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DEMANDANDSUPPLYOFREALESTATEMARKETINTURKEY:
ACOINTEGRATIONANALYSIS
AMastersThesis
by
ZEYNEPBURCUBULUT
Departmentof
BilkentUniversity
Ankara
January2009
-
ToMyHusbandandMyFamily
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DEMANDANDSUPPLYOFREALESTATEMARKETINTURKEY:
ACOINTEGRATIONANALYSIS
TheInstituteofEconomicsandSocialSciencesof
BilkentUniversity
by
ZEYNEPBURCUBULUT
InPartialFulfilmentoftheRequirementsfortheDegreeofMASTEROFARTS
in
THEDEPARTMENTOFECONOMICSBLKENTUNIVERSITY
ANKARA
January1999
-
I certify that I have read this thesis and have found that it is fullyadequate,inscopeandinquality,asathesisforthedegreeofMasterofArtsinEconomics.
Assoc.Prof.alaktenSupervisor
I certify that I have read this thesis and have found that it is fullyadequate,inscopeandinquality,asathesisforthedegreeofMasterofArtsinEconomics.
Asst.Prof.mitzlaleExaminingCommitteeMember
I certify that I have read this thesis and have found that it is fullyadequate,inscopeandinquality,asathesisforthedegreeofMasterofArtsinEconomics.
Assoc.Prof.ZeynepnderExaminingCommitteeMember
ApprovaloftheInstituteofEconomicsandSocialSciencesProf.ErdalErelDirector
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iii
ABSTRACT
DEMANDANDSUPPLYOFREALESTATEMARKETIN
TURKEY:
ACOINTEGRATIONANALYSIS
Bulut,ZeynepBurcu
M.A.,DepartmentofEconomics
Supervisor:Assoc.Prof.alakten
January2009
Since inacountrythehousingmarket isa leading indicatorfor
the whole economy, the determinants, that are affecting aggregate
housingsupplyanddemand,arewidelysearched.Inthisstudy,wetry
tofindthevariableswhichareaffectingthedemandandsupplyofreal
estatemarket inTurkeybetween theyears 1970 to 2007.We cannot
specialize on the housing market and rather study the real estate
market in the aggregatenumber of dwellings is our quantity
measuredue todata limitations.WechoseTopelandRosens (1988)
demandandsupplymodelsthatarebasicallybasedondifferentshort
andlongrunelasticity.Asdemandsideindependentvariables,interest
rate,valuevariable, incomeandpopulationarechosenandassupply
side independent variables, value, interest rate and costs are chosen.
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iv
Value isusedasaproxysince themarketpricedatadoesnotexist in
Turkey.Value isakindofcost that is taken from thebuilderwithout
interested inwhat thematerialsareandhowmuch the laborcosts to
the builder. Also, the annual data is used because of the data
limitations.Due to the fact that all these variables are I(1), Johansen
Cointegration and VECM are preferred. According to the empirical
findings, the signs of all the variables are as expected and are
significantinthelongrun.However,intheshortrun,onlyinterestrate
andcostvariablesaresignificantin90%confidencelevel.Furthermore,
thepriceelasticityofsupplyis1.5inthelongrunwhileitis0.13inthe
shortrun. This shows us that the adjustment costs for a change in
Turkey issignificantlyhigh.Moreover, the longrunpriceelasticityof
demandis4.97.
Keywords: Housing supply, housing demand, cointegration,
vectorerrorcorrection
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v
ZET
TRKYEDEGAYRMENKULPYASASIARZVETALEP
DENGES:
EBTNLEMEANALZ
Bulut,ZeynepBurcu
YksekLisans,ktisatBlm
TezYneticisi:DoDr.alakten
Ocak2009
Bir lkede konut piyasas, genel ekonomi asndan gsterge
nitelii tadndan dolay, konut piyasas toplam arz ve talep
bileenleriyaygnbirekildearatrlmtr.Bualmada,1970ve2007
yllar arasnda Trkiye gayrimenkul piyasas toplam arz ve talebi
oluturan deikenler bulunmaya allmtr. Veri eksikliinden
dolay, zel olarak konut piyasas incelenememiti. Birbirinden farkl
uzun ve ksadnem fiyat elastikiyetleri esasnadayal olanTopel ve
Rosen(1988)konutarzvetalepmodelitercihedilmitir.Konutpiyasas
yaznnda ska kullanlan talep/arz deikenleri esas alnarak, bu
almada telep deikenleri olarak, nfus, faiz oran, gelir ve deer
deikenleri;arzdengedeikenleriolarak,deer,faizoranvemaliyet
endeksi deikenleri kullanlmtr. Deer datas, Trkiyede evlerin
piyasafiyatlarbulunmadndandolay,fiyatdeikeninevekilolarak
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vi
kullanlmtr.Ayrca, bina says verisi yllk olarak toplanmasndan
dolay, bu alma yllk veri ile gerekletirilmitir. Btn
deikenlerin birinci farklar duraan olduundan dolay, Johansen
EbtnlemeveHataDzeltmeModelitercihedilmitir.Bualmann
ampirik sonularna gore, uzun dnemde sz konusu arz/talep
deikenlerianlamlkmtrvebeklenen iaretlergrlmtr.Buna
karn, ksa dnemde faiz oranlar ve maliyet deikenleri dnda
btndeikenler%90 gven seviyesinde anlamsz kmtr.Ayrca,
uzundnem arz fiyat esneklii 1.50 olarak karken, ksadnem arz
fiyatesneklii0.13olarakkmtr.Szkonusuesnekliksaylarbize,
konut piyasasnda olan bir deiikliin ksa dnemde gerekleme
maliyetininokyksekolduunugstermektedir.Ayrca,uzundnem
talepfiyatesneklii4.97olarakkmtr.
Anahtar kelimeler: Konut Talebi, konut arz, Ebtnleme,
VektrHataDzeltme
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ACKNOWLEDGEMENTS
Ifeelmostfortunatetohavebeenguidedandsupervisedbymyadvisor,Assoc.Prof.alaktenandwouldliketoexpressmydeepestgratitude to her for her valuable recommendations, patience andguidancewhichhelpedmefinishthisstudy.IwouldalsoliketothankAsst. Prof. mit zlale and Assoc. Prof. Zeynep nder for theirvaluable critique and comments on my thesis. Without theirsuggestions, I would not have been able to improve the academicqualityofmythesis.
My thanks shouldgoalso tomyhusband,myparentsandmybrotherfortheircontinuoussupport,encouragementandmotivationinthereallyhardtimesIlivedthrough.
IamgratefultomyfriendsatBilkentUniversityfortheirusefulcomments,moral support and close friendship.Without their help, Iwouldneverbeabletocompletethisstudy.
I alsowant to thank toVakfbank for the support duringmygraduate study. Especially I owe thanks to my colleagues and mymanager,CemErolu,inEconomicResearchDepartmentatVakfbankfor theirgreatunderstandingandenforcingme to finishmygraduatestudy.
My thanks also go to Tbitak for their financial support.
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TABLEOFCONTENTS
ABSTRACT... iii
ZET v
ACKNOWLEDGEMENTS... vii
TABLEOFCONTENTS. viii
LISTOFTABLES x
LISTOFFIGURES xii
CHAPTER1:INTRODUCTION.........
1
CHAPTER2:LITERATUREREVIEW...........................................
5
CHAPTER3:HOUSINGINVESTMENTTHEORY...................
10
3.1HousingSupply.. 12
3.2HousingDemand 19
3.3ImplicationsofTheory 25
CHAPTER4:HOUSINGMARKETINTURKEY..
29
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ix
CHAPTER5:ECONOMETRICMETHODOLOGYANDDATA.......................................................................
35
5.1Methodology..... 35
5.1.1PhillipsPerronUnitRootTest.. 36
5.1.2JohansenCointegrationTest......... 38
5.1.3VectorErrorCorrectionModel 42
5.2Data........
44
5.3EconometricModel.. 48
CHAPTER6:ESTIMATIONRESULTS...
52
6.1EmpiricalResultsofHousingSupplyandDemand..
53
6.1.1LevelDataAnalysis. 54
6.1.2LogarithmicFormAnalysis 63
6.2LimitationsofResults....
67
CHAPTER7:CONCLUSION......................................................... 69
BIBLIOGRAPHY............................................................................ 72
APPENDICES
A.TURKEYBUILDINGCOSTINDEX.................................. 78
B.DESCRIPTIVESTATISTICS................................................ 80
C.TABLESOFESTIMATIONRESULTS............................... 81
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x
LISTOFTABLES
1. ExpectedSignsinDemandandSupply.....................................
51
2.ExponentialRegressionResult......................................................
79
3.DescriptiveStatististicsofRealLevelData................................. 80
4.DescriptiveStatisticsofLogarithmicData..................................
80
5.PhillipsPerronUnitRootTestStatisticResults1....................
81
6.ResultsofPhillipsPerronUnitRootTestStatistics2..............
82
7.TestsoftheCointegrationRankforTurkeyCostIndex...........
82
8.Chisquare(2)statisticsfortherestrictionsunderHo:restrictionsareappropriateTurkeyCostIndex................
83
9.LongRunEquilibriumResults1..................................................
83
10.VectorErrorCorrectionResults1................................................
84
11.TestsoftheCointegrationRank2.................................................
85
12.Chisquare(2)statisticsfortherestrictionsunderHo:restrictionsareappropriate2...............................................
85
13.LongRunEquilibriumResults2.................................................
86
14.VectorErrorCorrectionResults2................................................
87
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xi
15.TestsoftheCointegrationRank3..............................................
88
16.Chisquare(2)statisticsfortherestrictionsunderHo:restrictionsareappropriate3............................................
88
17.LongRunEquilibrium3.............................................................. 89
18.VectorErrorCorrectionResults3.............................................. 90
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LISTOFFIGURES
1.ShortrunEquilibrium.............................................................
11
2.LongrunEquilibrium.............................................................
11
3.TheShareofHousingInvestmentinGrossFixedInvestments1998CurrentPrices.........................................
32
4.WholeBuildingCostIndex(19912007)andIstanbulConstructionMaterialsIndex(19702007)................
