Finance and Economics Discussion Series (FEDS)

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September 2018 (Revised August 2019)

Measuring Aggregate Housing Wealth: New Insights from Machine Learning

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

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.

Accessible materials (.zip)

Original paper: PDF | Accessible materials (.zip)

Keywords: Consumer economics and finance, Data collection and estimation, Flow of funds, Residential real estate

DOI: https://doi.org/10.17016/FEDS.2018.064r1

PDF: Full Paper

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Last Update: January 09, 2020