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ISSN 2587-814X (print),
ISSN 2587-8158 (online)

Russian version: ISSN 1998-0663 (print),
ISSN 2587-8166 (online)

Mikhail Laskin 1, Lyudmila Gadasina2
  • 1 Saint Petersburg Federal Research Center, Russian Academy of Sciences, 39, 14 line, Vasilevskiy Island, St. Petersburg 199178, Russia
  • 2 St. Petersburg State University, Center for econometrics and business analytics (CEBA), 7/9, Universitetskaya emb., Saint-Petersburg 199034, Russia

Peculiarities of applying methods based on decision trees in the problems of real estate valuation

2022. No. 4 Vol.16. P. 7–18 [issue contents]
      The increasing flow of available market information, the development of methods of machine learning, artificial intelligence and the limited capabilities of traditional methods of real estate valuation are leading to a significant increase of researchers’ interest in real estate valuation by applying methods based on decision trees. At the same time, the distribution of real estate prices is well approximated by a lognormal distribution. Therefore, traditional methods overestimate the predicted values in the region below the average of the available data set and underestimate the predicted values in the region above the average. This article shows the reasons for these features and proposes an adaptive random forest algorithm which corrects the results of the basic algorithm prediction by revising the bias of these predicted values. The results were tested on the real estate offer prices in St. Petersburg.

Citation: Laskin M.B., Gadasina L.V. (2022) Peculiarities of applying methods based on decision trees in the problems of real estate valuation. Business Informatics, vol. 16, no. 4, pp. 7–18. DOI: 10.17323/2587-814X.2022.4.7.18
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