Housing market volatility connectedness among G7 countries

Lee, Hahn Shik; Lee, Woo Suk

doi:10.1080/13504851.2017.1305069

Cited by 26 publications

(12 citation statements)

References 16 publications

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“…However, spillovers are bi-directional, time varying and state dependent, and the "Greater China" markets have become more influenced by other markets during the GFC. Lee and Lee (2018) find that spillovers account for only about 10% of the forecast error variance of G7 countries real housing quarterly returns over the period 1970-2014. However, spillovers vary over time and account for about 40% of the system's forecast error variance around the GFC, with strong net spillovers from the United States.…”

Section: Introductionmentioning

confidence: 89%

Spillovers between US real estate and financial assets in time and frequency domains

Tiwari

André

Gupta

2020

JPIF

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PurposeAssessing the strength and time variation of spillovers between returns on residential real estate, real estate investment trusts (REITs), stocks and bonds in the United States. Spillovers reduce the benefits of portfolio diversification, especially in crisis times, when asset returns tend to be more correlated.Design/methodology/approachThe Diebold–Yilmaz approach in the time domain and the Baruník–Krehlík methodology in the frequency domain are used. The latter allows distinguishing spillovers generating only short-lived volatility from those with a more persistent effect.FindingsOn average, spillovers between housing, stock and bond returns are relatively modest and shocks to stock and bond markets affect housing returns more than the other way round, even though with variations over time. Spillovers in both directions are much stronger between REITs and stocks than between REITs and housing. Shocks originating in the housing market are most persistent, particularly in the aftermath of the subprime crisis.Practical implicationsHousing provides a hedge against volatility in financial (including REITs) markets. However, hedging strategies involving housing need to take into account potential tail events such as the GFC and the investment horizon.Originality/valueTo the best of the knowledge of the authors, this paper is the first to apply the Baruník–Krehlík methodology to real estate price spillovers. Although the Diebold–Yilmaz methodology has been used in several studies on spillovers between residential real estate and financial asset returns, this paper covers a new set of variables and time span.

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Section: Introductionmentioning

confidence: 89%

Spillovers between US real estate and financial assets in time and frequency domains

Tiwari

André

Gupta

2020

JPIF

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“…They confirmed the First Home Owner Grant (FHOG) scheme could undermine the real estate market if the relationship with real estate price volatility was ignored. In addition Lee and Lee (2018) examined the global relation among real estate markets in the G7 area and found that volatility connectedness fluctuated considerably over time and that the connectedness index revealed an unprecedented rise during the GFC. In the same vein, Kaulihowa and Kamati (2019) investigated the causal relationship between real estate price volatility and its determinants in Namibia.…”

Section: Ijhma 132mentioning

confidence: 99%

The effect of negative information on the volatility of real estate residential prices in Abuja, Nigeria

Alkali

Sipan

Razali

2019

IJHMA

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Purpose The purpose of this paper is to determine the effect of negative information on the volatility of real estate residential prices in Abuja, Nigeria. Design/methodology/approach The empirical research covers a sample period of 17 years from the first quarter of 2000 to the fourth quarter of 2017. The leverage effect of Abuja’s real estate residential price volatility is determined. Exponential generalised autoregressive conditional heteroscedasticity is used to determine the ARCH shock, GARCH persistence and the leverage effect of the volatility of residential prices in Abuja. Findings The research found that the volatility of real estate prices varies from one category of residential property to another. The leverage effect was found only in the price of two and three bedroom flats in Abuja. Originality/value The findings provide useful information on the volatility of real estate prices for real estate investors. The study has policy implications for the regulation of measures that gradually checkmate the patterns of volatility in the Nigerian real estate market. It also controls negative information (such as a fall of crude oil prices, high costs of building materials, inconsistency of macro-economic policies and insecurity and political uncertainty) which mainly raises the level of uncertainty in the market and exposes investors to risk.

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“…Some studies explore the housing prices relationship with macro-economic variables (Mohan et al , 2019; Panagiotidis and Printzis, 2016), business cycles (Vogiazas and Alexiou, 2017; Caspi, 2016; Yiu et al , 2013), stock prices (Shi et al , 2017; Zheng and Osmer, 2019). However, very few studies have been conducted that have examined the forecasting of housing prices Wei and Cao (2017), Kishor and Marfatia (2018) or the spillover and connectedness among housing markets Lee and Lee (2018), Liow (2015) or housing market volatility Lee and Lee (2018), Liu et al (2019) or housing market efficiency (Liu et al , 2019; Chae and Bera, 2019). These studies have explored this association by using traditional times series techniques.…”

Section: Introductionmentioning

confidence: 99%

“…Given the effect of interest rates on commercial housing investment (Peng and Thibodeau, 2020), monetary policy synchronization in G7 economics is likely to bear a similar impact on housing prices in these countries. Moreover, there is significant spillover among the housing prices in G7 countries (Lee and Lee, 2018). The investor’s life cycle pattern and the income distribution in G7 countries are relatively homogenous than other countries, which potentially supports the Q theory of housing investment similarly (Jud and Winkler, 2003).…”

Section: Introductionmentioning

confidence: 99%

A multifractal detrended fluctuation analysis of housing market: a role of financial crises in developed economies

Raza

Shah

Suleman

et al. 2021

IJHMA

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Purpose This study aims to examine the house price fluctuations in G7 countries by using the multifractal detrended fluctuation analysis (MF-DFA) for the years 1970–2019. The study examined the market efficiency between the short-term and long-term in the full sample period, before and after the global financial crisis period. Design/methodology/approach This study uses the MF-DFA to analyze house price fluctuations. Findings The findings confirmed that the housing market series are multifractal. Furthermore, all the markets showed long-term persistence in both the short and long-term. The USA is identified as the most persistent house market in the short run and Japan in the long run. Moreover, in terms of efficiency, Canada is identified as the most efficient house market in the long run and the UK in the short run. Finally, the result of before and after the financial crisis period is consistent with the full sample result. Originality/value The contribution of this study in the literature is fourfold. This is the first study that has examined the house prices efficiency by using the MF-DFA technique given by Kantelhardt et al. (2002). Previously, the house market prices and efficiency has been investigated using generalized Hurst exponent (Liu et al., 2019), Quantile Regression Approach (Chae and Bera, 2019; Tiwari et al., 2019) but no study to the best of the knowledge has been done that has used the MF-DFA technique on the housing market. Second, this is the first study that has focused on the house markets of G7 countries. Third, this study explores the house market efficiency by dividing the market into two periods i.e. before and after the financial crisis. The study strives to investigate if the financial crisis determines the change in the degree of market efficiency or not. Finally, the study gives valuable insights to the investors that will help them in their investment decisions.

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Housing market volatility connectedness among G7 countries

Cited by 26 publications

References 16 publications

Spillovers between US real estate and financial assets in time and frequency domains

Spillovers between US real estate and financial assets in time and frequency domains

The effect of negative information on the volatility of real estate residential prices in Abuja, Nigeria

A multifractal detrended fluctuation analysis of housing market: a role of financial crises in developed economies

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