1 30 Can Invesors Hedge Residenial Price Dynamics? INTERNATIONAL REAL ESTATE REVIEW 200 Vol. 3 No. : pp Can Invesors Hedge Residenial Price Dynamics of Ausralia s Capial Ciies? Le Ma School of Archiecure and Building, Geelong Waerfron Campus, Deakin Universiy, Gheringhap Sree, Geelong, Vic 327, Ausralia; Tel: ; Fax: ; Chunlu Liu School of Archiecure and Building, Geelong Waerfron Campus, Deakin Universiy, Gheringhap Sree, Geelong, Vic 327, Ausralia; Tel: ; Fax: ; The sudy described in his paper focuses on esing he shor-run and long-run relaionships beween house price and consumer price indices in Ausralia s capial ciies from 998 o The auoregressive disribued lag model is adoped o obain he esimaes of he shor-run relaionships, while he error correcion model is used o invesigae he long-run relaionships. The -saisic is used o compue he significance of hese relaionships. The research resuls give no evidence ha house price indices are correlaed wih consumer price indices in he shor run. However, he long-run relaionships beween house and consumer price indices exis in mos of he ciies. Keywords House price indices; Consumer price indices; Auoregressive disribued lag model; Error correcion model Correspondence auhor
2 Ma and Liu 3. Inroducion Real esae is convenionally regarded as an efficien hedge agains price inflaion. In Ausralia, he increase rae in he house price index (HPI) has far oupaced he growh rae of he consumer price index (CPI), which is widely used o measure he inflaion rae. For example, he annual average percenage increase of he Ausralian HPI from 986 o 2006 was abou 7.5%, while he annual average CPI increase was abou 3.6% (ABS 2009a; ABS 2009b). Since Fama and Schwer (977) firs esimaed he inflaion-hedging characerisics of differen asses and argued ha real esae is a complee hedge agains inflaion, numerous sudies have analysed inflaion-hedging characerisics of real esae asses in differen counries and concluded wih various findings. I was found ha commercial propery does no hedge agains he inflaion rae in he shor run, bu a posiive correspondence exiss beween such propery and inflaion (Maysiak e al. 996). In anoher sudy, Newell (996) compares he hedging characerisics of Ausralian commercial properies wih differen ypes, places and marke facors, which indicae ha he hedging characerisics of Ausralian commercial properies would be differen. I was concluded ha commercial properies can provide a perfec hedge agains acual inflaion in he observed ciies; namely, Adelaide, Brisbane, Canberra, Melbourne, Perh and Sydney, during 984 o 995. The commercial properies in Adelaide, Brisbane and Canberra can also hedge expeced inflaion while he oher ciies are no able o do so. Moreover, excep for Canberra, he unexpeced inflaion hedging characerisics of he commercial properies are proven in he oher six ciies. Liu e al. (997) sudies he performance of real esae in foreign counries given securiy design differences, and he resuls show ha real esae securiies ac as an efficien hedge in some counries, bu inefficien in ohers. Barber e al. (997) find ha commercial properies can provide a hedge agains inflaion in some forms, based on an invesigaion of saisical similariies among UK commercial propery capial and renal values, and he price levels. The relaionship beween he reurn of real esae invesmen russ and inflaion is deemed o be a manifesaion of he effecs of changes in moneary policies (Glascock e al. 2002). Kolari (2002) examines he long-run impac of inflaion on homeowner equiy and concludes ha here is a long-run relaionship beween he house prices and he non-housing goods and services prices. Anoher