78
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CHAPTER1
INTRODUCTION
Thehousingmarketisdifferentfrommostoftheothermarkets
goodsandservices.Onereasonforthisisthedualfunction;itisbotha
commodity by yielding a flow of consumer services and also an
investmentassetbybeinga largeportionofhouseholdnetworth.So,
alltheanalysisofthehousingmarketincludesbothproperties.Dueto
not only including these properties but also having different other
features, theanalysisof thehousing is furthercomplicated.According
to Palmquist (1983), the housing market is a kind of differentiated
productduetotheheterogeneousstructure,i.e.ithasastructurebased
on the characteristics of houses like the structures of house or the
location.Also,accordingtoQuigley(1992),therearefourbasicfeatures
that differentiate housing from other goods and services. These are,
high cost of supply because it takes long time to build, durability,
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2
heterogeneity no two houses are identical in every respect and
location fixity. These features of housing, in particular its durability,
heterogeneity and location fixity together imply that the housing
marketisacollectionofconnectedbutsegmentedmarkets.
Accordingtotherealestatefinanciersandeconomists,becauseof
the relationbetween themacroeconomicvariables andhousing such
as, the relation between employment and housing construction
housing investment,madebyboth thebuildersand theconsumers in
ordertoincreasetheirworth,isaleadingindicatorofeconomicactivity
(Smith and Tesarek 1991;Wheeler andChowdhury 1993).Holly and
Jones(1997)alsoagreewiththisopinion;duetothefactthathousingis
anelementofpersonalwealth,itsoperationmaybesignificantlylinked
toeconomicconditionsofthatcountry.Theincreasedindemandinreal
estatemarketresultsincapitalgainininvestmentforrealestate.Inthis
environment, households observe two effects depending onwhether
they are the owners of real estate orplanning to acquire one. In the
former group, the rise in asset prices alongwith the decline in the
interestratesasaresultofcontinuinggoodeconomicenvironmentlead
to the so calledwealtheffect.Apositive shock tohouseholds total
wealthleadstoanincreaseintheircurrentandfutureconsumption.In
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thelattergroup,wherehouseholdsareonthebuyersideofthemarket,
thedecline in interest ratesgenerates an income effect thatmotivates
households to purchase houseswhereas the increase in house prices
leads them to substitute away. The resultant impact depends on
whichever force is greater. (Binay and Salman, 2008) These types of
effects bring about the housing market to be too important and
interesting.
Inaddition,governmentpolicycanhaveaprofound impacton
the operation of the housing market. The vouchers or subsidies to
homeowners in the form of themortgage interestdeduction increase
demandforhousingservices.Thelongrunimpactonpricedependson
the supply response determined by the price elasticity of supply.
Government policy has also impacted the supply side of themarket
directly through the construction of public housing and tax policy
designed toencourage theprivateconstructionofnewhousing.These
interventionsraisean importantpolicyquestionconcerning theextent
towhich thesepolicies result innetadditions to thehousing stockor
simplycrowdoutprivateactivity.
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4
Economistshaveusedthefactthatthehousingpriceisanatural
outcomeof thedemand forhousing,equatingwith itssupply.So, the
demand and supply for housing interact to determine the price of
housingrelativetoothergoodsandservices.Basedonthisfact,which
basicallydependson the idea that theprice is formedby supplyand
demandmarketmakers simultaneously, I try to estimate the supply
anddemandequationsforthehousingmarketactivity.Inthefirstpart
of this study, Iwill give some information about the literature about
housingmarketstudies.Inthesecondpart,Iwill introduceTopeland
Rosenhousing investment theory that isconsistentwithmyempirical
researchandwiththestructureoftheTurkishhousingmarket.Then,I
will briefly explain the housingmarket structure in Turkey and the
studiesaboutTurkishhousingmarket.Inthefourthpartofmystudy,I
willexplainmymethodselectionfortheestimationaswellasthedata
and theory underlying the estimationmethod with the econometric
model.Inthelastpart,theestimationresultswillbedisplayed.
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CHAPTER2
LITERATUREREVIEW
In the literature, while modeling housing market, various
methods are used. Poterba (1984) takes an assetmarket approach to
modeling the housing market. His model of the housing market
examines the impact of a shock to the steady state,mapping out the
adjustmentprocesstoanewsteadystate.Ashocksuchasadeclinein
usercostresults initially inan increase in realhousingpricesince the
housing stock is fixed. Themarket then adjustswith growth in the
housingstockandadeclineinrealpricetoanewsteadystate.
Urban spatial theory, which provides equilibrium models in
which the stock of housing always equals the urban population, is
anotherwayofmodelinghousingmarket.Inthesemodels,there isno
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supply theorydealingwithconstruction flows sincenewconstruction
or the flow of housing simply equals the growth in population.
DipasqualeandWheaton (1994)use this theoryeffectively inorder to
disproveoneoftheassumptionsaboutthehousingmarketwhichtells
housingmarketclearsquickly.Theyquestion thisbyusingstockflow
approachandshowthehousingmarketsinabilitytorapidlyclear,and
alsoshowtheinefficiencyofhousingmarket.Inordertogetridofthe
problemofslowmarketclearing,theyusepriceadjustmentmechanism
andannex it todemandsupplyequations.Theyestimate theirmodels
by using two quite different approaches in the way of forming
consumersexpectationsabout futurehouseprices,and they find that
the gradual price adjustment statistically holds strongly both when
consumersdevelopexpectationsby lookingbackwardathistoricprice
movementsandwhenhousingdemandisbaseduponrationalforward
lookingforecasts.Moreover,theyuselandfactor,whichdependsonthe
stock of housing not the level of building activity, in defining the
supplyequationofthehousingmarket.
SomeresearcherssuchasPalmquist(1983)thinkthathousingisa
goodexampleforadifferentiatedproduct.So,Palmquistestimatesthe
demand for the characteristic of housing by using hedonic demand
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theory. He chooses this because previous studies about hedonic
regression could not find any weakness of this theory and also
nonlinear hedonic equation with the data of seven standard
metropolitan areas provides elimination of identification and
endogeonity ofmarginal prices problems. In his paper, he assumed
there is nomarket segmentationwithin an urban area since there is
mobilityamonghousingtypesandlocationsandlittleevidenceofprice
discrimination.Alsoheassumes thatdifferences in consumerswithin
andbetweencitiesaremeasurableandcanbecontrolled.
UnlikePalmquist(1983),Reichert(1990)thinksthattherearebig
differences in housing demand or supply between regionswithin a
country. So his research is based on effects of somemacroeconomic
variablesuponregionalhousingpricesbyconstructingaregionspecific
housingsupplyanddemandfunctionofUnitedStates.
Topel and Rosen (1988) examine the extent towhich housing
investmentdecisionsaredeterminedbycomparingcurrentassetprices
withcurrentmarginalcostsofproduction.Theyarguethatcurrentasset
prices are sufficient statistics forhousing investment if shortrun and
longrun investment supplies are the same. If changes in the level of
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constructionactivity impactthecostofproduction,thensupply is less
elastic in the short run than the long run. This divergence between
shortterm and longterm elasticity indicates that current asset prices
are not sufficient and buildersmust form expectations about future
pricesinordertomakeinvestmentdecisions.
Besidesthesetheoreticalstudiesabouthousingmarket,thereisa
huge literature based on empirical analysis of housingmarket in the
countrylevelinthelightoftheseabovetheories.
SincethehousingmarketofUnitedStates isthemostadvanced
one in theworld, there is somuch empirical analysis about housing
marketaboutthewholecountryaswellasaboutwithinthecountry.
The housing supply and housing demand studies will be
presentedinlatersections.
Other than focusing the supply and demand analysis, the
interaction between the income and price iswidely searched. Joshua
Gallin(2006)searcheswhetherthereisalongrunrelationshipbetween
housepricesand incomebyusing95UnitedStatesmetropolitanareas
for23years.Manyhousingmarketobservershavebecomeconcerned
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9
thathousepriceshavegrowntooquicklyandarenowtoohighrelative
topercapitaincomes.Gallinadmitsthatundertheideathatthereisa
longrun relationship between prices and income, prices will likely
stagnate or falluntil they arebetter alignedby income.However,he
finds that with the standard tests, there is little evidence for the
cointegration of housing prices and income in 95 United States
metropolitanareasfor23years.
UnlikeGallin,Malpezzi(1999)findsthathousepricechangesare
not randomwalksandareat leastpartlypredictable. Inhiswork,by
constructingasimplemodelthattestswhetherpricestendtorevertto
some equilibrium ratio of house price to income. Furthermore, he
investigates how supply conditions affect both the equilibrium price
and the time path of adjustment to equilibrium in 133United States
metropolitan areas from 1979 through 1996.According to his results,
the stringency of the regulatory environment was a particularly
powerfuldeterminantof the equilibriumhouseprice to income ratio.
Also, faster rates of population growth and of income growthwere
associatedwithhigherconditionalpricechanges,suggestingalessthan
perfectlyelasticshortrunhousingsupply.
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CHAPTER3
HOUSINGINVESTMENTTHEORY
Housingstockdependsondepreciatednumberofdwellingsand
numberofhousingcompletionsasinperpetualinventory.
Itisacommonassumptionthathousingsupplyisinelasticinthe
shortrun than in the longrun,sincehousingcompletions isrelatively
smallerthanhousingstock.(Kenny,1998)AlsoTopelandRosen(1988)
explained the reason of this assumption by the high costs of
constructionactivitywhenrapidchangesoccur.So,intheshortrun,the
demandforhousingdrivenbytheexogenousfactorswilldeterminethe
priceofhousingrelativetoothergoodsandservices.
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Figure1.Shortrun Figure2.Longrunequilibrium equilibrium
InFigure 1, for any levelofhousepricesbelowP1, there is an
excessdemandforhousingandforanylevelofhousepricesaboveP1,
there isanexcesssupply forhousing.From thegraph, it isquiteclear
thatunderconditionsofshortrunequilibrium,anystimulustohousing
demandwillresultinarisemoreinhousepricesrelativetoothergoods
and services than house dwellings as mentioned in Kenny (1998).
Hence, the microeconomic studies of house market predict a very
strongrelationshipbetweentheargumentsofhousingdemandfunction
andtherealpriceofhousingintheshortrun.
However, in the longrun,asudden increase indemandresults
again rise in house prices, this time construction firms will find it
P
P1
S
H H2
DD2
P*
S0
S
D
D
H1 H
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12
profitabletosupplymorehousingunitstothemarketwhichmakesthe
supplycurvemoreelastic.
