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Forecasting the Real Estate Price Index in Russia
[Прогнозирование Индекса Цен На Недвижимость В России]

Author

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  • Natalia S. Nikitina

    (Russian Presidential Academy of National Economy and Public Administration)

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.

Suggested Citation

  • Natalia S. Nikitina, 2022. "Forecasting the Real Estate Price Index in Russia [Прогнозирование Индекса Цен На Недвижимость В России]," Russian Economic Development, Gaidar Institute for Economic Policy, issue 6, pages 23-28, June.
    • Handle: RePEc:gai:recdev:r2250
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    References listed on IDEAS

    as
    1. Gerhard Rünstler & Marente Vlekke, 2018. "Business, housing, and credit cycles," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 33(2), pages 212-226, March.
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    More about this item

    Keywords

    forecasting; real estate price index; machine learning;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • R30 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - General

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