머신러닝을 이용한 부동산 지수 예측 모델 비교

초록

As the real estates occupy major portion of domestic households assets, relevant issue has been dealt seriously by the Korean government. However, apartment prices in downtown Seoul, the capital city, have soared despite various policies. Forecasting the real estate market trendhas become an important research topic in order to provide information for establishing policies. In the prediction of the real estate market inthe previous studies, two research directions were classified as follows: quantitative economic models and machine learning models. Regardingthis trend, there was a need for comparative research on machine learning models, emerging methods, that are used to compare and predictvarious real estate indices. In this study, the machine learning model RF(Random Forest), XGBoost(eXtreme Gradient Boosting), and LSTM(Long Short Term Memory) are used to select suitable machine learning models for selected real estate index and conduct a comparativestudy to validate predictive power of machine learning models. Apartment sales index, land price index, charter price index, and real estatepsychological index using univariate variables are predicted. In addition, RF, XGBoost and LSTM models all tended to be generally marginalwith RMSE values of 0.0268, 0.0296, and 0.0259 in charter(Jeonse), Korean traditional pre-deposit rental system, price index data with linearbut small variants. This shows that the prediction of the real estate index is deviated from the prediction accuracy of machine learningmodels depending on the periodic characteristics and data characteristics of the real estate index.

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