after the fall jpm

8/13/2019 After the Fall JPM

1/24

AfterTheFall:RealEstateintheMixedAssetPortfoliointhe

AftermathoftheGlobalFinancialCrisis.

ColinLizieri

GrosvenorProfessorofRealEstateFinance

Universityof

Cambridge

DepartmentofLandEconomy

19SilverStreet

CambridgeCB39EP,UK

Versionof29January2013:PleaseDoNotCiteWithoutPermission

Email:[email protected]

Tel:+44(0)1223

337

114

Theglobalfinancialcrisischanged theperceptionof realestateasa riskdiversifier inmixedasset

portfoliosasfallingrealestatecapitalvaluescoincidedwiththebearmarketinequities:privatereal

estateappearednotdeliverdiversificationwhenitwasneeded.AnanalysisofUKprivaterealestate

returns,desmoothedusingaregimebasedfilteringprocessconfirmsthattherelationshipbetween

realestateandotherfinancialassetsistimevarying:butsuggeststhatthereremainstrongbenefits

fromincludingcommercialrealestateinthemixedassetportfolio.


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1

AfterTheFall:RealEstate in theMixedAssetPortfolio in theAftermathof

theGlobalFinancialCrisis.

Abstract:

Theglobal

financial

crisis

changed

the

perception

of

real

estate

as

arisk

diversifier

in

mixed

asset

portfoliosasfallingrealestatecapitalvaluescoincidedwiththebearmarketinequities:privatereal

estateappearednotdeliverdiversificationwhenitwasneeded.AnanalysisofUKprivaterealestate

returns,desmoothedusingaregimebasedfilteringprocessconfirmsthattherelationshipbetween

realestateandotherfinancialassetsistimevarying:butsuggeststhatthereremainstrongbenefits

fromincludingcommercialrealestateinthemixedassetportfolio.

Keywords:PrivateRealEstate,MixedAssetPortfolios,DiversificationBenefits,TimeVaryingReturns,

VarianceDecompositionRealEstateandtheGlobalFinancialCrisis

Therecentglobalfinancialcrisismayhavechanged investorsperceptionofcommercialrealestate

asanassetclass.Realestatesposition inthemixedassetportfoliohastraditionallybeenjustified

with reference to a combination of apparently favourable riskadjusted returns, inflation hedging

qualitiesandthebenefitsofdiversification,basedonexpostevidenceof lowcorrelationbetween

property returns and financial assets. An extensive literature exists that seeks to assess those

benefits and to explain the apparent mismatch between hypothetical allocations in a Markowitz

optimiser frameworkandtheactualholdingsofprofessional investors. Nonetheless,theevidence

appears to be consistent that adding private real estate to a portfolio of stocks and bonds is

expected to enhance riskadjusted portfolio returns and insulate the portfolio against drawdown

duringbearmarkets.

However,intheglobalfinancialcrisisthatbeganin2007,realestateperformedexceptionallypoorly

across different property types and locations in many countries. Moreover, correlations between

realestateandotherassetclassesappearedtoincreasemorethanduringotherrecentcrises,such

asthe199798RussianandAsian financialpanics. Ifthebenefitsofdiversificationofferedbyreal

estatedonotholdinsharpmarketdownturns,perhapsduetoasymmetricreturndependencewith

other asset classes, then the gains from including real estate in a mixed asset portfolio may be

misplaced:asKnightetal.(2005)putit,doesrealestatedeliverdiversificationwhenithurts? Time

varying dependence between real estate returns and the returns from other asset classes under

wouldcompromise results from standard optimisationexercises:expectedportfolio returnsmight

be systematically overstated while risk particularly downside risk might be understated. As a

result,frequent

rebalancing

of

investment

positions

would

be

required

to

maintain

achosen

target

levelofrisk,withsubstantialconsequencesforassetclassessuchasrealestatewithhightransaction

costs.

This paper contributes to the debate about the role of commercial real estate in mixed asset

portfolios in the aftermath of the global financial crisis by examining the changing relationship

betweenpropertyreturnsandthereturnsoffinancialassets.Thefocus ison investment inprivate

realestatetheownershipofportfoliosofbuildingsfortheirrentalincomeandcapitalappreciation

ratherthanon investment inpublic listedrealestate.Thegrowthofprivate,unlisted investment

fundsacrossthe1990sandfirsthalfofthe2000smadesuchaninvestmentstrategymoreaccessible

for both professional and retail investors, previously constrained by lot size and capital entry

barriers.


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2

While much recent research has focussed on dynamics or the asymmetry in the relationships

between asset returns or examined longrun relationships using VAR or VECM approaches1, this

paperadoptsamoredirectapproach,focussingonthetimevaryingrelationshipbetweenprivately

ownedrealestateandother financialassetsthatmoreclosely linkedtotopdownassetallocation

approachesusingportfoliooptimization.Inpartthisisdrivenbydataissues:manyofthetechniques

usedin

the

cited

studies

require

high

frequency

data

that

are

neither

available

for

private

real

estate

nornecessarilyappropriatewhenconsideringplausibleriskmanagementstrategiesforprivatereal

estate investors. We examine correlation structures before and during the financial crisis and

decompose variation in real estate returns into distinct factors on a rolling window basis, the

window based on typical holding periods of professional investors. We analyse the relationship

betweenrealestateandotherassetclassesdirectly,ratherthanviaamacrofactormodel.

WeconductourempiricalanalysisusingUnitedKingdomprivatecommercialrealestatereturns.The

UK formsavaluablecasestudyduetotheavailabilityoftimeseriesofreasonablyrobustmonthly

investment return data from the InvestmentProperty Databank monthly index.While the returns

from IPD are based on appraisals (and hence are subject to valuation effects), all assets in the

monthlydatabase

are

valued

each

month

(albeit

largely

on

adesk

based

appraisal

basis).

Moreover,

the overall size of the index is sufficient to provide some comfort that it is representative of the

overall behaviour of investment grade assets in the UK market. Nonetheless, it was necessary to

transformthedatatodealwithserialcorrelationandthepotentialpresenceofappraisalsmoothing

effects, using an innovative, regimebased desmoothing technique. More importantly, since the

majorityofthefundsreportingtotheIPDmonthlyindexareunittrusts,itis,inprinciple,investible,

by buying units in the largest funds subject to bidask spreads on entry and exit and potential

delaysinredemptionandtheimpactsofmodestgearingandcashholdings2.

Usinga rollingwindowcorrelationapproach, the timevaryingnatureof the relationship between

privaterealestateandfinancialassetsisevident.Thereisclearevidencethat,inthefinancialcrisis,

correlation

increased.

However,

the

rise

in

correlations

is,

in

no

measure,

a

case

of

all

correlations

goingtoone.Weusefactormodelstodecomposethevariationinrealestatereturns,showingthat

theinfluenceofequityandbondreturnsvariesovertime.Theequityinfluenceonprivaterealestate

returns becomes stronger in the financial crisis, but never dominates: there still appears to be a

substantialdiversificationbenefitfromholdingrealestateassets.Theresultsalsosuggestthatlisted

realestatebehavedmore likeprivaterealestateand less likecommonequity inthefinancialcrisis

phase.

