This article examines issues related to the development of the residential real estate market as an independent component of the market economy, addressing the pressing issue of housing provision for the country's population. The development of the residential real estate market has been accompanied by rapid growth in prices per square meter of housing, amid declining purchasing power and declining volumes of residential buildings being commissioned. These processes have made it impossible for most Russians to purchase apartments and detached houses, prompting the revival of mortgage lending in our country. The phenomena and processes resurfacing in Russia pose a challenge for modern statistical science to improve their statistical analysis through the application of quantitative methods that characterize the patterns of these phenomena. In this regard, this article examines the problems of mortgage lending from a statistical perspective. The primary focus of this study is the development and refinement of methodological aspects for quantitative analysis of mortgage lending. The theoretical and methodological foundations of the research were drawn from the works of Russian and international scholars in statistics, finance, and lending, legislative acts and regulatory documents of government agencies, official statistical data from the Federal State Statistics Service (Rosstat), the Central Bank of the Russian Federation (CBRF), commercial banks, and research conducted by academic institutions. Modern statistical tools and research methods were used to address these objectives, including tabular and graphical analysis, comparative analysis, correlation analysis, multivariate statistical analysis, and time series analysis. The study yields a spatial typological grouping of Russian regions by mortgage lending levels, as well as identifies the influence of macroeconomic factors on mortgage lending volumes in Russia. These findings can be used in developing new government policies aimed at providing housing to the Russian population.
mortgage housing lending, housing construction indicators, mortgage affordability indicators, international comparisons, regional differentiation in the Russian Federation
1. Anikanova V.V. Sovremennoe sostoyanie i problemy razvitiya ipotechnogo zhilischnogo kreditovaniya v RF [Tekst] / V.V. Anikanova // Forum molodyh uchenyh. 2024. — № 1.
2. Gamukin V.V. Vyyavlenie osobennostey sberegatel'nogo i kreditnogo povedeniya naseleniya v regionah Rossii [Tekst] / V.V. Gamukin // Ekonomika regiona. — 2020. № 3.
3. Gafarova E.A. Geterogennost' kanala refinansirovaniya ipoteki v Rossiyskih regionah [Tekst] / E.A. Gafarova // Finansy: teoriya i praktika. — 2023. — № 4.
4. Golodova Zh.G. Podhody k ocenke i povysheniyu urovnya dostupnosti zhil'ya v Rossii [Tekst] / Zh.G. Golodova, P.A. Smirnov // Vestnik RUDN. Seriya: Sociologiya. 2023. — № 4.
5. Nikitina N.S. Analiz faktorov, vliyayuschih na dinamiku cen na zhiluyu nedvizhimost' v Rossii [Tekst] / N.S. Nikitina // Finansy: teoriya i praktika. — 2023. — № 1.
6. Abiodun M. Ikotun, Absalom E. Ezugwu, Laith Abualigah, Belal Abuhaija, Jia Heming. Kmeans clustering algorithms: A comprehensive review, variants analysis, and advances in the era of big data, Information Sciences. Vol. 622, 2023, pp. 178–210. DOI: https://doi.org/10.1016/j.ins.2022.11.139
7. Absalom E. Ezugwu, Olaide O. Oyelade, Abiodun M. Ikotun, Laith Abualigah, Jeffery O. Agushaka, Christopher I. Eke, Andronicus A. Akinyelu. A comprehensive survey of clustering algorithms: Stateoftheart machine learning applications, taxonomy, challenges, and future research prospects, Engineering Applications od Artificial Intelligence. Vol. 110, 2022. DOI: https://doi.org/10.1016/j.engappai.2022.104743
8. Petr Polak & Lubos Komarek. Mortgage loan regulation instruments around the world," Occasional Publications Chapters in Edited Volumes, in: CNB Global Economic Outlook — July 2020, pp. 13–19.
9. Libuse Svobodova, Martina Hedvicakova. Mortgage Loans and Impacts of the Global Pandemic COVID 19 in the Globalized Society. SHS Web of Conf. Volume 92, 2021. DOI: https://doi.org/10.1051/shsconf/20219201047
10. Makram ElShagi, Jarko Fidrmuc, Steven Yamarik. Inequality and credit growth in Russian regions, Economic Modelling. Vol. 91, 2020, pp. 550–558. DOI: https://doi.org/10.1016/j.econmod.2019.11.003
11. Jaroslav Harvath, Philip Rothman. Mortgage spreads, asset prices, and business cycles in emerging countries, Journal of International Money and Finance. Vol. 115, 2021. DOI: https://doi.org/10.1016/j.jimonfin.2021.102370
