딥러닝 모형을 활용한 서울 주택가격지수 예측에 관한 연구: 다변량 시계열 자료를 중심으로 (Prediction of Seoul House Price Index Using Deep Learning Algorithms with Multivariate Time Series Data)

서지정보

· 발행기관 : SH공사 도시연구원
· 수록지 정보 : 주택도시연구 / 8권 / 2호 / 39 ~ 56페이지
· 저자명 : 이태형, 전명진

초록

This study aims to evaluate the predictability of Deep Learning Neural Network algorithms (RNN and LSTM) in the forecast of the Seoul apartment price index. For the empirical analysis, we collect monthly housing price index data for the medium-sized and large-sized apartment units in Seoul during January 2006-October 2017 period. We also collect six macroeconomic variables that are known to affect housing price including expected inflation rate, rental price index, debt interest rate, stock price index, consumer price index, and unemployment rate. For the comparative purpose, we build Vector Autoregressive model (VAR) for multivariate time-series forecast. The analysis results indicate that the LSTM model best performed with the lowest RMSEs (0.826 and 1.038) for the medium-sized and large-sized apartment price indices, respectively, which is about 52 and 63 percent reductions from the VAR’s RMSE (1.708 and 2.825). We also found that standard deviations of predicted values from the LSTM are substantially lower than those of simple RNN, indicating higher stability of predicted price index from the LSTM than simple RNN.

영어초록

This study aims to evaluate the predictability of Deep Learning Neural Network algorithms (RNN and LSTM) in the forecast of the Seoul apartment price index. For the empirical analysis, we collect monthly housing price index data for the medium-sized and large-sized apartment units in Seoul during January 2006-October 2017 period. We also collect six macroeconomic variables that are known to affect housing price including expected inflation rate, rental price index, debt interest rate, stock price index, consumer price index, and unemployment rate. For the comparative purpose, we build Vector Autoregressive model (VAR) for multivariate time-series forecast. The analysis results indicate that the LSTM model best performed with the lowest RMSEs (0.826 and 1.038) for the medium-sized and large-sized apartment price indices, respectively, which is about 52 and 63 percent reductions from the VAR’s RMSE (1.708 and 2.825). We also found that standard deviations of predicted values from the LSTM are substantially lower than those of simple RNN, indicating higher stability of predicted price index from the LSTM than simple RNN.

참고자료

· 없음