The next section briefly reviews the extensive research on the role of real estate in the portfolio

beforeconsideringthe implicationofthesharpcorrection inrealestatevalues inmanydeveloped

economiesinthelatteryearsofthe2000sforrealestatesplacealongsidefinancialassets.Next,the

data

and

the

methods

used

are

described.

The

fifth

section

sets

out

our

empirical

results:

the

final

sectionsummarisesthefindingsandconsiderstheimplicationsforportfolioriskmanagement.

PrivateRealEstateintheMixedAssetPortfolio

ThecaseforincludingrealestateinthemixedassetportfoliohasbeensetoutbyHudsonWilsonet

al.(2003)whosefirstreasonforinclusionistoreducetheoverallriskoftheportfoliobycombining

asset classes that respond differently to expected and unexpected events. Similarly, Chun et al.

1 Somerecentexamplesofanextensiveliteratureinclude,Boudryetal.(2012),Caseetal.(2012),Hoesli&

Oikarinen(2012),Liowetal.(2009),Lizierietal.(2012),Michayluketal.(2006), Oikarinenetal.(2011),

Stevenson

et

al.

(2007).,

Yang

et

al.

(2012),

Zhou

and

Gao

(2012).

2Forexample,thequarterlypooledpropertyfundindexhasacorrelationof0.978withtheIPDmonthlyindex

between1989and2011andatrackingerrorofaround1.4%.


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3

(2004) argue that real estate and real estate diversificationpays off at the very timewhen the

benefitsaremostneeded Analysing private commercial real estate returnsalongside financial

assetsinaMarkowitzportfoliooptimizationframeworktendstosuggesthighallocationsfarhigher

than isgenerallyobserved inprofessionalinvestmentportfolios.Therearemanyreasonsadvanced

forthismismatch:forexample,thattheallocationssimplyreflectmeasurementerrorandtheuseof

appraisalbased

data;

that

reported

returns

do

not

account

for

illiquidity

in

private

markets,

or

for

thehightransactioncostsofrealestate;thatitisnotpossibletoachieveindexedrealestatereturns

due to lot size, heterogeneity and capital constraints; or technical issues in the application of

portfoliomodelsconcerningdistributionalassumptionsandinvestmenthorizons.Nonetheless,even

wherethesefactorshavebeenaccountedfor,mostresearch,acrossawiderangeofcountries,still

findsthatcommercialrealestatehasaroletoplayinthemixedassetportfolio3.

Theonsetofwhathasbecomeknownastheglobalfinancialcrisisinthelate2000shadaprofound

impactonassetpricesinthemajorityofdevelopedeconomies.IntheUnitedKingdom,theFinancial

TimesAllSharetotalreturn indexfellby41%fromOctober2007, intheaftermathoftheLehman

BrothersfailuretoFebruary2009;UKsmallcapstocksfell59%fromOctober2007toFebruary2009;

andlisted

UK

property

company

returns

fell

76%

from

their

peak

in

December

2006

to

March

2009

or66%fromOctober2007.Privatecommercialrealestatewasnotimmunefromthesefalls.TheIPD

monthlycapitalvalueindexpeakedinJune2007.ItthenfelleverymonthuntilAugust2009,witha

peak to trough fall of44%.The IPD monthly total return index, partially protected by the income

returndeliveredby investmentproperty,stillfell37%peaktotrough4.That listedandprivatereal

estate performed so badly while equity markets were in a bear phase cast doubt on the claimed

diversification benefitsof real estate as anassetclass, raising the question: doescommercial real

estatestillhavearoleinriskmanagementinthemixedassetportfolio?

DataEmployedintheStudy

The

commercial

real

estate

returns

used

in

the

study

are

the

total

returns

for

standing

investments

on the Investment Property Databank Monthly Index database (IPDMI). These are available from

December1986,althoughwebeginouranalysisinJanuary1990andendinDecember2011,giving

264observations.TheindexconsistsofthevalueweightedreturnsfromthosebuildingsontheIPD

databank thatare valued on amonthlybasismanyofwhich willbeproperties thatare inopen

endedfundsandunittruststhatrequirefrequentregularappraisalssinceunitsarepricedonanet

assetvaluebasis.AtDecember2011,theindexwasbasedonreturnsfrom3,595propertieswitha

total capital value of 34 billion (US$55 billion). Over the analysis period, the index has grown as

morefundshavebeenvaluedonamonthlybasis:inDecember1989,theindexwasbasedonaround

1,700 properties. While it does not perfectly track the IPD Annual Index5, the Decemberto

DecembertotalreturnseriesfromIPDMIhasacorrelationof0.98withthemainindex.

The reported total return series is valuationbased and, hence, both income return and capital

appreciation are subject to appraisal effects. There is strong evidence of serial correlation in the

returns(althoughonamonthlybasisthat isnotunreasonable,giventhecontractualnatureofthe

incomeflowsandthefiveyearlyrentreviewcyclethatistypicalofUKcommercialleases). Inusing

appraisal based real estate data, researchers have typically attempted to remove the impact of a

valuation updating process, initially identified by, interalia, Quan and Quigley (1989), using some

3RecentreviewsanddiscussionsincludeBondetal.,2007a,2007b,Chunetal.,2004;Hoeslietal.,2003,2004;

HoesliandLizieri,2007,MacKinnonandZahman,2009;Rehring,2012.4

By

contrast,

UK

owner

occupied

house

prices

fell

only

22%

peak

to

trough:

however,

sales

transactions

fell

dramatically,from110,000permonthin2006tolessthan49,000permonthin2008.5SeeCrosbyetal.(2010)foradiscussionofpossiblevaluationdistortionsrelatingtotypeofownership.


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4

form of filtering process: for reviews see, for example, Fisher et al. (2003) Geltner et al. (2003),

Marcato&Key(2007)orLizierietal.(2012).

To correct for appraisal smoothing effects, we use the regimebased desmoothing procedure

introducedbyLizierietal. (2012).Thisapproachsuggests thatboth theunderlyingreturnprocess

and the valuation effects may be timevarying, conditional on the underlying economic

environment.For

example,

there

may

be

regimes

where

the

underlying

market

behaves

stably

and

others where there is considerable volatility; similarly, there may be periods where appraisal

smoothing is marked (for example when there is a dearth of transaction evidence), and others

whereit islessprevalent.Regimesareidentifiedusingadoublethresholdautoregressiveapproach

derivedfromTong().Theprocessisdescribedinmoredetailbelow.Lizierietal.suggestavarietyof

candidate variables to delimit the regimes, including GDP, employment, inflation, equity market

performance,interestrates,exchangeratesandvariousrealestatemeasures.Wefollowtheirpaper

inusingequityreturnstodefiningreturnandsmoothingregimes. The impactoftheprocess isto

reduce the first order serial correlation from 0.900 to 0.301 (similar to that found for our bond

returnseries)whilenearlydoublingthestandarddeviationofreturns6.

