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Measuring Housing Stability With Consumer Reference Data

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Demography

Abstract

Housing instability for low-income renters has drawn greater attention recently, but measurement has limited research on policies to stabilize housing. Address histories from consumer reference data can be used to increase the quantity and quality of research on low-income renters. Consumer data track housing moves throughout the entire United States for most of the adult population. In this article, I show that such data can measure housing stability for groups with very low income and extreme instability. For example, the data can track housing moves during natural disasters, at demolition of public housing, for households at high risk of homelessness, and during gentrification. Consumer data can track housing instability outcomes that are more common than shelter entry and less expensive to collect than surveys. Relative to existing administrative address histories, consumer data allow researchers to track housing moves to exact addresses and across jurisdictions.

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Data Availability

The raw data used for this study are proprietary and owned by Infutor Data Solutions, Inc. from whom it can be purchased.

Notes

  1. See https://www.hudexchange.info/programs/hdx/pit-hic/.

  2. See Evans et al. (2019b) for a more complete review of popular policy responses and the existing evidence base.

  3. Public-use microdata areas are those that contain 100,000 to 200,000 residents within a state.

  4. In that case, a population with an overall move rate of 12% should have about 1.4% moving more than once because .014 = .122.

  5. The soundex of a word removes all vowels, treats consonants that can have the same sound as identical, and removes consecutively repeated letters. For example, “PHILLIPS,” “PHILIPS,” “PALEFACE,” and “PLAYOFFS” all have the same soundex.

  6. This study was conducted entirely in STATA 15 on a server with 256GB RAM, dual 12-core Intel Xeon CPU E5-2680 v3 @ 2.50GHz Haswell processors, and a 1.4TB solid-state drive. Because STATA rather inefficiently stores entire large data sets in working memory, the large amount of available RAM was important for the completion of this study using that tool.

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Acknowledgments

Thanks to the editors, three anonymous referees, Rebecca Diamond, Ingrid Gould Ellen, Bill Evans, Gary Painter, Jim Sullivan, and participants in the Notre Dame applied micro brownbag and the APPAM Fall Research Conference for comments and questions that have improved this article. Thanks to Dan Hartley for providing the Robert Taylor Homes move dates. Tessa Bonomo and Becca Brough provided excellent research assistance. This project received financial support from the Wilson-Sheehan Lab for Economic Opportunities.

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  1. Wilson Sheehan Lab for Economic Opportunities, Department of Economics, University of Notre Dame, 3060 Jenkins Nanovic Hall, South Bend, IN, 46556, USA

    David C. Phillips

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  1. David C. Phillips
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Correspondence to David C. Phillips.

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This study was approved under University of Notre Dame IRB 18-05-4712.

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Phillips, D.C. Measuring Housing Stability With Consumer Reference Data. Demography 57, 1323–1344 (2020). https://doi.org/10.1007/s13524-020-00893-5

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