Pricing modeling in the housing market with urban infrastructure effect

Описание

Тип публикации: доклад, тезисы доклада, статья из сборника материалов конференций

Конференция: International Conference on High-Tech and Innovations in Research and Manufacturing, HIRM 2019

Год издания: 2019

Идентификатор DOI: 10.1088/1742-6596/1353/1/012139

Аннотация: Nowadays in many large industrial enterprises, one of the motivational incentives for hiring employees, as well as for encouraging them, is the provision of temporary or permanent use of residential real estate. This raises the question of choosing the best option of the property from a variety of proposals on the market. This artiПоказать полностьюcle addresses the issue of estimating the value of residential property in the city of Krasnoyarsk. The descriptive signs of the apartment were not only its internal parameters, such as the area or number of rooms, but also the external characteristics that describe the environment of the apartment house. The data on the apartments were taken from the website of the apartment sales announcements and from various open data sources. The number of organizations of each type considered within a radius of 1000 m serves as a quantitative measure of the house environment. The model built using a random forest showed good results and solved the problem. The relative error of the forecast was 8%. In addition, it was shown the positive impact of the apartment external characteristics on the quality of the constructed model. As a result, an easily scalable model was built that can be applied to other cities. © 2019 Published under licence by IOP Publishing Ltd.

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Издание

Журнал: Journal of Physics: Conference Series

Выпуск журнала: Vol. 1353, Is. 1

Номера страниц: 12139

ISSN журнала: 17426588

Издатель: Institute of Physics Publishing

Авторы

  • Koktashev V. (Siberian Federal University, 79 Svobodny Avenue, Krasnoyarsk, 660041, Russian Federation)
  • Makeev V. (Siberian Federal University, 79 Svobodny Avenue, Krasnoyarsk, 660041, Russian Federation)
  • Shchepin E. (Siberian Federal University, 79 Svobodny Avenue, Krasnoyarsk, 660041, Russian Federation)
  • Peresunko P. (Siberian Federal University, 79 Svobodny Avenue, Krasnoyarsk, 660041, Russian Federation)
  • Tynchenko V.V. (Siberian Federal University, 79 Svobodny Avenue, Krasnoyarsk, 660041, Russian Federation, Reshetnev Siberian State University of Science and Technology, 31 Krasnoyarsky Rabochy Avenue, Krasnoyarsk, 660037, Russian Federation)

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