Application of methods of neural networks in the analysis of the market of mortgage housing loan

Abstract

The article analyzes the state of the mortgage lending market in the Russian Federation. The classification of regions by indicators characterizing the mortgage lending market is given. The factors affecting the state of the mortgage lending market are studied. A forecast for 12 months is proposed. The methods of neural networks were used as the main analysis methods. In particular, the paper presents examples of the use of neural networks for classifying, evaluating factors, and constructing regression models. The choice of this technology is due to the fact that over the past few years, interest in the use of neural networks in the study of statistical data, analysis and forecasting has increased significantly. Such popularity is due to the fact that neural networks significantly complement the usual traditional statistical methods of data analysis, which can be used to build complex nonlinear models and dependencies. The article is intended for researchers in the mortgage market who want to get acquainted with the use of neural network technology for analyzing the mortgage market.