Determinants of housing price volatility in Canada: a dynamic analysis

Hossain, Belayet; Latif, Ehsan

doi:10.1080/00036840701522861

Cited by 50 publications

(56 citation statements)

References 17 publications

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“…In other words, a negative shock would raise the uncertainty and volatility of these markets dramatically. These results are also consistent with Miles (2008) in the U.S. housing market and Hossain and Latif (2007) in the Canada market,…”

Section: (Insert Table 6)supporting

confidence: 81%

“…Additionally, the estimated volatility series with a GARCH model is Granger-caused by the home appreciation rate and Gross Metropolitan Product (GMP) growth rate. More recently, Hossain and Latif (2007) have also offered the evidence of time varying housing price volatility in the Canadian housing market. The results also demonstrated that the gross domestic product growth rate, house price appreciation rate and inflation are the determinants of house price volatility with using an impulse responses analysis.…”

Section: Literature Reviewmentioning

confidence: 99%

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Housing price volatility and its determinants

Lee

2009

International Journal of Housing Markets and Analysis

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Purpose -The purpose of this paper is to examine the housing price volatility for eight capital cities in Australia over 1987Australia over -2007. Specifically, the volatility of Australian housing and its determinants were investigated. Design/methodology/approach -An exponential-generalised autoregressive conditional heteoskedasticity (EGARCH) model was employed to analyse the volatility for eight capital cities in Australia. The Engle LM test was also utilised to examine the volatility clustering effects in these cities. Findings -The volatility clustering effects (ARCH effects) were found in many Australian capital cities. The importance of estimating each individual city's EGARCH model was also demonstrated in which the determinants of housing volatility vary from a city to another city. Asymmetric of the positive and negative shocks were also documented. Research limitations/implications -This study has implications for investors and policy makers in which housing investors should estimate the conditional variance (EGARCH process) of a housing market in respect to the volatility of housing series is not always constant over time. Furthermore, policy makers should also address the importance of considering the sub-national factors in formulating the national housing policy. The analysis and results are limited by the quality of the data. Originality/value -This paper is one of the few studies in housing volatility. Additionally, it is probably the first attempt to assess the volatility spillover effects in the Australian housing market.

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confidence: 81%

Section: Literature Reviewmentioning

confidence: 99%

Housing price volatility and its determinants

Lee

2009

International Journal of Housing Markets and Analysis

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“…Sivitanides (2015) verified the effect of GDP per capita, as well as the effect of the number of households and construction costs on Cyprus house prices over the period 2006-2014. That study also verified a structural break in the effect of GDP over the period immediately following the beginning of the global financial crisis. Hossain and Latif (2009), in a study of the intertemporal behaviour of house prices in Canada, also verified the effect of GDP on house prices, and particularly on house price volatility. Furthermore, their study revealed an asymmetric effect of GDP on house price volatility.…”

Section: Macroeconomic Determinants Of House Pricesmentioning

confidence: 66%

Macroeconomic drivers of London house prices

Sivitanides

2018

JPIF

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Purpose -The purpose of this paper is to validate and quantify the effect of key macroeconomic drivers on London house prices using annual data over the period 1983-2016. Design/methodology/approach -Within this context, the authors estimate alternative error-correction and partial-adjustment models (PAMs), which have been widely used in the empirical literature in modelling the slow adjustments of house prices to demand and supply shocks. Findings -The results verify the existence of a strong long-term relationship between London house prices and key macroeconomic variables, such as UK GDP, London population and housing completions. A key finding of the study relevant to the debate on the causes of the housing affordability crisis is that the results provide little evidence in support of the argument that user demand, which is captured in the author's model by Greater London population, may have had a diminished role in driving house price inflation in London. Practical implications -The practical and policy implications of the results are that increased homebuilding activity in London will undoubtedly help limit house price increases. Also, any potential reduction of immigration and economic growth due to Brexit will also have a similar effect. Originality/value -The originality of this research lies in the use of annual data that may better capture the long-term effect of macroeconomic drivers on house prices and the estimation of such effects through both error-correction and partial-adjustment models.

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“…The ARCH/GARCH methodologies by Bollerslev (1986) and Engle (1982), as well as the SV model (Taylor, 1994), attempt to simulate this empirical phenomenon in financial markets. Indeed, the Univariate Stochastic Volatility (USV) models and ARCH-type models use alternatively in accounting for both conditional and unconditional properties of volatility (Kim et al, 1998;Shephard, 2004;Gerlach and Tuyl, 2006;Hossain and Latif, 2009;Tsai et al, 2010).…”

Section: Methodsmentioning

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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Determinants of housing price volatility in Canada: a dynamic analysis

Cited by 50 publications

References 17 publications

Housing price volatility and its determinants

Housing price volatility and its determinants

Macroeconomic drivers of London house prices

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

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