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Big Data Approach to the Analysis of Real-estate Prices

ФИО студента: Umnov Andrey

Руководитель: Mariam Mamedli

Кампус/факультет: Faculty of Economic Sciences

Программа: Applied Economics (Master)

Оценка: 9

Год защиты: 2021

This work is devoted to the study of the application of Big Data and machine learning algorithms to the analysis of the real estate market. In particular, the emphasis is on the implementation of real estate appraisal activities using information from online portals for the sale of real estate and official statistics. The article considers the legal and regulatory framework of the Russian Federation governing the procedure for appraising real estate objects on the territory of the country, and an attempt is made to substantiate the legality of applying the methodology for appraising real estate objects based on the use of Big Data and machine learning algorithms. On the example of apartments in the secondary housing market in Moscow, a methodology for evaluating apartments is considered using data from the CIAN online portal obtained through web scraping technology, official statistics from the Housing and Utilities Reform portal, and machine learning algorithms such as Elastic Net Regularization, Random Forest and Gradient Boosting. At the end of the work, the best of the considered algorithms is selected, its results are interpreted using the Shapley vector, and issues that require further research in this area are identified.

Full text (added May 10, 2021)

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