3.1.HousingSupply
Muchof the literaturehas focusedon thedeterminantsofnew
housing supply, particularly the supply of single family detached
homes,andtherenovationandrepairdecisionsofhomeowners.Ithas
focusedonaggregatedatabecause there isso little informationwhere
theunitofobservationisthebuilder,investor,orlandlord.Inaddition,
sincehousingisadurablegood,housingsupplyisdeterminednotonly
by the productiondecisionsofbuildersofnewunitsbutalsoby the
decisionsmade by owners of housing (and their agents) concerning
conversionoftheexistingstockofhousing.(Dipasquale,1999)
While modeling supply side of the market, Poterba (1984)
assumes that thehomebuilding industry is composedof competitive
firms and that the industrys aggregate supply depends on its input
pricesandtherealmarketpriceofhousing.Assumingthereare limits
to supply of any factor of production (such as lumber), increases in
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demand for construction increase the equilibrium price of structures.
Poterbadefines supply asnet investment in structures, ignoring land
prices;heacknowledges the importanceof landbutomits land inhis
empiricalstudiesbecauseofthedataissuesforhisempiricalwork.
Adisadvantageofacoststructurebasedonrisingsupplyprice
aloneisthatitdoesnotmaketheMarshalliandistinction,inwhichthe
longertheperiod,thefewerthingsthatyouareholdingconstantwhile
you analyze the response of amarket to an external shock, between
shortrunandlongrunsupplyresponses:theindustrysupplycurveis
fixed,andhasno timedimension.Thisassumptiongivesan industry
versionof theadjustmentcost theoryof investment,but isunlikely to
bevalid,becausesupplyislikelytobemoreinelasticintheshortrun.
Therefore,thenatureoftheshortandlongrunsupplyconditions
offactorsofproductiontotheindustryisspecified.Thus,forexample,
labordoesnotmovecostless inandoutof the industry.Neitherdoes
capital.Shortrunfactorsuppliesarelesselasticthanlongrunsupplies.
Togointhisdirection,itrequiresintroducingadditionalstatevariables
into the analysis, which increases the complexity of the model,
especially forempiricalwork. InsteadTopelandRosen (1988)adopta
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14
more tractable alternative where supply conditions of factors are
approximately incorporated into an expanded cost function which
includestherateofchangeofindustryoutput.Shortrunoutputsupply
inelasticityisimpliedbycostpenaltiestorapidchangesinthelevelof
constructionactivity.
A complete model of the dynamics of new housing supply
requires detailed specification of supply dynamics for all factors of
productiontotheindustry.Byallowingmarginalcosttovarywithboth
the levelofoutputand its rateofchange,TopelandRosen (1988)cut
throughtheimmensecomplications.
Inhousing literature, there isa large literatureonmodeling the
housingsupplyofnewhomes.WhileTopelandRosen(1988)modelthe
housing investment under the assumption of perfect foresight, they
focus on housing supply. On the supply side of the market, the
representative building firms maximizes discounted profits over an
infinite horizon. Since themarket isperfectly competitive,profits are
definedas
, ,
edt 3.1.1
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15
whereP(t) is theprice foroneunitofhousing stockat time t, is
grossinvestmentinhousingattimet,Crepresentsthecostsattimet
andisapositiveconstantrepresentingtheinterestrate.Furthermore,
theindustryscapitalevolutionequationis
3.1.2
Thecostfunctionisspecifiedas
, , , 3.1.3
TotalcostCattimetisafunctionofthelevelofproduction,the
change in production and a number of cost function shifters
representedbya factory.Note that the inclusionof thechangeof the
gross investment level is the difference between the cost function in
Poterba (1984),who includes only the level of investment, andTopel
andRosen (1988),who includeboth the leveland thechange ingross
investment. Third change in the gross investment level denotes the
adjustmentcostthatthefirmfaceswhenchangingitsoutputlevel.
They imposethatC istwicecontinuouslydifferentiableand
thatmarginal costs are positive and increasing in the level of gross
investmentIandthattheadjustmentcostsareincreasing.
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16
0,
0
/ 0, / 0
Furthermore, the nonnegative constraints for the derivative
cost function (C2andC22)prevent the infiniteproduction sinceas the
rateofchangeofinvestmentincreases,thecostalsoincreases.
Given these assumptions, we can solve the maximization
problem of the representative building firm by constructing the
Hamiltonianequationandtakingthefirstderivativeswithrespectto,
and .Thenecessarycondition for theoptimalpath isgivenbyEuler
equation.
/
3.1.4
If
0, in other words there is no adjustment cost, firms should
choose such that the price equals to themarginal cost. In such a
situation, therighthandsideofaboveequation (3.1.4)reduces tozero
andcurrentpricesbecomesufficientinordertodetermineproduction.
When the change in appears as an argument in the cost
function, therebecomesadifferencebetweenpriceandmarginal cost
-
17
that consists of the right hand side of equation (3.1.4). By the
linearizationofeulerequation,wecanderive,
1
3.1.5
where the terms inandarederivativesof thecost function
evaluatedatstationarypoint, / and /.
If the crucialparameter is zero then the above equation
(3.1.5) tells us that the investment is a function of exogenous cost
shiftersandtheprice.
By rewriting the equation (3.1.5) slightly different,we can
havethefollowingexpression,
1 16
where thescanbeobtained from theequation (3.1.5). In themodel
without adjustment costs = 0, that is, changes in exogenous cost
shiftersareimmediatelyreflectedinthelevelofinvestment.Inthecase
wherethereareadjustmentcosts( 0),thereisalagbeforethenew
levelofinvestmentisreached.
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18
In the literature, Topel and Rosenmodel is used for different
purposes. Kenny (1999) has considered the potential effects of
asymmetric adjustment costs on the dynamics of housing supply by
utilizing from the Topel and Rosen (1988) supply model with the
flexible adjustment costs function advocated in Pfann (1996). His
empirical results suggest Irish housing supply is unit elastic in
equilibrium in the longrun and also in the Irish housing market,
adjustment costs associatedwith an expansion inhousing output are
greaterthantheadjustmentcostsassociatedwithacontraction.
Furthermore,Kenny (1998) summarizes the housingmarket in
Irelandwhere his estimations about housing supply and demand is
basedonTopelandRosen(1988)housingmodels.Healsoexaminesthe
monetarypolicydevelopmentsabout Irishhousingmarketby looking
deeply the banking channels and also the inflation policy effects on
housingprices.
TopelandRosens(1988)ideassuchasthesupplyrestrictionson
constructionactivityarenotonlyusedinestimationsofsupplymodels
butalsousedinsettingupanequilibriumassetpricingmodelbetween
housepricesandrents(AyusoandRestoy,2006).Theyapplytheirown
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19
constructedmodeltoSpain,UKandUS.Andtheyconcludethatsharp
increases inhouseprices leadtopricetorentratiosaboveequilibrium
bymid2003inthosecountries.
Hakfoort and Matsyiak (1997) examine the determinants of
unsubsidizedhousingstarts inNetherlandsbyestimating thesupply
sideofthePoterba(1984)modelandthesupplysideoftheTopeland
Rosen(1988)model.Theformermodelyieldsasupplyelasticityofthe
order1.6whilethelatteryieldsashortrunelasticityof2.3andalong
runelasticityof6.
3.2.HousingDemand
Mostoftheliteratureforthedemandsideofthehousingmarket
isbasedontheestimationofpriceelasticityofdemand.Asmentioned
before,Palmquist (1983)estimates thedemand for thecharacteristicof
housingbyusing thehedonicdemand theory. Heestimates theprice
elasticityofdemandforlivingspacewhichcomesoutunitarywhilethe
other characteristics are more inelastic. The crossprice effects are
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20
significantwhiletheexpenditureandincomeelasticitiesarefoundtobe
inelastic.
Theempiricalresearchfordemanddiffereitherinvariablesused
fortheestimationorinthemethodchosenfortheestimation.JamesR.
Follain,Jr.(1979)examinestheeffectofanincreaseindemandonlong
run price of housing by finding the price elasticity of the longrun
supplyofnewhousingconstructioninperiod19471975.Heshowsthat
demand function depends on longrun price of a unit of housing,
permanent income ofhouseholds, interest rate and theprice of other
goods. Follainuses real value ofprivate residential construction as a
quantityinsupplyfunctionbyapplyingOLSandTSLSmethods.
Dipasquale and Wheaton (1994) estimates demand equation
whichiscomposedofstockofsinglefamilyunitsasafunctionofrent
index, age expected homeownership rate, permanent income per
household, price index of single family housing, annual user cost of
homeownership,andtotalhouseholds.Theycomparetwoeconometric
models for actual households as for tenure choice and age expected
households as forboth tenure choice andhousehold formation.They
find that all elasticities arehigherwhen age expectedhouseholds are
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21
used thanwhen actualhouseholds areused.The regionaldifferences
withinacountryareseennotonly for thesupplysideof thehousing
but also for the demand side of it.Alan K. Reichert (1990) searches
effectsofsomemacroeconomicvariablesuponregionalhousingprices
byconstructingregionspecifichousingequations.Hederivesdemand
function in theway of assuming utilitymaximization on the part of
homeowners andwealthmaximization on the part of investors. The
demandequationiscomposedofthequantityofnewhousingsoldasa
lefthand sidevariable and realhousingprices indexofnewhousing
quality, resident income, average employment rate, average loan to
value ratio, realmortgage interest rate, themeasureofacceleration in
regionalhousingpricesandseasonaldummyvariablesforeachspecific
region.
In housing economics literature, the demand for housing is
normally derived inmultiperiodmodelwhere consumersmaximize
utility subject to an intertemporal budget constraint. These models
incorporatevariousfeaturesofhousingmarketincludingthelargecost
of housing relative to the current disposable income and hence the
dependenceofhousingdemandsthesavingsinearlierperiodsandalso
theprice.(Kenny,1998)
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22
Considera simpledemand functionwhich ignores the frictions
generatedbytheheterogeneityofunitsandthematchingofbuyersand
sellers.(TopelandRosen,1988)Undertheassumptionofperfectcapital
market,theinversedemandequationofTopelandRosen(1988)model
becomes;
3.2.1
where is the rental price of a housing unit, is a vector of
exogenousdemandshifters, bethestockofhousingcapitaland
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23
3.2.3
wherekisthelifeofabuilding.
