Forecasting the Value of Real Estate Using Machine Learning Tools

Correct valuation of real estate plays a crucial role in the process of buying and selling. We have carefully studied the existing applications with which we carry out real estate transactions, described their features, advantages and disadvantages. The developed model will help sellers get an estimate of their property according to the parameters entered, which can serve as a starting point for establishing the final value. The computation of real estate values has historically been based primarily on the method of analyzing data manually and subjective estimates, often resulting in errors and delays. The use of machine learning algorithms in solving this problem turned out to be effective, since it has a number of advantages over the manual estimation method, namely: a high level of accuracy, elimination of subjectivity and bias in estimates, time efficiency, cost reduction, use of geospatial data and substantiation of results. The process of creating a machine learning model is conditionally decomposed into four stages, which include collecting data, filtering, processing, supplementing, dividing into different samples and training the model based on this data. We considered the most popular regression algorithms, briefly described the principle of their work, as well as metrics with which you can evaluate the quality of the predicted values of the models. Standard parameters were used to test linear regression algorithms, decision tree, nearest neighbor method, support vector method, and random forest. The determination coefficient R-square is chosen as the main metric. Comparing the coefficient of determination of the results, it became clear that the algorithm “random forest” showed the best result. Having manually selected hyper parameters for this algorithm, the average value of the absolute error of the predicted value is 8.49 %, and the median is 1.9 %. The constructed model meets the established quality requirements and is ready for implementation in the information system of forecasting the value of real estate. For buyers, this service will be relevant, since they will be able to search for real estate according to the parameters entered by them, which have a favorable price for the purchase.