For

equity

market

returns,

we

use

the

total

returns

for

the

Financial

Times

All

Share

index,

thebroadestmeasureofequitymarketperformance.Forbondreturns,weusean indexofreturnson

ten year maturity UK government gilts. To examine the impact of small capitalisation stocks, we

analyse theFTSESmallCapStock total return index. Theseseriesweresourced fromDataStream.

Finally,weuseFTSENAREITEPRAUKpropertycompanyreturnseriestoprovideameasureoflisted

realestateperformanceforcomparison,obtainedfromEPRA.Wedonotcorrectforleverageinthe

EPRAindex,sincethereportedreturnsarethosedeliveredtoshareholders.

Exhibit1showstheperformanceof the indicesover theanalysisperiod7:descriptivestatisticsare

showninExhibit2.ThesharpcorrectionfollowingtheglobalfinancialcrisisisclearintheFTAS,EPRA

anddesmoothedprivaterealestateseries.Bycontrastthebondreturnindexperformsstronglyover

this

period

driven

by

the

fall

in

yields

that

followed

both

Government

intervention

in

bond

and

moneymarketsandtheflighttosafetyasinvestors(domesticandforeign)soughtsecurehavensfor

capital.Exhibit3showstheyieldtomaturityforbenchmarkfiveyearGovernmentgilts,fallingfrom

anaverageof4.5%from2003to2008tobelow1%bytheendoftheanalysisperiod.Giveninflation

expectationsofover2%atthatpoint,suchyieldsoffernegativerealreturns foranewpurchaser.

However,thefallinyieldscreatecapitalgainsforexistingholdersofbonds,reflectedinthereported

returns.Theextremecycleofthelistedrealestatesectorisevident,inpartreflectingtheimpactof

leverageinboththegrowthandcorrectionphasesofthecycle.

Thedescriptivestatisticsconfirmthevisualimpression,withpublicrealestatedeliveringlowrelative

returns forhighvolatility; the low volatility andstrong returns of the bondseriesmeans that any

conventional

Markowitz

portfolio

optimised

using

a

Sharpe

ratio

and

with

ex

post

data

would

be

dominated by Government securities8. There is evidence of nonnormality in the skewness and

kurtosisfigures,withtherealestateandsmallcapstockdistributionsbeingnoticeablyfattailed.We

alsohighlighttheautocorrelationpresent inthedesmoothedprivaterealestatereturnsand inthe

bond returns: this is not unexpected, in that these are total return indices with sticky contractual

incomestreams.

6TheseresultsarebroadlyconsistentwiththefindingsofDevaneyandMartinezDiaz(2011)whoconstructa

transactionbasedindexforIPDquarterlyreturnsbasedontheFisheretal.(2003)modelandreportafallin

autocorrelationfrom0.77to0.39andanincreaseinthestandarddeviationofreturnsfrom4%to6.5%.7

We

omit

the

small

cap

stock

index

which

closely

tracks

FTAS.

8 Ontheotherhand,giveninterestsratesatornearthezerolowerbound,anexpectationsbasedapproach

mightfactorinrisingdiscountratesandpotentialfallingvalues.


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5

Exhibit1. RealEstate,EquityandBondIndices19892011

Source:IPDP,EPRA,DataStream

Exhibit2.DescriptiveStatistics,RealEstate,EquityandBondMonthlyReturnSeries19902011

PanelA:DescriptiveStatistics

IPDDes EPRA FTAS SmallCap Bonds

CompoundGrowth 0.57% 0.24% 0.62% 0.46% 0.71%

MeanReturn 0.59% 0.42% 0.72% 0.60% 0.73%

StandardDeviation 1.93% 6.06% 4.28% 5.34% 2.05%

Skewness 1.4547 0.1296 0.4619 0.1470 0.0592

Kurtosis 6.4955

1.7353

0.5674

3.1213

0.9887

Autocorrelation 0.3018 0.1961 0.0904 0.2538 0.0393

SharpeRatio 0.080 0.001 0.067 0.032 0.147

PanelB:CorrelationCoefficients19902011

IPDDes EPRA FTAS SmallCap Bonds

IPDDes 1.000

EPRA 0.265 1.000

FTAS 0.160 0.624 1.000

SmallCap 0.205 0.629 0.813 1.000

Bonds 0.188 0.172 0.169 0.024 1.000Notes: PanelA shows basic descriptive statisticsfor the monthly total returns of the Investment Property

Databankmonthly index,desmoothedusingtheTARapproach(IPDDes);propertycompanyreturnsfromthe

FTSENAREITEPRAUK index(EPRA),theFinancialTimesAllShareIndex(FTAS),theFTSmallCapStocksindex

(SmallCap)andabenchmarkGovernment10yearbondseries (Bonds).Skewnessandkurtosismeasuresare

zerocentred,autocorrelation isfirstorderserialcorrelationandtheSharperatioiscalculatedusingthemean

threemonthUKTreasuryBillrateoverthewholeperiodasaproxyfortheriskfreerate.PanelBshowsproduct

momentcorrelationsforthereturnseriesoverthewholeperiod.

0.0

100.0

200.0

300.0

400.0

500.0

600.0

700.0

Dec

89

Dec

90

Dec

91

Dec

92

Dec

93

Dec

94

Dec

95

Dec

96

Dec

97

Dec

98

Dec

99

Dec

00

Dec

01

Dec

02

Dec

03

Dec

04

Dec

05

Dec

06

Dec

07

Dec

08

Dec

09

Dec

10

IndexDec1989

=100

IPD

Des EPRA

UK FTAS Bonds


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6

Exhibit3:TheFallinUKBondYieldstoMaturity

Source:BankofEngland

AnalyticMethods

Initially we analyse the data using a rolling bivariate correlation approach, using a sixty month

windowtoaccountforthelongerholdingperiodsofprivatecommercialrealestate.Thistechnique

allows us to observe changes in the correlation structure between pairs of asset classes over our

analysisperiod.Volatility incorrelationwould implythatabasicsingleperiodcovarianceapproach

toriskdiversificationmaybemisleading (ifhistoricdataareusedas inputs); ifthereareapparent

structuralshifts in thepatternsofcorrelation, thismight imply that therearedifferent regimes in

which

the

diversification

benefits

of

particular

combinations

of

assets

vary.

There

is

some

relationship between volatility and correlation which needs to be considered in interpreting the

results.

Whiletherollingcorrelationanalysisallowsustoexaminethecomovementbetweenpairsofasset

classes, itnecessarily ignoresthe interactionsbetweenallthedifferentassetclasses. Ifrealestate

returns are, to some extent, linked to both equities and bonds and bonds and equities are

themselvesrelatedthenweneedtounpickthecombinedrelationship.Thissectionattemptstodo

thisusingafactordecompositionapproach.

