2020
DOI: 10.1002/ijfe.1962
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What determines China's housing price dynamics? New evidence from a DSGE‐VAR

Abstract: We investigate what determines China's housing price dynamics using a DSGE-VAR estimated with priors allowing for the featured operating of normal and "shadow" banks in China, with data observed between 2001 and 2014. We find that the housing demand shock, which is the essential factor for housing price "bubbles" to happen, accounts for near 90% of the housing price fluctuation. We also find that a prosperous housing market could have led to future economic growth, though quantitatively its marginal impact is … Show more

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Cited by 9 publications
(12 citation statements)
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References 81 publications
(124 reference statements)
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“…As we show in Section 4.4.1 below, this shock is mainly explained by migration and land prices (For a comparison, structural (DSGE) analyses typically attribute this shock purely to the demand side; the existing evidence (e.g. Ng, 2015;Wen & He, 2015;Liu & Ou, 2021) usually points to pure speculation, population and, for China, also gender imbalance). An inflation shock reduces house prices, as the income effect dominates the substitution effect.…”
Section: The Whole Regionmentioning
confidence: 99%
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“…As we show in Section 4.4.1 below, this shock is mainly explained by migration and land prices (For a comparison, structural (DSGE) analyses typically attribute this shock purely to the demand side; the existing evidence (e.g. Ng, 2015;Wen & He, 2015;Liu & Ou, 2021) usually points to pure speculation, population and, for China, also gender imbalance). An inflation shock reduces house prices, as the income effect dominates the substitution effect.…”
Section: The Whole Regionmentioning
confidence: 99%
“…The focus is on the time series properties of local house prices, including their lead-lag relations. Ng (2015), Wen and He (2015) and Liu and Ou (2021) are among the first who study what determines the house price dynamics in China using a dynamic stochastic general equilibrium (DSGE) model of the type of Iacoviello and Neri (2010). It is generally agreed that house price fluctuations in China are dominated by demand disturbances, of which Ng points to variations in gender imbalance, stock market performance, the number of potential buyers, and urban unemployment.…”
Section: The Literaturementioning
confidence: 99%
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“…The former is subject to velocityrelated transaction costs similar to Barrdear andKumhof (2016, 2022). At its core, without the different currencies and central banking policies, the model has a " housing as collateral for commercial bank loan" set-up similar to China-based studies such as Minetti et al (2019), Liu and Ou (2021) to account for the fact that housing assets are the most dominant financial assets held by Chinese households, accounting for almost 70% of the asset values of the majority of the households. To examine the cyclical implications and the qualitative differences between the current and the post-CBDC world (beyond that of a one-off deterministic shock to money stocks), we distinguish between a benchmark model and a " Post-CBDC world" model, where prior to the implementation of CBDC the households pay digitally using PDC (Hong et al, 2018;Schilling and Uhlig, 2019;Giudici et al, 2020), albeit with a significant holding/access cost due to the direct trading of PDC within China being restricted since 2018.…”
Section: Introductionmentioning
confidence: 99%