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Can property tax curb housing costs in China? New insights from Chongqing with Bayesian synthetic control

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  • Zhang, Jinyu
  • Tang, Yinghan
  • Liu, Tianyi
  • Zhang, Yuan

Abstract

This study explores whether property taxes can effectively curb housing costs, contributing new evidence to the literature on fiscal policies and housing markets. Existing research highlights the regulatory potential of property taxes in developed economies but offers limited insights from emerging markets. We employ monthly data from 64 Chinese cities (2009–2012) and a Bayesian synthetic control approach that addresses the challenge of many potential control units with limited observations. Our findings reveal that Chongqing’s pilot tax reduced average rents by 6.51%, with a delayed peak impact of around 10% in the eighth month. These results highlight how increased holding costs curb speculative incentives, eventually passing through to rental markets. By demonstrating the tax’s substantial dampening effect and the importance of anticipating implementation lags, this study provides critical insights into how property taxes regulate housing markets and offers implications for policymakers seeking to curb speculation while improving housing affordability.

Suggested Citation

  • Zhang, Jinyu & Tang, Yinghan & Liu, Tianyi & Zhang, Yuan, 2025. "Can property tax curb housing costs in China? New insights from Chongqing with Bayesian synthetic control," Economic Modelling, Elsevier, vol. 147(C).
  • Handle: RePEc:eee:ecmode:v:147:y:2025:i:c:s0264999325000641
    DOI: 10.1016/j.econmod.2025.107069
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    1. Eli Ben-Michael & Avi Feller & Jesse Rothstein, 2021. "The Augmented Synthetic Control Method," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1789-1803, October.
    2. Sheng, Yu & Xu, Xinpeng, 2019. "The productivity impact of climate change: Evidence from Australia's Millennium drought," Economic Modelling, Elsevier, vol. 76(C), pages 182-191.
    3. Ashok Kaul & Stefan Klößner & Gregor Pfeifer & Manuel Schieler, 2022. "Standard Synthetic Control Methods: The Case of Using All Preintervention Outcomes Together With Covariates," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 40(3), pages 1362-1376, June.
    4. Valen E. Johnson & David Rossell, 2012. "Bayesian Model Selection in High-Dimensional Settings," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 649-660, June.
    5. Alberto Abadie & Javier Gardeazabal, 2003. "The Economic Costs of Conflict: A Case Study of the Basque Country," American Economic Review, American Economic Association, vol. 93(1), pages 113-132, March.
    6. Ko, Dong Gyun, 2025. "Did the American Rescue Plan cause inflation? A synthetic control approach," Economic Modelling, Elsevier, vol. 143(C).
    7. Nikolay Doudchenko & Guido W. Imbens, 2016. "Balancing, Regression, Difference-In-Differences and Synthetic Control Methods: A Synthesis," NBER Working Papers 22791, National Bureau of Economic Research, Inc.
    8. Cheng Hsiao & H. Steve Ching & Shui Ki Wan, 2012. "A Panel Data Approach For Program Evaluation: Measuring The Benefits Of Political And Economic Integration Of Hong Kong With Mainland China," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(5), pages 705-740, August.
    9. Carvalho, Carlos & Masini, Ricardo & Medeiros, Marcelo C., 2018. "ArCo: An artificial counterfactual approach for high-dimensional panel time-series data," Journal of Econometrics, Elsevier, vol. 207(2), pages 352-380.
    10. Zheng, Shanshan & Wang, Derek D., 2024. "The local economic impacts of mega nuclear accident: A synthetic control analysis of Fukushima," Economic Modelling, Elsevier, vol. 136(C).
    11. Abadie, Alberto & Diamond, Alexis & Hainmueller, Jens, 2010. "Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 493-505.
    Full references (including those not matched with items on IDEAS)

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