research sudies he relaionships beween Ausralian housing dynamics and macroeconomic facors; namely, all ordinaries index, he real household disposable income per capia, rade-weighed exchange rae, consumer price index, unemploymen rae, and deached housing sock per capia (Abelson e al. 2005). The resuls showed ha he consumer price index has a posiive and significan impac on Ausralian housing dynamics. Newell (2007) shows ha srong risk-adjused reurns are delivered by boh direc indusrial propery and indusrial lis propery russ. Furher research on
3 32 Can Invesors Hedge Residenial Price Dynamics? Ausralian residenial propery invesmens indicae ha he main facors which drive he invesmens, are wealh-relaed facors raher han life-syle facors (Brown e al. 2008). Previous research has invesigaed relaionships beween house and consumer prices a naional levels raher han regional levels, alhough he laer is valuable for boh local governmen and invesors. The presen sudy esimaes he shor-run and long-run relaionships beween he HPI and CPI in Ausralia s capial ciies. The nex secion will inroduce he economeric heories used in he sudy, including he auoregressive disribued lag (ADL) model and he error correcion model (ECM) for building shor-run and longrun relaionships beween he HPI and CPI, respecively. The following secion presens he colleced daa and a summary of preliminary es resuls. The empirical resuls secion idenifies he parameers ha are applied o he analysis, and he shor-run and long-run relaionships beween he HPI and CPI of Ausralia s capial ciies. The conclusions of his sudy are provided in he final secion. 2. Economerical Models for Relaionship Analyses Beween he HPI and CPI The mehodology invesigaes he relaionships beween he HPI and CPI. Previous research shows ha a linear relaionship exiss beween he naural logarihm values of he house prices and he CPI, when he annual ren and discoun rae are consan (Kolari 2002). In he research of Kolari, he relaionship a period, beween house prices, HP, and consumer price index, CPI, is esimaed as: ln HP = (ln R lnr) + β lncpi, () where R and r, which are consans, sand for he annual ren and he discoun rae respecively. In his sudy, he HPI and CPI are used o esimae he relaionships beween house and consumer prices hrough economeric mehods. Mos of he economic daa is influenced by is pas values, and herefore, he ADL model is inroduced o esimae he regression. The ECM is also adoped o es wheher long-run relaionships exis beween he HPI and CPI. 2. ADL Model and Opimal Lags The ADL model is ofen inroduced o esimae he regression because mos ime series are auo-correlaed. The model is expressed as: Y m m = a+ bi Y i+ c i= i= X i + ε, (2)
4 Ma and Liu 33 where m is he opimal lag, a, b, and c are he esimaes, and ε is he residual erm. In his sudy, he ADL model is esimaed via he firs differenced ime series, hus Eq. (2) is rewrien as: where HPI and respecively, and c, n HPI = c + α HPI + β CPI + µ i i= i i n i CPI is he differencing HPI and CPI a period α, i β are esimaes, while i (3) µ is he error erm. The problem of esimaing he ADL model is deermining ways o choose he number of lags in he model. If here are no enough lags included, he esimaes will be biased and he saisics will be unreliable. If here are oo many lags included in he model, muli-collineariy may exis, which can make he -saisic unreliable (Gujarai 2003). The Akaike informaion crierion (AIC) (Akaike 974) and Schwarz Bayesian crieria (SBC) (Schwarz 978) are used o choose he opimal lags for he ADL model. The formulae for he AIC and SBC are: AIC = ( 2) ln( ML) + 2( p / n) (4) SBC = ( 2) ln( ML) + 2 ln n( p / n) (5) where ML represens he maximised likelihood esimaes of he model, while n is he number of observaions and p is he number of esimaes including he consan. In mos cases, ML could be calculaed as he sum of squared errors, known as he residual variaion (RSS). RSS represens wha he model canno explain or how closely he model fis he daa. If a model fis he daa well, he values of hese crieria should be small. Therefore, he judgemen of wheher he model is efficien is based on comparing he values of he crieria wih differen lenghs of lag. Including an exra lag could lead o an increase in p and decrease of he RSS. If he degree of decrease in RSS is greaer han he degree of increase of p, only hen will he value of hese crieria fall. 2.2 Error Correcion Model The long-run relaionships beween he HPI and CPI can be idenified by compuing a -saisic o deermine wheher he lagged HPI and CPI resul in significan coefficiens, as shown in he following ECM: n n i HPI i + βi CPI i + δhpi + ϕcpi i= HPI = c + α + µ If δ = ϕ= 0, no long-run relaionship exiss beween he HPI and CPI. Alernaively, if δ and ϕ are boh significanly differen from 0, he long-run relaionship exiss. (6)
5 34 Can Invesors Hedge Residenial Price Dynamics? 3. Hisorical Daa of House and Consumer Price Indices in Ausralia As menioned before, his sudy focuses on he relaionships beween he HPI and CPI in he eigh capial ciies of Ausralia. The HPIs are a series of price indices ha measure changes in he prices for each of he eigh capial ciies of Ausralia. Two ses of HPIs, which are indices for projec houses and esablished houses respecively, are published by he Ausralian Bureau of Saisics (ABS). The index for projec homes is compiled by he ABS for use in calculaing he house purchase expendiure class of he CPI, while he index for esablished houses does no conribue o he CPI, which is he one used in his sudy. The HPI is consruced wih reference o he curren and hisorical marke prices of he enire sock of residenial dwellings (ABS 2005; ABS 2009b). The CPI measures quarerly changes in he price of goods and services which accoun for a high proporion of expendiures by he CPI populaion group (ABS 2009a). The CPI covers a wide range of goods and services, which are arranged in eleven groups: food, alcohol and obacco, clohing and foowear, housing, household furnishings, supplies and services, healh, ransporaion, communicaion, recreaion, educaion, and miscellaneous. The housing expendiure ha conribues o he CPI is made up of rens, uiliies and oher housing, such as house purchases, propery raes, house mainenance and so on. The caalogue numbers of he wo indices are and respecively. The observaion period is from he March quarer of 998 o March quarer of Boh of hese wo indices were esablished quarerly and calculaed on he reference base =00. Figure describes he movemens of he HPI in he eigh Ausralian capial ciies. All HPIs have been increasing during he las decade. The HPI of Darwin has he highes rae of increase among he eigh ciies, while Hobar s HPI experiences he lowes increase. The HPI began o decline in Sydney afer 2004, while a huge increase came abou in Perh s HPI afer The HPI of he oher four ciies; namely Adelaide, Brisbane, Canberra and Melbourne, grew moderaely during 998 o Table describes he populaion and number of dwelling unis in he Ausralian capial ciies for he laes hree years. More han 60% of he Ausralian populaion live in he eigh capial ciies, especially in Sydney and Melbourne. From 2006 o 2008, he populaion increased by abou 6.88%, 5.49% and 5.47% in Brisbane, Darwin and Perh respecively, while he proporions in he oher five ciies were less han 4%.
