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@Andr ewMuhendaAboo kiNyakaa na Blair,B.:5c.(En g .)

A thesis sub m itte d to theSchoolofGraduate Studies in partialfulfilmentofthe

requirements fo r thedegreeof Master of Engineering

Facultyof Engineeringand Applied Science MemorialUniversityof Newfoundland


St••John's Newfound land Ca na da



N<ll00na\library or Canada

BibliolhbQucnasco aic co caoaca

Canadianrneses scrvce sevce desIhCscs cafla6.enncs

Theauthor hasgranted anirrevocable non- exclusivelicenceanowingtheNationalUbracy of Canadato reproduce,loan.distributeorsen

;opiesof his/her thesisby any meansandIn any foon orfannat,making:thisthesisavailable toint erestedpersons.

The author retainsownershipalthacopyri ght in hlsfherthesis.Neitherthethesis nor sub st antial extracts fromitmaybe printedOf otherw ise rep roduced without his/herper- mission.

L'auleur a eccordeunelicence jrrevocabte at nonexc lusive peemettant


Ia BibliotMQue natlonareduCanada dereprodulre .pret er, distribuer ou vendredes copies desathese dequelquemaraereet sousqoelqueforme que ce soupourrnet tcedes exemplaires de celie theseitla dispositiondes ocrso nncs inter essees .

L'autcur conserveIapmprietcdu droit d'euteur quIprotege sa these. NiIathese ni desextrens ecos tenuets de celle-ci no doivent 6tre imprimesau autrcmc ntreoroooustans son autortsation.



Escal a t ionof constru c tion costscoustinuca su bstant ial!'ilrtofIII<'t"l il]

costsof manyconst ruc t ionprojerts.DrOls l ir a lly"h;Hl ~i ll ~"S<".d;'lionf,llo'S callhave at!\'c rso;effect s 011theSU(TI'~Sofsuchprojects.""]10'ma inohj' ~'1in · of thisthesis is10,' XiII lli IWSronstne-tion rust"sl'"la li"nimdr""Ol1 I1IU'lid suitab leanelyticaltechuiqucsto 'lllanr,ifyits impMI.

TheCilUSNiof escalatio nafCt.'xamiu('d . :-'1'111)" of,Ill's!',' 1\1I,'« ' San- IoundI"~

be unpredic tableby their very nat ure.'-\5suchtill'main,-If"",of".s,·"I " l j"ll is toca use riskand uncer taintyregardin gII proj" d 's filS!..

Methodsof assessingtheamou nts allowedill('0l1s l1111"1iounltll r;I<"L~I"

cove r esc ala tion arere vie wed .Itist'st ahli sht'dthatllll '~"i1l!lOIIllIS";HIIJl"sl he assesse dusingthe co ncep tofcxrJl'ctl.'Jut ilityvahn-.lI;ls,'d011lilis""un'I,I.

financially st a b lecont rac t ors willincludelargerlIlo!l<·tar ysums10r-overrest.

escalationr.ekthana large owner,like governme nt .w<Juhl lll' willi nA10PilY forthesamerisk,

Forecastsof theamountofesca latio n. arcrequiredrllr],lI<ll!:{'la ryalllihid- dingpurposes.These forecastsmay beobtainedby for"r,'U>tingcost in/lir'"s that meaaaretheescalation rat eand app lying thisrIlt"tothe-,'!;lilll at",1n,sh flow.~tethod,offorecasting const ruction costindicesusin gtim/' se-riesanal- yeisare examined,Thetheories underlyin gthesemethodsan-cutfined.'1'111' applicationofthesemethodsusingII.compu te rsoftware pa<:kaw'isillllslral<·, 1.


I",Iiclatl'mit" ofC05tflow InOfld s basedonpol~'nolni alregression.The use IIf~II.-hr..~l110""1110,11'11illde mons trat ed.

