Paik, J.; Baek, S.-J.; Kim, J.-W.; Ko, K. Influence of Social Overhead Capital Facilities on Housing Prices Using Machine Learning. Appl. Sci.2023, 13, 10732.
Paik, J.; Baek, S.-J.; Kim, J.-W.; Ko, K. Influence of Social Overhead Capital Facilities on Housing Prices Using Machine Learning. Appl. Sci. 2023, 13, 10732.
Paik, J.; Baek, S.-J.; Kim, J.-W.; Ko, K. Influence of Social Overhead Capital Facilities on Housing Prices Using Machine Learning. Appl. Sci.2023, 13, 10732.
Paik, J.; Baek, S.-J.; Kim, J.-W.; Ko, K. Influence of Social Overhead Capital Facilities on Housing Prices Using Machine Learning. Appl. Sci. 2023, 13, 10732.
Abstract
In South Korea, the residential real estate market is influenced not just by traditional supply and demand dynamics, but also by external factors such as housing policies and macroeconomic conditions. Given the significant role of housing assets in individual wealth, market fluctuations can have profound implications. While prior research has utilized variables like GDP growth rate, patent issuance, and birth rates, and employed models like LSTM and ARIMA for housing price predictions, many overlook key localized factors. Notably, the impact of subway stations and living SOC facilities on housing prices, especially in metropolitan areas, remains underexplored. This study addresses these gaps by analyzing usage trends across subway stations, assessing the influence of living SOC facilities on housing values, and identifying the optimal machine learning model for price predictions near transport hubs. Through a comparative analysis of machine learning techniques, we aim to provide insights for more informed housing price determinations, promoting a more stable real estate market.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
