A Tale of Two Countries: Comparing the US and Chinese Housing Markets

Lai, Rose Neng; Order, Robert A. Van

doi:10.1007/s11146-018-9670-3

Cited by 10 publications

(5 citation statements)

References 26 publications

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“…This is particularly remarkable as anecdotal evidence suggests that shadow banking is an essential source of funding in second tier cities. As for the long run, we find that housing in China is priced like a growth stock (high P/E ratio) with different expected growth rates across cities, as in Lai and Van Order (2018), and cities tend to share common long run fundamentals and adjust relatively quickly to deviations from them, without bubbles. Prices appear to be rapidly chasing growing rents.…”

Section: Discussionmentioning

confidence: 88%

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International Real Estate Review

Lai¹,

Order²

2019

IRER

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This paper studies the evolution of property values and the connections between shadow banking and property markets in China. We use Pooled Mean Group estimation to analyze Chinese house prices in 65 cities from 2007-2016, define the "fundamentals¨ of housing prices with the Gordon dividend discount model, and use lagged rents, prices, real and nominal interest rates, and shadow banking activity as short term explanatory factors. We find that the cities tend to share long run fundamentals and adjust relatively quickly to deviations from the fundamentals. We do not find bubbles; rather houses are like growth stocks with house prices rapidly chasing growing rents. More importantly, we find that house prices increase more quickly with the availability of shadow banking funds, which have grown rapidly.

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Section: Discussionmentioning

confidence: 88%

“…A very recent study is . Lai and Van Order (2018) compare the housing bubbles between China and the US. To the best of our knowledge, there is no study on the effect of shadow banking as a source of funding on Chinese house prices.…”

Section: The Chinese Housing Marketmentioning

confidence: 99%

International Real Estate Review

Lai¹,

Order²

2019

IRER

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“…One of the contributors to the deviation of housing prices from fundamental is speculation. Some researchers have discussed the speculation in the Chinese housing market, including Lai et al ( 2020 ), Chen and Wang ( 2020 ), and Chen and Wang ( 2022 ), just to cite a few. Of course, a single model can't control everything.…”

Section: Empirical Results and Analysismentioning

confidence: 99%

Urban housing prices within a core urban agglomeration in China

Liu

Qiu²,

Yang³

2022

SN Bus Econ

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To address the imbalanced development in China, this study focuses on housing prices in China's large urban areas, referred to as “agglomerations” which is distinct from the label of “tier 1” versus “tier 2” (and three and four) that is used to categorize cities in China which have been the focus of previous research. It primarily uses a dummy variable approach to analyze whether, in China, housing prices in cities within a core urban agglomeration increase faster than those in a non-core urban agglomeration. This study also makes technical contributions in using altitude and other appropriate instrumental variables to address endogeneity issues, which is useful in studying Chinese cities. Our results show that housing prices are found to grow more rapidly in cities in the Pearl River Delta urban agglomeration, at a statistically significant level, than in other regions studied. Therefore, polarization in the core urban agglomeration is now much severer than we expect. Using housing prices as an indicator, this study reveals a sharpened trend of imbalanced development in Chinese cities. In addition to its empirical findings, this study also discusses some policies that policymakers can make on coordinating economic growth and stabilizing housing prices.

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“…We use an ARDL model to identify real estate return determinants; this model allows us to introduce lags of both the dependent and explanatory variables as regressors, and has also been used to forecast real estate returns in previous research (Mintah et al, 2020). A similar model has also been applied to the US and Chinese housing markets (Lai and Van Order, 2018). As the ARDL model requires all variables to be stationary, we use the first-order difference value of the economic variables in the model.…”

Section: = −mentioning

confidence: 99%

International Real Estate Review

Wang¹,

Hartzell²

2021

IRER

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We apply the dynamic Gordon growth model to the Hong Kong real estate market to analyze quarterly data on four kinds of real estate—housing, office, retail, and factory properties—from 1999 to 2020. We find that factories have the highest total returns among the four types of real estate, and also a larger Sharpe ratio. The total returns of these four kinds of real estate are highly correlated. The results of an autoregressive distributed lag model show that the gross domestic product growth rate is the key determinant of real estate returns, while changes in foreign direct investment also influence housing and retail returns. The expected value of the risk-free rate is the key factor that determines the rent-price ratio. The decline in the risk-free rate in Hong Kong is the main reason that the real estate price-rent ratio has increased from 20 to 40 in the last twenty years. Our research represents an early contribution that compares the performance of housing and commercial real estate at the city level, with both types of real estate having similar determinants. Finally, we find that the fall in risk-free interest rates worsens housing affordability in Hong Kong.

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A Tale of Two Countries: Comparing the US and Chinese Housing Markets

Cited by 10 publications

References 26 publications

International Real Estate Review

International Real Estate Review

Urban housing prices within a core urban agglomeration in China

International Real Estate Review

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