지역별 주택가격의 격차에 관한 연구 : 회귀분석과 인공신경망 활용

Abstract

The purpose of this study is to identify the determinants of housing prices in the region based on demand and supply variables and location considering housing preference. In order to analyze the determinants of the housing price among the city areas in the nation, the housing price model was established by using the location conditions considering the supply and housing preference, and the importance was analyzed by regression analysis and artificial neural network. Transportation, job, income, school districts, and educational level, which determine the price of the local housing market, show a strong influence. The model considering the location conditions gives clearer explanation and is more significant than the traditional demand supply model.. Capital was the most important factor in analyzing the importance of artificial neural networks, followed by income, education level, and job. The order of the overall importance is as follows: transportation, job, income, and neighborhood. In the accuracy analysis, artificial neural network analysis to which location variables are added was superior to individual regression analysis model.