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Although there is a consensus that the housing market is deeply affected by credit policies, little research is available on the impact of credit policies on housing market liquidity. Moreover, housing market liquidity is not scientifically quantified and monitored in China. To improve the government’s intelligence in monitoring the fluctuation of the housing market and make more efficient policies in time, the dynamic relationship between credit policy and housing liquidity needs to be understood fully. On the basis of second-hand housing transaction data in Beijing from 2013 to 2018, this paper uses a time-varying parameter vector autoregressive model and reveals several important results. First, loosening credit policies improves the housing market liquidity, whereas credit tightening reduces the housing market liquidity. Second, both the direction and the duration of the impacts are time-varying and sensitive to the market conditions; when the housing market is downward, the effect of a loose credit policy to improve market liquidity is weak, and when the housing market is upward, market liquidity is more sensitive to monetary policy. Finally, the housing market confidence serves as an intermediary between credit policy and housing market liquidity. These results are of great significance to improve the intelligence and efficiency of the government in monitoring and regulating the housing market. Several policy recommendations are discussed to regulate the housing market and to stabilize market expectations.


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Credit Policy and Housing Market Liquidity: An Empirical Study in Beijing Based on the TVP-VAR Model

Show Author's information Yourong Wang1( )Lei Zhao1
Department of Urban and Real Estate Management, Central University of Finance and Economics, Beijing 100081, China

Abstract

Although there is a consensus that the housing market is deeply affected by credit policies, little research is available on the impact of credit policies on housing market liquidity. Moreover, housing market liquidity is not scientifically quantified and monitored in China. To improve the government’s intelligence in monitoring the fluctuation of the housing market and make more efficient policies in time, the dynamic relationship between credit policy and housing liquidity needs to be understood fully. On the basis of second-hand housing transaction data in Beijing from 2013 to 2018, this paper uses a time-varying parameter vector autoregressive model and reveals several important results. First, loosening credit policies improves the housing market liquidity, whereas credit tightening reduces the housing market liquidity. Second, both the direction and the duration of the impacts are time-varying and sensitive to the market conditions; when the housing market is downward, the effect of a loose credit policy to improve market liquidity is weak, and when the housing market is upward, market liquidity is more sensitive to monetary policy. Finally, the housing market confidence serves as an intermediary between credit policy and housing market liquidity. These results are of great significance to improve the intelligence and efficiency of the government in monitoring and regulating the housing market. Several policy recommendations are discussed to regulate the housing market and to stabilize market expectations.

Keywords:

housing market liquidity, credit policy, Time On Market (TOM), Time-Varying Parameter Vector AutoRegressive (TVP-VAR) model
Received: 06 January 2022 Revised: 02 March 2022 Accepted: 02 March 2022 Published: 15 April 2022 Issue date: April 2022
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Publication history

Received: 06 January 2022
Revised: 02 March 2022
Accepted: 02 March 2022
Published: 15 April 2022
Issue date: April 2022

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Acknowledgment

This work was supported by the National Natural Science Foundation of China (No. NSFC71804206).

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