Abstract
We study housing dynamics in China using vector autoregressions identified with theory-consistent sign restrictions. We study seven potential drivers: (1) population increases; (2) a relaxation of credit standards, for example, due to the shadow banking system; (3) increasing preferences towards housing, for example, due to a housing bubble, or to housing being a status asset in the marriage market; (4) an increase in the savings rate; (5) expected productivity progress; (6) changes in land supply; and (7) tax policy, a proxy for policy stimulus. Our results show that, even if all shocks play relevant roles, productivity, savings glut, and policy stimulus have been the dominant drivers. When the sample is closer to 2014, housing preferences and credit shocks increase their importance to explain house prices and volume, while population shocks explain a larger share of the dynamics of residential investment. The results show some differences if we use house price indices constructed by the government or by private sources. The official indices show smaller increases in house prices and assign a smaller role to credit and preference shocks.
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