Forecasting the Real Estate Price Index in Russia

Natalia S. Nikitina – Junior Researcher of the Russian Presidential Academy of National Economy and Public Administration (Moscow, Russia). E-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

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.

Key words: forecasting, real estate price index, machine learning.

JEL: C32, C53, R30.