Forecasting the Real Estate Price Index in Russia

Прогнозирование индекса цен на недвижимость в России

Natalia S. Nikitina
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Natalia S. Nikitina: Russian Presidential Academy of National Economy and Public Administration

Russian Economic Development, 2022, issue 6, 23-28

Abstract: This article is devoted to choosing the best model for short-term forecasting of Russia’s real estate price index. Popular machine learning methods: Ridge and Lasso regressions, Elastic Net regression and methods of working with time series were considered: Naive, Exponential smoothing, ARIMA, OLS. The set of variables includes the values of GDP, inflation, effective exchange rate, interbank lending rates, and oil prices. Machine learning methods – Ridge Regression and Elastic Net regression – show the high quality of forecasting the real estate price index compared to standard ways of working with time series – Naive, Exponential smoothing, ARIMA. The article was prepared in the framework of execution of state order by RANEPA.

Keywords: forecasting; real estate price index; machine learning (search for similar items in EconPapers)
JEL-codes: C32 C53 R30 (search for similar items in EconPapers)
Date: 2022
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