Thebroadapproachhereistoestimateamodelofthegeneralform:

tBtBSCtSCEtEREt FFFR 0 (1)

where RREt is the real estate return for time t, the Fs are independent (orthogonal) factors

representingequities (subscriptE),smallcapstocks(subscriptSC)andbonds(subscriptB)andthe

betas represent real estates sensitivity to those factors. The key here is that the factors are

unrelated orthogonal in an attempt to isolate the pure effect of a particular asset class on

propertyreturns.

Withsuchamodeldefined,itisthenpossibletoanalysetheinfluenceofeachofthefactorsonthe

overallvarianceofrealestatereturns,since:

22222222

iBBSCSCEERE (2)

0.00

1.00

2.00

3.00

4.00

5.00

6.00

7.00

Yieldto

Maturity

(%)

UKFiveYearBondRedemptionYield


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7

where2isthevarianceoftheassetreferencedbythesubscript,thefinaltermbeingtheresidualor

idiosyncraticvariance9.Dividingeachproductbythetotalrealestatevarianceallowsustoestimate

the influence the variation in the returns on one asset class has on the variation in real estate

returns.Thus,theinfluenceofequityreturnvarianceonrealestatereturnvariancewouldbe:

%2

22

RE

EE

(3)

Tomodeltheinfluenceofdifferentassetclasses,factorswereconstructedusingtwoprocedures.It

isnecessarytocalculatefactorssincetherawreturnsarelikelytobecorrelated(forexample,theFT

All Share returns include returns that reflect real estate price movements and bond market

movements)whichcreatesestimationissuesandmaymasktheinfluencessought.

The first procedure used is thatutilised by ClaytonandMackinnon (2003): to orthogonalise the

returnseriesbysuccessivelyregressingthereturnsfromoneassetclassontheothersandretaining

the residuals as a pure asset class factor as in equations (4), (5) and (6). Thus small cap stock

returns

are

regressed

on

the

FT

All

Share

index

with

the

returns

representing

a

small

cap

effect

purgedoftheoverallinfluenceoftheequitymarket.Next,bondreturnsareregressedontheFTAS

returnsandthesmallcapresidualstoproduceapurebondeffect;andthenrealestatereturnsare

regressedonFTASand thesmallcapandbond residualstoyieldarealestateseries.Wedescribe

thisastheresidualfactorapproach.

SCtFTtSCt RR

10 (4)

BtSCtFTtBt RR 310 (5)

REtBtSCtFTtREt RR

3210 (6)

Theproblemwithsuchaprocedure isthatthereareorderingeffects: inthemodeldescribed,any

realestate influenceson theoverallequitymarketareretained intheFTAS returns10.Thesecond

procedure, here described as the factor approach, by contrast, uses a factor analysis / principal

components approach to generate three factors that can represent bond, equity and real estate

performancethatareuncorrelated,butnotaffectedbyorderingeffects.Firstabroadsetof index

returns and interest rate variables over the whole analysis period are analysed using a principal

components analysis which transforms a set of observations to produce orthogonal factors that

summarisethevariationinthedataset.Thosecomponentshavinganeigenvalueinexcessofoneare

retained and rotated to maximise the variable loadings on each factor using the varimax rotation

procedure to make interpretation clearer. Finally the individual factor scores from those retained

factors

are

retained

and

used

as

the

independent

(and,

by

construction,

orthogonal)

factors

in

equation1.Inpractice,itdidnotprovepossibletogenerateasmallcapstockfactorusingthefactor

approachsothesmallcapeffectvariablefromtheresidualapproachwasusedwhereappropriate.

Withthefactorsconstructed,weonceagainexaminesuccessiveperiodsofsixtymonthsonarolling

basis.Foreachfiveyearperiod,wecalculatethebetasofthefactorsandusethesetodecompose

the variation in the property returns into that explained by the equity market, small cap stocks,

bondsandrealestate.Iftheinfluenceof,forexample,theequitymarket,changesovertime,thenit

willexplainagreaterorlessershareoftherealestatereturns.

9

This

holds

since

the

explanatory

variables

are

orthogonal,

removing

covariance

terms

.

10Foramoredetaileddiscussionofissueswitharegressionbasedorthogonalizationmethod,seeBrooksand

Tsolacos(2000).


9/24

8

REtSCtBndtEQtREt FFR 3210 (7)

As in prior research, the direct private real estate returns, even afterdesmoothing, have typically

lowcorrelationswiththeotherassetclassfactorsandexhibitlaggingeffectswhichmeansthatthe

realestate factorsdominateexplanation. For that reason,we focussolelyon the influenceof the

otherassetclassesandtestdifferentlagstructures.FortheEPRApublicrealestatereturns,wecan

additionallyexaminetheinfluenceofthe(private)realestatefactor,sheddingadditionallightonthe

diversificationcharacteristicsofprivaterealestate.

The factormodel testing revealed the presenceof a lags in the responseofprivate real estate to

changes inthedefinedassetmarketfactors.Therefore,asafinalsetoftests,weexamine leadlag

relationships between private and public real estate returns and the financial asset series using

GrangercausalitytestsanduseanunrestrictedVARmodeltoobservetheeffectsofshocks inthe

property and equity markets on the subsequent performance of real estate returns, using an

impulseresponse framework. Since the main focus of the paper is on timevarying short run

relationshipsbetweentheassetclasses,weadoptstandardmethodsfortheseanalyses.

EmpiricalResults19902011

RollingCorrelationsIn this section, we examine rolling 60 period correlation coefficients between real estate and the

othervariables,first,toassesstheextenttowhichtherelationshipsbetweenpropertyandfinancial

assetsaretimevarying,andsecondtotesttheassertionthatcorrelationsincreasedmarkedlyinthe

global financial crisis, such that the theoretical diversification benefits of real estate were not

delivered in practice at exactly the point when they would have been most beneficial. While five

years may seem a relatively low holding period for commercial real estate, Collett etal.s (2003)studypointedtosharplyfallingholdingperiodsintheUKmarket,withamedianholdofsevenyears

attheendoftheiranalysisperiod in1998.Sincethen,therapidgrowthofunlistedfunds,manyof

whicharefinitelife,hasprobablyfurtherreducedaverageholdingperiods.

Exhibit4showstherollingcorrelations.Itisevidentthattheyarenotstableovertime,althoughitis

not possible to distinguish between volatility effects and changes in the underlying relationships.

Panel A shows the correlations between FTAS and the desmoothed IPD series. To the turn of the

centurytheseare low,oftennegative.They thenbegintorise,stabilisingataround+0.2.There is

thenasharpspikeupwards,peakinginOctober2008at+0.505.However,therollingcorrelationsare

plottedattheendofthetimeperiod.UKrealestatevaluesbegin falling inAugust2007:thepeak

correlationthus

includes

14

months

of

falling

values

and

36

months

which

coincided

with

the

final

yearsoftheassetvalueboom.Asthisfallsaway,socorrelationsfallbacktoanewrelativelystable

levelaround+0.40. PanelBshowstheIPDDescorrelationwithSmallCapstocks:thepatternisvery

similartothatobservedfortheFTcorrelations.

