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

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Presentation transcript:

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 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.

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

Introduction Previous studies -the conditional volatility of a housing market. The conditional volatility could be further decomposed into: (Pagan and Schwert, 1990; Nelson, 1991). a) “permanent” component – persistent b) “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)

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).

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.

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

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.

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

Data and Methodology C-GARCH Mean Equation: Variance Equations:

Results and Discussion CitiesQ(3)Q 2 (3)ARCH(3) Australia ( p-value) (0.007)*** (0.012)** (0.000)*** Sydney (p-value) (0.013)** (0.047)** (0.005)*** Melbourne (p -value) (0.091)* (0.062)* (0.057)* Brisbane (p-value) (0.839) (0.052)* (0.070)* Perth (p-value) (0.878) (0.000)*** (0.000)*** Adelaide (p-value) (0.407) (0.915) (0.931) Hobart (p-value) (0.001)*** (0.011)** (0.016)** Darwin (p-value) (0.370) (0.972) (0.725) Canberra (p-value) (0.547) (0.733) (0.754) Table 5: ARCH Tests Volatility Clustering

Results and Discussion CitiesAustraliaSydneyMelbourneBrisbanePerthHobart (4.285)*** (4.014)*** (4.498)*** (2.131)*** (3.147)*** (4.668)*** (11.090)*** (10.282)*** (3.667)*** (10.215)*** (9.674)*** (-1.210) (3.884)*** (2.144)** (14.865)*** (-7.288)*** ( )*** (85.969)*** (1.203) (1.843)* (0.554) ( )*** (18.587)*** (19.051)*** (7.171)*** (1.020) (8.682)*** (16.581)*** (1.125) (-0.166) (-0.412) (0.960) (6.422)*** (-0.432) (-1.654)* (0.155) (0.495) (-0.045) (-4.770)*** (1.051) (3.132)*** (-0.096) (0.776) (0.064) (-7.665)*** (-0.273) Log-likelihood Table 6: C-GARCH(1,1) Model

Results and Discussion CitiesQ(6)Q 2 (6)Q(12)Q 2 (12)ARCH(6)ARCH(12) Australia (0.096)* (0.394) (0.413) (0.615) (0.342) (0.514) Sydney7.892 (0.246) (0.444) (0.570) (0.791) (0.730) (0.839) Melbourne (0.010)** (0.903) (0.000)*** (0.976) (0.847) (0.968) Brisbane2.968 (0.813) (0.616) (0.966) (0.782) (0.733) (0.827) Perth1.832 (0.934) (0.377) (0.764) (0.604) (0.497) (0.761) Hobart4.150 (0.656) (0.874) (0.817) (0.937) (0.882) (0.913) Table 7: Specification Tests for the C-GARCH Model Correct specifications

CitiesAustraliaSydneyMelbourneBrisbanePerthHobart Real GDP0.005 (3.195)*** (5.789)*** (-1.330) (2.922)*** (-2.084)** (-1.253) Income0.000 (0.178) (4.268)*** (-1.594) (-0.161) (-4.173)*** (1.219) Population0.033 (0.056)* (0.258) (1.965)** (0.024) (1.065) (3.278)*** Unemploy ment (-1.466) (-0.390) (-0.294) (0.242) (-3.164)*** (2.694)*** Lending rate (0.388) (1.205) (1.530) (0.074) (0.393) (-0.754) Inflation0.007 (2.546)** (-2.697)*** (1.657)* (-7.898)*** (1.287) (-3.756)*** Building approval (0.106) (0.424) (-0.490) (3.472)*** (0.934) (-2.125)** Table 8: “Permanent” Volatility Spillover Results and Discussion

CitiesAustraliaSydneyMelbourneBrisbanePerthHobart Real GDP (-0.391) (2.578)*** (-2.495)** (3.745)*** (2.274)** (-0.820) Income0.000 (0.078) (4.240)*** (-3.333)*** (3.181)*** (1.484) (1.083) Population0.090 (3.618)*** (1.239) (2.460)** (2.468)** (2.986)*** (-2.542)** Unemploym ent (-0.566) (-1.947)* (0.755) (0.678) (-0.918) (-2.538)*** Lending rate0.000 (1.922)* (0.811) (0.837) (2.666)*** (4.018)*** (2.755)*** Inflation0.011 (1.849)* (-4.633)*** (1.941)* (-2.745)*** (-6.852)*** (-0.935) Building approval (1.690)* (1.486) (-0.285) (12.305)*** (-2.946)*** (-1.657)* Table 9: “Transitory” Volatility Spillover

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.