NATIONAL BUREAU OF ECONOMIC RESEARCH
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Valuing Housing Services in the Era of Big Data: A User Cost Approach Leveraging Zillow Microdata

Marina Gindelsky, Jeremy Moulton, Scott A. Wentland


This chapter is a preliminary draft unless otherwise noted. It may not have been subjected to the formal review process of the NBER. This page will be updated as the chapter is revised.

Chapter in forthcoming NBER book Big Data for 21st Century Economic Statistics, Katharine G. Abraham, Ron S. Jarmin, Brian Moyer, and Matthew D. Shapiro
Conference held March 15-16, 2019
Forthcoming from University of Chicago Press
in NBER Book Series Studies in Income and Wealth

Historically, residential housing services or “space rent” for owner-occupied housing has made up a substantial portion (approximately 10%) of U.S. GDP final expenditures. The current methods and imputations for this estimate employed by the Bureau of Economic Analysis (BEA) rely primarily on designed survey data from the Census Bureau. In this study, we develop new, proof-of-concept estimates valuing housing services based on a user cost approach, utilizing detailed microdata from Zillow (ZTRAX), a “big data” set that contains detailed information on hundreds of millions of market transactions. Methodologically, this kind of data allows us to incorporate actual market prices into the estimates more directly for property-level hedonic imputations, providing an example for statistical agencies to consider as they improve the national accounts by incorporating additional big data sources. Further, we are able to include other property-level information into the estimates, reducing potential measurement error associated with aggregation of markets that vary extensively by region and locality. Finally, we compare our estimates to the corresponding series of BEA statistics, which are based on a rental-equivalence method. Because the user-cost approach depends more directly on the market prices of homes, we find that since 2001 our initial results track aggregate home price indices more closely than the current estimates.

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