Sua Kim  Mi Ju Kwon  Hyon Hee Kim

https://doi.org/10.3745/TKIPS.2024.13.5.209

Generated AI Prediction of Real Estate Price News Sentiment Index Multi-head Attention LSTM Vetor Auto Regression

Real estate market prices are determined by various factors, including macroeconomic variables, as well as the influence of a varietyof unstructured text data such as news articles and social media. News articles are a crucial factor in predicting real estate transactionprices as they reflect the economic sentiment of the public. This study utilizes sentiment analysis on news articles to generate a NewsSentiment Index score, which is then seamlessly integrated into a real estate price prediction model. To calculate the sentiment index,the content of the articles is first summarized. Then, using AI, the summaries are categorized into positive, negative, and neutralsentiments, and a total score is calculated. This score is then applied to the real estate price prediction model. The models used forreal estate price prediction include the Multi-head attention LSTM model and the Vector Auto Regression model. The LSTM predictionmodel, without applying the News Sentiment Index (NSI), showed Root Mean Square Error (RMSE) values of 0.60, 0.872, and 1.117for the 1-month, 2-month, and 3-month forecasts, respectively. With the NSI applied, the RMSE values were reduced to 0.40, 0.724,and 1.03 for the same forecast periods. Similarly, the VAR prediction model without the NSI showed RMSE values of 1.6484, 0.6254,and 0.9220 for the 1-month, 2-month, and 3-month forecasts, respectively, while applying the NSI led to RMSE values of 1.1315, 0.3413,and 1.6227 for these periods. These results demonstrate the effectiveness of the proposed model in predicting apartment transaction price index and its ability to forecast real estate market price fluctuations that reflect socio-economic trends.