Title Comparison of Models to Forecast Real Estates Index Introducing Machine Learning
Authors 이주미(Lee, Ju-mi) ; 박성훈(Park, Sung-Hoon) ; 조상호(Cho, Sang-ho) ; 김주형(Kim, Ju-Hyung)
DOI https://doi.org/10.5659/JAIK.2021.37.1.191
Page pp.191-199
ISSN 2733-6247
Keywords Real Estates Index Forecasting; Machine Learning; Long Short-Term Memory
Abstract 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 trend has become an important research topic in order to provide information for establishing policies. In the prediction of the real estate market in the previous studies, two research directions were classified as follows: quantitative economic models and machine learning models. Regarding this trend, there was a need for comparative research on machine learning models, emerging methods, that are used to compare and predict various 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 comparative study to validate predictive power of machine learning models. Apartment sales index, land price index, charter price index, and real estate psychological index using univariate variables are predicted. In addition, RF, XGBoost and LSTM models all tended to be generally marginal with RMSE values of 0.0268, 0.0296, and 0.0259 in charter(Jeonse), Korean traditional pre-deposit rental system, price index data with linear but small variants. This shows that the prediction of the real estate index is deviated from the prediction accuracy of machine learning models depending on the periodic characteristics and data characteristics of the real estate index.