Abstract
In this article, in order to optimize the economic policy in the field of mortgagehousing lending, the clustering of Russian regions by the most optimal method was carried out and analyzed. The main limitations arising from the application of the most popular k-means clustering algorithm for analyzing mortgages are considered and ways to correct them are suggested. The regions were grouped using clustering algorithms using medians and medoids that are more resistant to outliers. A comparison was made of the results of the k-means, k-medians and k-medoids algorithms, and the optimal number of groups of regions with similar indicators in the field of mortgage lending and their relevant regions representatives were found. A hierarchical clustering algorithm based on the Ward method was used, the result of which was the use of five mortgage clusters in Russia. The study of the characteristics of these groups of regions will help in creating a mortgage policy that takes into account the peculiarities of the regions of Russia. All calculations were made in the R programming language; graphics were created in the Rstudio development environment.
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