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Volatility Decomposition of Australian Housing Prices

Volatility Decomposition of Australian Housing Prices. Chyi Lin Lee and Richard Reed The 17 th European Real Estate Society Conference. Outlines. Introduction Objectives Data and Methodology Results and Findings Conclusions. Introduction.

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Volatility Decomposition of Australian Housing Prices

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  1. Volatility Decomposition of Australian Housing Prices Chyi Lin Lee and Richard Reed The 17th European Real Estate Society Conference

  2. Outlines • Introduction • Objectives • Data and Methodology • Results and Findings • Conclusions

  3. Introduction • Australia- high homeownership -70% (IBISWorld, 2007). • Housing- 57% of the total value of Australian household assets (ABS, 2007). • The determinants of housing prices (first moment)- attention. • BUT the volatility patterns in housing prices- limited.

  4. Introduction • Several studies: • Volatility clustering in housing prices (Dolde and Tirtiroglu, 1997; Crawford and Fratantoni, 2003; Wong et al., 2006). • The determinants of housing price volatility: • Miller and Peng (2006) -the home appreciation rate and GMP growth rate. • Hossain and Latif (2007)- GDP growth rate, house price appreciation rate and inflation

  5. Introduction • Previous studies -the conditional volatility of a housing market. • The conditional volatility could be further decomposed into: (Pagan and Schwert, 1990; Nelson, 1991). • “permanent” component – persistent • “transitory” trend –strong impact • The transitory volatility is caused by noise trading (e.g. speculation activities and trading by irrational investors) • The permanent (fundamental) volatility is caused by the arrival of new information (Hwang and Satchell, 2000)

  6. Introduction • Real estate literature: • A common (fundamental) component of volatility shared by direct properties and securitised real estate (Bond and Hwang, 2003). • The strong evidence of long-term memory volatility is also observed in most international real estate markets (Liow, 2009). • Significant differences between the “permanent” and “transitory” volatility movements (Liow and Ibrahim, 2010) .

  7. Introduction • Previous studies - securitsed real estate • Exception - Fraser et al. (2010) - real house prices have a long-run relationship (permanent) with real income in the UK, the US and New Zealand.

  8. Objective • To provide an insight into the pattern of housing price volatility by decomposing the volatility of housing price into permanent and transitory components.

  9. Data and Methodology • Quarterly data of 8 capital cities for the period Q4:1987-Q3:2009, for a total of 88 observations were obtained from the ABS. • These capital cities are Sydney, Melbourne, Brisbane, Perth, Adelaide, Hobart, Canberra and Darwin, as well as the Australian housing market on aggregate. • Returns are calculated by the first difference of the natural logarithm of the quarterly indices.

  10. Data and Methodology • Engle and Lee (1993,1999) and Liow and Ibrahim (2010)- Component-GARCH model.

  11. Data and Methodology • C-GARCH Mean Equation: Variance Equations:

  12. Results and Discussion Table 5: ARCH Tests Volatility Clustering

  13. Results and Discussion Table 6: C-GARCH(1,1) Model

  14. Results and Discussion Table 7: Specification Tests for the C-GARCH Model Correct specifications

  15. Results and Discussion Table 8: “Permanent” Volatility Spillover

  16. Results and Discussion Table 9: “Transitory” Volatility Spillover

  17. Conclusion • Volatility Clustering • The volatility of housing price can be decomposed into permanent and transitory components  Differences between both components. • Both volatilities capture different sets of information and have different determinants.

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