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A Tale of Two Policies: Examining Treatment Effects on Housing Prices in Shenzhen, China

Author

Listed:
  • Luya Wang

    (School of Statistics, Capital University of Economics and Business)

  • Zheng Li

    (Department of Agricultural and Resource Economics, North Carolina State University)

  • Qi Li

    (Department of Economics, Texas A&M University)

Abstract

The city of Shenzhen has seen a surge in housing prices. In response, the Shenzhen government implemented policies to make housing more affordable. Two notable policies were implemented between 2016-2018. The first policy increases the supply of land for housing and raises down payment rates. The second policy restricts the sale of houses for a certain period of time. We use the Hsiao et al. (2012) method and factor model method to assess the effectiveness of these policies. Our empirical results suggest that the first policy had significant effects on housing prices while the second policy had no significant effect.

Suggested Citation

  • Luya Wang & Zheng Li & Qi Li, 2023. "A Tale of Two Policies: Examining Treatment Effects on Housing Prices in Shenzhen, China," Annals of Economics and Finance, Society for AEF, vol. 24(2), pages 277-288, November.
  • Handle: RePEc:cuf:journl:y:2023:v:24:i:2:wanglili
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    References listed on IDEAS

    as
    1. Du, Zaichao & Zhang, Lin, 2015. "Home-purchase restriction, property tax and housing price in China: A counterfactual analysis," Journal of Econometrics, Elsevier, vol. 188(2), pages 558-568.
    2. Laurent Gobillon & Thierry Magnac, 2016. "Regional Policy Evaluation: Interactive Fixed Effects and Synthetic Controls," The Review of Economics and Statistics, MIT Press, vol. 98(3), pages 535-551, July.
    3. Jushan Bai & Serena Ng, 2002. "Determining the Number of Factors in Approximate Factor Models," Econometrica, Econometric Society, vol. 70(1), pages 191-221, January.
    4. Jushan Bai & Serena Ng, 2021. "Matrix Completion, Counterfactuals, and Factor Analysis of Missing Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(536), pages 1746-1763, October.
    5. Bai, Jushan, 2004. "Estimating cross-section common stochastic trends in nonstationary panel data," Journal of Econometrics, Elsevier, vol. 122(1), pages 137-183, September.
    6. 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.
    7. Fan, Ying & Yang, Zan & Yavas, Abdullah, 2019. "Understanding real estate price dynamics: The case of housing prices in five major cities of China✰," Journal of Housing Economics, Elsevier, vol. 43(C), pages 37-55.
    8. Xu, Yiqing, 2017. "Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models," Political Analysis, Cambridge University Press, vol. 25(1), pages 57-76, January.
    9. Bai, ChongEn & Li, Qi & Ouyang, Min, 2014. "Property taxes and home prices: A tale of two cities," Journal of Econometrics, Elsevier, vol. 180(1), pages 1-15.
    10. Zhentao Shi & Jingyi Huang, 2019. "Forward-Selected Panel Data Approach for Program Evaluation," Papers 1908.05894, arXiv.org, revised Apr 2021.
    11. Chan, Mark K. & Kwok, Simon, 2016. "Policy Evaluation with Interactive Fixed Effects," Working Papers 2016-11, University of Sydney, School of Economics.
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    More about this item

    Keywords

    Housing prices; Program evaluation; Panel data;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • R31 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Housing Supply and Markets
    • R38 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Government Policy

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