Inthenextperiod, thepriceofahouse isstillsumof therental
pricesbutthereisadepreciationsinceyoudidnotsellthehouseinthe
previousperiod.Also,youhaveadepreciatedincomeandincomethat
areexposingtotheinterestgain.
3.2.4
3.2.5
Equation (3.2.5) is the same with the equation (3.2.2), just
written indiscretetime.Furthermore,thevalueofhousingstockmust
beboundedsothatthediscountedfuturepriceofcapitalconverges:
lim
0 3.2.6
By taking the integral of equation (3.2.2)with respect to t
undertheboundarycondition,wecanwrite;
3.2.7
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24
Above equation (3.2.7) tellsus that theprice of a house is the
accumulationofalldiscountedrentalincomethroughitslife.
Hence, the completemarketdynamicsof stocks andprices are
describedbytwolineardifferentialequations:
1 3.2.8
3.2.9
Given the initial conditions 0 and 0with the boundary
condition (3.2.6), by differentiating (3.2.9) with respect to t and
substitutingfrom(3.1.2)yields
1 3.2.10
where .
Thisdemandmodel(TopelandRosen,1988)hasitsoriginsinthe
workofWalras (1954)andmuch laterbyFriedman (1963)andTobin
(1969).Theydealwitha linear structure foranalytical tractabilityand
presentadeterministic (perfect foresight) formulation to illustrate the
key ideas.Toavoid expositorydistractions,whicharewell treated in
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25
the literature, they also ignore the special and peculiar income tax
provisionsofhomeownership.
This demand part of the Topel and Rosen (1988) model
completes the housing supply model since the market should be
thoughtsimultaneously.
3.3ImplicationsofTheory
Topel andRosen (1988) housing investment theory provides a
frameworktoanalyzethepossibledeterminantsofthehousingsupply
as well as the allowance of shortrun and longrun analysis in my
empiricalwork. Inaddition,TopelandRosenmodelalsocontains the
expected present value theory of asset pricing which supports my
empiricalanalysisandbecomessuitablefortheTurkishhousingmarket
inthewayofhouses,notbeingonlyconsumptiongoodbutalsoapart
ofahouseholdwealth.Thereforetheirmodel isakindofanextended
versionofPoterbas(1984)model.
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26
As in thismodel,bynotomitting the longrun relations, short
runrelationscanbefoundandbeinterpretedinmystudywiththehelp
ofVectorErrorCorrectioneconometricmethodologywhichprovidesus
tostudyonshortrundynamicsbyrestrictingthevariablestoconverge
to their cointegrating longrun relations. (Known asRestrictedVector
AutoRegression).
Inmy empirical framework, the cost indexbehaves likeoneof
theelementofthecostfunctioninTopelandRosen(1988)model,which
isdenotedasy(t).Because,thecostindexhastheconstructionmaterial
prices and in the Topel and Rosen (1988) model the cost shifter is
definedas the factorprices thatare supplied to the industry, thecost
indexcanbeusedasacostshifter.Theotherdynamics,representedas
gross investment level is composed of the quantity of dwellings,
constructed for the defined period, because the investment level
depends on the change of capital stockwith the depreciated capital,
equation(3.1.2).Lastly,therateofchangeoftheinvestmentisaddedto
themodelbecauseoftheslowadjustmentmechanismofthemarketin
the shortrun, so it isused in the shortrun empirical analysis. Inmy
empiricalframework,thelongrunerrorsthatcanbefoundbyJohansen
Cointegration econometric methodology and used in the restricted
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27
vector autoregression model, and also the first differences of the
variables are the representatives of the rate of change of gross
investmentlevelintheshortrunanalysis.
According to the equilibrium equation (3.2.10),when demand
side shifter, , increases under the assumption that the other
variables stay the same, the investment level, , increases since
is positive and is negative. Moreover, as
increases, the capital stock increases. Inmy empirical study, the
demand side shifters are population and income. So as population
increases,theneedforhousesincreasessoquantitydemandedincreases
andasincomeincreases,thedemandofhousesincreases.Ontheother
hand, when the supply side shifter, increases, the price of the
investment, , increases then decreases since again
ispositiveand isnegative. Inmyempirical framework,
the supply side shifter is the cost index since it includes factorprices
affectingthesupplyandascostindexincreases,thedesireforbuilding
willdecreaseduetolessprofit.Hence,ascostindexincreases,thelevel
of investmentandso thecapitalstockwilldecrease.Furthermore, the
interest rate, in the equation (3.2.10) affects both the supply side
andthedemandside.Theinterestratehasanegativerelationshipsince
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28
isnegative.Inthisstudy,theinterestratehasalsoanegativeeffecton
the quantity of dwellings for both sides of the market. The other
variable,affectingboth thedemandand thesupply, is thepriceof the
investment, . The effect of price to the demand side and to the
supply side is different.According to the supply equation (3.2.8), as
priceincreases,thelevelofinvestmentincreasessinceispositiveand
so the capital stock increases. However, according to the equation
(3.2.9), as the price of investment increases, the capital stock directly
decreasesdue to the fact that ispositiveand isnegative.The
value,which isusedasaproxy for theprice,hasanegativeeffecton
quantity demanded since as prices increases, less people can buy
houses. However, the value has a positive effect on the supply of
housessincebuildingahousemaybecomemoreprofitablethanbefore.
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29
CHAPTER4
HOUSINGMARKETINTURKEY
Housing was not ranked among the most important socio
economic issues inTurkeyuntil theearly1960s.Themainreasons for
thislackofinterestmaybesummarizedasfollows.First,themigration
fromruraltourbanareaswasrelativelyslowandtherewasnomarked
deficit in the housing supply at least quantitatively until that era.
Second, the slowpaceof industrializationdidnotmake theworkers
housing question an important source of discontent before the early
1960s.Finallyuntilthebeginningoftheplanneddevelopmentperiod,
housing had not been taken up within the broader context of its
positionrelativetothewholeoftheeconomy.Therefore,itseffecttothe
economywaslargelyneglected.(Keles,1990)
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30
After 1960s, transition from the traditional family to nucleus
familyandrapidrisingofpopulationincreasesthedemandforhouses,
especially the housing type called apartments which have smaller
gardens andmore than one floor. Due to Turkeys problems about
economics such as low level of Gross National Product per capita,
chronical high inflation and high interest rates, enough savings for
house building and buying can not be formed. The implemented
policiesabouthousing isnotefficientenoughtosolvetheproblemsof
Turkeyhousingmarket. In thepast, land is allowed tobuildbut the
infrastructure is not constructed for a living place, this reduces the
investmentdesireoftheinvestors.Also,theunavailabilityofmortgage
credits causes more people not to be able to buy houses for long
periods. So, building shanty houses (gecekondu) and unhealthy,
unplanned urbanization spread widely. The promises before each
electionandfrequentlyacceptedconstructionforgivenesscausetoraise
theproblemexponentially.(Gurbuz,2002)
In addition, deficient municipal income is not enough to
construct infrastructure services to the new streets and new counties
wheretherearealreadylotsofshantyhouses.Furthermore,deficiency
in communication between the municipalities who construct
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31
infrastructure and theutilityunitswhoprovide electricity andwater
causeswastefulexpenditure.
Increasing investment to the infrastructure services with the
renovationinhousingpolicyin1980smaintainstheconstructionsector
to rally.Collective housing fund, housing aid fund to the employees
and especially the Turkey Emlak Bank had assumed the role of the
leader for the construction sector.With the guidance of these funds,
housesuppliesandcooperatives,whicharesupportedbythemortgage
credits, increase rapidly. Housing Development Administration of
Turkeystartstobuildhousesforthelowincomefamilieswithfacilities
inpayment.Thishelps reducing the inequalitybetweendemand and
supplyinTurkey.
Therewas seen a significantdecline inhousing investments in
themiddleofthe1970sandalsointhebeginningofthe1980swiththe
effect of the crisis seen induring 1970s. Sincehousing investment is
oneofthemostimportantexpenditureofahouseholdandithasahigh
portion in the expenditure of a household, this investment is an
importantsourcefortheotherinvestmentsotherthantheinfrastructure
and utility investments. (Malpezzi, 1990) In the 1980s, the housing
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32
investment increases, especially with the help of government
investment, and then starts to decline in the last years of twentieth
centuryand inthebeginningoftwentyfirstcentury.Byobservingthe
figure3,wecaneasilynoticethatafter1998,theratioofhousingfixed
investmenttothegrossfixedinvestmentisrapidlydeclining.
Figure3:Theshareofhousinginvestmentingrossfixedinvestments1998currentprices
With this decrease investment in housing, Turkey housing
investmentsislowerthantheinvestmentratiosofdevelopedcountries,
whereasin1988sthisratioisneartothedevelopedcountries(Eraydin
etal.,1996)
05
1015202530
Housing Investment / Total Investment (% percent)
*Expectation ** Target of the government
Source: DPT, State Planning Organization, www.dpt.gov.tr
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33
The literature abouthousing inTurkey iswidelybased on the
inefficiencyofthehousingpolicies;littleempiricalanalysisisdonedue
tothedeficiencyofdata.However,asthehousingsectorimportanceis
understood, various data collection increases and more studies are
done.ForexampleoneofthelateststudiesisdonebySari,Ewingand
Aydin(2007).Theyinvestigatetherelationbetweenhousingstartsand
macroeconomic variables in Turkey from 1961 to 2000. They use
generalized variance decomposition approach for examining the
relations between housingmarket activity and prices, interest rates,
output,money stock and employment.Their results indicate that the
effect of thehousingmarket on output is notnecessarily reflected in
labormarket.Moreover,theshocksto interestrates,outputandprices
havenotableeffectsonhousingactivityinTurkey.
Theprovisionofhousingfinanceindevelopingcountriesisoften
problematic,because of thevolatilemacroeconomic environment and
thelackoflegalandregulatoryframeworkthatsupportscollateralized
lending.ErolandPatel (2004)evaluateTurkishgovernmentshousing
policy for financing the public sector housing and discuss the
appropriatetypeofmortgagesfromthelendersperspective.According
totheirresults,wageindexedpaymentmortgages(WIPM)arefoundto
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34
be desirable mortgage instruments in periods of persistent high
inflation from the lendersperspective.Thereasonbehind this finding
is that WIPM eliminate the real interest rate risk, credit risk of
adjustable rate mortgages and the wealth risk of the fixed rate
mortgages.
