhD student, Chapel Hill, NC
Michal Grinstein-Weiss, PhD , University of North Carolina at Chapel Hill, Assistant Professor, Chapel Hill, NC
Andréa Taylor, MSW , University of North Carolina at Chapel Hill, Research Coordinator, Chapel Hill, NC

Neighborhood satisfaction is an important component of individuals' quality of life, and is associated with social capital, community involvement, and residential stability. Although research has found that neighborhood satisfaction is linked with homeownership, this correlation appears to diminish for the poorest homeowners. Little is known about the association between homeownership status and neighborhood satisfaction for low- and moderate-income (LMI) households. Given that LMI households are more likely to purchase houses in lower-income, distressed neighborhoods, it is important to discover whether they still experience higher rates of neighborhood satisfaction than LMI renters. This study aims to examine the role of homeownership among LMI households in explaining neighborhood satisfaction. Further, this study examines individual and neighborhood characteristics to explore important predictors of neighborhood satisfaction among LMI households.


The data for this study come from the 2007 Community Advantage Secondary Market Program (CAP) survey. CAP is a secondary mortgage market pilot program for LMI households, operated in 49 states. This study uses a quasi-experimental design of LMI homeowners (n=689); a comparison panel of LMI renters (n=764); and U.S. Census tract information to represent neighborhood characteristics. Given the nested data, we use hierarchical generalized linear modeling (HGLM) to assess the differences between homeowners and renters on two dichotomous neighborhood satisfaction outcomes: rating of neighborhood and recommending of neighborhood. Individual-level covariates (level 1) include demographic characteristics and household income; social capital measured by resource generation scale (alpha=0.81); and new neighborhood status. Census tract covariates (level 2) include the neighborhood economic disadvantage scale (alpha=0.91); the neighborhood stability scale (alpha=0.68); and the proportion of non-White population. All variables are grand mean centered for analysis.


First, homeownership is significantly related to neighborhood satisfaction for the LMI sample population. Controlling for individual and neighborhood characteristics, the odds of LMI homeowners positively rating and recommending their neighborhood are 2.3 times (<.001) and 2.7 times (<.001) higher, respectively, than for LMI renters. Second, we demonstrate that all neighborhood characteristics (level 2) are significantly related to neighborhood satisfaction. For example, neighborhood economic disadvantage is associated with less likelihood of positively rating (<.001) and recommending (<.001) the neighborhood. Third, among individual level variables, social capital and movement to a new neighborhood are significant predictors for neighborhood satisfaction among LMI households. For example, a one point increase on the social capital scale is associated with a 15% (<.001) and a 19% (<.001) increase in the odds of positively rating and recommending the neighborhood, respectively.


This study makes several unique contributions, most notably the focus on LMI households, which are under investigated in neighborhood satisfaction research. Increased knowledge of the impacts of homeownership for LMI families is of critical importance to the field of social work. Moreover, we account for nested data (individual within neighborhood) with HGLM, correcting for biases generated when researchers use uncorrected models such as ordinary least squares. Finally, we discuss the importance of using experimental and quasi-experimental designs and sophisticated statistical methods, to advance the rigor of social work research.