6 Figure HPI of he Ausralian Eigh Capial Ciies from Ma and Liu 35 Ma and Liu 35
7 36 Can Invesors Hedge Residenial Price Dynamics? The monhly average numbers of dwelling unis in Adelaide and Melbourne coninued o increase from 2006 o 2008, while a huge decrease is eviden in Perh. Moreover, he number in Brisbane flucuaes significanly and moderae movemens are observed in Canberra, Darwin, Hobar and Sydney. Surprisingly, he dwelling number of Sydney, which has he larges populaion, is fourh afer Melbourne, Perh and Brisbane. Darwin has he lowes number during hese hree years. Table Populaion and he Monhly Average Number of Dwelling Unis in Eigh Capial Ciies Ciies Populaion Number of dwelling unis ADE BRI CAN DAR HOB MEL PER SYD Moreover, Table 2 shows he means and sandard deviaions of he HPI and CPI for each capial ciy. Over he observaion period, Darwin has he highes average HPI a , while Hobar has he lowes one, a During 998 o 2008, surprisingly, he house prices of residenial propery in Darwin have he highes increase, followed by Brisbane, Sydney and Melbourne, while he house prices in Hobar have he smalles growh. Aside from he housing assisance o Ausralians, such as home purchase assisance, he special housing assisance policy for he Aboriginal and Torres Srai Islander people may parly accoun for he huge increase of house prices in Darwin (ABS 2008). The sandard deviaion of HPI in Perh is he highes among he eigh ciies, and he lowes one is in Hobar. This indicaes ha he residenial propery marke of Perh has he highes price volailiy. One of he poenial reasons for he high volailiy in he Perh residenial housing marke is ha is marke has he highes rae of increase, which is abou 0.87% yearly during he decade. Paricularly during he December quarer of 2005 o December quarer of 2006, he residenial house prices of Perh increase by nearly 50%. On he oher hand, he house prices in ciies, where sandard deviaions are low, move moderaely. House prices in Hobar, Sydney and Melbourne are less volaile han he house prices in he oher capial ciies. The saisic values for he CPI vary lile from ciy o ciy, compared o he resuls of he
8 Ma and Liu 37 HPI. The highes average CPI is in Adelaide a 42.26, and he lowes one is in Darwin a The sandard deviaions for each ciy s CPI do no appear as differen from each oher as he average CPI. Adelaide has he highes sandard deviaion a 3.48, while Darwin has he lowes a.. Table 2 Saisics for HPI and CPI of Ausralia s Capial Ciies HPI CPI ADE BRI CAN DAR HOB MEL PER SYD Mean Min Max Sd. Dev Mean Min Max Sd. Dev Numerical Resuls 4. Saionary Tes A ime series is said o be saionary if is mean and variance are consan and he variance depends on he disance of wo ime periods. The saionary process plays an imporan role in he ime series analysis. I is difficul o generalise valuable informaion from is behaviour because he non-saionary ime series behaviour is unpredicable over ime. Before esimaing he model wih a ime series, i is imporan o es he saionariy of hese daa. A uni roo es is used o deec he variable saionariy and he order of inegraion. Three mehods are widely used; namely, he Dicky-Fuller uni roo es, he augmened Dicky-Fuller (ADF) uni roo es (Dicky and Fuller 979) and he Phillips-Perron uni roo es (Phillips and Perron 988). In his sudy, he ADF es is adoped o es he saionariy of he ime series. y = δy p + βi y i+ + e i = 2 p + βi y i+ + e β0 i = 2 y = δ y + (8) p y = δ y + βi y i + + e + β0 + β i= 2 (7) (9)
9 38 Can Invesors Hedge Residenial Price Dynamics? Eq. (7) is used o es wheher he ime series are non-saionary, because of a p β is he lagged values simple random walk. Where δ is he esimae, i= 2 i y i+ of o eliminae auocorrelaion from he equaion, and e is he error erm. y If δ is equal o 0, which means ha he daa is generaed by a random walk, i is proven ha he daa is non-saionary, while β in Eq. (8) represens a drif 0 and Eq. (9) is used when esing for he presence of boh he drif and rend, where he rend is expressed byβ. Table 3 is he summary of he ADF uni roo es resuls for HPI and CPI. According o he ADF es, none of he house price indices are saionary. However, a he firs difference, mos of hem become inegraed, excep hose in Brisbane and Darwin. Table 3 also shows ha consumer price indices of he eigh capial ciies are inegraed a he firs difference. 4.2 Selecion of Opimal Lags Since boh HPI and CPI are saionary a he firs difference, he ADL model can be esimaed wih hose differenced variables. Several lags are inroduced ino he model, in order o find he opimal lag. In his sudy, lags, 2, 3, and 4 are involved. The lag, wih which he model has he smalles value of he AIC and SBC, is deermined o be he opimal lag. Table 4 shows he AIC and SBC values wih differen lags for he eigh ciies. According o he values of he AIC, he ADL models for Brisbane and Canberra are efficien wih lag, while he daa of Adelaide, Perh and Sydney fi he model well when lag 2 is also included. Moreover, more lags (lag 3) are needed for Darwin, Melbourne and Hobar. On he oher hand, he SBC values for Adelaide, Brisbane and Canberra poin o lag while he values for Hobar, Melbourne, Perh and Sydney poin o lag 2. I is also indicaed ha lag 3 is needed for he model of Darwin. Since a large number of lags will lead o loss of freedom of he regression, he models for each of he eigh capial ciies are esimaed under he SBC crierion.
