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http://dx.doi.org/10.5762/KAIS.2021.22.4.228

Prediction of Housing Price Index Using Artificial Neural Network  

Lee, Jiyoung (KIS Pricing)
Ryu, Jae Pil (KIS Pricing)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.22, no.4, 2021 , pp. 228-234 More about this Journal
Abstract
Real estate market participants need to have a sense of predicting real estate prices in decision-making. Commonly used methodologies, such as regression analysis, ARIMA, and VAR, have limitations in predicting the value of an asset, which fluctuates due to unknown variables. Therefore, to mitigate the limitations, an artificial neural was is used to predict the price trend of apartments in Seoul, the hottest real estate market in South Korea. For artificial neural network learning, the learning model is designed with 12 variables, which are divided into macro and micro factors. The study was conducted in three ways: (Ed note: What is the difference between case 1 and 2? Is case 1 micro factors?)CASE1 with macro factors, CASE2 with macro factors, and CASE3 with the combination of both factors. As a result, CASE1 and CASE2 show 87.5% predictive accuracy during the two-year experiment, and CASE3 shows 95.8%. This study defines various factors affecting apartment prices in macro and microscopic terms. The study also proposes an artificial network technique in predicting the price trend of apartments and analyzes its effectiveness. Therefore, it is expected that the recently developed learning technique can be applied to the real estate industry, enabling more efficient decision-making by market participants.
Keywords
Artificial Neural Network; Machine Learning; Deep Learning; Housing Price; Seoul Apartments;
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