# Influence of Social Overhead Capital Facilities on Housing Prices Using Machine Learning
## 2 Related Research  
The housing market, along with its myriad influencing factors, has been the subject of extensive academic exploration, especially around station proximity areas [27; 28; 29; 30; 31; 32; 33; 34; 35]. To provide a comprehensive context for our research within this vast domain, it is imperative to delve into the methodologies, findings, and implications of prior studies. This section aims to review the relevant literature, highlighting the evolution of predictive modeling in the housing market and the integration of machine learning techniques. Historically, predictions in the housing market have been deeply rooted in traditional econometric models [36; 37]. These models primarily emphasize macroeconomic indicators. One notable study [36] delved into the complexities of the Japanese housing market, shedding light on the challenges faced by the average Japanese individual when purchasing a house. This research introduced the innovative \"asset market approach\", which conceptualizes houses as unique asset classes. By combining the consumption capital asset pricing model with housing and residential land supply functions, a robust theoretical framework was crafted. However, the study's focus on data from the 1970s and 1980s and its primary emphasis on the Japanese context limit its applicability to contemporary global housing markets. [PERSON] and [PERSON]'s research [38] provided a fresh perspective on the U.S. housing market. They meticulously investigated whether house prices genuinely reflected the underlying economic fundamentals. Their findings, derived from both aggregate and panel data, revealed significant discrepancies between house prices and their determinants, which were especially evident before the 2006 market correction. While their insights were invaluable, the study's reliance on traditional statistical methodologies suggests potential enhancements through the integration of modern machine learning techniques. [PERSON] and [PERSON]'s work [39] is particularly noteworthy for its in-depth exploration of housing market liquidity. By leveraging a unique dataset spanning diverse geographical areas and timeframes, they offered a panoramic view of market dynamics. Their innovative approach to understanding demand shocks and their subsequent impact on liquidity was groundbreaking. However, the study's reliance on data from the National Association of Realtors and its traditional methodologies indicate areas for potential improvement. The hedonic pricing model, a cornerstone in real estate economics, has been instrumental in understanding how various factors, such as location and amenities, influence property values. Building on this foundation, several studies have investigated the relationship between housing prices and transportation infrastructure [12; 32; 40; 41; 42; 43; 44]. One study in Beijing [32] emphasized the positive correlation between proximity to rail transit systems and elevated property values, while another study in Tianjin [40] highlighted the transformative impact of subway systems on urban landscapes and housing prices. Machine learning's integration into housing price predictions has been a game changer [45; 46; 47; 48; 49; 50; 51; 52]. One recent study [45] has showcased the potential of various algorithms, with the RIPPER algorithm standing out for its superior predictive capabilities. This research underscored the transformative power of machine learning, suggesting a paradigm shift from traditional methodologies. By 2018, the field of housing market research had matured considerably, with researchers employing more intricate datasets and refining their methodologies. A testament to this progression is the seminal work by [46]. This study provided a fresh perspective on the subject, specifically focusing on Ames, Iowa. The authors meticulously dissected the Ames Housing dataset, employing regression-based supervised learning methodologies to predict housing prices.