# Influence of Social Overhead Capital Facilities on Housing Prices Using Machine Learning
## 4 Discussion  
In the rapidly changing field of housing market research, our study offers a fresh perspective by examining the interplay between traditional econometric models and the latest machine learning techniques. By combining data from SOC facilities and subway stations, we present a comprehensive method to understand the multifaceted factors influencing housing prices. Our integration of diverse datasets provides a holistic view of urban amenities and their impact on housing prices. The rigorous preprocessing and data integration methods we employ can serve as a reference point for future studies, emphasizing robustness and reliability. Moreover, our comparative evaluation of eight advanced machine learning models highlights the transformative potential of machine learning in reshaping housing price predictions. Our model not only delivers noteworthy predictive accuracy, it illuminates the intricate relationships between various determinants. Although many studies have focused on either transportation infrastructure or the significance of SOC facilities, our research stands out through its integrated approach. We move beyond traditional approaches, offering a deeper understanding of housing prices by considering both subway accessibility and the importance of SOC facilities. This comprehensive perspective ensures a more accurate prediction model, setting our research apart from others. We recognize that the vast landscape of urban economics holds many areas ripe for exploration. In particular, national policies related to real estate, especially housing, play a pivotal role in influencing prices. The implementation of transportation infrastructure, expansion of social amenities, land development, and other state-driven initiatives form the backbone of these influences. We believe that integrating information from news reports, articles, and social networks related to these policy measures with the data we have gathered could yield more accurate and trustworthy predictions for real estate prices, including housing. As a future direction, we aim to collate policy data in order to examine its impact on price forecasting. Moreover, we intend to delve deeper into the influence of proximity to urban centers, schools, and public facilities in subsequent studies, harnessing these data to refine our predictive models. Building on these efforts, we lastly aim to perform comparisons with other machine learning-based prediction studies while leveraging our improved predictive model. We intend to compare and analyze the results using predictive models from other studies, employing features extracted by integrating housing transaction data near subway stations with SOC-related data. Figure 8: Comparison of feature importance in the LightGBM model based on the inclusion or exclusion of the WTD_SUBW_RANK and NEAR_SUBW_DIST features: (**a**) feature importance when excluding WTD_SUBW_RANK and NEAR_SUBW_DIST and (**b**) feature importance when including WTD_SUBW_RANK and NEAR_SUBW_DIST. Our research provides valuable insights and charts a path for future inquiries. By emphasizing the importance of a comprehensive approach and the potential of machine learning, we aspire to stimulate continued innovation in housing market research. Conceptualization, J.P. and S.-J.B.; methodology, J.P., S.-J.B. and J.-W.K. ; software, S.-J.B. and J.-W.K. ; validation, S.-J.B., J.-W.K. and K.K. ; formal analysis, J.P.; investigation, S.-J.B. and J.-W.K. ; resources, S.-J.B. and J.-W.K. ; data curation, S.-J.B., J.-W.K. and K.K. ; writing--original draft preparation, S.-J.B. and J.-W.K. ; writing--review and editing, J.P. and K.K. ; visualization, S.-J.B. and J.-W.K. ; supervision, J.P.; project administration, J.P.; funding acquisition, J.P. All authors have read and agreed to the published version of the manuscript. This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2021R1F1A1064073). Not applicable.