Fully Utilizing Housing Cost Data in the American Community Survey PUMS Data: Identifying Issues and Proposing Solutions

Keith E. Wardrip

Danilo Pelletiere

Data Shop

Data Shop, a department of Cityscape, presents short papers or notes on the uses of data in housing and urban research. Through this department, PD&R introduces readers to new and overlooked data sources and to improved techniques in using well-known data. The emphasis is on sources and methods that analysts can use in their own work. Researchers often run into knotty data problems involving data interpretation or manipulation that must be solved before a project can proceed, but they seldom get to focus in detail on the solutions to such problems. If you have an idea for an applied, data-centric note of no more than 3,000 words, please send a one-paragraph abstract to david.a.vandenbroucke@hud.gov for consideration.

The American Community Survey (ACS) is emerging as a valuable tool for analyzing annual trends and patterns in housing in the United States. Researchers often use the housing cost-to-income ratios (HCIRs) provided in the ACS Public Use Microdata Sample housing file to evaluate the level of housing cost burden for renters and owners and to estimate the proportion of households spending more than a specified level of income, often 30 percent or 50 percent, on shelter. In this article, we show that these variables should be used with caution, identifying 3.2 million households in the 2006 ACS for which the Census Bureau does not calculate an HCIR, even though useful housing cost and income data are available for these households. We also identify 2.8 million owner households for which the HCIR is underestimated because monthly costs do not include mobile home fees. This article explores these issues, explains how researchers can develop an alternative HCIR, and describes the resulting distribution of households by housing cost burden.