Standard

Peculiarities of applying methods based on decision trees in the problems of real estate valuation. / Laskin, Mikhail ; Gadasina, Lyudmila .

в: Business Informatics, Том 16, № 4, 2022, стр. 7-18.

Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование

Harvard

Laskin, M & Gadasina, L 2022, 'Peculiarities of applying methods based on decision trees in the problems of real estate valuation', Business Informatics, Том. 16, № 4, стр. 7-18. https://doi.org/10.17323/2587-814X.2022.4.7.18

APA

Laskin, M., & Gadasina, L. (2022). Peculiarities of applying methods based on decision trees in the problems of real estate valuation. Business Informatics, 16(4), 7-18. https://doi.org/10.17323/2587-814X.2022.4.7.18

Vancouver

Laskin M, Gadasina L. Peculiarities of applying methods based on decision trees in the problems of real estate valuation. Business Informatics. 2022;16(4):7-18. https://doi.org/10.17323/2587-814X.2022.4.7.18

Author

Laskin, Mikhail ; Gadasina, Lyudmila . / Peculiarities of applying methods based on decision trees in the problems of real estate valuation. в: Business Informatics. 2022 ; Том 16, № 4. стр. 7-18.

BibTeX

@article{0644ff31ab194bebb259f9c7455c43f3,
title = "Peculiarities of applying methods based on decision trees in the problems of real estate valuation",
abstract = " 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{\textquoteright} 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.",
keywords = "decision trees, random forest, real estate market, price-forming factors, market value apprising",
author = "Mikhail Laskin and Lyudmila Gadasina",
note = "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",
year = "2022",
doi = "10.17323/2587-814X.2022.4.7.18",
language = "English",
volume = "16",
pages = "7--18",
journal = "Business Informatics",
issn = "2587-814X",
publisher = "National Research University, Higher School of Econoimics",
number = "4",

}

RIS

TY - JOUR

T1 - Peculiarities of applying methods based on decision trees in the problems of real estate valuation

AU - Laskin, Mikhail

AU - Gadasina, Lyudmila

N1 - 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

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

KW - decision trees

KW - random forest

KW - real estate market

KW - price-forming factors

KW - market value apprising

U2 - 10.17323/2587-814X.2022.4.7.18

DO - 10.17323/2587-814X.2022.4.7.18

M3 - Article

VL - 16

SP - 7

EP - 18

JO - Business Informatics

JF - Business Informatics

SN - 2587-814X

IS - 4

ER -

ID: 103471619