Estimation and Updating Methods for Hedonic Valuation

27 Pages Posted: 13 Dec 2018 Last revised: 4 Jan 2019

See all articles by Michael Mayer

Michael Mayer

Consult AG Bern

Steven C. Bourassa

Florida Atlantic University

Martin Hoesli

University of Geneva - Geneva School of Economics and Management (GSEM); Swiss Finance Institute; University of Geneva - Research Center for Statistics; University of Aberdeen - Business School

Donato Flavio Scognamiglio

University of Berne, Institut für Finanzmanagement

Date Written: December 12, 2018

Abstract

Purpose – We use a large and rich data set consisting of over 123,000 single-family houses sold in Switzerland between 2005 and 2017 to investigate the accuracy and volatility of different methods for estimating and updating hedonic valuation models.

Design/methodology/approach – We apply six estimation methods (linear least squares, robust regression, mixed effects regression, random forests, gradient boosting, and neural networks) and two updating methods (moving and extending windows).

Findings – The gradient boosting method yields the greatest accuracy while the robust method provides the least volatile predictions. There is a clear trade-off across methods depending on whether the goal is to improve accuracy or avoid volatility. The choice between moving and extending windows has only a modest effect on the results.

Originality/value – This paper compares a range of linear and machine learning techniques in the context of moving or extending window scenarios that are used in practice but which have not been considered in prior research. The techniques include robust regression, which has not previously been used in this context. The data updating allows for analysis of the volatility in addition to the accuracy of predictions. The results should prove useful in improving hedonic models used by property tax assessors, mortgage underwriters, valuation firms, and regulatory authorities.

Keywords: Hedonic models, Appraisal accuracy, Appraisal volatility, Machine learning, Robust regression, Mixed effects models, Random forests, Gradient boosting, Neural networks

JEL Classification: R31, C45, C53

Suggested Citation

Mayer, Michael and Bourassa, Steven C. and Hoesli, Martin Edward Ralph and Scognamiglio, Donato Flavio, Estimation and Updating Methods for Hedonic Valuation (December 12, 2018). Swiss Finance Institute Research Paper No. 18-76. Available at SSRN: https://ssrn.com/abstract=3300193 or http://dx.doi.org/10.2139/ssrn.3300193

Michael Mayer

Consult AG Bern ( email )

Steven C. Bourassa

Florida Atlantic University ( email )

777 Glades Road
Boca Raton, FL 33431
United States
5028077642 (Phone)

Martin Edward Ralph Hoesli (Contact Author)

University of Geneva - Geneva School of Economics and Management (GSEM) ( email )

40 Boulevard du Pont d'Arve
Geneva 4, 1211
Switzerland
+41 22 379 8122 (Phone)
+41 22 379 8104 (Fax)

Swiss Finance Institute

c/o University of Geneva
40, Bd du Pont-d'Arve
CH-1211 Geneva 4
Switzerland

University of Geneva - Research Center for Statistics ( email )

Geneva
Switzerland

University of Aberdeen - Business School ( email )

Edward Wright Building
Dunbar Street
Aberdeen, Scotland AB24 3QY
United Kingdom
+41 22 379 8122 (Phone)
+41 22 379 8104 (Fax)

Donato Flavio Scognamiglio

University of Berne, Institut für Finanzmanagement ( email )

Engehaldenstrasse 4
Bern, CH-3012
Switzerland
+41 43 501 06 00 (Phone)
+41 43 501 06 03 (Fax)

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