PanelCshowstherollingcorrelationsbetweenthedesmoothedIPDseriesandthepublicrealestate

companyreturns.Inthefirsthalfoftheseries,correlationsareinsignificant.Theriseincorrelations

begins with the boom phase in asset values and continues across the financial crisis, peaking at

+0.720 in November 2008and remainingabove+0.50until IPD capitalvalues begin to rise in July

2009.Itappearsthatcorrelationsstrengthenedinboththeupanddownphasesofthemarketcycle.

FinallyPanelDshows the correlationsbetween IPDDesandtheBondseries.Thecorrelationsare

lower,

often

insignificant

with

58%

of

the

correlations

negative.

In

the

financial

crisis

phase,

correlations become increasingly negative which can be related to the strong performance of


10/24

9

governmentbondindices,withpricerisesdrivenbyfallingyieldstomaturityfromgovernmentand

centralbankinterventionandfromflighttosafetyeffects.

Exhibit5shows,forcomparison,therollingcorrelationsbetweenthe listedrealestatereturnsand

the twoequitymarket indices. Correlations fell in thedot.com and technologystockboom in the

late

1990s

(in

common

with

other

value

sectors)

but

rise

across

the

asset

value

boom

phase.However, they fall sharply as the impacts of the global financial crisis hit equity and real estate

markets,fallingaslowas0.36inMay2008.ReferringbacktoPanelCofFigureY,thiscoincideswith

the increase incorrelationbetweenprivateandpublicrealestate.Fromvisual inspection, itseems

thatpublicrealestatebehavedmorelikeapropertyassetinthefinancialcrisis. Inthenextsection,

wedecomposethevarianceofassetreturnsoverthesamesixtyperiodwindowstoshedmorelight

onthisobservation.


11/24

10

Exhibit4:RollingCorrelationsBetweenRealEstateandOtherFinancialAssets

Notes:ThePanelsshowrolling60monthproductmomentcorrelationsbetweentheTARdesmoothed InvestmentPropertyDat

financialassetvariables,theFinancialTimesAllShareIndex(FTAS),theFinancialTimesSmallCapStockIndex(SmallCap)and

series(EPRA).

0.400

0.200

0.000

0.200

0.400

0.600

Dec94

Dec95

Dec96

Dec97

Dec98

Dec99

Dec00

Dec01

Dec02

Dec03

Dec04

Dec05

Dec06

Dec07

Dec08

Dec09

Dec10

PanelA:IPDDes&FTAS

0.400

0.200

0.000

0.200

0.400

0.600

Dec94

Dec95

Dec96

Dec97

Dec98

Dec99

Dec00

Dec01

PanelB:IPDDes&SmallCap

0.600

0.400

0.200

0.000

0.200

0.400

0.600

0.800

Dec94

Dec95

Dec96

Dec97

Dec98

Dec99

Dec00

Dec01

Dec02

Dec03

Dec04

Dec05

Dec06

Dec07

Dec08

Dec09

Dec10

PanelC:

IPD

Des

&

EPRA

0.6000.5000.4000.3000.2000.100

0.000

0.1000.200

0.300

De

c94

De

c95

De

c96

De

c97

De

c98

De

c99

De

c00

De

c01

PanelD:

IPD

Des

Bonds


12/24

11

Exhibit5:RollingCorrelationsBetweenListedRealEstateandEquityIndices

DecomposingtheVarianceofReturnsCreatingOrthogonalFactors

Exhibit6setsouttheresultsoftheorthogonalisationregressionsusingtheresidualapproachas in

ClaytonandMcKinnon(2001).Thedegreeofexplanationfortheprivaterealestateseriesislowand

it was necessary to include lagged values of the equity market variable. While a critical

interpretationmightsuggestthis isanartefactofmeasurementand indexconstruction, it isworth

notingthattherealestateresidualhasastrong,positive,significantandcontemporaneouseffecton

listedpropertyreturns.

Exhibit6:OrthogonalisationRegressionsfromtheAssetClasses

Dependent: SmallCap Bonds IPDDes EPRA

Constant 0.001

(0.515)

0.006

(5.074)***

0.004

(2.637)**

0.002

(0.568)

FTAS 1.023

(15.815)***

0.093

(2.706)**

0.291(a) 0.857

(10.832)

SmallCapResid 0.133

(3.191)**

0.061

(1.582)

0.414

(3.600)**

Bond

Resid

0.163

(2.282)** 0.379

(2.232)**

RealEstateResid 0.565

(2.567)**

AdjustedR2 65.8% 7.0% 1.4% 45.7%

FStatistic 482.275*** 10.384** 7.920** 52.953***

Notes:Othogonalisation regressionsforthedataseries,OLSwithWhitesheteroscedasticitycorrection.Figures

inparenthesisindicatetstatistics.(a)FTASbetaforIPDDes isaDimsonbetaoverthreelaggedmonths,jointly

significantatthe0.01level.Asterisksdenotesignificance,*0.05level,**0.01level,***0.001level.

0.000

0.100

0.200

0.300

0.400

0.500

0.600

0.700

0.800

EPRAFT EPRASC


13/24

12

To estimate the factor model, the return series for equities, the small cap stock series, EPRA UK

propertycompanyreturns,privaterealestate(inbothoriginalsmoothedIPDreportedreturnsand

two desmoothed formats), bond returns and interest rate variables were subject to a principal

components analysis. Factors with eigen values in excess of one were retained, which were then

rotatedusingavarimaxprocedure(tomaximiseloadingsonindividualvariableswhileretainingthe

orthogonal

qualities

of

the

original

component

extraction).

The

analysis

clearly

identified

threevariables:anequity factor,a bond/interest rate factoranda realestate factor (where theprivate

realestateloadingsweremuchhigherthanforEPRAwhichappearedtohavepropertiesmoreakin

totheequity indices).Thefactorscoresfortheextractedandrotatedfactorswereretainedasthe

assetclassfactorsinsubsequentanalysis.

The first three factors explained 74% of the total variation of the eight variables included in the

analysis.Factoroneexplained38%ofvariationand,afterrotation,hadstrongpositive loadingson

FTAS(0.903),SmallCaps(0.890)andEPRA(0.817): itthuscapturesequitymarketbehaviour.Factor

twoexplained24%ofthevariationandhadstrongpositiveloadingsonthetwodesmoothedseries,

thethresholddesmoothing(0.910)andconventionaldesmoothing(0.904)andtheunsmoothedIPD

returns(0.783);

EPRA

returns

have

aweak

positive

loading

(0.178).

This

thus

seems

to

be

adirect

realestatemarketfactor.Finally,Factorthreeexplains12%ofvariation;theBondseries(0.549)and

LIBOR (0.866) loadpositively implying this isabond/interestrate factor.Byconstruction,all three

factorsareorthogonalanduncorrelatedinriskreturnspace.

