| Title |
Analysing Impact of Price Ceiling System on Housing Market using Machine Learning |
| Authors |
윤규섭(Yoon, Kyu-Seup) ; 이주미(Lee, Ju-Mi) ; 고선주(Ko, Seon-Ju) ; 김희정(Kim, Hee-Jeong) ; 김주형(Kim, Ju-Hyung) |
| DOI |
https://doi.org/10.5659/JAIK.2021.37.8.221 |
| Keywords |
Price Ceiling System; XGBoost; Random Forest |
| Abstract |
Housing prices in Korea continued to rise with the consumer’s desire for asset increase and suppliers’ desire for profit, and the government
introduced a price ceiling system in 1972 to stabilize housing prices. The government expected that housing stability would be achieved by
introducing the price ceiling system, but there was a constant controversy over the effect of it. Some experts argued that the price ceiling
system does not affect existing housing prices, but rather reduces housing supply, which increases price in the long run. In this study, we
intend to analyze the impact of the price ceiling system on housing market forecast by predicting housing market based on the presence or
absence of the price ceiling system and comparing the accuracy. Several previous research have studied the factors affecting housing price,
but this only identified the correlation, and there was insufficient of research on housing price prediction according to actual policy. To
overcome such statistical limitations, this paper predicts housing price using machine learning that can be interpreted with high accuracy even
if input variables are incomplete, wide, or irregular. As a result of analysis, the difference between RMSE(Root Mean Error), error rate
mean, and error rate standard deviation was insignificant even if the price ceiling system was applied or not. This means that the housing
market is more affected by other factors than the price ceiling system. |