A Comprehensive Study on the Saturation and Unequal Distribution of Urban Residents' Housing in China

Based on China's census yearbook, China's statistical yearbook, CSS public data and other data sources, this study calculates China's housing saturation, housing wealth and area Gini coefficient respectively. Then use Pyatt's ternary decomposition method to decompose the Gini coefficients and further explore the sources of the degree of housing distribution imbalance. Further integrate the two core indicators of housing saturation and uneven distribution in multiple dimensions to construct a binary quadrant classification model. This model accurately locates 29 provincial-level administrative regions in China into four characteristic quadrants. This intelligent classification provides data-driven decision-making basis for implementing differentiated housing policies. Finally, based on the development prospects of artificial intelligence, a policy optimization path for empowering artificial intelligence is proposed.