With the continuous advancement of urbanization and population growth, the issue of housing security is receiving increasing attention. This paper will use big data technology, combined with data mining and machine learning methods, to analyze large-scale housing data and socio-economic data to construct a predictive model for predicting housing demand. It will also analyze the relationship between housing demand and urban development and economic conditions. The research findings show that big data technology can effectively predict changes in housing demand trends, providing important basis for policy making. Therefore, based on the research results, countries can tailor policies to meet the housing demand of different groups, improve the scientificity and targeted nature of policies, promote social equality and sustainable development, and provide scientific support and decision-making reference for the formulation and implementation of housing security policies.