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Article type: Research Article
Authors: Fagereng, Andreasa; d | Holm, Martin Blomhoffb; d; * | Torstensen, Kjersti Næssc; d
Affiliations: [a] Department of Finance, BI Norwegian Business School, Oslo, Norway | [b] Department of Economics, University of Oslo, Oslo, Norway | [c] Norges Bank, Oslo, Norway | [d] Research Department, Statistics Norway, Oslo, Norway
Correspondence: [*] Corresponding author: Martin Blomhoff Holm, Department of Economics, University of Oslo, Oslo, Norway Tel.: +47 46410134; E-mail: m.b.holm@econ.uio.no.
Note: [1] The article should not be reported as representing the views of Statistics Norway or Norges Bank. The views expressed are those of the authors and do not necessarily reflect those of Statistics Norway or Norges Bank.
Abstract: We provide a new estimate of household-level housing wealth in Norway between 1993 and 2015 using an ensemble machine learning method on housing transaction data. The new housing wealth measure is an improvement over existing data sources for two reasons. First, the model outperforms previously applied regression models in out-of-sample prediction precision. Second, we extend the sample of estimated housing wealth by including cooperative units, non-id apartments, and cabins.
Keywords: Machine learning, housing wealth, house prices
DOI: 10.3233/JEM-200471
Journal: Journal of Economic and Social Measurement, vol. 45, no. 1, pp. 65-81, 2020
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