Machine Learning, Architectural Styles and Property Values

34 Pages Posted: 11 Jun 2020 Last revised: 15 Dec 2020

See all articles by Thies Lindenthal

Thies Lindenthal

University of Cambridge

Erik Barry Johnson

University of Alabama - Department of Economics, Finance and Legal Studies

Date Written: December 15, 2020

Abstract

This paper couples a traditional hedonic model with architectural style classifications from human experts and machine learning (ML) enabled classifiers to estimate sales price premia over architectural styles, both at the building and the neighborhood-level. We find statistically and economically significant price differences for houses from distinct architectural styles across an array of specifications and modeling assumptions. Comparisons between classifications from ML models and human experts illustrate the conditions under which ML classifiers may perform at least as reliable as human experts in mass appraisal models. Hedonic estimates illustrate that the impact of architectural style on price is attenuated by properties with less well-defined styles and we find no evidence for differential price effects of Revival or Contemporary architecture for new construction.

Suggested Citation

Lindenthal, Thies and Johnson, Erik Barry, Machine Learning, Architectural Styles and Property Values (December 15, 2020). Available at SSRN: https://ssrn.com/abstract=3604052 or http://dx.doi.org/10.2139/ssrn.3604052

Thies Lindenthal (Contact Author)

University of Cambridge ( email )

Trinity Ln
Cambridge, CB2 1TN
United Kingdom

Erik Barry Johnson

University of Alabama - Department of Economics, Finance and Legal Studies ( email )

P.O. Box 870244
Tuscaloosa, AL 35487
United States
(720)234-7675 (Phone)

HOME PAGE: http://https://github.com/erikbjohn

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