As the population in urban areas increases, the importance of adequate public-led development policies for sustainable cities with affordable housing is becoming more highlighted. In this regard, this study aims to determine the effectiveness of publi...
As the population in urban areas increases, the importance of adequate public-led development policies for sustainable cities with affordable housing is becoming more highlighted. In this regard, this study aims to determine the effectiveness of public-led urban development policies for sustainable growth in urban areas, specifically measuring the effect of Housing Site Development Project on housing prices. The geographical scope of the study is the project sites and surrounding areas of the project sites in South Korea, and the temporal background is from 2006 to 2023. The project sites were subdivided into four groups through utilizing Self-organization Map (SOM), a machine learning-based clustering analysis, to collect characteristics of each region. Then, the impact of the policy on real estate market of each cluster was analyzed by utilizing DID analysis. The result shows that each cluster had different characteristics and effect on the housing site development projects, depending not simply on the location but on several characteristics, including the level of size, infrastructure installation, input cost, etc. Therefore, it is significant for policy makers to understand the characteristics of each real estate market in different regions, so as to implement inclusive housing policy. Furthermore, it is expected for the future studies that more detailed research should be conducted with larger datasets of the regional characteristics.