Housing prices and business cycle in China: A DSGE analysis

He, Qing; Liu, Fangge; Qian, Zhongxin; Chong, Terence Tai Leung

doi:10.1016/j.iref.2017.01.012

Cited by 35 publications

(19 citation statements)

References 15 publications

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“…Compared with the housing price in 2015, for example, the average housing price in Shenzhen increased by ~ 60% in 2016, even though the nationwide control policies have been strictly issued. The various negative effects of the high housing price have been widely discussed (He et al, 2017; Hu, Su, & Ye, 2019; Yang & Pan, 2020; Zhou et al, 2020). For instance, the housing price can negatively impact economic development in the long run (Yang & Pan, 2020).…”

Section: Introductionmentioning

confidence: 99%

Housing price, talent movement, and innovation output: Evidence from Chinese cities

Lin

Ren

et al. 2020

Review Development Economics

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How housing costs would influence the job‐housing choice of talent and associated city‐level innovation performance is a question of interest for urban development policies. Recently, considerable attention has been paid to the influence of rising housing prices on the attraction of talent and the associated innovation output in major Chinese cities. In this paper, we use the housing price data of 51 cities from the China Real Estate Index System database and the corresponding macro data of China City Statistical Yearbooks from 2005 to 2014 to analyze this focal research question. The empirical analysis shows that the increase in city housing prices generally correlates positively with city innovation outcome and talent attraction, suggesting no crowding‐out effect on the innovative performance of the city. However, the positive association between housing prices and innovation outcome and talent attraction has started to disappear in first‐tier cities in recent years, suggesting potential crowding‐out effect if the increasing housing prices transform to bubbles. This research thus provides considerable policy implications concerning the impacts of housing prices on talent movement and innovation output.

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Section: Introductionmentioning

confidence: 99%

Housing price, talent movement, and innovation output: Evidence from Chinese cities

Lin

Ren

et al. 2020

Review Development Economics

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“…For example, Wen and He () use quarterly data from 2001 to 2012 to estimate a DSGE model for China and find that monetary policy shocks only account for approximately 1% of the housing price cycle. He, Liu, Qian, and Chong () demonstrate that monetary policy shocks can only explain 0.2% of the housing price fluctuations from 1998 to 2013 using a Bayesian DSGE model. These results differ from those for the USA.…”

Section: Resultsmentioning

confidence: 99%

“…Iacoviello and Neri () demonstrate that in the USA, monetary policy shocks play an important role in housing price fluctuations in recent years. According to He et al (), this might be because the Chinese Government prefers to use administrative measures rather than monetary policies to stabilize the economy.…”

Section: Resultsmentioning

confidence: 99%

“…These studies find that LTV shocks have non‐negligible effects on housing price dynamics in China (e.g. Bian & Gete, ; He et al, ). One possible explanation is that the LTV shocks in my study only capture the changes in LTV ratios that are caused by the changes in the minimum down payment ratios, because I use the minimum down payment ratios that are set by the Chinese Government to estimate the standard deviations of the LTV shocks.…”

Section: Resultsmentioning

confidence: 99%

“…For example, Bian and Gete () use the relationship between the ratio of aggregate consumption to GDP and housing prices to identify LTV shocks. He et al () use five data series to estimate a DSGE model that includes LTV shocks. However, these five data series do not include credit policy indicators.…”

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confidence: 99%

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Housing prices and population dynamics in urban China

Ding

2018

Pacific Economic Review

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In this study, a dynamic stochastic general equilibrium (DSGE) model is used to investigate the influence of population on housing price dynamics in urban China. The contribution of population growth to the housing price trend and the contribution of population shocks to housing price fluctuations are quantitatively measured. The empirical results indicate that the ongoing policy to control the population size in large cities in China can decrease the growth rate of housing prices by 0.49% every year, which accounts for 1/10 of the total housing price trend. Furthermore, this policy cannot stabilize housing prices because population shocks have negligible effects on the housing price cycle. Housing technology shocks and housing preference shocks are responsible for most of the housing price fluctuations.

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China's business cycle forecasting: a machine learning approach

Tang,

Zhang

2024

Comput Econ

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Housing prices and business cycle in China: A DSGE analysis

Cited by 35 publications

References 15 publications

Housing price, talent movement, and innovation output: Evidence from Chinese cities

Housing price, talent movement, and innovation output: Evidence from Chinese cities

Housing prices and population dynamics in urban China

China's business cycle forecasting: a machine learning approach

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