PrivateRealEstateVariabilityandtheFactorModels

Theoptimal lagstructureforthefactormodels laggedtheequityandbondfactorsoneperiodand

thesmallcapstockfactortwoperiods.Itisevidentthattheexplanatorypowerofboththeresidual

approachmodelestimatedusingequation6andthefactorbasedmodelestimatedusingequation7

increasesmarkedlyas theestimationwindow includes returns in the global financial crisisperiod.

Exhibit

7

shows

the

adjusted

R

2

from

the

rolling

regressions

which

spike

upwards

for

the

models

that

end in the second half of 2008 and remain at an elevated level thereafter. It should be stressed,

however,thatevenwiththatupwardshift,the levelsofexplanationare low:amaximumvalueof

24.7%fortheresidualmodeland(intheGFCperiod)13.7%forthefactorapproach.Thetwomodels

differintheinfluenceoftheequitymarketandbondfactors.

Exhibit7ExplanatoryPoweroftheFactorModels

0.1000

0.0500

0.0000

0.0500

0.1000

0.1500

0.2000

0.2500

0.3000

AdjustedR

Squa

red

ResidualApproach Factor

Approach


14/24

13

Turning, first, to the factorbasedmodel, theequitymarket factorbeta ispositiveandstatistically

significantforallregressionwindowsuntilmid1997:theaveragecoefficientis,however,just0.109.

FromthatpointuntilthewindowendinginDecember2008,itisstatisticallyinsignificant.Thereafter

itturnssignificantlypositivewithanaveragevalueof0.249,peakingat0.333inthewindowending

March2009.

The

equity

market

betas

are

statistically

significantly

positive

for

43%

of

the

five

year

windows. Exhibit 8 plots the evolution of the equity market beta factor. In the early part of the

analysis period, the bond/interest rate factor and the small cap residual factor betas are,

respectively, negatively and positively significant; in the financial crisis period, they remain

insignificant.

Exhibit 9 shows the influence of the equity market, small cap stocks and bond factors on the

variationinrealestatereturnsusingthefactorbasedmodel.Intheearlypartoftheseries,thethree

factorsappeartohaveastronger influence,attimesexplainingsome30%ofreturnvariation,with

equity, bond and small caps all playing a part. This influence declines, with adjusted Rsquared

figuresdecliningtozeroacrossthe2000s.There isasmallspikeupwards intheearlyyearsofthe

century,perhaps

associated

with

asset

price

falls

at

the

end

of

the

technology

stock

boom.

However,

attheendoftheanalysisperiod,astheimpactoftheglobalfinancialcrisisbecomesevident,initially

weseean increase inbondfactor influence,rapidlyreplacedbyastrongequitymarketeffect.The

earlybondfactoreffectislinkedtonegativebetasandmaythusreflecttheinterestrateimpactsat

theendoftheboom/bubblephaseofrisingassetprices.Bycontrast,theequityimpactisdrivenby

positiveandrisingbetaswithequitymarketpricesfallingduringthefinancialcrisis(andthen,toan

extent,recoveringattheveryendoftheperiod)thissuggeststhattheeventsof20072009brought

morecommonmovementandlessdiversificationthanwasevidentoutsidethecrisisperiod.Thisis

emphasisedbynoting that theseriesofadjustedRsquared foreachof thewindowshasa 0.843

correlationwiththeannualisedpropertyreturnoverthewindow:thatistheinfluenceofotherasset

classes increases as real estate returns deteriorate. Nonetheless, the combined influence of the

asset

factors

on

real

estate

variation

never

exceeds

20%

in

the

financial

crisis

phase,

suggesting

that

thereremainsubstantialdiversificationbenefits.

Exhibit8:FactorBasedModel:EvolutionoftheEquityFactorBeta

0.1500

0.1000

0.0500

0.0000

0.05000.1000

0.1500

0.2000

0.2500

0.3000

0.3500

0.4000

B

eta


15/24

14

Exhibit9:FactorBasedModel:DriversofPrivateRealEstateVariation

Theresultsfromtheresidualfactormodelpresentasomewhatdifferentstory.Aswiththefactor

based model, the equity market beta is statistically significant at the beginning of the period;

however, in theglobal financial crisisphase, it is thebondmarketbeta thatbecomes significant,

withanegativesignandthere issomeevidenceofan influenceofsmallcapstocksonrealestate

variability thatwasnot clearlypresent in the factorbasedmodel.Thegrowing influenceofbond

marketreturns

coincides

with

the

sharp

fall

in

Government

bond

yields

illustrated

in

Exhibit

3.

Exhibit10showsthechangingproportionofrealestatevariationexplainedbytheresidual factors

overtheanalysisperiod.

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

30.00%

35.00%

%o

fVariation

SmallCap Bond Equity


16/24

15

Exhibit10:ResidualBasedModel:DriversofPrivateRealEstateVariation

To provide a point of comparison, Exhibit 11 shows the factor influences on the variability of

propertycompany returnsasmeasuredby theEPRAUK index,using the factorbasedmodelover

thesame60monthrollingwindow,butincludingtherealestatefactor.Itisclearthatoverallequity

market returnsplayasignificantrole inexplainingmovementpropertycompany returns,at times

explainingover90%ofthevariation.That influencethough, isagaintimevarying,fallingsharply in

thedot.com

and

technology

stock

bubble

and,

more

significantly

for

this

paper,

falling

back

in

the

global financial crisis period. In the window ending December 2006, equity market variability

explains73%ofproperty company returnvariability.For thewindowendingDecember2008, the

proportion of variation explained has fallen to 37%. At the same time, the contemporaneous

influenceoftherealestatefactorbecomesstronger,peakingataround19% inmid2008.Itseems

thatpublicandprivaterealestatearebehaving inamoresimilarmannerinthefinancialcrisisand

propertymarketcorrectionthantheywereintheassetpricegrowthphase11.Resultsfortheresidual

approachmodelareverysimilarwith,ifanything,therealestatefactorstillmoreprominentinthe

globalfinancialcrisisphase.

11

One

possible

explanation

is

that,

as

transaction

volumes

in

the

private

market

slowed,

so

UK

valuers

were

preparedtouseevidenceofpublicmarketpricemovementstoadjusttheirappraisalsanecdotalevidence

forthisisprovidedinIPF(2009)

0.00%

5.00%

10.00%

15.00%

20.00%

25.00%

%o

fVariation

SmallCap Bond Equity


17/24

16

Exhibit11:FactorBasedModel:DriversofPublicRealEstateVariation

LaggingRelationshipsandtheInfluenceofEquityMarketsonPrivateRealEstateWhiletheemphasisinthispaperisontheshortrunrelationshipbetweenUKprivaterealestateand

theothermainassetclasses,thissectionbrieflyconsidersthetransmissionofpricesignalsfromthe

equitymarketintotherealestatemarket.First,Grangercausalitytestsareusedtodeterminelead

lagrelationshipsbetweenrealestateandthefinancialassets,giventheevidenceoflagstructuresin

the factormodels. Second, a simpleVARmodel is run and impulseresponse analysis is used to

explorehow

equity

market

signals

are

transmitted

to

real

estate

markets.

