A Study on the Determinants of Housing Price Using Data Mining

Abstract

This study empirically analyzed the determinants of housing price using decision tree analysis, multiple regression analysis, and neural network analysis. The dependent variables are apartment real transaction price, independent variables are apartment size, apartment floor, construction year, city hall straight distance, 2 lane straight distance, 4 lane straight distance, subway station straight distance, Gangnam area, Gangseo area, urban area and Gangbuk area. As a result, the mean absolute error (MAE) of the neural network analysis is the smallest, and the linear correlation value shows the highest correlation between the real and predicted values. According to the decision tree analysis, the small size is divided into 70m2 instead of 59m2 , so it can be seen that the small and medium area is popular because 2-3 person households are popular. As a result of multiple regression analysis, apartment size, number of floors, 2 lane straight distance, and 4 lane straight distance were positively affected by housing price, while city hall straight distance, subway station straight distance, population density and region from Gangnam area to Gangseo area, Gangbuk area and urban area were negative.