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Finance and Credit
 

Cluster technologies in researching the inequality of mortgage development of Russian regions

Vol. 28, Iss. 8, AUGUST 2022

Received: 20 June 2022

Received in revised form: 11 July 2022

Accepted: 25 July 2022

Available online: 30 August 2022

Subject Heading: THEORY OF FINANCE

JEL Classification: C38, G21, R38

Pages: 1808–1830

https://doi.org/10.24891/fc.28.8.1808

Tat'yana S. KOROSTELEVA Samara National Research University (Samara University), Samara, Russian Federation
korosteleva75@mail.ru

https://orcid.org/0000-0002-8519-5956

Vladimir E. TSELIN Samara National Research University (Samara University), Samara, Russian Federation
vtzelin@mail.ru

https://orcid.org/0000-0001-8657-9903

Subject. The article discusses the uneven development of mortgage lending in Russian regions.
Objectives. The aim is to cluster regional mortgage markets in the Russian Federation to identify uneven mortgage development in regions; to test the hypothesis about the possibility to base the differentiated approach to the State mortgage policy on the results of clustering of Russian regions.
Methods. The study employs cluster technologies. The basic method is a hierarchical cluster analysis. The optimal number of clusters was selected by finding the ‘elbow’ point based on the study of the distance of clustering. Agglomerative clustering rests on the method of weighted pairwise comparison.
Results. We performed hierarchical clustering of regional mortgage markets. Nine clusters were taken as the optimal number. The clustering results were analyzed with a search for their semantic interpretation. We revealed socio-economic reasons that determine the regional membership in the selected clusters, proved that the differentiated public policy of regional mortgage systems development to tackle the housing problems can be implemented on the basis of the results of clustering of regional mortgage markets, but not be limited to them.
Conclusions. The findings can be useful for Federal authorities of the Russian Federation in the search and study of anomalies in regional mortgage development. Cluster technologies, as a tool for system classification of regions, are effective, if the cluster analysis is complemented by other methods of multivariate statistical analysis and the development of procedures for their joint constructive application.

Keywords: cluster technology, hierarchical cluster analysis, uneven development, regional mortgage market, State policy

References:

