Who performs better? AVMs vs hedonic models
| DOI | https://doi.org/10.1108/JPIF-12-2019-0157 |
| Pages | 213-225 |
| Date | 27 March 2020 |
| Published date | 27 March 2020 |
| Author | Agostino Valier |
| Subject Matter | Property valuation & finance,Property management & built environment |
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/approach–All 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 evaluator’s 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.
AVMs vs
hedonic models
213
© Agostino Valier. Published by Emerald P ublishing Limite d. This article is pub lished under the
Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and
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http://creativecommons.org/licences/by/4.0/legalcode
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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