Effective use of machine learning to solve socio-economic problems

Abstract

To study of the dependence of the offer price of a residential real estate object in Almaty on a number of factors (apartment area; number of rooms; material from which the house is built; floor on which the studied object is located; kitchen area; type of bathroom; presence of a balcony; distance from the city center; availability of an elevator; condition of the apartment in terms of the need for repairs; age of the house; belonging to the primary or secondary real estate market). The obtained machine learning models can be used to make forecasts of the cost of residential real estate, which can be reflected in the development of programs for the socio-economic development of municipalities and the region as a whole, will be in demand by investors, other counterparties in the residential real estate market and individuals. when assessing the acquired social and domestic benefits. The authors see the continuation of research based on the accumulation of an array of information in expanding the possibilities of modeling the cost of apartments, depending not only on the characteristics of apartments, but also on characteristics that reflect the influence of external factors on the regional market of residential real estate.