Abstract
The article is devoted to the description of the real estate market, the situation in the market and services in the real estate market of Almaty and Kazakhstan as a whole. The factors influencing the market, such as external and internal, are considered, and further prospects for the development of the real estate market in the context of digitalization are presented The housing market in Kazakhstan is intricate, comprising numerous unique features among hundreds of thousands of apartments. It's important to note that even minor shifts in this market can trigger speculative behavior and intentional inflation of property prices. Thus, grasping the actual worth of an apartment and recognizing its prime location becomes crucial. The primary focus of the research was to construct a predictive model for real estate valuation using machine learning techniques. Various machine learning algorithms were employed, encompassing linear regression, lasso, ridge, decision tree regression, random forest regression, and SVM (utilizing a Gaussian kernel). The study illustrates the outcomes and precision of these algorithms, offering insight into their effectiveness. Based on the findings of this study, the Random Forest regression algorithm demonstrated the highest performance, achieving a 90% score.