Abstract
This study proposes a deep learning method for predicting real estate prices utilizing deep learning with real estate price time-series data, text data such as news, and experts’ opinions. This study provides a method to integrate qualitative data into quantitative data when developing a deep learning model to predict time series. We employed house sales prices and economic data to build a prediction model for real estate prices from January 2006 to June 2021. In addition, we crawled 150,000 news information related to real estate and applied the TF-IDF method to identify the meaning of the news. We integrated the sentiment analysis and the experts’ opinions into our proposed model. The experiment results show that our proposed method is valid in RMSE performance. It was also confirmed that LSTM was superior to short-term prediction in long-term prediction.
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