# Influence of Social Overhead Capital Facilities on Housing Prices Using Machine Learning
## 2 Related Research
Through a rigorous comparative analysis of multiple models, they identified an optimal model, which they then used as a foundation for amalgamating predictions. Their innovative approach to feature engineering and categorization stands out, offering a blueprint for future research in the domain. They delved deep into the intricacies of the dataset, exploring factors such as neighborhood characteristics, property age, and amenities. Despite securing the 35 th position out of 2221 entries on Kaggle.com's public leaderboard, a prominent platform for data science competitions [47; 48], this study acknowledged its limitations while emphasizing the need for broader validation and exploration. This work underscores the burgeoning potential of machine learning in real estate economics, reinforcing the notion that the horizon of possibilities in housing market research continues to expand as datasets grow and computational techniques advance. Fast-forwarding to 2023, the research landscape has witnessed further advancements. A study by [PERSON] et al. [51] introduced LUCE, a novel predictive model tailored for the Toronto housing market. LUCE was designed to address two pivotal challenges in real estate evaluation: the scarcity of recent sales prices, and the sparsity of housing data. This model's ingenuity lies in its ability to structure housing data in a Heterogeneous Information Network (HIN), where graph nodes represent crucial housing entities and attributes pivotal for price evaluation. By leveraging Graph Convolutional Networks (GCNs), LUCE extracts spatial information, such as the geographical locations of housesm from the HIN. Subsequently, the model employs Long Short-Term Memory (LSTM) networks to capture the temporal dependencies in housing transaction data over time. This dual approach allows LUCE to provide a comprehensive and up-to-date housing evaluation dataset, significantly simplifying downstream appraisal tasks. In the same year, another groundbreaking study [52] presented at the European Conference on Social Media delved into the nexus between social media sentiment and housing prices. This research probed the influence of Twitter sentiment, specifically pertaining to the COVID-19 pandemic, on the resale prices of Housing Development Board (HDB) apartments in Singapore. The study utilized the VADER lexicon-based tool for sentiment analysis and employed the Granger Causality method to discern the relationship between sentiment scores and reported COVID-19 cases. The research harnessed the power of neural networks for prediction, emphasizing the advantages of using Twitter sentiment over traditional predictors. The findings revealed that the incorporation of Twitter sentiment can augment prediction accuracy, surpassing models that rely solely on traditional predictors. This study underscored the pivotal role of sentiment analysis derived from Twitter data in urban economics, shedding light on the profound capability of social media platforms to encapsulate the behavioral economic nuances of a populace. The progress in the housing market research domain over the years highlights the significance of exploring diverse determinants impacting housing prices. Beyond the conventional indicators, recent investigations have particularly gravitated towards more contemporary factors. These include aspects such as the influence of transportation infrastructure, notably rail transit systems, and the burgeoning relevance of social media sentiment. Building upon these existing findings, our research takes a nuanced approach. Rather than studying transportation infrastructure and SOC facilities in isolation, our study uniquely combines these elements. By integrating data related to SOC facilities with subway station information, we present a more nuanced perspective on the influencers of housing prices. The distinctiveness of our research is further amplified through our methodological approach, which leverages advanced machine learning techniques to further refine the understanding of this complex domain.