A comprehensive dataset for uncovering housing price drivers in emerging cities: Nanning as a case study

This dataset supports the research article titled "Exploring housing price dynamics in sustainable cities through a cooperated big data driven machine learning method: case study on a typical city in China", published in City and Environment Interactions. The data were collected from multiple sources, including web-scraped real estate listings, air quality monitoring stations, public amenities using the Gaode Map API, and population data from the LandScan global dataset. The dataset includes variables describing property characteristics, accessibility, environmental quality, and land use patterns. Random Forest modeling and SHAP values were used to interpret the contribution of each feature to housing price volatility. This dataset is valuable for urban economists, planners, and data scientists studying housing market dynamics, land use policy, or spatial machine learning. It enables replication, benchmarking, and comparative studies in similar urban contexts across developing cities.