Assessing the Predictive Power of Urban Green Spaces in Machine Learning Models for Chicago Housing Prices

Urban Green Space (UGS) plays an important role in the urban environment. This study compares the predictive power of two types of UGS measurements — park proximity and NDVI — in the machine learning models to predict housing prices in Chicago. By incorporating real estate big data, GIS analysis, and Machine Learning techniques, the result indicates NDVI is a strong predictor for single-family residential properties on the fringe of the urban core.