Neighborhood Affordability and Housing Market Resilience

Abstract

Problem, research strategy, and findings: Although many researchers have examined factors associated with vulnerability to foreclosure, few have investigated the role neighborhood affordability plays in foreclosures in metropolitan areas. In this study, we examine the effects of location affordability (i.e., housing and transportation affordability combined) on resilience to foreclosure in more than 300 U.S. metropolitan areas during the U.S. housing recovery period. Using hierarchical linear regression with changes in zip code–level home foreclosure rates, our findings suggest the relationship between affordability and foreclosure resilience varies according to urban form (central/high-density city versus suburban low-density area) and types of metropolitan housing markets (boom–bust versus strong versus weak). In the national analysis, where location affordability was high, home foreclosure rates dropped substantially in central/high-density areas but not in suburban low-density areas. When we disaggregated the zip codes according to the market type, location affordability contributed to recovery in central cities in strong and weak metros and in the suburbs of boom–bust metros. There was no positive association in the suburbs of strong and weak metros. With improved data, future studies could measure an association between affordability and lower income renter households.

Takeaway for practice: Our study of the affordability crisis that followed the foreclosure crisis shows that planners can foster resilient and affordable housing markets by expanding and densifying affordable neighborhood locations and considering interactions between the costs of housing and transportation. Planners can improve neighborhood affordability with local and regional strategies based on the local residential density and the type of metropolitan housing market.

Keywords:

• foreclosure
• housing affordability
• location affordability
• resilience
• urban form

ACKNOWLEDGMENTS

We would like to thank former and current Editors and three anonymous reviewers for their constructive comments.

RESEARCH SUPPORT

This study is partially supported by the Community and Economic Development Department of the Federal Reserve Bank of Atlanta through its Co-op researcher program.

Supplemental Material

Supplemental data for this article can be found on the publisher’s website.

Notes

Notes

1 The eight household types in the Location Affordability Index are 1) median-income families, 2) very-low-income individuals, 3) working individuals, 4) single professionals, 5) retired couples, 6) single-parent families, 7) moderate-income families (80% median income for a region), and 8) dual-professional families (150% of median income for a region; HUD, 2017).

2 The structural equation model uses housing costs, auto ownership, and transit use for homeowners and renters as endogenous variables, which are similar to dependent variables because of their interrelationship; other exogenous variables that are similar to independent variables include various household (e.g., median income, household size, and household commuters) and geographic (e.g., regional earning, single family home share, density, rental unit share, and commute distance) characteristics (Haas et al., 2016).

3 We used boom–bust recovery periods based on Figure 1: The housing boom occurred from August 2000 to August 2006, the bust from August 2006 to August 2011, and the recovery from August 2011 to August 2014, the last year in which housing price index (HPI) data were available in this study. To measure the degree of shocks, we selected the peak HPI from the highest prices during the housing boom from August 2005 to August 2008 and the lowest HPI from the lowest prices during the housing recovery from August 2009 to August 2013 (Dong & Hansz, 2016; Wang, 2018). We used the HPI from CoreLogic, which includes value-weighted repeat sales and is normalized by setting the index value for January 2000.

4 For this study we did not assume a certain number of clusters but instead used a simple two-step cluster analysis. The results of the silhouette measure of cohesion and separation exceeded 0.5, which confirmed that three clusters were meaningful and distinctive (Norusis, 2011).

Additional information

Notes on contributors

Kyungsoon Wang

KYUNGSOON WANG ([email protected]) is a founder and research director of the Housing and Urban Research Institute.

Dan Immergluck

DAN IMMERGLUCK ([email protected]) is a professor in the Urban Studies Institute at Georgia State University.