# Influence of Social Overhead Capital Facilities on Housing Prices Using Machine Learning  
[PERSON]  
1 Department of Data Information and Statistics, Pyeongtaek University,  
Pyeongtaek-si 17869, Cyeonggi-do, Republic of Korea; [EMAIL_ADDRESS] (J.P.); [EMAIL_ADDRESS] (S.-J.B.);  
[EMAIL_ADDRESS] (J.-W.K.) 2 Department of Smart Mobility, Pyeongtaek University, Pyeongtaek-si 17869, Cyeonggi-do, Republic of Korea 2  
S.-J.B.
provides a visual representation. Figure 1a visualizes the number of housing transactions in South Korea's metropolitan area over a year. The broad orange region represents the metropolitan area centered around Seoul, with the narrower orange extensions depicting areas connected by subway lines emanating from the center. Figure 1b displays a South Korean metropolitan subway route map. The densely packed blue dots represent subway stations in the metropolitan area; similarly, the lines of blue dots extending from the center show the subway routes. Through this visual representation, it is evident that housing transactions in South Korea predominantly occur centered around the metropolitan area and major subway stations. Building on this observation, it is clear that these transit-oriented developments play a pivotal role in shaping the real estate landscape of South Korea. The convenience and accessibility offered by these areas results in heightened preference, significantly impacting housing prices in South Korea (2020, 2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029, 2020, 2021). In light of global urbanization trends and the push towards sustainable urban living, such station proximity areas in South Korea not only offer logistical advantages, they contribute to urban efficiency. In light of the importance of the  
Figure 1: Visualization of the relationship between areas where housing transactions occurred in South Korea from November 2021 to November 2022 and the distribution of metropolitan and regional subway routes. (**a**) Visualization of housing transaction counts centered in the metropolitan area; (**b**) Visualization of the metropolitan and regional subway routes (Source: jido45.com). housing market in the national economy of South Korea, price stability in station proximity areas becomes even more paramount. Understanding and predicting the dynamics of housing prices in these areas is crucial for all relevant stakeholders and for the broader national economy of South Korea. Modeling housing price forecasting in densely populated areas of South Korea offers significant research benefits. The implications of price forecasting extend beyond just the housing and real estate markets, encompassing factors such as transportation hubs (stations), transportation infrastructure (railways, highways), potential traffic bottlenecks, utilities (electricity, water, sewage systems), and educational institutions. Accurate forecasting can furnish individuals and organizations with critical insights, aiding informed decisions when purchasing, selling, or investing in real estate. Moreover, this research can bolster the case for new investments in rail and other infrastructure. Effective forecasting models can provide transactional advantages while tempering the volatility inherent to housing prices [15; 16; 17; 18; 19; 20; 21]. Such models are pivotal for regulators and policymakers, allowing them to pinpoint, monitor, and address anomalies in the housing market, thereby fostering a more stable and predictable market landscape. In this study, we aim to construct a comprehensive prediction model for housing prices in South Korean station proximity areas. We employ machine learning methods predicated on regression models. Regression models, statistically sophisticated tools that predict a dependent variable based on the values of one or more independent variables, have proven effective in identifying and mapping complex interdependencies among variables [22; 23].