Exhibit12setsoutGrangercausalitytestsforbothprivaterealestate(thedesmoothed IPDseries)

andpublicrealestate(theEPRAUKreturns)forthewholeanalysisperiod.Theresultsarenothighly

sensitive to lag structure: we report results with four lags. The tests show that, even after

desmoothing,theequitymarketandthesmallcap indexleadtheprivaterealestatereturns. Asin

priorresearch,thepublicrealestateindexleadsthedesmoothedprivaterealestatereturns.Inpart,

this reflects differences in the nature of the two indices, despite having corrected for appraisal

effects.Inpart,though,itmayalsoreflecttheinertialeffectofcontractualrentalincomeinthetotal

returnfigure(whichwould leadtoautocorrelation intruereturns).Theequityandsmallcapstock

indicesalsoGranger cause thepublic realestate returns,emphasising thispoint.The sample size

whenrestricting

the

analysis

to

the

2007

2011

period

is

too

short

for

stable,

robust

results.

However,wenote that there isno longerevidenceofa leadlag relationshipbetweenpublicand

privaterealestate(consistentwiththeresultsfromthedecompositionofpublicrealestatereturns,

above)andthereissomeevidenceofbondreturnsleadingpublicrealestate.

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

%o

fVariation

Equity Bond RealEstate


18/24

17

Exhibit12:GrangerCausalityTests:RealEstateandFinancial Assets19902011

PrivateRealEstate(IPD) PublicRealEstate(EPRA)

NullHypothesis FStatistic Prob FStatistic Prob

EquitydoesnotGrangerCauseRealEstate 3.115** 0.016 2.482** 0.044

RealEstatedoesnotGrangerCauseEquity 1.352 0.251 0.541 0.706

SmallCaps

do

not

Granger

Cause

Real

Estate 4.067***

0.003

2.290*

0.060

RealEstatedoesnotGrangerCauseSmallCaps 1.178 0.321 0.905 0.461

BondsdonotGrangerCauseRealEstate 2.389* 0.052 3.224** 0.013

RealEstatedoesnotGrangerCauseBonds 2.576** 0.038 3.155** 0.015

PublicRealEstatedoesnotGrangerCause

PrivateRealEstate4.373*** 0.002

PrivateRealEstatedoesnotGrangerCause

PublicRealEstate1.812 0.127

TableshowsFstatisticsandprobabilityunderthenullhypothesisforGrangercausalitytestswithfourlags

betweenrealestateandfinancialassets,usingmonthlyreturnsJan1990Dec2011.Asterisksindicate

significance

of

F

statistic:

*

=

10%

level,

**

=

5%

level,

***

=

1%

level

and

beyond.

We turn now to the VAR analysis. We ran a variety of models, having tested the variables for

stationarityanddistributionalqualities: stationaritytestsusingbothPhillipsPerronandAugmented

DickeyFullertestsrejectaunitrootinthedifferenced(returns)seriesforallvariables;alltheindex

number series however, fail to reject the null hypothesis of a unit root. We can assume that the

indexnumberseriesareI(1)andthereturnseriesareI(0).Wereporttheresultsofanunrestricted

VAR 12; our focus is on impulseresponse analysis. The analysis examines the impact of a one

standarddeviationshockonthereturnsofoneofthevariablesintheVARsystemonthereturnsof

theother variables.Are therespillover impactsand, if so,how largeandpersistentare they?We

focusonthedirectrealestatemarketproxiedbythedesmoothedIPDreturnandontheimpactson

thepropertycompanyreturnsproxiedbytheEPRA,publicrealestate,returnseries.

Examining, first, the responsesofthepublicrealestateseriestoshocks inthesystem,Exhibits13

and14showtheresponsetostandardisedonestandarddeviationshocksin,respectively,thepublic

realestateseries(EPRA) itselfandtheFTASallequity returnseries,withthe95%confidenceband

plottedaroundtheresponseline).ReturnshockstoEPRAdonotpersistbeyondamonth;however,

thereissomeevidencethattheresponsetoshocksinthewiderequitymarketaremorepersistent,

remainingsignificantlyabovezerofortwomonthswithanechooccurringatmonthfive(theechois

presentinFTASsresponsetoFTASshocks,butitislesspronounced).

12FulltechnicaldetailsoftheunrestrictedVARareavailablefromtheauthors. Weassumesymmetricshocks,

althoughrecognisethattheremaybeasymmetriceffectspresent.


19/24

18

Exhibit13PublicRealEstateResponsetoInternalShock

Exhibit14:

Public

Real

Estate

Response

to

Equity

Market

Shock

Theresponseofthedesmoothedprivaterealestatereturnstopropertymarketshocks(whetherin

thepublicorprivaterealestatemarkets)appearstobepersistent (Exhibits15and16).This isnot

unexpected: the desmoothing process has not completely removed the serial correlation in the

series.Asnotedabove,thecontractualnatureofleasecontractsmeansthatincomereturnswillnot

varygreatlyfromperiodtoperiod.Thesameeffectispresentintheincome(coupon)returnsfrom

bonds.

This

stickinessmay

well

contribute

to

the

pattern

of

response

to

shocks

observed

in

the

EPRA,propertycompanyseries,althoughitis importanttonotetheverticalscaleofthegraphand

themutednatureoftheresponse.

Finally, Exhibit 17 shows the private real estate markets response to equity market shocks. Once

again, there is evidence of stickiness in the response, although the extent of the movement is

relativelysmall.Evenwithanexacting twostandarderrorconfidenceband, the response remains

statistically significant four months out, with the response still positive and at the margin of

significance after ten periods. We emphasise that the response is not large consistent with the

factor models described above and our other analyses, it seems that private real estate has a

(somewhat lagged) response to equity market movements, but a response that is masked by

valuation

effects

and

the

stickiness

that

results

from

the

bond

like

income

returns

of

the

asset

class.

2.00%

1.00%

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

6.00%

1 2 3 4 5 6 7 8 9 10

LaginMonths

2.00%

1.00%

0.00%

1.00%

2.00%

3.00%

4.00%

5.00%

1 2 3 4 5 6 7 8 9 10

LaginMonths


20/24

19

Exhibit15:PrivateRealEstateResponsetoPrivatePropertyMarketShocks

Exhibit16:PrivateRealEstateResponsetoPublicRealEstateMarketShocks

Exhibit17:PrivateRealEstateMarketResponsetoEquityMarketShock

0.50%

0.00%

0.50%

1.00%

1.50%

2.00%

1 2 3 4 5 6 7 8 9 10

LaginMonths

0.40%

0.20%

0.00%

0.20%

0.40%

0.60%

0.80%

1 2 3 4 5 6 7 8 9 10

LaginMonths

0.20%

0.10%

0.00%

0.10%

0.20%

0.30%

0.40%

0.50%0.60%

0.70%

1 2 3 4 5 6 7 8 9 10LaginMonths


21/24

20

TheVARmodelsextendourunderstandingoftherelationshipbetweenrealestatereturnsandother

asset classes, and between public and private real estate. The Granger models suggest that the

returnsintheoverallequitymarketleadpropertycompanyreturnswhich,inturn,leadprivatereal

estate returns, despite correction of valuation effects via desmoothing. There appear to be more

complextwowayrelationshipsbetweenbondandpropertyreturns,perhapsmediatedby interest

rateeffects.

