The Predictability of House Prices: "Human Against Machine"

Kristoffer B. Birkeland (), Allan D. D'Silva (), Roland Füss () and Are Oust ()
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Kristoffer B. Birkeland: Norwegian University of Science and Technology
Allan D. D'Silva: Norwegian University of Science and Technology
Are Oust: Norwegian University of Science and Technology

International Real Estate Review, 2021, vol. 24, issue 2, 139-183

Abstract: We develop an automated valuation model (AVM) for the residential real estate market by leveraging stacked generalization and a comparable market analysis. Specifically, we combine four novel ensemble learning methods with a repeat sales method and tailor the data selection for each value estimate. We calibrate and evaluate the model for the residential real estate market in Oslo by producing out-of-sample estimates for the value of 1,979 dwellings sold in the first quarter of 2018. Our novel approach of using stacked generalization achieves a median absolute percentage error of 5.4%, and more than 96% of the dwellings are estimated within 20% of their actual sales price. A comparison of the valuation accuracy of our AVM to that of the local estate agents in Oslo generally demonstrates its viability as a valuation tool. However, in stable market phases, the machine falls short of human capability.

Keywords: AVMs; Housing Market; Machine Learning; Repeat Sales Approach; XGBoost. (search for similar items in EconPapers)
JEL-codes: L85 (search for similar items in EconPapers)
Date: 2021
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