~OllO'ofthcavallable forc("asl"lgtech niq ues arefound to provid eApanacea for"I,vial iugfln-"'fre t ofconstructioncostcSCillll.l io n The..rfl'Ctofesca- latien r-a.n1M'minimizedhycllrdullyll11o("lltingthe rlskof escalationusing .'S..alntiuudallse:<.Guidelinesfcrtheuseofescalation clausesMesrlpulated.

Iti~roudll'lrd thatexceptforthan insho rtco nst ruct io nprojectstobebuilt under~lahlecondit ions,theriskofescalationshoul dbe bornebythe owner. V...rinusty pes ofescalati o nclau ses Arerevie wed.Useofescalat io nclau ses iu('cUIKJrll. ti ngaform ulabasedon indices isrecom men ded.Itis also recoin- mc'n. I('<1tha tf('gulll.fl)· pu blished indic essho uld he mainta inedtoprovidefor const ruct ioncost escalatio nandfor useinothe restimatin gsituations.



Iamgreatly indebted and wishtoexp ressIllysincere grntithll,·toPro- fessorW.J.Ca m pbell for hisenco ur age ment andguid ancethro ughout my stay atthe Memorial Universityof Newfoundland. lIist'"p"rl iscpru\""dt..

bean invaluable succour witho utwhichtheachievem ent


my gonls within the requ isite tim ewouldhavebeenvirt ul\lIy impossible.

ralsowishto register mygreat appreciationfortill'expert.opinionre-n- deredb), Dr.A.S.Hanna throughout all stagesofthisthesis. Sincerethanks are alsodueto ProfessorL.Lye for his adroit suggestionsduring the'lll'\.g- mireexperiencedinselectingfrom somewhat esotericIorccastiugtl'd lll i qll t's and histechnical assistan cein applying them.

~rystudiesat MemorialUniversityweremade possiblethrough financial aidin form of assistantsh ipsand by the award of a GraJuatcFellowship.

Forthese andthevarious universityfacilitiesprovided, Iwish to express gratitude to Dr.J.Melpee, Dean or GrarluateStudies,andindeed to the entire MemorialUniversityof Newfoundland,

Thanksarefurthe rdueto Mr. T. OgwangIo-his elucidation ofOh.~f:1JI"C econometricsandtoProfessorD.Friis for all theencouragement andsugges- tions given.Spedal thanksare due to Ms.D.Poole,Professor G.Sabln nnd the WritingClinic,MemorialUniversityfortheeditorial assistan cerendered.

Iwish toexp ressheartfeltgratitudeto Mr. R. Wood for thesacrlflce enduredand toHilda.forthe patience,encourage mentandprayer.Allfor my friend,Mr.R.S.Owor,laimply"have no words."Specialthanks are extended to myfamily at home and totheHosaes, my HostFamilyinNewfoundland.

Last,but not least,it is right and fitt ing to express acclamationto the


Lord who has madeeverything possible.







1 IN TROD UCTION 1.1 Background. .

1.2 Expected benefitand objectives . . .. 1.3 Met hodology . . .

1.4 Scope • • . .. . . • • • . . . .. 1.5 Thesisorganizat ion•.

2 CAUSES AND EFFECT OFESCALAT ION 2.1 Causesof escalation ..

2.2 Effectofescalation.. . 2.2.1 Effectin fixedpricecontracts



10 10 13 13


2.2.2 Effectin cont ractswitha comp ensationorescalatio n

2.3 Con st ruct ion costrisk

:1,.1 Measur esofvalueem bodyingrisk .

2.'1.1 Expectedmonetary value. 2..1.2 Expectedutilityvalue 2.5 Choiceofmeasureof value . 2.6 Establishingaparty'sutility function. 2.7 Classificationofindividualrisk preferences

2.7.1 General classification.. .

14 14 15 1.5 16 16 17 18 18 2.7.2 Classificationofcontractorsrisk preferen ce, 20 2.7.3 Classificationof owners risk preference . .. 21 2.8 Risk alloca tionbasedon utilityvalueconsiderations. ... 22


3.1 Int roduction . 24

3.2 Forecasting Methods 26

3.2.1 Subject iveMethods.. 3.2.2 Univar iatemethods . 3.2.3 Multivariate Methods. 3.3 Applicationorforecasting techniques..

