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Analysis and Modeling of the Impact of Macroeconomic Factors on the Commissioning of Residential Real Estate in Russia

https://doi.org/10.34023/2313-6383-2023-30-1-27-41

Abstract

The system of statistical indicators, which is necessary for the construction of mathematical and statistical models that reflect modern domestic trends in the development of the residential real estate market is explained. The official data from the Federal State Statistics Service (Rosstat), the Unified Interdepartmental Information and Statistical System (EMISS), the Central Bank of the Russian Federation (CBR), and the Unified Housing Construction Information System (UIIS) served as information sources for the empirical component of the study.

Based on quarterly data for 2010–2021 using ARIMA and SARIMA models, a time series of residential real estate commissions in the Russian Federation was modeled and predicted for 2022. Both models make it possible to account for the influence of the seasonal component. Based on results of the time series regression analysis, the authors selected a mathematical and statistical model with the best approximating characteristics. To model the volume of commissioning of residential real estate in the Russian market, with due regard to the influence of macroeconomic factors, the ARMAX model was used, which has significant explanatory power.

The results of the study presented in the article may be of interest to analytical agencies, developers, banking professionals, financiers, economists, analysts of the real estate market or related areas, as well as authorities for strategic planning of the development of the real estate market.

About the Authors

N. V. Zvezdina
National Research University Higher School of Economics (HSE University)
Russian Federation

Nataliya V. Zvezdina – Cand. Sci. (Econ.), Associate Professor; Associate Professor, Department of Statistics and Data Analysis

11, Pokrovsky Bulvar, Moscow, 109028



A. V. Saraev
Cooper Vision
Russian Federation

Anton V. Saraev – Analyst

6, Presnenskaya Embankment, Bldg. 2, Moscow, 123112



References

1. Information about Housing (Mortgage) Loan Market in Russia. Bank of Russia Information Bulletin. Statistical Indicators. 2022;6(26):60. (In Russ.)

2. Housing Construction. Bank of Russia Analytical Notes. 2021;1(4):37. Moscow. (In Russ.)

3. Radonjić M. et al. The Impact of Macroeconomic Factors on Real Estate Prices: Evidence from Montenegro. Ekonomski Pregled. 2019;70(4):603–626.

4. Gőkkent G., Ucal M.S. Macroeconomic Factors Affecting Real Estate Markets in Turkey: A VAR Analysis Approach. Briefing Notes in Economics. 2009;(80).

5. McCue T.E., Kling J.L. Real Estate Returns and the Macroeconomy. The Journal of Real Estate Research. 1994;9(3):277–287.

6. Chaudhry M.K., Rohan A.C, William H.C. Long-Term Structural Price Relationships in Real Estate Markets. Journal of Real Estate Research. 1999;18(2):335–354.

7. Lorenz F. et al. Interpretable Machine Learning for Real Estate Market Analysis. Real Estate Economics. 2022;00:1–31.

8. Potrawa T., Tetereva A. How Much Is the View from the Window Worth? Machine Learning-Driven Hedonic Pricing Model of the Real Estate Market. Journal of Business Research. 2022;144:50–65.

9. Tchuente D., Nyawa S. Real Estate Price Estimation in French Cities Using Geocoding and Machine Learning. Annals of Operations Research. 2022;308(1):571–608.

10. Yu Y. et al. Research on Real Estate Pricing Methods Based on Data Mining and Machine learning. Neural Computing and Applications. 2021;33(9):3925–3937.

11. Yasnitsky L.N., Yasnitsky V.L. Development and Application of Complex Neural Network Models of Mass Assessment and Forecasting of the Cost of Residential Objects on the Example of Real Estate Markets in Yekaterinburg and Perm. Property Relations in the Russian Federation. 2017;3(186):68–84. (In Russ.)

12. Hylleberg S. et al. Seasonal Integration and Cointegration. Journal of Econometrics. 1990;44(1-2):215–238.

13. Magnus Ya.R., Peresetsky A.A., Katyshev P.K. Econometrics. Beginner Course: Textbook. 9th Ed., Revised. Moscow: Delo Publ.; 2021. 504 p. (In Russ.)

14. Podkorytova O.A., Sokolov M.V. Time Series Analysis: A Study Guide for Bachelor's and Master's Degrees. 2nd Ed., Rev. and Add. Moscow: Yurayt Publishing House; 2017. 267 p.


Review

For citations:


Zvezdina N.V., Saraev A.V. Analysis and Modeling of the Impact of Macroeconomic Factors on the Commissioning of Residential Real Estate in Russia. Voprosy statistiki. 2023;30(1):27-41. (In Russ.) https://doi.org/10.34023/2313-6383-2023-30-1-27-41

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ISSN 2313-6383 (Print)
ISSN 2658-5499 (Online)