Abstract
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.
Highlights
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)
Using a data set of 3,906 observations and 108 times the comparison with data sets of different sizes, they show that artificial neural networks (ANNs) exceed the predictive capacity of multiple regression only when the sample is of medium-large size
Summary
Artificial intelligence is bringing about a radical change in many activities traditionally carried out by human work: among them, real estate valuation. 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%. Automated value prediction models are gradually replacing the evaluator’s work. In the past, these models only used regression analysis. The new learning techniques are able to provide predictions with a very high degree of accuracy
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