With

alonger,

six

month,

lag

structure,

there

is

some

evidence

of

switching

behaviour

between the equity market and private real estate (with no comparable switching behaviour

manifestbetweengeneralequitiesandpropertycompanies). TheVAR impulseresponseanalyses

pointtoapersistentimpactofshocksintheequitymarketonrealestatereturnsanimpactthatis

much more pronounced for public real estate than for private real estate. In combination, these

resultspointtolongruninfluencesthatrunfromequityreturnstopropertyreturns,themagnitude

varying sharply between public and private markets. Stock market shocks (which, given the

skewnessshown inExhibit2,aremore likelytobenegativethanpositive)aretransmitted intothe

realestatemarket, with a sharp and rapid effect on propertycompany returns and asmaller but

moreprotractedandpersistentimpactonprivaterealestate.

Discussion

In this paper, we explored the extent to which the relationship between real estate returns and

otherassetclassesvariesovertime.Specifically,thepaperaddressedthreebroadquestions:

(a) Do the correlations between real estate returns (in particular, private real estate returns)andthoseofotherassetclassesvaryovertime?

(b) Howsignificant is the influenceof theother financialassetclasseson thevolatilityof realestatereturnsanddoesthatinfluencevaryovertime?

(c) Aretherelongrunlinkagesbetweenpropertyandotherassetclassesthatleadtoshocksinone

market

being

transmitted

to

another?

To an extent, all three questions address the same issue: to what extent do the apparent

diversificationbenefitsofholdingrealestateinamixedassetportfoliopersistoverthemarketcycle

andoverdifferenteconomicenvironments?Do investorsgetdiversificationwhentheyneed itand

do investorsneed to adjust their risk management strategies toaccount for any time variation in

thosediversificationbenefits?

Theresultsclearlyshowthatpropertyscorrelationswithotherassetclassesvarymarkedlyoverthe

analysisperiod,forbothpublicandprivaterealestate.Publicrealestateismorecloselycorrelated

with the equity market than is private real estate, although the correlation between public and

private

real

estate

returns

has

been

trending

upwards

over

the

analysis

period.

Nonetheless,

there

appeartobeperiodswheretheoverallequitymarketandpropertycompanyreturnsappeartobe

less closely related, and periods when the equity and bond markets appear to have a stronger

influenceon thedirect,privaterealestatemarket. Inparticular, thereseems tobeanassociation

betweenpoorperformanceinthestockmarketandanincreaseincorrelationbetweenequitiesand

realestate.Thismightindicatethatdiversificationdiminisheswhenitwouldbemostbeneficial.

Examiningthedecompositionofrealestatevarianceusingfactormodels,themoststrikingresultis

thehighproportionofvariationinprivaterealestatemarketsthatcannotbeexplainedintermsof

variation intheequity,smallcapandbondfactorswhichcouldsuggestthat,at least intermsof

meanvariance,privaterealestatedoesoffersubstantialdiversificationbenefits,assuggestedinthe

conventional riskreturn models. There are, once again, periods in the marketwhere the financial


22/24

21

assets seem to be more closely influencing private property returns, periods generally linked to

equitymarketpoorperformance:theearly2000sandtheglobalfinancialcrisisera.

Bycontrasttoprivaterealestate, influences fromotherassetclassesonpublicrealestatereturns

are much stronger (particularly from the overall equity market, as might be expected) but also

complex

in

terms

of

variation.

Intriguingly,

for

the

periods

that

include

the

earlier

phases

of

theglobal financial crisis, the influence of the equity market seems to fall (and the influence of the

underlyingprivaterealestatemarketappearstostrengthen).Whilethediversificationpotentialof

publicpropertycompaniesdoesseem less than forprivate realestate (at leastascaptured inthe

index),thereremainssufficientunexplainedvariationtosuggestthattherewouldberiskreduction

benefits.

The longrun analysespoint to lags in the relationshipbetween direct realestate returns and the

publicmarketindices:wecannotdistinguishbetweendataconstructioneffectsandmorestructural

leadlagresults.Stockmarketshocks(which,giventhedistributionofequityreturnsaremorelikely

tobenegativeshocks)areclearlytransmittedintobothpublicandprivaterealestatemarkets.The

impacton

public

property

is

sharp

and

comparatively

rapid;

the

impact

on

private

real

estate

is

less

markedandmoreextended.Again,itishardtosaywhetherthisreflectsfundamentalfeaturesinthe

private market such as illiquidity and thin trading or is a function of lags in valuations processing

relevantmarketinformation.

Combining these insights, the evidence presented here confirms that both private and public real

estateofferdiversificationbenefitsinthemixedassetportfoliocontext.Bothareinfluencedbythe

performanceoffinancialassets,butretainindependence.Privaterealestateseemstooffergreater

diversificationpotential,butthishastobesetagainstconcernsovertherobustnessofdataandthe

practical issues and obstacles associated with investment in the direct market. What is clear,

however, is that adopting a single time period, meanvariance optimisation approach does not

capture

the

changing

risk

return

characteristics

of

property.

Betas

are

time

varying;

correlations

are

timevarying;theinfluenceofotherassetsonrealestatevolatilityistimevarying.Thereareperiods

inthemarketwhenthebehaviouroftheequitymarketisstronglylinkedtothatofrealestate,and

thoseperiodstendtobewhenthestockmarketisperformingbadly.Shocksnegativeshocks in

equity returns are transmitted to real estate returns and have a significant effect. A risk

managementstrategyneedstoaccountforthesetimevaryinginfluences.

There is one further implication of the results. The focus here has largely been on riskreturn

featuresoftheassets,onmean,standarddeviationandcovariance.Withinthat,however,there is

evidencethattherelationshipbetweenassetclassesdependsonthereturnsinthoseassetclasses:

inparticular,thattheremaybestrongercorrelationsbetweenassets,betweenpropertyandequity,

when

both

are

underperforming.

This

has

further

significance

given

the

distribution

of

returns

in

propertymarketsand,toalesserextent,inequitymarkets:negativeskewnessandpositivekurtosis

indicatingahigherprobabilitythannormaloftherebeingpoorreturns(withtheserialcorrelation

evidentinprivaterealestatesuggestingthatthosepoorreturnsmaypersist).Thatsuggeststhat,for

a more complete view of the riskreturn characteristics of real estate we need to consider the

relationship betweenassetsat the extremesof their returndistributions toseek to identifyany

taildependence.


23/24

22

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and

Satchell,

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(2007b)

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