Another research paper on the Turkish real estate market is
basedon the idea that thehousing isbothan incomedecrease for the
tenantsandanincomeproviderforthelandlords.Sohousinghassome
kind of wealth effect for the households that can affect the whole
economy.BinayandSalman(2008)discusstheextentofwealtheffects,
affordability, financial deepening and creditmarket risks in Turkish
realestatemarket.Theyusepricerentratio to testwhether there isa
realestatepricebubble inTurkeyornot.Asaresult, theydonot find
enoughevidencesupportingthatthereisarealestatebubbleinTurkey,
contradictingwhatmanybelieve.
Therefore,thereisnodirectandcollectivestudywhichisbased
on formulating both the supply and thedemand side of theTurkish
housingmarket.So, thisstudyaims todetermine the factorsaffecting
thehousingsupplyanddemandinTurkey.
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35
CHAPTER5
ECONOMETRICMETHODOLOGYANDDATA
In thischapter,econometricmethodology that is foundsuitable
to use in this study is introduced with the data descriptions.
Furthermore,econometricmodelisbrieflyexplained.
5.1Methodology
This chapter presents and discusses a brief review of the
empiricalmethodology employed. In section 5.1.1,we brieflypresent
PhillipsPerronUnitRootTests. In 5.1.2, JohansenCointegrationTest
andVectorErrorCorrectionMethodsarepresented.
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36
5.1.1PhillipsPerronUnitRootTest
A stationary time serieshas a constant longrunmean, a finite
variance(timeinvariant)andatheoreticalcorrelogramthatdiminishes
as lag length increases.On theotherhand, foranonstationaryseries,
there exists no longrun mean and its variance is time dependent.
Therefore, under the condition of nonstationarity, to use classical
statisticalmethods such as ordinary least squares (OLS),usual ttests
andFtests,areinappropriate.However,inordertodecidethepresence
ofunitrootswhichcanbedefinedasatendencyforchangesinasystem
topersist, inotherwordsnonstationarity inasystem,only lookingat
the sample correlogram is unreliable. A formal test to detect the
possible presence of unit roots is developed by Phillips and Perron
(1988)
The distribution theory supporting the DickeyFuller tests
assumes that the errors are statistically independent and have a
constant variance. In using thismethodology, caremust be taken to
ensurethattheerrortermsareuncorrelatedandhaveconstantvariance.
Phillips and Peron (1988) developed a generalization of the Dickey
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37
Fullerprocedurethatallowsforfairlymildassumptionsconcerningthe
distributionoftheerrors.
ThePhillipsPerontestisexplainedinEnders(1995)asfollows:
Suppose that we observe observations 1,2,...,T of the {yt}
sequenceandestimatetheregressionequation:
2
Fortunately,thechangesareminor;simplyreplace with,
with,and with.Thus,supposewehaveestimatedtheregression:
2
where , , and are the conventional least squares regression
coefficients.
PhillipsPerronderiveteststatisticsfortheregressioncoefficients
underthenullhypothesesthatthedataaregeneratedby
wherethedisturbancetermissuchthat 0.
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38
There is no requirement that the disturbance term be serially
uncorrelated or homogenous. Instead, the PhillipsPerron test allows
the disturbances to be weakly dependent and heterogeneously
distributed.
ThePhillipsPerronstatisticsmodifytheDickeyFullertstatistics
byallowingforanadjustmenttoaccountforheterogeneityintheerror
process.
The appropriate critical values are given inMacKinnon (1991)
samewiththeDickeyFullertestcriticalvalues.
5.1.2.JohansenCointegrationTest
Thesequences{yt}and{zt}arecointegrated,iftheyareintegrated
of thesameorder, letussayd,or I(d),and their residualsequence is
stationary. It is a known fact thatOLS estimation procedure can be
appliedifthevariablesinvolvedinthemodelareI(0).Theviolationof
thisassumptioncausesus toobtainspuriouscorrelation (Grangerand
Newbold,1974).Whiledealingthisproblem,Davidsonetal.(1978)state
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39
thatfittingtheregressionbyusingthefirstdifferencesofthevariables
would result in a loss of valuable information about the longrun.
Therefore, they propose an error correction mechanism (ECM) by
combining the first differences of the shortrun and undifferenced
valuesof the longrundynamics.However,EngleandGranger (1987)
provethatthismethoddevelopedbyDavidsonetal.(1978)istrueifthe
variablesinthemodelarecointegrated.
A theoretically more satisfying approach is developed by
Johansen (1988) to consider the cointegration relationshipwhen there
aremorethantwovariables.ThisprocedureisexplainedinWatsonand
Teelucksingh (2002) as follows; xt is composed of (n,1) vector of I(1)
variableswhosevectorautoregressive(VAR)representationisgivenas,
(5.1.2.1)
whereare(n,n)matrices.Itcanalsobewrittenas,
(5.1.2.2)
where
and
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40
ThepurposeoftheJohansenprocedurecanbestatedasfollows;
1. To determine the maximum number of cointegrating
vectors
2. To obtain the maximum likelihood estimators of the
cointegrating matrix () and adjustment parameters () for a given
valueofr.
The rank of the matrix , r, is equal to the number of
independent cointegrating vectors. There can be at most n1
cointegratingvectorsandifr=0,itisaknownfactthatthevariablesare
not cointegrated and equation (5.1.2.2) is VAR model in first
differences. If r=n, the vector process is stationary. For 0
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41
Equation (5.1.2.2) isdenotedasavectorerror correctionmodel
(VECM). When there are r cointegrating vectors, r error correction
terms appear in each of the n equations. For instance, in the first
equation(explainingx1t),xt1consistsofterms,
. .
It isknown that thenumberofcointegratingvectors isequal to
thenumberofsignificantcharacteristicsrootsofthematrix.Suppose
theordered characteristic rootsof thematrix are; .
Toobtainthenumberofcharacteristicrootsthataredifferentfromzero,
Johansen proposes the following tests, that are based on trace and
maximumeigenvaluestatistics,respectively,
ln 1 (5.1.2.4)
, 1 ln1 (5.1.2.5)
whereistheestimatedvaluesofcharacteristicroots(eigenvalues)of
theestmatedmatrixandTisthenumberofusableobservations.
The trace statistic testswhether the number of cointegrating
vectorsislessthanorequaltoragainstageneralalternativewhilethe
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42
alternative hypothesis for maximum eigenvalue statistic is r+1. The
criticalvaluesforthesestatisticsarecalculatedbyJohansenandJuselius
(1990)withthehelpofsimulation.
5.1.3VectorErrorCorrectionModel(VECM)
A vector error correction model (VECM) is a restricted VAR
designed for use with nonstationary series that are known to be
cointegrated. The VEC has cointegration relations built into the
specificationsothatitrestrictsthelongrunbehavioroftheendogenous
variablestoconvergetotheircointegratingrelationshipswhileallowing
forshortrunadjustmentdynamics.Thecointegrationtermisknownas
theerrorcorrectiontermsincethedeviationfromlongrunequilibrium
iscorrectedgraduallythroughaseriesofpartialshortrunadjustments.
Formally, the (nx1) vector , , , has no error
correctionrepresentationifitcanbeexpressedintheform:
(5.1.2.6)
where,
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43
0=an(nx1)vectorofintercepttermswithelementsi0
i=(nxn)coefficientmatriceswithelementsjk(i)
=isamatrixwithelementsjksuchthatoneormoreofthejk0
=an(nx1)vectorwithelements
Note that the disturbance terms are such that may be
correlatedwith
Thekeyfeaturein(5.1.2.6)isthepresenceofthematrix.There
aretwoimportantpointstonote:
1. If all elements of equal zero, (5.1.2.6) is a traditional
VAR in first differences. In such circumstances, there is no error
correction representation since xt does not respond to the previous
periodsdeviationfromlongrunequilibrium.
2. Ifoneormoreofthejkdiffersfromzero,xtrespondsto
the previous periods deviation from longrun equilibrium. Hence,
estimatingxtasaVAR in firstdifferences is inappropriate ifxthasan
errorcorrection representation. The omission of the expression xt1
entails a misspecification error if xt has an errorcorrection
representationasin(5.1.2.6)
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44
5.2.Data
All the data are obtained from the Turkish Statistical Institute
(TurkStat)overtheperiod19702007(annually).
First variable is the quantity of dwellings (q) which is the
number of buildings including apartment houses, houses, other
buildings (commercial, industrial, medical and social, cultural,
religious,administrativeandother).Itistakenascompletedorpartially
completednewbuildingsandadditionsbyuseofbuildingaccordingto
occupancypermitsfromTurkStat.Thisvariablerepresentsthequantity
demanded and quantity supplied in the equilibrium. Also, the
Occupancy permit is preferred in this study since it is a certificate
which must be given to building owners by municipalities to be
constructed in boundaries ofmunicipalities and itmust be given to
building owners by governorships if the construction is out of
boundariesofmunicipalities.
Thesecondvariableistheinterestratewhichdirectlyaffectsboth
thesupplysideandthedemandside.TheCentralBankoftheRepublic
ofTurkey (CBRT)nominaldiscount interest ratesareusedasaproxy
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45
for all other interest rates, since the aim of CBRT for this discount
interest rate isbeingabenchmark for theother interest rates, suchas
deposit or loan rates. Under the assumption that the inflation
expectationsareequal to theactual inflation, it is transformed to real
valuesbyusingtheFishersRulewhichis;
1 1 1
where,isthenominalinterestrate
istherealinterestrate
istheexpectedinflation.
The thirdvariable isGrossDomesticProductatconstantprices
(1987),calculatedbyTurkStat.Itisusedasaproxyforrealincome.
Theothervariableisthevalueofdwellings,whichistakenfrom
TurkStat.Thedescriptionofthedataisasfollows:
Unitprice ofm2 are calculated four times at a year byprovince for reinforced concreteandbearingwall construction,forbuildingthattheiruseofbuildingsincludeapartmenthouses,houses, other buildings (commercial, industrial, medical andsocial,cultural,religious,administrativeandother)byprovince.Value is multiplication of floor areas indicated in OccupancyPermits.Thecostoflandisexcluded.
