Determination of outliers when building a multivariate regression model of prices in residential real estate market of the region

Abstract

The paper is aimed at selecting the optimal method for identifying outliers in the initial data when building a multivariate regression model of prices in the regional residential real estate market. The study was based on offers for sale of apartments in prefab flat blocks located in Irkutsk. In this regard, a basic multiplicative multivariate regression model was built, describing the dependence of cost indicators on the pricing factors of real estate. The identified outliers were iteratively removed from the basic model. The methods for detecting outliers included calculation of standard deviation (z-score), calculation of the Mahalanobis distance, as well as a method developed in the study for bringing the prices of objects to the characteristics of the reference object. The optimal method for detecting outliers in the initial data was selected by comparing the characteristics of the final variable-based multivariate regression models obtained after removing outliers from them. The analysis of the results proved the method of bringing the prices of objects to the characteristics of the reference object to be the optimal method of identifying outliers when building a multivariate regression model of prices in the regional residential real estate market. This method significantly reduces the approximation errors of the basic multivariate regression model of the market, thereby increasing the adequacy of the results of the real estate valuation conducted on its basis.