摘要：The article considers the issue of pricing for the secondary real estate market regarding local causes (physical properties of housing). The aim of the study was to verify the following hypotheses: the influence of the pricing factors of residential real estate on its value is determined by its price segment and the influence of infrastructure on the value of apartments in different cities is the same. In the hypothesis test, data were used on the secondary housing market of the cities of Novosibirsk and Krasnoyarsk, taken from the site of «CIAN» apartment sale announcements and from various open data sources. During the study, non-parametric methods of machine learning, model-agnostic methods for the interpretation of predictive models, hierarchical clustering are involved. As a result of the work, the first hypothesis was confirmed and the second hypothesis was refuted, the high accuracy of forecasting the cost of an apartment was achieved, and the peculiarities of price formation for secondary housing objects were revealed and described.