10 Ma and Liu 39 Table 3 Augmened Dicky-Fuller Tes for HPI and CPI of Ausralia s Capial Ciies ADE BRI CAN DAR HOB MEL PER SYD -es for level p-value HPI -es for firs difference p-value es for level p-value CPI -es for firs difference p-value Noes: es criical values: % level , 5% level , 0% level Ma and Liu 39
11 40 Can Invesors Hedge Residenial Price Dynamics? Table 4 Values of AIC and SBC of ADL Models for Ausralia s Capial Ciies AIC SBC Lag Lag 2 Lag 3 Lag 4 Lag Lag 2 Lag 3 Lag 4 ADE * * BRI 5.937* * CAN * * DAR * * HOB * * MEL * * PER * * SYD * * Noe: * indicaes he smalles values of AIC and SBC 4.3 Idenificaion of Relaionships beween he HPI and CPI of Ausralia s Capial Ciies The resuls show several characerisics of he relaionships beween he HPI and CPI. The esimaions and p-values of he -saisic of he esimaors are shown in Table 5. The ADL models for Adelaide, Brisbane and Canberra are esimaed wih one lag, while wo lags are inroduced in he models for Hobar, Melbourne, Perh, and Sydney. The model for Darwin is esimaed wih hree lags. In he firs row of he able, c denoes he consan, while α and β i ( i=, 2, and 3) sand for he correlaion coefficiens of he lagged movemens of he HPI and CPI respecively. The p-values of α sugges ha i he movemens of he HPI are influenced by hemselves significanly, in he shor run. The ciies of Adelaide, Brisbane, Canberra and Sydney have posiive relaionships wih he dynamics of HPI in he previous quarer. No only do he changes of he HPI in Perh have a posiive relaionship wih is value for he previous quarer, bu i also has a negaive relaionship wih he one quarer before he previous quarer. Hobar and Melbourne are influenced by he HPI dynamics a lag 2, while he HPI dynamics a lag 3 have a posiive significan influence in Darwin. According o he p-values of heβ i, none of he correlaion coefficiens of he HPI and CPI are significanly differen from 0 a he 5% criical level, alhough he coefficiens in he models for Hobar and Melbourne are significan a he 0% criical level. This implies ha shor-run relaionships beween he HPI and CPI do no exis in mos of Ausralia s capial ciies. i
12 Ma and Liu 4 Table 5 The Esimaes and he P-Values of he ADL Models for Ausralia s Capial Ciies ADE BRI CAN DAR HOB MEL PER SYD c α β α 2 β α 2 3 β 3 Esimaes n/a n/a n/a n/a p-value ** n/a n/a n/a n/a esimaes n/a n/a n/a n/a p-value ** n/a n/a n/a n/a esimaes n/a n/a n/a n/a p-value ** n/a n/a n/a n/a esimaes p-value ** esimaes n/a n/a p-value * ** n/a n/a esimaes n/a n/a p-value * ** n/a n/a esimaes n/a n/a p-value ** * n/a n/a esimaes n/a n/a p-value ** n/a n/a Noes: c denoes he esimaed consan in he regressions, α i and β i denoe he correlaion coefficiens of he lagged movemens of HPI and CPI. ** and * denoes ha he coefficiens are significanly differen from 0 a he 5% and 0% criical levels respecively. Ma and Liu 4
13 42 Can Invesors Hedge Residenial Price Dynamics? Table 6 shows he correlaion coefficiens of he lagged HPI and CPI Eq. (6) for each of he eigh capial ciies. The p-values of hese coefficiens can also be found in Table 6. The saisical es indicaes ha he coefficiens of he lagged HPI and CPI are significanly differen from 0 in he models for Adelaide, Brisbane, Canberra and Hobar a he 5% criical level. In he models for Melbourne and Sydney, hese relaionships only appear o be significan a he 0% criical level. Moreover, he coefficien of he CPI in he model for Darwin and he coefficien of