The impact of income, economic openness and interest rates on housing prices in China: evidence from dynamic panel quantile regression

ABSTRACT

The rapid growth of housing prices has attracted the attention of the whole of society in China. This article adopts the dynamic panel quantile regression to investigate the impact of income, economic openness and interest rates on housing prices in China, based on the panel data of 35 major cities from 2002 to 2012. Compared with previous studies, we can more precisely and reasonably discuss the impact of these variables on different levels of housing prices. The empirical results indicate that the impact of independent variables on housing prices is heterogeneous across quantiles. Specifically, the impact of income is positive and significant across quantiles, and the impact becomes greater at the 90th and 95th quantiles. Economic openness has a positive and significant effect at the 5th–80th quantiles, which support the Balassa–Samuelson effect, but it is insignificant at the 90th and 95th quantiles. The impact of interest rates is positive and significant at low quantiles, but the impact is negative and insignificant at high quantiles. Furthermore, we also find that the coefficients of interest rates at various quantiles are smaller. In addition, the population has a significant positive effect across quantiles. Finally, we provide important policy implications.

KEYWORDS:

• Housing prices
• income
• economic openness
• interest rates
• dynamic panel quantile regression

JEL CLASSIFICATION:

• C23
• E44

Acknowledgments

We thank the editor and the anonymous referee for their highly constructive comments to help improve the quality of this article. We also wish to thank Professor Joakim Westerlund for providing the GAUSS codes and guidance. This research is partly supported by the National Natural Science Foundation of China, grant numbers 71671062, 71431008 and 71521061.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This research is partly supported by the National Natural Science Foundation of China, grant numbers 71671062, 71431008 and 71521061.