The United States housing market has historically exhibited regional imbalances in housing supply and demand, which have contributed to reduced housing affordability and market volatility. The application of big data technology enables the utilisation of data to enhance comprehension of these imbalances, thereby informing the formulation of policy. We put forth a model for analyzing the housing supply and demand based on big data, which employs a comprehensive approach to examine both the demand and supply sides. With regard to the demand side, the model incorporates a multitude of data sources to ascertain and delineate the pivotal elements influencing housing demand. These include population growth rate, household income level, employment opportunity distribution, migration and flow trends, and cost of living. By constructing cubes, the model is capable of capturing the characteristics of dynamic demand changes in different regions. With regard to the supply side, the model assesses land use, building materials and labor costs, the timeliness of building permitting and approval processes, and the impact of regional policies and regulations. By means of a quantitative analysis of the aforementioned factors, the model is able to identify housing supply bottlenecks in different regions. The model's efficacy in identifying significant imbalances between supply and demand in the United States housing market was validated through experimental analysis of historical data.