The Society for Social Work and Research

2014 Annual Conference

January 15-19, 2014 I Grand Hyatt San Antonio I San Antonio, TX

Predicting Housing Stability in a Population Study of Privately Subsidized Housing

Sunday, January 19, 2014: 11:45 AM
Marriott Riverwalk, Alamo Ballroom Salon B, 2nd Floor Elevator Level BR (San Antonio, TX)
* noted as presenting author
Daniel Brisson, PhD, Associate Professor, University of Denver, Denver, CO
Jennifer Covert, Director, Programs and Evaluation, National Resident Services at Mercy Housing, Inc, Mercy Housing, Denver, CO
Background and Purpose

For many low-income families and individuals subsidized, affordable, rental housing is the most viable option to achieving long-term, stable housing. There are 1.2 million households receiving publicly subsidized housing through the U.S. Department of Housing and Urban Development, and many more households receiving subsidized housing through private providers. Despite the tremendous need, surprisingly little is known about keeping vulnerable citizens in permanent and stable homes.  This research addresses the following questions.

  1. Do household characteristics predict housing stability for low-income families in subsidized housing?
  2. When assessing housing stability, do household characteristics matter differently for different types of housing (family, senior, and supportive)?


Mercy Housing is one of the nation’s largest providers of subsidized housing and, as such, their data present a unique opportunity for research. The data set for this research represents 23,817 residents in 12,392 subsidized housing units across 17 states.  Females are the head or co-head of 65% of the households.  Nine percent of households identified as Asian, 25% as Black, 19% as Hispanic, 30% as White, and 16% as some other race or ethnicity.  The head of the household mean age was 51 years old and the mean household income was $14,976.  On average, there were 2.28 people per household. Housing stability is measured by the receipt of a warning for rules violations, which could result in eviction.  A total of 4.6% of households, or 816 of the 17,844 households received a warning. 


Logistic regression tested the multivariate relationship between receiving a rules violation and the head of household’s age, sex, race and ethnicity, number of people in the home, and income.  After analysis of the full sample, analyses were conducted on subsets representing housing types: family, senior, and supportive. Results demonstrate that predictors of housing stability depend on the type of housing.  For female heads of household, the odds of receiving a rules violation increase by 41% in family housing, decrease by 57% in senior housing, and are not statistically significant in supportive housing.  For those identifying as Black, there was a 31% increase in the odds of receiving a rules violation in family housing.  However, no effect was discernible in senior or supportive housing.  Surprisingly, in both family and supportive housing, as income increased so did the likelihood of receiving a rules violation. The only variable that was consistently predictive across the three housing types was age.  As the age of the head of household increased, the likelihood of receiving a rules violation decreased.


The analyses suggest that household characteristics predict housing stability and that significant characteristics differ across housing type.  This is important as housing providers work to assess the most important characteristics for housing success.  Next, assessing service effectiveness across household characteristics and housing type will help direct programming for housing success.  Assessing the health and well-being of families will also be important to guide providers and policy makers as they work to identify approaches to housing that address the needs of low-income families.