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46
Thisvaluem2isturnedintorealtermswiththebaseyear1970by
using the Fishermethod. This real value is used as a proxy for real
housingprices.
Anothervariable isBuildingsConstructionCostIndex,which is
calculated by Turk Stat. Since the aim of forming a building
constructionpriceindexistodeterminethequantitiesofinputsusedin
building construction and to show the yearly cost changes of these
quantitiesofinputs,thisindexcanbeusedascostinthesupplysideof
themarket.This studybegan in1989andand resultswerepublished
first inNovember 1992. 1991wasdetermined as thebaseyear and a
weightedLaspeyresindexformulawasusedinthiscalculation.
Theindexisconstructedasbelow;
Outofa totalof295 items in thebuildingsconstructioncost index,20encompassworkmanship,7aremachinery,146 are construction materials and 122 are installationmaterials. Price of these items are gathered from 24provinces (for every item prices are collected from 3separateestablishments)Thesepricesarecollectedonthe15th of the last month of every quarter from 1292establishments which are producers, wholesalers orretailers who do business with construction firms andcontractors.
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47
The Turkey BuildingCost Index starts from 1991 till 2007 but
IstanbulConstructionCostIndexstartsfrom1970to2007.Sobyusing
IstanbulConstructionCostIndexasaleadingvariable,TurkeyBuilding
Cost Index can be generated for the years before 1991. The detailed
calculationisintheappendix.Thisindexisalsoturnedintorealterms
to the base year 1970 by using Fishermethod. In addition, another
studyisestablishedbyusingtheIstanbulConstructionCostIndexsince
this index is highly correlatedwith Turkey BuildingCost Index and
IstanbulConstructionCostIndexstartsfrom1970to2007.Thisindexis
transformed intoreal termsbyusingFishersRuleandIstanbulactual
inflation.
InBuildingCostIndex,presetmaterialsandlaborarecalculated
within the preset weights. The weights and the materials are not
changedduringtheyears.Inaddition,thesepricesaretakenfromthe
producers.On theotherhand, invalue eachbuildings cost are taken
fromthebuilderwithoutinterestedinwhatthematerialsareandhow
much the labor costs to thebuilder.So thematerialsand theweights
probably change over time.Another difference is that value is taken
and calculated for each citywhile building cost index is taken from
presetfourregions.
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48
The last variable is the population. Mid year population
(populationonJuly1)estimateistakenfromTurkStat.Thiscoversdata
relatedtotheresultsobtainedinGeneralPopulation.
5.3.EconometricModel
The credit restrictionshave a crucial impacton the signof the
effectofvariousvariablesonhousingdemand.Under theassumption
ofperfectcapitalmarkets,i.e.nocreditrestrictions,boththecurrentand
future income and the expected increase in real house prices have a
positiveeffectonhousingdemandduetothefactthatwhenthecurrent
andfutureincomeofahouseholdincrease;hewantstobuynewhomes
in order to increase his monthly income by taking rent from each
additional home if the benefit from consumption or the return from
alternative investments are less than the housing investment; or he
wantstobuyanewhomeinordertoraisehisstandardofliving.Also,a
householdwhowants tomaximizehisprofit fromahousebuysnew
homeswhenhousepricesareexpectedtoincrease.Sinceaspopulation
rises, theneed for thehouses increase, there isapositive relationship
betweenhousingdemandandpopulation.Conversely,thedemandfor
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49
housing is negatively related with the interest rate because higher
interest rates increase the cost of borrowing as well as the cost of
housingservices.
As the credit restrictions increase, theeffectof thevariableson
housingdemandmayvary.Forexample,afallinfutureincomehasan
immediateeffectonfutureconsumption.Sincethehouseholdswantto
smooththeirconsumption,fromnowontheystarttosave.Sincethere
isashortageofalternativesavings(otherthanhousingmarket)because
of the credit restrictions, currentdemand forhousing increaseswhen
futureincomefalls.
Inthisstudy,itisassumedthatthequantitydemanded(qd)isa
function of real value (p), real income (y), real interest rate (c) and
population(n)
Qd=f(P,Y,R,N) (6.2.1)
where Qd=quantityofdwellings
P=realvalue
Y=GDPat1987prices
R=realCBRTdiscountrate
N=population
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50
The impacts of these variables are basically based on the
consumerbehaviors.However,thechangesofexogenousfactorsonthe
equilibrium level of value will also depend on how the supply of
housing adjusts both to the changes in demand and to the other
exogenousfactors.
Inthesupplysideofthehousingmarket,thereisabuilderwho
wantstomaximizehisprofit.Sothemoretheconstructioncost,theless
thebuilderwantstocontinuebuilding,whichmeansthereisanegative
relativerelationbetweenhousingsupplyandconstructioncostlikethe
relationbetweensupplyand interestrate.Ifthe interestrate increases,
thecostofbuildingnewhousesincreases,becausetheyhavetoaccept
topaymoreinterestforhavingenoughcapitalforbuildingahouse.On
theotherhand,increaseinboththecurrentandthefuturehouseprices
will increase current supply of housing due to the fact that selling
houses may be more profitable than the other investments. The
populationand the incomeofahouseholdhavenotadirecteffecton
supplyofhousingbutanindirecteffectthroughthehousingdemand.
In this study, it is assumed that thequantity supplied (qs) is a
functionofrealvalue(p),realinterestrate(r)andrealcost(c)
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51
Qs=f(P,R,C) (6.2.2)
where Qs=quantityofdwellings
P=realvalue
R=realCBRTdiscountrate
C=realconstructioncostindex
So,inmyanalysistheinstrumentalvariablesarepopulationand
incomeforthesupplysideofthemarketandconstructioncostforthe
demandsideofthemarket.Thesevariableshelpmetoestimatesupply
anddemand.TheexpectedsignsofthevariablesareasintheTable1.
Table1.ExpectedSignsinDemandandSupply
Variables ExpectedSignsinDemand
ExpectedSignsinSupply
RealHousePrices +RealIncome + NoDirectEffectPopulation + NoDirectEffectRealInterestRate ConstructionCost NoDirectEffect
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CHAPTER6
ESTIMATIONRESULTS
Thedescriptive statisticsof the levelsand logarithmic formsof
allthevariablesinvestigatedinthisstudyaregivenintheappendix2.
The timesseriesplotsof the levelsof thevariablespurport tobenon
stationaryprocesses.However,toobtaintheexactintegrationlevelsof
thevariables,only considering theplots isnot reliable.Therefore, the
PhilipsPerronUnitRoottestsareapplied.Theresultsofthesetestsare
giveninTable2inAppendixC.
According to the Phillips Perron test results (Table 2), all the
variablesareof integratedoforder1,I(1)atthe0.1significance level.
Therefore, the results of Phillips Perron unit root tests can be
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53
interpreted not to preclude the validity of employing the Johansen
Cointegrationprocedureforoursample.
The logarithmic forms of the variables are also checked by
PhilipsPerronunitroottestandweobservetheresultsthatcanbeseen
fromTable3inAppendixC
AccordingtoTable3results,allthevariablesareofintegratedof
order1,thatis,thefirstdifferenceofallthevariablesarestationaryat
90%confidence level.Sinceallthevariablesare inthesameorder,we
canuseJohansenCointegrationprocedure.
6.1 Empirical Results of Housing Supply AndDemand
In this section the empirical analysis is employed for two
differenteconometricmodels.Inthefirstmodel,allthevariablesarein
the level formswhile in the secondmodel, in order to observe the
elasticitythevariablesareinlogarithmicforms.Insidethefirstmodel,
thereexistalso twodifferentanalysisbasedondifferent cost indexes,
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54
TurkeyBuildingConstructionCostIndexorIstanbulConstructionCost
Index.
6.1.1.LevelDataAnalysis
In this part, we employ the Johansen (1988) cointegration
procedure to investigate the presence of a longrun relationship
between thevariablesofhousingmarketbyusingTurkeyCost Index.
Consideringtheresultsthatallthevariablesareofintegratedoforder1,
I(1),weconsiderallthevariablessimultaneously.Wetestthenullofno
cointegrationbyusingboth the Johansenmaximumeigenvalue (max)
andtrace(trace)statisticsforaVARmodelwithaconstantandwithout
trend.
In table 4 (Appendix C), eigenvalues (i), the maximum
eigenvalue(max)andtraceeigenvalue(trace)statisticsarereported.The
appropriatelaglengthsfortheVARmodelareselectedaccordingtothe
sequential modified likelihood ratio (LR) test, final prediction error
(FPE)andAkaikeInformationCriterion(AIC).
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55
FromTable4,itisseenthattherearetwocointegratedvectorsin
ordertoexplainthelongrunrelationbetweenthehousingvariablesat
the0.05significancelevel.Thisresultisconsistentwithourexpectations
sincethehousingmarketconsistsofsupplyanddemandsides.
The economic assumptions tell us that in supply side of the
market,theincomeandpopulationdoesnotaffectthequantitywhilein
the demand side of the housing market, the instrumental variable
should be the cost. In order to test whether these restrictions are
significant in themodel or not, the chi square (2) is estimated and
interpreted.TheTable5inAppendixCshowsthesestatisticsforsome
specifiedrestrictions.
CE1standsforthedemandside,CE2standsforthesupplyside.
Inthefirstrestriction,Ionlyimposetherestrictionsthatareconsistent
with the economic theory during analysiswhich tells cost has not a
directeffectondemandand incomeandpopulationdonotaffect the
supplydirectly. BylookingattheresultsofTable5,atthe30%level,
the first restrictions are appropriate. In the second restriction, in
addition to the first restriction, I restrict the coefficient of quantity
supplied to take thevalueof 1, inordernot tonormalize the supply
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56
model.Accordingtotheresults,thesecondrestrictionisappropriateat
the99%conficencelevelInthethirdrestriction,therestrictionsthatare
used in the second restriction is still valid and also the coefficient of
quantitydemanded isrestricted to thevalue1.At the99%confidence
level, third restriction is found significant.Therefore, sincepvalueof
the second restriction is thehighest,whichmeans the restrictions are
themost appropriate among these three restrictions, the second one
shouldbeusedduringtheanalysis.
