Abstract
Using data from the Community Advantage Panel Survey and a multivariate proportional hazards framework, we investigate relationships among house price dynamics, unemployment, and the mobility and housing tenure decisions of low-income homeowners in the United States between 2005 and 2012. In recent years, house prices have declined and unemployment rates have increased in many parts of the country, and existing literature provides mixed insights into whether housing market conditions have impeded recovery in the labor market. Our results suggest that negative equity and falling house prices are associated with delayed homeowner mobility, while finding employment or living in an area of increasing unemployment is associated with increased mobility. Those homeowners who experienced a change in employment status during the study period are largely distinct from those who experienced negative equity. In addition, most movers who remained homeowners relocated because of changes in family structure, while those movers who became renters were more likely to move because of housing costs or changes in employment status. Therefore, we infer that a transition out of homeownership may be more likely for those low-income homeowners who move for employment reasons, but that low-income homeowners generally do not appear to be prevented from moving to take advantage of employment opportunities when necessary.
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Notes
Authors’ calculations based on national all-transactions house price index data from the Federal Housing Finance Agency.
Unemployment statistics obtained from the Bureau of Labor Statistics.
In the United States, an individual’s credit history is summarized using a credit score. The score is generated by one of the major credit reporting agencies that collect consumer credit records from lenders and other organizations, and it is widely used as an indicator of the individual’s demonstrated capacity to carry and repay credit. An individual’s credit score is often requested from the credit reporting agencies by lenders or landlords during mortgage loan or rental tenancy applications, so it has a substantial impact on potential opportunities for residential mobility. A credit score normally ranges between 300 and 800, with 800 indicating the lowest credit risk.
The existing paper most closely related to our analysis is previous work by Spader and Quercia (2008), who use the same data set to consider the mobility and tenure choice decisions of low-income households. However, their analysis spans a shorter time period preceding the financial crisis and does not consider the mobility effects of house price dynamics and unemployment conditions.
Most of the cases with missing data (96) involve missing values for the original credit score; we exclude these cases because we cannot observe changes in credit score over time for this group. The remaining missing observations are distributed throughout the other variables in the analysis.
Each of these categories is calculated independently, and they are not mutually exclusive. Note that counties do not cross state lines but that some metro areas do. Therefore, moves within the county will also be counted as moves within the state, and moves out of state will also be counted as moves out of county. However, in some cases, it is possible to leave a state but stay within the same metro area.
Note that the overall household size change measures do include changes resulting from marriage/divorce or cohabitation/separation.
About 31 % of CAP borrowers refinanced the original CAP mortgage between loan origination and the end of 2012.
Given that the geographic patterns of high rates of negative equity and unemployment in the US overall would seem to suggest that negative equity and unemployment should coincide for individual cases, we ran robustness checks to verify that our results were not an artifact of how we had chosen to construct the data set. In particular, we tried alternative specifications in which (1) we used the level of employment status rather than the change, and (2) we used employment status changes between baseline and the move/censoring date rather than changes that occurred in the year prior to the move/censoring date. In both of these cases, the results are quite similar to our original results. Our intuition for explaining the apparent disconnect between unemployment and negative equity at the individual level is as follows: while areas of negative equity may tend to be areas of high unemployment, the relationship of these variables to individual outcomes will depend on tenure status. In the US overall, renters are much more likely to be or become unemployed than homeowners; therefore, given that we begin with a sample of homeowners, unemployment rates tend to be low in the sample as a whole. Moreover, unemployment will tend to be less geographically concentrated for this population, leading to a mismatch between negative equity incidence (which is quite concentrated) and most cases of unemployment in our sample. We ran state-level frequencies of the incidence of negative equity and unemployment in our sample to confirm our intuition, and these tabulations do suggest that unemployment is much more geographically dispersed for this population than is negative equity.
We also consider specifications that include dummies for loan origination year, but these are not significant predictors of either of the outcomes, and including them in the model does not substantively alter the coefficients of the other covariates. Therefore, we omit them for simplicity. Similarly, we omit changes in income and credit scores over time, as these tend to be highly correlated with changes in household structure and employment.
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Acknowledgments
We thank the editor and two anonymous reviewers for helpful comments on a prior version of this paper. We thank the Ford Foundation for financial support. All opinions and any errors remain our own.
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Riley, S.F., Nguyen, G. & Manturuk, K. House price dynamics, unemployment, and the mobility decisions of low-income homeowners. J Hous and the Built Environ 30, 141–156 (2015). https://doi.org/10.1007/s10901-014-9400-y
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DOI: https://doi.org/10.1007/s10901-014-9400-y