Taking the housing prices of 151 districts and counties in the Chengdu-Chongqing urban agglomeration from 2017 to 2019 as the basic data, the housing price data is geospatially expressed in ArcGIS, and the Theil index is calculated to analyze the differentiation pattern of the housing prices of the Chengdu-Chongqing urban agglomeration, using geo-detector. The effect of different factors on housing prices in different regions shows that the geographical distribution of housing prices in the Chengdu-Chongqing urban agglomeration is basically the same as the city scale and economic development of each city. The housing prices of the Chengdu-Chongqing urban agglomeration show a dual-core dominance and a collapse in the central region. The characteristics of industrial structure, housing supply, population scale, land cost, economic strength, real estate investment scale, housing policy, and higher education resources have a significant impact on the housing prices of Chengdu-Chongqing urban agglomeration. Different indicators have different effects on housing prices in different cities.