The Journal of Information Systems (한국정보시스템학회지:정보시스템연구)

Korea Association of Information Systems (한국정보시스템학회)
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Real Estate Price Forecasting by Exploiting the Regional Analysis Based on SOM and LSTM

SOM과 LSTM을 활용한 지역기반의 부동산 가격 예측

  • 신은경 (부산대학교 경영대학) ;
  • 김은미 (경희대학교 스마트관광연구소) ;
  • 홍태호 (부산대학교 경영대학)
  • Received : 2021.06.01
  • Accepted : 2021.06.22
  • Published : 2021.06.30
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Abstract

Purpose The study aims to predict real estate prices by utilizing regional characteristics. Since real estate has the characteristic of immobility, the characteristics of a region have a great influence on the price of real estate. In addition, real estate prices are closely related to economic development and are a major concern for policy makers and investors. Accurate house price forecasting is necessary to prepare for the impact of house price fluctuations. To improve the performance of our predictive models, we applied LSTM, a widely used deep learning technique for predicting time series data. Design/methodology/approach This study used time series data on real estate prices provided by the Ministry of Land, Infrastructure and Transport. For time series data preprocessing, HP filters were applied to decompose trends and SOM was used to cluster regions with similar price directions. To build a real estate price prediction model, SVR and LSTM were applied, and the prices of regions classified into similar clusters by SOM were used as input variables. Findings The clustering results showed that the region of the same cluster was geographically close, and it was possible to confirm the characteristics of being classified as the same cluster even if there was a price level and a similar industry group. As a result of predicting real estate prices in 1, 2, and 3 months, LSTM showed better predictive performance than SVR, and LSTM showed better predictive performance in long-term forecasting 3 months later than in 1-month short-term forecasting.

Keywords

Acknowledgement

이 논문은 2019년 대한민국 교육부와 한국연구재단의 지원을 받아 수행된 연구임. (NRF-2019S1A3A2098438)

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