Measuring aggregate housing wealth: New insights from machine learning ☆

Joshua Gallin, Raven Molloy, Eric Nielsen, Paul Smith and Kamila Sommer

Journal of Housing Economics, 2021, vol. 51, issue C

Abstract: We construct a new measure of aggregate housing wealth for the U.S. based on (1) home-value estimates derived from machine learning algorithms applied to detailed information on property characteristics and recent transaction prices, and (2) Census housing unit counts. According to our new measure, the timing and amplitude of the recent house-price cycle differs materially but plausibly from commonly-used measures, which are based on survey data or repeat-sales price indexes. Thus, our methodology generates estimates that should be of considerable value to researchers and policymakers interested in the dynamics of aggregate housing wealth.

Keywords: Residential real estate; Consumer economics and finance; Data collection and estimation; Flow of funds (search for similar items in EconPapers)
JEL-codes: C82 E21 R31 (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (5)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jhouse:v:51:y:2021:i:c:s105113772030070x

DOI: 10.1016/j.jhe.2020.101734

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