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ИД «Финансы и кредит»






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


Tat'yana S. KOROSTELEVA Samara National Research University (Samara University), Samara, Russian Federation


Vladimir E. TSELIN Samara National Research University (Samara University), Samara, Russian Federation


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


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