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Integration and risk transmission across supply, demand, and prices in China’s housing market

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Abstract

This study explores integration and risk transmission in the Chinese (residential) housing market using variance decompositions from the vector autoregression model. The study covers the period from January 2000 to September 2022. The results indicate that short-term total connectedness changes over time and is significantly larger than long-term connectedness, suggesting that total connectedness is sensitive to time-specific developments and short-term events. Supply-side shocks typically lead demand-side shocks in the long term. However, because of changes in the economy, market conditions, government policies, and investor sentiment, the direction of short-term risk transmission between supply and demand sides varies. Moreover, long-term shocks usually flow from housing prices to the supply side and from the demand side to housing prices. In the short term, prices alternately affect the supply and demand sides of the housing market at different points in time. This study also supports the predictive ability of connectedness measures for macroeconomic conditions, suggesting meaningful implications for investors, real estate developers, and policymakers.

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Fig. 1

Source: Data are collected from the National Bureau of Statistics of China

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Data availability

Data used in this study will be made available upon reasonable request.

Notes

  1. This conceptualization is in line with existing literature in this field. For instance, Conefrey and Whelan (2013) use new homes entering the market as an indicator of housing supply, while new home sales are considered as an indicator of housing demand. Glaeser et al. (2008) use the flow of new construction as a measure of the supply of homes. Varli and Erdem (2014) identify the number of construction permits as a measure of housing supply. Ooi and Le (2012) regard the quantity of new units launched by homebuilders as an indicator of housing supply.

  2. The exchange rate of Chinese yuan vs. USD was 6.45:1 on average in 2021.

  3. The most popular criteria for selecting lag VAR models, including the Akaike Information Criterion (AIC), Hannan–Quinn (HQ), final prediction error (FPE), and Schwartz criteria (SC), all recommend setting the lag length of the VAR model to one in order to generate the most parsimonious model. We also calculate the results based on different lag lengths, and although the values slightly changed, the pattern in Table 2 remains. Therefore, the results are robust and independent of the lag lengths used in the VAR model.

  4. A larger window size with more observations improves parameter estimation accuracy but reduces representativeness in the presence of heterogeneity. In contrast, a smaller window size increases representativeness but decreases accuracy.

  5. We also experiment with different horizon values, and find that the results do not materially change and are robust with respect to the window sizes and horizon selection.

  6. There are three major theories that provide insight into the relationship between sales area and housing prices in the housing market. These are the down-payment model (Ortalo-Magne and Rady 2006), the search and matching model (Ngai and Tenreyro 2014), and the loss aversion model (Genesove and Mayer 2001). The search and matching model emphasizes the leading effect of sales area, while models based on loss aversion and credit constraints highlight the leading effect of price shocks.

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Funding

This research is supported by the program for scientific research start-up funds of Guangdong Ocean University (No. YJR24008).

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  1. Business School, Guangdong Ocean University, No. 1, Luoqin Lu, Yangjiang, 529500, China

    Huifu Nong

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Nong, H. Integration and risk transmission across supply, demand, and prices in China’s housing market. Econ Change Restruct 57, 126 (2024). https://doi.org/10.1007/s10644-024-09713-x

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