@ARTICLE{26583204_803939459_2022, 
	author = {Mikhail Laskin and Lyudmila Gadasina}, 
	keywords = {, decision trees, random forest, real estate market, price-forming factorsmarket value apprising},
	title = {<p class="text">Peculiarities of applying methods based on decision trees in the problems of real estate valuation</p>},
	journal = {},
	year = {2022},
	number = {4 Vol.16},
	pages = {7-18},
	url = {https://bijournal.hse.ru/en/2022--4 Vol.16/803939459.html},
	publisher = {},
	abstract = {&nbsp; &nbsp; &nbsp; 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&rsquo; 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.},
	annote = {&nbsp; &nbsp; &nbsp; 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&rsquo; 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.}
}