By using the second type of restrictions and normalize the
demand side,wewill get the resultswhich can be seen in Table 6
(AppendixC)
These results (Table6)provideus toanalyze themarket in the
longrun. So, according to these results, all the variables forming the
supplyanddemandsidesaresignificantin99%confidencelevelinthe
longrun.Inthedemandsideofthehousingmarket,aspricesincrease,
the quantitydemanded of buildingsdecrease. In addition, as income
rises,thequantitydemandedincreasessincethehouseholdshavemore
money tobuyhouses.Populationalsoaffects thequantitydemanded
positively in the long run,due to the fact thataspeople increases the
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57
need for houses increases. Also the interest rate effect is negative,
meaning, as interest rate increases the households are lesswilling to
buyahouse.Thesesignsareconsistentwithourexpectationsaswellas
theeconomictheory.
Inthesupplysideofthehousingmarket,allthesignsarealsoas
expected.Asprices increase, thequantity supplied rises;on theother
handcostsandinterestratesarenegativelyrelated.
Thecoefficientsofthevariablesmaynotbeinthesamescalethat
we face in reality, due to the fact that these variables are real, not
nominal.Ontheotherhand,ifweobservethecoefficientmeaningsby
transforming the variables into nominal terms,we can interpret the
coefficientsasfollows;
Firstlythedemandsideofthemarketisanalyzed.Therealvalue
at2007 is0.0006, ifwedecide to increase thevalue0.0025%, inother
words 1.5x108, then the quantity of dwellingwill decrease by 1. By
applying theFisher formulawith theactual inflationof2007,wecan
have the nominal values. The results tell us thatwhen themean of
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58
dwellingvalues increaseby9,495YTL inTurkey, thedemand for the
dwellings decrease by 1. The same procedure is applied to the real
interest rate.When thenominal interest rate increasesby1point,100
basepoints, thedemand to thedwellingsdecreases by 64units.The
population and income can directly be interpreted. When the
populationincreasesby1,000peoplethenthedemandincreasesby6.44
numbers of dwellings. Also, when the GDP increases 1.63x108 to
1.64x108,1,000dwellingsaredemanded.
Secondly,whenwe lookat thesupplyside,wecansee that the
responseofbuilderislesssensitivethantheresponseofahouseholdto
thechangesofvalues.Thesameprocedurethatisappliedaboveisalso
doneforthesupplyside.So,ifthemeanofdwellingsvaluesincreaseby
35,140YTL, builderswant to build onemore dwelling. Furthermore,
whenthenominalinterestrateincreasesby1point,100basepoints,44
less dwellings are supplied.Whenwe observe the effect of real cost
which is 141.2 in 2007,by applying the sameprocedurewehave the
followingresult:whenthenominalcostindexincreasesby1,044,which
is the 2percent ofnominal cost index in 2007, the quantity supplied
decreasesby840units.
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59
Whenwe lookat thevectorerrorcorrectionmodelresults from
Table7 (AppendixC)whichprovideus toobserve themarket in the
shortrunsimplybydifferencing thedata,wecansee thatmostof the
variables are not significant in the shortrun. In the vector error
correctionmodel,thesequenceisimportantinordertounderstandthe
pathofthespeedofadjustmentwhichcanbedecreasingorincreasing,
inotherwordswhether theshortrundynamicsconverge to the long
rundynamicsbyfollowinganincreasingpathoradecreasingpath.In
myanalysis,thequantityofdwellingsiswrittenfirstandthentheother
variables follow thequantity.Thecoefficientof theerror termsshows
thespeedofadjustment,soaccording to theresulting table, theshort
rundynamics follows an increasingpath in order to converge to the
longrun equilibrium. In addition, the error correction terms are
significant at 99% confidence level. Other than the error correction
terms,therealcostindexandtherealinterestratecomeoutsignificant
at85%confidencelevel.Despitetheinsignificanceinthevalue,wecan
noticeintheshortrunthevalueoftheresponse(coefficientis9.4x105)is
lessthaninthelongrun.ThissupportstheideasofthemodelofTopel
andRosen (1988) since in theirmodel; the shortrunpriceelasticity is
lesselastic, inotherwordssmallercoefficient, than the longrunprice
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60
elasticity. This shows that the adjustment costs for a change in the
housingmarketaresignificantinTurkey.
In thedemand sideofTopelandRosen (1988)model,demand
sideshiftersarepopulationand incomewhichcomeoutsignificantat
99%confidencelevelinthelongrun;howeverintheshortruntheyare
insignificant.Furthermore,priceandcommonvariable,interestrate,are
allsignificantinthelongrunandbehaveinthesamemannerasitdoes
inthemodelinthewayofcoefficients.
Now,thesameanalysisisappliedbyonlychangingthevariable,
TurkeyConstructionCost Index to IstanbulConstructionCost Index.1
Firstly,wetestthenullofnocointegrationbyusingboththeJohansen
maximumeigenvalue(max)andtrace(trace)statisticsforaVARmodel
withaconstantandwithouttrend.
From Table 8 inAppendixC, it is seen that there are two co
integratedvectorsinordertoexplainthelongrunrelationbetweenthe
housingvariablesatthe0.05significancelevel.
1Thisanalysiscanbeinterpretedasroboustnesscheck.
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61
TherestrictionsarethesamewithTable5whereCE1standsfor
thedemandsideandCE2standsforthesupplyside.Bylookingatthe
results of Table 9 in Appendix C, the first restrictions are not
appropriate for this analysis; at the 99% confidence level, the second
restriction is appropriate and third restriction is significant at 75%
confidencelevel.Therefore,sincepvalueofthesecondrestrictionisthe
highest,thesecondoneisusedduringthisanalysis.
By using the second type of restrictions and normalize it, the
associatedresultsarefound,showninTable10(AppendixC).
Theseresultsprovideustoanalyzethemarketinthelongrunby
using IstanbulConstructionCost Index.So,according to theseresults,
allthevariablesformingthesupplyanddemandsidesaresignificantin
99%confidencelevelinthelongrun.Inthedemandsideofthehousing
market, value and interest ratehave a negative relationshipwith the
quantitydemanded.Ontheotherhandpopulationandincomehavea
positive relationship with the quantity demanded. These signs are
consistentwithourexpectationsaswellastheeconomictheory.
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62
Inthesupplysideofthehousingmarket,allthesignsarealsoas
expected.Asprices increase, thequantity supplied rises;on theother
handcostsandinterestratesarenegativelyrelated.
Whenwelookatthevectorerrorcorrectionmodelresults(Table
11AppendixC)whichprovideustoobservethemarket intheshort
run,we can see thatmost of the variables are not significant in the
shortrun.Errorcorrectiontermthatiscomingfromdemandequation,
the lag ofquantitydwellings and lag ofpopulation are significant at
90% confidence level.Despite the insignificance,we cannotice in the
shortrunthepriceoftheresponse(coefficientis5.4x106)islessthanin
the longrunwhenIstanbulConstructionCostIndex isused.Thisalso
supportstheideasofthemodelofTopelandRosen(1988).Thisshows
againthattheadjustmentcostsforachangeinthehousingmarketare
significantinTurkey.
Thedifferencebetweentheanalysis,madebyusingTurkeyCost
Index,andtheanalysis,madebyusingIstanbulCostIndexcanbeseen
in the shortrun results. In the former one, all error termswith real
interestrateandtherealcostaresignificantwhileinthelatteranalysis,
theerror termcoming fromdemandequationand the lagofquantity
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63
and lagofpopulationaresignificant.On theotherhand, the longrun
analysis
6.1.2LogarithmicFormAnalysis
Inthissection,allthevariablesaretransformedintologarithmic
termsthenthesameanalysiswithsection7.1.1isapplied.Sinceallthe
variables are in the same order, we can test whether there is a
cointegrationrelationbetweenthevariablesbyusingboththeJohansen
maximumeigenvalue(max)andtrace(trace)statisticsforaVARmodel
withaconstantandatrend.
AccordingtotheTable11inAppendixC,atthe0.05significance
level, twocointegratedrelationshipbetween thesevariablesare found
outinthelongrun.
Therestrictionsareasdescribed in7.1.1.,whereCE1stands for
thedemandside,CE2standsforthesupplyside.InTable12(Appendix
C), the first and second restrictions are significant at 1% confidence
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64
level.However,thesecondrestrictionisappropriateat99%confidence
level.
Byusingthesecondtypeofrestrictionsandnormalizeit,wecan
findtheresultspresentedinTable13(AppendixC).
Theseresults(Table13)provideustoanalyzethemarket inthe
longrun elasticities. So, according to these results, all the variables
formingthesupplyanddemandsidesaresignificantin99%confidence
level in the longrun.Thepriceelasticityofdemand is 4.97while the
priceelasticityofsupplyis1.5inthelongrun.Thesecoefficientsmean
that when the prices increase by 1%, the demand to the buildings
decrease by 4.97% on the other hand the supply of the buildings
increaseby1.5%.Furthermore,theincomeelasticityofdemandis10.28,
thatis,whentheincomeofahouseholdincreaseby1%,thedemandof
buildingsincreaseby10.28%.Thishighcoefficientshowsusthatwhen
the income of a household increases, the buying a house iswidely
preferredinTurkey.
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65
Whenwe look at the vectorerror correctionmodel results in
Table14 (AppendixC)whichprovideus toobserve themarket in the
shortrun, we can see that the logarithmic forms of real price, real
interestrate,realcostandrealincomeareinsignificant.Converselythe
error correction terms that are coming from demand and supply
equations,thelagofquantitydwellingsandlagofpopulationwiththe
constant term are significant at 99% confidence level. Despite the
insignificance,wecannoticethepriceelasticity is0.13,meaningwhen
thepriceofadwellingincreasesby1%,thequantitysuppliedincreases
by0.13% in the short run.This shows that therearehighadjustment
costsforachangeintheshortruninTurkeysinceforinstanceinUSA
the shortrunprice elasticityof supply is1.0 (TopelandRosen, 1988)
while it is 3 in the longrun during the period 1963 to 1983 with
quarterlydata.TheseresultsareallconsistentwiththemodelofTopel
andRosen(1988).
According toHakfoortandMatsiyak (1997), inNetherlands the
shortrun price elasticity of supply is 2.3 while the longrun price
elasticityofsupplyis6overtheperiod1977to1994withquarterlydata.
