| DC Field | Value | Language |
|---|
| dc.contributor.author | Алехина, А. Э. | - |
| dc.contributor.author | Федюкович, Т. В. | - |
| dc.date.accessioned | 2017-05-06T09:36:30Z | - |
| dc.date.accessioned | 2017-07-18T11:53:56Z | - |
| dc.date.available | 2017-05-06T09:36:30Z | - |
| dc.date.available | 2017-07-18T11:53:56Z | - |
| dc.date.issued | 2017 | - |
| dc.identifier.citation | Алёхина, А. Э. Статистический анализ и моделирование стоимости квартир на вторичном рынке жилой недвижимости г. Минска / А. Э. Алёхина, Т. В. Федюкович // BIG DATA and Advanced Analytics: collection of materials of the third international scientific and practical conference, Minsk, Belarus, May 3–4, 2017 / editorial board : М. Batura [et al.]. – Minsk : BSUIR, 2017. – С. 301-305. | ru_RU |
| dc.identifier.uri | https://libeldoc.bsuir.by/handle/123456789/12810 | - |
| dc.description.abstract | The purpose of this work is improvement of processes of economic and statistical modeling of residen-
tials of real estate on the basis of multivariate statistical analysis. The statistical analysis of the residentials of real estate
market in Minsk is carried out in the context of administrative districts of the city, types of buildings and number of
rooms. Apartments were divided into clusters at a cost of one square meter. The regression model of the cost of residential
real estate objects depending on the most significant indicators affecting the price of the apartment was constructed. | ru_RU |
| dc.language.iso | ru | ru_RU |
| dc.publisher | БГУИР | ru_RU |
| dc.subject | материалы конференций | ru_RU |
| dc.subject | Big Data | ru_RU |
| dc.subject | статистический анализ | ru_RU |
| dc.subject | жилая недвижимость | ru_RU |
| dc.subject | моделирование стоимости | ru_RU |
| dc.subject | рынок | ru_RU |
| dc.subject | продажа квартир | ru_RU |
| dc.title | Статистический анализ и моделирование стоимости квартир на вторичном рынке жилой недвижимости г. Минска | ru_RU |
| dc.type | Article | ru_RU |
| Appears in Collections: | BIG DATA and Advanced Analytics. Использование BIG DATA для оптимизации бизнеса и информационных технологий (2017)
|