26 27 34 37 3.3.1 Expone ntialSmoothing Modelli ng. . . . .. 39

3.3.2 Box-J enkinsModelling 40

3.3.3 DynamicRegressionModelling . . . . 43 3..1 Choice ofForecastingModel ... .... .... . . .•. 50



1.1 Introduct ion. . ~,(;

-1.2 Generalcomputatio nprocedure. . . . •.. . . .... . . .. ,>6 -1.3 Cashnow projectionsfrom a planm-dprogress~r1ll'dlll,'. ;,!I

4.3.1 Use ofcomp uterschedulingpl\ck;J~e"

4.3.2 Use ofthe ProgrammeEvaluationand Review'Ior-h- nique(PERT ).

4.3.3 Shortcomir,gsiatheuse of scheduk'S


4.4 Cash now projectionsfrom cost flowmod els

. . . . . . .


4.4.1 Bas is0(costflow models... ... . .. 65 4..1.2 Characteristicshapes ofcost flowprofiles. r..

4..1.a Histori cal costanalysi ~.•. 68

4.4.4 Interpolation 7:!

4.4.5 Useofan equation 72

4.' Exam inationofthe use ofpolynomia l regression. 78 4.5.1 Descrip tionofdatausedfor analysis

. ... ...


4.5.2 Appli cation ofregression analysis

, .

4.5.3 Accu racy of regressionmodels .•.. • . •.... 82


Usefulnessandlim itations of polynomia.lregressionmodt"l~ 1!6 5 USE OF ESCAL ATIO N CLAUSES

5.1 Introduct ion.• . . . •.. . .

88 88 5.2 Meritsofvarious riskallocation policies. . .... . . . .. !)() 5.2.1 Advan tages of ownerbearingescalationrisk ... .. 90 5.2.2 Disad vanta ges ofownerbearin g escalationri~k... !)J

~.3 Guidelines {or the use of escalatio nclauses ... .. 92



,'j..l ltc(ju ircdeurlbutcsofescalat ionclauses .1..') Priceadjustm e nt.methods

.'>"1.1 Cost-plusmethod .

.')..5.2 Brit ishtrad itionalfluctua tionmethod.

.5.fi.:J Formula escalation .

5.6 Applicat ionof formulaescalation 95


6.1 Reasons for quantifyingescalation. ,,101

6.2 Cost indices. .. . . 101

6.2.1 Cleselflcationof indices 102

6.2.2 Index formulation. . 103

6.2.3 Sources ofindices. ., ..103

6.3 Forecasting the amount of escalation 6..1 Escalationtracking andpriceadjustment_ 6.5 Escalat iontreatment inthethird world.



..106 , .107 . .. 108

n o




A.l Assumptions , ,. , ,.,,_ , • , ,., ,121 A.2 Projectdescriptio nand escalation scenarios , ." ,.,... 122


:\.5 lnterpretarion


... .•I:!(;







SHEETS . [.j!!


List of Figures

1. 1 Impac t ofvariousannualescalationfatesonconstructi oncoots 3 1.2 Flowdaigremofstepstobeta kenin examin ingconstruc tion

cost escalation.

2.1 Principle causesof escalation..

3.1 Stages intheiter ativeapproach to modelbuilding(fromBox andJenkins.1976). ... . . . .•..•. .. ...


33 3.2 PlolofthePrdabricatedWoodenBuildingInd ustr yIndex

(PWBII ).. . .•.... . . .•..•.. . ... . 38 3.3 Forecas t com pa rison:\Vinters 3parameter exponentialsmooth -

i~.. . . .. . .......... .... ..... 41

:1..1 f'ON"Ca.,tcomparison:Box-Jenkins 44

3.5 Forecastecmparisom DynamicRegressio n • •... 49

-l.I Formof computer gene rated cost envelope(Subenic,1986) 61 4.2 CostRow profilesfor twohospitalproject! (DP WT, 1990). 67 4.3 Variousformsof S·curve s(Campbell,1990).. . . ..• . , ,..69 ..IA Exampleof normaliz h .ghistoricalcostsofselected completed

project (Hanna,1990) 71


.J.5 Examp le ofC'Slimati n! monthly ('xpt' lltlil u n 'IJ~'illl.·q...I"l iu lI from selectedS-cun"('.•

