The relationship between social overhead capital facilities and housing prices has been a subject of interest and debate in the field of urban economics for several decades. Social overhead capital refers to essential public infrastructure and services that are fundamental for the wellbeing of a community. These include schools, hospitals, transportation networks, parks, and more. It is widely believed that the presence and quality of such facilities can significantly impact housing prices in a given area. In this article, we delve into this intricate relationship between social overhead capital facilities and housing prices, employing a machine learning approach to analyse data and draw insights. Machine learning has emerged as a powerful tool in urban economics, enabling researchers to uncover patterns and correlations in vast datasets that were previously challenging to process manually. By leveraging this technology, we can gain a deeper understanding of how social overhead capital influences housing prices and its implications for urban development and policy.
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