ECONOMIC AND MATHEMATICAL MODELING OF THE INFLUENCE OF MACROECONOMIC FACTORS ON REAL ESTATE MARKET PRICING
Abstract
The process of determining and forecasting the pricing of the real estate market has a high degree of complexity due to the dependence of market processes on many meso- and macroeconomic factors of the national economy. The purpose of the work is. to create a model for assessing the impact of various factors on the price per square meter of the primary market in Kyiv. It has been identified potential macroeconomic and regional factors influencing the pricing of the real estate market in Kyiv. The construction of a linear multivariate model was based on statistical data from the civil service of Ukraine, This model allows us to estimate the correlation between two or more independent variables and one dependent variable. The regression-correlation analysis was conducted to avoid multicollinearity. The following criteria were applied: correlation coefficient Pearson, direct selection method, Fisher's criterion, and p-value. Some factors were excluded from the process. As a result, a model with fairly high accuracy was obtained. Also, the expediency of using the machine learning method was substantiated and the model of Random Forest was built by using the R programming language. The sample was divided into two parts, one of the artificial intelligence was trained (80%), and on the other, there was a model testing by forecasting (20%). The big advantage of the random forest is that we can find out which factors are more important for our model. In the process of model building, the most insignificant factors were excluded and we got a model with a quite high accuracy. The results of the comparison of models showed that machine learning, namely the Random Forest method, is more effective for the study of complex issues, such as the impact of macroeconomic factors on the pricing of the real estate market. During the study, it was found that the price per square meter in new buildings is influenced by such factors as the discount rate, exchange rate, per capita expenditure, gross regional product, subsistence minimum, and the number of the available population.
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