Who performs better? AVMs vs hedonic models

• Published in: Journal of property investment & finance Vol. 38; no. 3; pp. 213 - 225
• Main Author: Valier, Agostino
• Format: Journal Article
• Language: English
• Published: Bradford Emerald Publishing Limited 08.06.2020; Emerald Group Publishing Limited
• Subjects: Accuracy; Algorithms; Artificial intelligence; Automation; Cost estimates; Datasets; Econometrics; Investigations; Machine learning; Neural networks; Prices; Regression analysis; Software; Statistical methods; Valuation; Valuation; Mass appraisal; Machine learning; AVM; Econometric model; Automated valuation models; Real estate
• ISSN: 1463-578X; 1470-2002
• DOI: 10.1108/JPIF-12-2019-0157

PurposeIn 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 comparing regression analysis and AVMs machine learning on the same data set have been identified. The scores obtained in terms of accuracy were then compared with each other.FindingsMachine 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 implicationsAVMs 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/valueAccording to the author, this is the first systematic review that collects all the articles produced on the subject done comparing the results obtained.