Applied statistical analysis and forecasting in the real estate sector

The real estate market is constantly changing and is one of the most signi cant indicators of the state of the economy. In terms of constantly changing demand, price and supply, market participants often face the problem of correctly assessing the value of real estate objects and determining the factors a ecting the price. In this paper, various parameters a ecting the value of residential real estate were identi ed and evaluated. The Python programming language and its libraries such as Matplotlib and Seaborn were used for data visualization, as well as Pandas, NumPy and Sklearn for data processing. The methodology included the analysis of various parameters of houses, such as area, location, number of oors and rooms using correlation, regression and graphical analysis. The factors identi ed at the stage of studying the characteristics of houses were used to build a linear regression model. To evaluate the model, the classical accuracy metric was used the square root of the root mean square error between the test sample and the model prediction. The resulting model also demonstrated high accuracy of predictions. The results of the study might allow to rationalize the actions of buying and selling real estate to both real estate agencies and developers, as well as individuals. They can also be useful for real estate aggregators to evaluate objects before putting them up for sale.