Who performs better? AVMs vs hedonic models

DOIhttps://doi.org/10.1108/JPIF-12-2019-0157
Publication Date26 March 2020
Pages213-225
AuthorAgostino Valier
SubjectProperty management & built environment,Real estate & property,Property valuation & finance
Who performs better? AVMs vs
hedonic models
Agostino Valier
Department of Civil Building and Environmental Engineering,
Universit
a degli Studi di Padova, Padova, Italy
Abstract
Purpose In the literature there are numerous tests that compare the accuracy of automated valuation models
(AVMs). These models first train themselves with price data and property characteristics,then they are tested
by measuring their ability to predict prices. Most of them compare the effectiveness of traditional econometric
models against the use of machine learning algorithms. Although the latter seem to offer better performance,
there is not yet a complete survey of the literature to confirm the hypothesis.
Design/methodology/approachAll tests comparingregressionanalysisand AVMs machinelearningon the
same data set have been identified. The scores obtained in terms of accuracy were then co mparedwit h each other.
Findings Machine learning models are more accurate than traditional regression analysis in their ability to
predict value. Nevertheless, many authors point out as their limit their black box nature and their poor
inferential abilities.
Practical implications AVMs machine learning offers a huge advantage for all real estate operators who
know and can use them. Their use in public policy or litigation can be critical.
Originality/value According to the author, this is the first systematic review that collects all the articles
produced on the subject done comparing the results obtained.
Keywords Real estate, Mass appraisal, Valuation, AVM, Automated valuation models, Machine learning,
Econometric model
Paper type Literature review
Introduction
Artificial intelligence is bringing about a radical change in many activities traditionally
carried out by human work: among them, real estate valuation. Innovation affects the nature
of evaluations, operational procedures and the skills required of the professional sector
(Rics, 2017).
Frey and Osborne (2017) have carried out an extensive survey that assigns to each
profession the degree of possible computerization, that is, the possibility that the work
currently done by man can be entirely replaced by the work of a machine. In this survey, the
profession of real estate valuers is estimated to be susceptible to computerization at 90%. The
whole field of evaluation therefore wonders what the future of estimates will be, what impact
automatic value prediction models will have on professional evaluation practice (Cook, 2015).
Automated value prediction models are gradually replacing the evaluators work. In the
past, these models only used regression analysis. Now these models are improved by
self-learning algorithms. The new learning techniques are able to provide predictions with a
very high degree of accuracy.
Many stakeholders are interested in the use of machine learning models in mass appraisal:
among them, real estate companies, public authorities, banks and so on. The use of these new
techniques requires the inclusion, within the traditional evaluation groups, of new
professional figures such as data analysts.
Some real estate companies already successfully use machine learning models in
estimates. The best known case is the home valuation model Zestimate© by the American
agency Zillow. It is not yet known, at least to the authors knowledge, if machine learning
models are used in public policies. For banks the possibility of using self-learning algorithms
was introduced by the Basel II Accord in 2004. It allows the use of statistical methods to
AVMs vs
hedonic models
213
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1463-578X.htm
Received 18 December 2019
Revised 20 January 2020
Accepted 24 January 2020
Journal of Property Investment &
Finance
Vol. 38 No. 3, 2020
pp. 213-225
© Emerald Publishing Limited
1463-578X
DOI 10.1108/JPIF-12-2019-0157

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