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Прогнозирование цен на рынке жилой недвижимости г. Владивостока

Прогнозирование цен на рынке жилой недвижимости г. Владивостока

Abstract

The real estate market is the subject of analysis in this paper, namely, the prediction of prices at this market. The authors consider the variables that affect the price of a square meter of the real estate. The characteristics of the property, also called "object properties" are listed. It has been found, that multiple regression analysis cannot be used as a method for the real estate prices forecasting. Also, the article presents results of the forecasting with application of ARIMA models, and it is shown that these forecasts differ slightly.

Объектом анализа в статье является рынок недвижимости, а именно прогнозирование цен на этом рынке. Выделяются переменные, от которых зависит цена квадратного метра недвижимости. Перечисляются свойства объекта недвижимости, иначе называемые «качествами объекта». Определяется, что множественный регрессионный анализ не может являться методом прогнозирования цен на недвижимость. Также в работе представлены результаты прогнозирования с использованием ARIMA-моделей и показано, что прогнозы различаются незначительно.

Keywords

РЫНОК НЕДВИЖИМОСТИ,REAL ESTATE MARKET,СВОЙСТВА ОБЪЕКТА НЕДВИЖИМОСТИ,REAL ESTATE PROPERTIES,ПРОГНОЗИРОВАНИЕ ЦЕН НА РЫНКЕ НЕДВИЖИМОСТИ,PRICE FORECASTING IN THE REAL ESTATE MARKET,КОРРЕЛЯЦИОННО-РЕГРЕССИОННЫЙ АНАЛИЗ,REGRESSION ANALYSIS,МНОЖЕСТВЕННАЯ РЕГРЕССИЯ,MULTIPLE REGRESSION,ТРЕНДОВОЕ ПРОГНОЗИРОВАНИЕ,TREND FORECASTING,ВРЕМЕННОЙ РЯД,TIME SERIES,МОДЕЛЬ ARIMA,ARIMA MODEL

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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