he HPI in he model for Perh are no significanly differen from 0 even a he 0% criical level. This suggess ha long-run relaionships beween he HPI and CPI exis in he ciies of Adelaide, Brisbane, Canberra and Hobar. Weaker long-run relaionships are found in Melbourne and Sydney. The exisence of long-run relaionships beween he HPI and CPI canno be proven in Darwin and Perh. Table 6 Idenificaion of Long- run Relaionships of he HPI and CPI of Ausralia s Capial Ciies δ p-value ϕ p-value ADE ** ** BRI ** ** CAN ** ** DAR * HOB ** ** MEL * * PER * SYD ** * Noes: δ and ϕ denoe he correlaion coefficiens of he lagged HPI and CPI respecively. ** and * denoes ha he coefficiens are significanly differen from 0 a 5% and 0% criical levels respecively. The saisical resuls indicae ha from he March quarer of 998 o March quarer of 2008, he HPI does no correlae wih he CPI in mos Ausralian capial ciies, in he shor run. Weak shor-run relaionships can be found in Hobar and Melbourne. Long-run relaionships beween HPI and CPI are proven o exis in Adelaide, Brisbane, Canberra, Hobar, Melbourne and Sydney. This suggess ha he movemen of consumer prices is no an imporan facor in he dynamics of house prices in he shor run. In he long run, however, house prices in mos of Ausralia s capial ciies are correlaed wih consumer prices.
14 Ma and Liu Conclusion This paper has examined he shor-run and long-run relaionships beween he HPI and CPI in Ausralia s capial ciies. The ADL model and ECM are adoped o esimae he regressions, and he -saisic es is used o invesigae he significance of he shor-run and long-run relaionships. The empirical resuls and findings are discussed. In summary, he dynamics of he HPI srongly depend on heir pas values in he shor run. However, he lenghs of lags are differen in ciies. The movemens of he HPI in Adelaide, Brisbane, Canberra and Sydney are significanly impaced by he values in he previous quarer, while he HPI movemens in Hobar and Melbourne are impaced by he changes in he one quarer before he previous quarer. The movemens of he HPI in Perh are influenced no only by he values a lag, bu also by he values a lag 3. Darwin is he leas sensiive o he self-impac of he HPI, which is influenced by he HPI movemens of wo quarers before he previous quarer. The resuls of his sudy also indicae ha he relaionships beween he HPI and CPI are no saisically significan in mos of Ausralia s capial ciies in he shor run. This implies ha he inflaion risk may no be effecively hedged by shor-erm invesmen in Ausralian residenial real esae markes. Moreover, he resuls sugges ha inflaion hedging characerisics of Ausralian residenial properies vary across ciies in he long run. Invesors, herefore, may have o adop differen sraegies in differen markes. Since here is no evidence o suppor he exisence of long-run relaionships beween he HPI and CPI in he ciies of Darwin and Perh, he long-erm invesmens in hese wo residenial markes may no be a good choice for hedging he inflaion risk. As he relaionships are found o be significan in Adelaide, Brisbane, Canberra and Hobar, long erm invesors may selec he properies in hese markes as he inflaion hedges for invesmens. The weak long-run relaionships in Sydney and Melbourne reflec he uncerainy of inflaion hedges in hese wo larges residenial propery markes in Ausralia and hey are jus regarded as a diversified long-erm invesmen opporuniy.
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