Study of Guangzhou House Price Bubble Based on State‐space Model

Hui, Eddie C.M.; Gu, Qi

doi:10.3846/1648-715x.2009.13.287-298

Cited by 25 publications

(13 citation statements)

References 23 publications

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“…They find evidence of time-varying volatility in about 17% of the MSAs. Furthermore, the volatility is Granger-caused by both home appreciation rate and Gross Metropolitan Product (GMP) growth rate (see more examples in Genesove and Mayer (1997), Evenson (2003), Hui and Gu (2009) …”

Section: Literature Reviewmentioning

confidence: 99%

The dynamic correlation and volatility of real estate price and rental: an application of MSV model

Hui

Zheng

2011

Applied Economics

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This article investigates the dynamic conditional correlation between price and rental among four types of real estate markets, namely housing, retail, office and factory. In the case of Hong Kong (from January 1993 to December 2008), the Dynamic Conditional Correlation Multivariate Stochastic Volatility (DCC-MSV) model and Bayesian Markov Chain Monte Carlo (MCMC) are employed to capture the volatility and time varying correlation of property price and rental in each market. The empirical results demonstrate that the volatilities of price are significantly larger than those of rental for each type of real estate markets throughout the whole sample period. Besides, the findings indicate that the correlations between price and rental are time-varying, and the average correlation in the housing market is much larger than those in the other three markets. Also, the conditional correlations in the factory market are the most volatile, while those in the office market are relatively stable. Additionally, two particularly volatile periods have been identified in and around 1997 and 2003.

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Section: Literature Reviewmentioning

confidence: 99%

The dynamic correlation and volatility of real estate price and rental: an application of MSV model

Hui

Zheng

2011

Applied Economics

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“…Owing to a deeper integration of secondary and tertiary industries in the Pearl River Delta in recent years, the rapid industrial upgrading has facilitated a more flexible mobility of migrant workers across the different cities, which assume the complementary functions in the urban cluster in the Pearl River Delta [107]. These advantages have strongly attracted millions of low-skilled migrants to move in and settle down, but these low-skilled migrants have to bear a high rental housing stress against the background of a serious housing bubble [125].…”

Section: Variablesmentioning

confidence: 99%

Regional Heterogeneity of Migrant Rent Affordability Stress in Urban China: A Comparison between Skilled and Unskilled Migrants at Prefecture Level and Above

Liu

2019

Sustainability

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Rental housing unaffordability has been widely used to assess the housing poverty problem among immigrants in the developed and developing countries. China is experiencing an unprecedented urbanization process, with two-thirds of its 250 million migrants now being sheltered in private rental housing in the host cities. In this paper, we aimed to examine the rental housing unaffordability problems faced by migrant workers in urban China and provide policy recommendations for a more accessible and affordable migrant housing provision system. We used the household data on China’s Migrant Dynamics Monitoring Survey (MDMS), released in 2016, across China’s 329 prefecture-level cities and above to look into the sociality and spatiality of migrant rent expenses and rent-income ratio at the prefecture-level cities and above. The statistical tests were conducted to examine the socio- and spatial-variance of these rent stress indexes, and it was found that educational level is a significant and quite powerful indicator in predicting who will or will not assume the heavier rental housing pressure. We then continued to reveal the different spatiality of high-rent-stress migrants across the high- and low-skilled categories. An agglomeration of the high-skilled high-rent-stress migrants was witnessed in the coastal growth engines of urban clusters, while a more spillover-like pattern among the low-skilled high-rent-stress migrants was reported in our study. An ordinary least square and spatial regression analysis was conducted to explain their respective mechanisms.

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“…Sim and Chang (2006) explain such relationship in Korea using vector autoregression (Var). likewise, Hui and Shen (2006) examine the house price bubbles in Hong Kong, Beijing and Shanghai. More recently, Hui and Gu (2009) study the Guangzhou market based on state space model.…”

Section: Literature Reviewmentioning

confidence: 99%

Examining the Relationship Between Real Estate and Stock Markets in Hong Kong and the United Kingdom Through Datamining / Ryšio Tarp Honkongo Ir Jungtinės Karalystės Nekilnojamojo Turto Bei Akcijų Rinkų Nagrinėjimas Analizuojant Duomenis

Hui

Zuo

Hu³

2011

International Journal of Strategic Property Management

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This paper aims to examine the relationship between real estate market and stock market in the United Kingdom and in Hong Kong, from 1993 to 2007, using the method of datamining. The results provide evidence for the existence of not only a positive correlation, but also a co-movement, between the two markets. Such interactions reflect the similarities among these two regions, which can be explained by two transmission mechanisms: wealth effect and credit-price effect. However, the two real estate markets respond differently upon similar adjustments of the respective stock markets. Such dissimilarity is attributed to their respective local factors. It is shown in the paper that datamining could be an appropriate option for studying this kind of relationships. Santrauka Šiame darbe siekiama duomenų analizės metodu išnagrinėti ryšį tarp Jungtinės Karalystės ir Honkongo nekilnojamojo turto rinkų ir akcijų rinkų nuo 1993 iki 2007 m. Remiantis gautais rezultatais matyti, kad tarp minėtų dviejų rinkų egzistuoja ne tik teigiama koreliacija, bet ir kovariacija. Tokios sąveikos rodopanašumus tarp šių dviejų regionų, o tai paaiškinama dviem perdavimo mechanizmais: turto poveikiu ir kredito bei kainos poveikiu. Tačiau dvi nekilnojamojo turto rinkos skirtingai reaguoja į panašų akcijų rinkų reguliavimą. Taip vyksta dėl jų vietinių veiksnių įtakos. Duomenų analizė galėtų būti tinkama alternatyva tiriant tokius ryšius.

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Study of Guangzhou House Price Bubble Based on State‐space Model

Cited by 25 publications

References 23 publications

The dynamic correlation and volatility of real estate price and rental: an application of MSV model

The dynamic correlation and volatility of real estate price and rental: an application of MSV model

Regional Heterogeneity of Migrant Rent Affordability Stress in Urban China: A Comparison between Skilled and Unskilled Migrants at Prefecture Level and Above

Examining the Relationship Between Real Estate and Stock Markets in Hong Kong and the United Kingdom Through Datamining / Ryšio Tarp Honkongo Ir Jungtinės Karalystės Nekilnojamojo Turto Bei Akcijų Rinkų Nagrinėjimas Analizuojant Duomenis

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