Since the implementation of Taiwan’s Integrated Real Estate Income Tax in 2016, the policy has been more effective at reducing transaction volume than at curbing housing prices. Its actual impact remains contested, particularly regarding whether tax burdens have been shifted to buyers or if extended holding periods have indirectly contributed to price increases. Grounded in tax incidence theory, this study investigates the unintended consequences of the reform on the housing market. Utilizing nearly 48,000 repeat-sales transactions in Taipei City from 2014 to 2020, we apply the Difference in Differences (DID) method to assess whether the policy effectively sup-pressed price growth and to identify behavioral responses among sellers. By leveraging large-scale transactional data, this research demonstrates the analytical potential of big data in detecting time-varying policy effects that are difficult to observe through traditional methods. The results offer empirical insights into the dynamics of housing tax reform and support evidence-based decision-making in future housing policy.