On theotherhand,Follain (1979) finds the longrunpriceelasticityof
supply for United States as 1.48 over the period 1947 to 1975. In
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66
additionDipasquale andWheaton (1992) finds the price elasticity of
supplyforthelongrunasatleast1.2byusingtheirconstructedmodel.
Inmostof thedeveloping countries,housingmarketdatadoes
not exist completely, however for United States and within United
States; data about housingmarket data is one of themost available.
Hence there isahuge literatureabout finding thepriceelasticities for
UnitedStates.AccordingtoPalmquist(1983),intheshortruntheprice
elasticity of demand is approximately unitary while the income
elasticityisinelasticforUnitedStates.Reichert(1990)findstheincome
elasticity of demand is 3.78 inUnited States over the period 1975 to
1987,withquarterlydata.Heexamines thepriceelasticityofdemand
for the specific regions in United States and finds that the price
elasticityofdemandchangesbetween0.13and0.22withinthecountry.
Green et. al. (1999) estimate the elasticity of housing supply
based upon contemporaneous price change for 44 United States
metropolitan areas over the period 1979 to 1996. According to his
findings,thepriceelasticitiesareintherangeof38.6to0.6
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67
6.2.LimitationsofResults
This is the firststudy thatattempts toanalyze thedemandand
supply relationships in the real estate market of Turkey using a
structuralmodel.However there are serious limitations to this study
due to lack of appropriate data. For instance, since the realmarket
housingpricedoesnotexistforTurkey,valuepereachdwellingisused
asaproxyfortheprice.Thevalueisakindofcostthatistakenfromthe
builderwithoutinterestedinwhatthematerialsareandhowmuchthe
labor costs to thebuilder.So, thevalueper eachdwellinghasahigh
correlationwith the cost index;however theyarenot the same.They
haveslightdifferenceswhicharediscussedindetailinSection6.1.
Anotherlimitationofthisstudyisthatinordertousethevalue
data,numberofallthebuildings,suchasresidential,commercial,social
culturalbuildings,aretakenasthequantityofbuildings.Asaresultof
this restriction,we cannot focuson thedynamicsofhousingmarket.
Furthermore, the number of buildings data is constituted annually,
whichmeansforalongperiod,1970to2007,only38dataexists.Infact
thenumberofdwellingsstartsfrom1961buttheinterestratedoesnot
existbeforetheyears1970.
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68
Inmy empirical framework, all the variables are transformed
intoreal termsbyusing theFishersrule.Fishersrule isbasedon the
real interest rate, nominal interest rate and inflation expectations.
However, in Turkey the expectation survey data starts in 2001 in
Turkey. So, in this study it is assumed that inflation expectations are
equaltotheactualinflation.
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CHAPTER7
CONCLUSION:
Thisstudyhasattemptedtomodelthedemandandsupplysides
of the Turkish real estate market using a structural model and an
econometric framework which clearly distinguishes the long and
shortruninformationamongarelevantsetofeconomicvariables.
In this study Topel and Rosens (1988) housing demand and
supplymodelsareusedduetothefactthat inthesemodelsshortand
longrun elasticities are different; shortrun price elasticity is more
inelasticwhich fits the Turkish real estatemarket structure since the
adjustmentcostforshortrunequilibriumishigh.
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70
In addition, since all the variables used in this study are of
integrated of order 1 (I(1)), in order not to lose information by
differencing data, cointegration analysis is found appropriate to be
used. Johansen Cointegration test is preferred because there has not
been found a significantweaknesson this test so far. In addition the
VectorErrorCorrectionModel (VECM) isused to find the shortrun
relationsby imposing some restrictionsonVARmodel.Furthermore,
VECMtakesintoaccountthelongrunrelationswhilefindingshortrun
relations,whichisconsistentwiththeTopelandRosen(1988)housing
investmenttheory.
Inthisstudysincethemarketpriceofahousedoesnotexist in
Turkey,thevalueisusedasaproxyforprice.Inaddition,thevalueof
buildings isnotdivided into theuseofbuilding types,sowecannot
observe thedynamicsof theresidentialbuildingsbut thedynamicsof
realestatemarketintheaggregate.Furthermore,becausethenumberof
buildingsdata is formedannually,annualdata isused for theperiod
1970to2007
All the variables, which are taken from Turkish Statistical
Institute,are transformed into real formsbyusingFishers ruleunder
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71
theassumptionofactualinflationisequaltotheinflationexpectations.
Theempiricalstudyisdividedintotwogroups,leveldataanalysisand
logarithmicformanalysis.Accordingtothebothoftheanalysis,interest
rate, value, income and population are found to be significant in
explainingthequantitydemandedofdwellingsinthelongrunwiththe
expectedsignsandforthesupplyside,value,costandinterestrateare
foundtobesignificantinexplainingthequantitysuppliedintheshort
runwith the expected signs.On theotherhand, in the shortrun, the
variables,thosearesignificant,aredifferentforthetwoanalyses.
According to the results of the logarithmic form analysis, the
longrun price elasticity of supply is 1.5 while the shortrun price
elasticityof supply is0.13.This shows that therearehighadjustment
costs for a change in the shortrun in Turkey. These results are all
consistentwiththemodelofTopelandRosen(1988).Furthermore,the
longrunpriceelasticity is 4.97which ismoreelasticcomparingwith
the longrun price supply elasticity, that is, consumers are more
sensitivethatthebuilders.
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72
SELECTBIBLIOGRAPHY
Ayuso,JuanandFernandoRestoy.2006.HousePricesandRents:AnEquilibriumAssetPricingApproachJournalofEmpiricalFinance,13/3:371388
Binay, kr and Ferhan Salman. 2008. ACritique on TurkishReal
EstateMarket, Turkish Economic Association, Discussion Paper2008/8
Campbell,JohnY.andPierrePerron.1991.PitfallsandOppurtunities:
WhatMacroeconomicstsShouldKnowAboutUnitRootsNBERTechnicalWorkingPaperNo.100
Case,KarlE.andRobertShiller.1989.TheEfficiencyoftheMarketfor
SingleFamily Homes The American Economic Review, Vol.79,No.1:125137
Davidson,J.H.,Hendry,D.H.,SrbaF.andS.Yeo.1978.Econometric
Modelling of theAggregate TimeSeries Relationship BetweenConsumers Expenditure and Income in theUnitedKingdomEconomicJournal,88:661692
-
73
Dipasquale,Denise.1999.WhyDontWeKnowMoreAboutHousingSupply?JournalofRealEstateandEconomics,18:1:923
Dipasquale,Denise andWilliamC.Wheaton. 1994. HousingMarketDynamics and the Future ofHousing Prices Journal ofUrbanEconomics,35:127
Enders,Walter.1995.AppliedEconometricTimeSeriesJohnWiley&
Sons,Inc.
Engle,Robert F. andC.W.J.Granger. 1987. Cointegration and Error
Correction: Representation, Estimation and TestingEconometrica,Vol.55,No.2:251276
Eraydin,Ayda,AliTrelandAlperGzel.1996.KonutYatrmlarnn
EkonomikEtkileriT.C.BabakanlkTopluKonutdaresiBakanl,KonutAratrmalarDizisi3.
Erol, Isil and Kanak Patel. 2004. Housing Policy and Mortgage
Finance inTurkeyDuring theLate1990s InflationaryPeriod ,InternationalRealEstateReview,Vol.7No.1:98120
Eisner,Robert andRobertH. Strotz. 1963. Determinants ofBusiness
Investment in Impacts of Monetary Policy, by Commission onMoneyandCreditEnglewoodCliffs,H.J.PrenticeHall
Follain,JamesR.Jr.1979.ThePriceElasticityoftheLongRunSupply
ofNewHousingConstructionLandEconomics,Vol.55
Friedman,Milton. 1963. Price Theory: A Provisional Text Chicago:
AldinePublishingCompany
Gallin,Joshua.2006.ThelongrunRelationshipbetweenHousePricesand Income:Evidence fromLocalHousingMarketsRealEstateEconomics,Vol.34/3:417438
-
74
Gould,JohnP.1968.AdjustmentCostsinthetheoryofinvestmentofthefirmReviewEconomicStudies,35:4755
Granger, C. W. J. and P. Newbold. 1974. Spurious Regressions inEconometricsJournalofEconometrics,2:111120
Green, Richard K., Stephen Malpezzi and Stephen K. Mayo. 1999.Metropoplitian Specific Estimates of the price elasticity ofhousingandtheirresourcesDraft
Gurbuz, Ayhan. 2002. potekli Konut Kredisi ve Trkiyede
UygulamasTCMBUzmanlkTezleri
Hakfoort,JaccoandGeorgeMatysiak.1997.HousingInvestmentinthe
NetherlandsEconomicModelling,Vol.14:5015
Holly, Sean andNatasha Jones. 1997. House prices since the 1940s:
Cointegration, demography and asymmetries EconomicModelling,14/4:549565
Johansen, Soren and Katarina Juselius. 1990. Maximum Likelihood
EstimationandInferenceonCointegrationWithApplicationstothe Demand for Money Oxford Bulletin of Economics andStatistics,Vol.52/2:169210
Johansen,S.1988.StatisticalAnalysisofCointegrationVectorsJournal
ofEconomicDynamicsandControl,12:231254
Keles, Rusen. 1990. Housing Policy in Turkey inHousing Policy in
DevelopingCountries,GilShidlo:140172
Kennedy,Peter.2003AGuidetoEconometricsBlackwellPublishing
-
75
Kenny,Geoff.1999.AsymmetricAdjustmentCostsandtheDynamicsof Housing Supply Central Bank of Ireland, Technical Paper3/RT/99.
..1999.Modelingthedemandandsupplysidesofthehousing
market:evidence from IrelandEconomicModelling,Vol.16:389409
. 1998.The Housing Market a nd the Macroeconomy:
Evidence from Ireland.CentralBankof Ireland,TechnicalPaper1/RT/98
Leeuw,Frankde.1971.TheDemand forHousing:AreviewofCross
Section Evidence The Review of Economics and Statistics,Vol.53,No.1:110
Lucas,RobertE., Jr.1967.Optimal InvestmentPolicyand theFlexibl
AcceleratorInternationalEconomicReview,8:4755
MacKinnon, J.