  1. Belyaeva T.A., Bessonova E.N., Kozieva I.A. Development and Implementation of the Spatial Development Strategy: Effectiveness Challenges. Complex Systems: Innovation and Sustainability in the Digital Age. Studies in Systems, Decision and Control, 2021, vol. 283, pp. 133–141. URL: Link
  2. Ara-Aksoy S., Irwin E. Cluster Analysis for Housing Market Segmentation. Sosyoekonomi, 2021, vol. 29, iss. 49, pp. 11–32. URL: Link
  3. Gabrielli L., Giuffrida S., Trovato M.R. From Surface to Core: A Multi-layer Approach for the Real Estate Market Analysis of a Central Area in Catania. Proceedings of International Conference on Computational Science and Its Applications (ICCSA 2015), vol III. Cham, Springer, 2015, pp. 284–300.
  4. Napoli G., Giuffrida S., Valenti A. Forms and Functions of the Real Estate Market of Palermo (Italy). Science and Knowledge in the Cluster Analysis Approach. In: Stanghellini S., Morano P., Bottero M., Oppio A. (eds) Appraisal: From Theory to Practice. Green Energy and Technology. Springer, Cham, 2017, pp. 191–202. URL: Link
  5. Guo K., Wang J., Shi G.S., Cao X.H. Cluster Analysis on City Real Estate Market of China: Based on a New Integrated Method for Time Series Clustering. Procedia Computer Science, 2012, vol. 9, pp. 1299–1305. URL: Link
  6. Kowalczyk-Rolczynska P. An Application of Cluster Analysis on the Polish Housing Market. Proceedings of the 8th International Days of Statistics and Economics, 2014, Prague, 2014, pp. 766–775. URL: Link
  7. Tomal M. Housing Market Heterogeneity and Cluster Formation: Evidence from Poland. International Journal of Housing Markets and Analysis, 2021, vol. 14, iss. 5, pp. 1166–1185. URL: Link
  8. Hwang S., Thill J.-C. Delineating Urban Housing Submarkets with Fuzzy Clustering. Environment and Planning B: Planning and Design, 2009, vol. 36, iss. 5, pp. 865–882. URL: Link
  9. Vatansever M., Demir I., Hepsen A. Cluster and Forecasting Analysis of the Residential Market in Turkey: An Autoregressive Model-Based Fuzzy Clustering Approach. International Journal of Housing Markets and Analysis, 2020, vol. 13, iss. 4, pp. 583–600. URL: Link
  10. Gabrielli L., Giuffrida S., Trovato M.R. Gaps and Overlaps of Urban Housing Sub-market: Hard Clustering and Fuzzy Clustering Approaches. In: Stanghellini S., Morano P., Bottero M., Oppio A. (eds) Appraisal: From Theory to Practice. Green Energy and Technology. Springer, Cham, 2017, pp. 203–219. URL: Link
  11. Alkan L. Housing Market Differentiation: The Cases of Yenimahalle and Çankaya in Ankara. International Journal of Strategic Property Management, 2015, vol. 19, no. 1, pp. 13–26. URL: Link
  12. Wang K. Housing Market Resilience: Neighborhood and Metropolitan Factors Explaining Resilience Before and After the U.S. Housing Crisis. Urban Studies, 2018, vol. 56, iss. 13, pp. 2688–2708. URL: Link
  13. Kostylev A.V. [Regional housing markets: Experience of classification]. Aktual'nye problemy ekonomiki i prava = Actual Problems of Economics and Law, 2014, no. 1, pp. 181–185. URL: Link (In Russ.)
  14. Tokarev Yu.A., Belanova N.N., Guzhova O.A., Glukhov G.V. Region Clustering and Modeling Indices for Housing Market. In: Mantulenko V. (ed.) Global Challenges and Prospects of the Modern Economic Development, 2019, vol. 57, pp. 1408–1417. URL: Link
  15. Babich S.G., Karmanov M.V., Toropova N.N. et al. Statistical Study of Current Trends in Mortgage Lending in Russia. Universal Journal of Accounting and Finance, 2021, vol. 9(2), pp. 170–177. URL: Link
  16. Chukanov A. Clusterization of Russian Regions by the Level of Mortgage Developing. Scientific Research and Development. Economics, 2019, vol. 7, no. 1, pp. 31–36. URL: Link
  17. Sirotkin V.A. [Clustering of the regional real estate market: The example of Sverdlovsk region]. Zhilishchnye strategii, 2016, vol. 3, no. 3, pp. 163–178. (In Russ.) URL: Link
  18. Derkachenko V.N. [Forecasting and cluster analysis of the development of the regional residential real estate market]. Nauchno-metodicheskii elektronnyi zhurnal Kontsept, 2014, vol. 20, pp. 11–15. (In Russ.) URL: Link
  19. Korosteleva T.S., Tselin V.E. [Assessing the mortgage potential capacity of regions: A methodology and indicators]. Regional'naya ekonomika: teoriya i praktika = Regional Economics: Theory and Practice, 2020, vol. 18, iss. 2, pp. 381–396. (In Russ.) URL: Link
  20. Korosteleva T.S., Tselin V.Y. Management of Regional Imbalances in the Russian Mortgage Market Using the Principal Components Method. Journal of Economics Studies and Research, 2021, vol. 2021, 126542. URL: Link
  21. Byuyul' A., Tsefel' P. SPSS: iskusstvo obrabotki informatsii. Analiz statisticheskikh dannykh i vosstanovlenie skrytykh zakonomernostei [SPSS: The Art of Information Processing. Analysis of statistical data and restoration of hidden patterns]. Moscow, St. Petersburg, Kiev, DiaSoftYuP Publ., 2005, 602 p.
  22. Korosteleva T.S., Tselin V.E. [Assessment of regional mortgage inequality: Comparative analysis of methods and results]. Vestnik Samarskogo universiteta. Ekonomika i upravlenie = Vestnik of Samara University. Economics and Management, 2020, vol. 11, no. 3, pp. 92–106. (In Russ.)
  23. Tagirova E.I. [Criteria for assigning regions to depressive territories]. Vestnik Altaiskoi akademii ekonomiki i prava = Bulletin of Altai Academy of Economics and Law, 2020, no. 10-3, pp. 309–313. (In Russ.) URL: Link

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