Regional Housing Supply and Demand Imbalance Qualitative Analysis in U.S. based on Big Data

Regional mismatches in U.S. housing supply and demand have created huge blind spots in home affordability and housing market volatility. Big data technologies can be a powerful tool to analyze these imbalances and to generate policy advice as such. When establishing a housing supply and demand aggregate based on big data in this study, both housing supply and demand are evaluated with the same importance for an overall view of current housing status. In the demand section of the model, a number of data sources are integrated to identify and explore key drivers impacting demand for housing such as population growth, household income, employment and labour forces, migration and cost of living. It uses multidimensional data structures to capture properties of demand (i.e., its dynamic and regional aspects). On the supply side, the model assesses factors including land use, costs of construction, efficiency in permitting and approvals, and regional policy and regulation. The model assists in the identification at the regional level of supply bottlenecks or constraints by discovering quantative relationships between these three variables. The model is tested on past data; its function validates that it reveals misalignments between supply and demand in the U.S. housing market. These insights they can help with how policymakers work on the complexities involved in establishing a more stable and equitable housing market, in the context of these challenges.