.J.6 Regression model fit to IIct u al '"lIlu("!I

A.l Contractor sutilityfunctionoverrelevantrll n~.·llfprofits..•12:1

s.i PlotofthePWOIl B.' PlotoftheUWRI. B.3Plot oftheCBMPI



8.5Plot oftheS$.:PhIP I B.6PlotoftheA~lI...,.


• . . . .. . . 1:12

" .1:1:1

". .1:11

••.. ••.••••.• .••1:1rl

" .131;



List of Tables

l.l Cost estimateforla ndtypecablesin tunnel(from Tcshmcnt.

]l)7 ·I}.

2.1 Classificationof individual risk preference(from Erikson and

O'Connor,1 979l. 19

:U Computeroutput for Expo nentialSmoothingmode l. 39 a.2 COInputcroutputforBox- Je nkins model 42 :L1C'omputeroutput forDyn a mic Regression model:initial tr ia l. 46 :1,.1 Computer output forDyna mic Regressionmodel:final tri a l..47 3.5 ~Ieanabsolute perce ntage error ofvariousfo recast modelsand

scenarios. 51

- 52 :J.G Mea n absolute perce ntage error ofvariousfore cast models and

sce narios (con tinued)...

3.7 Mea n absolut epercentageerr orofvariousforecastmodels and scenarios(cont inued)... •... ., , ...,•.. . . •. 53 4.1 Computat io nof escalationamountfromest imatedmonthly

expendit ureand forecastedcostindex values.... . . 58

·1.2 Tota lproje ctcost andtimefor fourhospitalprojec ts 79

4.3 Summ ary ofregress ion analysisres ults 81


~A II"sulls ofescalation computat ion liNingvar-ious m"<1" I,, ~;i


1.1 Examp leofapplicationol~EDOFormu la~1('lho(1


6.1 Constructionelementbreakdo wnoftypicalnewroadroustruc-

tion •.•..1111

A.I Contractors utilityfunctionover relevantTi\Ul/;eofI.rulils . .I:.!:!

B.l ~Ionthlyvaluesofselectedindices. ..I:!S

B.2 ~Ionthlyva lues ofselectedin dices[conr'd] .I:!!I

B.3 ~Ionthlyvalves ofselectedindices(ccnt'd] ..I:\u C.I Datafor Hos pital l

C.2DataforHc;>spital2 C.3 DataforHospital3 CA Data for Hospital4..

xi v

... . ... . .. ..I;IS


.... .. . . ... .I-ln ...•.... . . ... ..1·11


(I i Ale AMI AMI[-12J AR /\IU1.IA ,\Se E









I I,


pa ramete r(constant)estimatedbyre g ression analysis weightof commcdhyj

Ake tkeInformationCriterion ArchitecturalMater-lalsIndex theA~lIlagged12 months autoregressive

AutoregressiveIntegratedMoving Average Am er ican Society ofCivil Eng ineers

parameter (constant) estimat ed by regressionanalysis betafunctionof rands

Building Cost Index Business Foreceet Systems, Inc.

Bayesia nInforma tion Criteri o n Breakth roughSoftwa reCorporat ion parameterto be estimated

paramet er(constant)estimatedbyregression analysis cost index ofcommodi tyiat time of valuation cost index of commodityiat date oftender Commercial BankLen dingRate index Const ruction BuildingMateri alsPrice Index Constructio nCostInd ex

Crit icalPath Method

parameter (constant)estimat ed by regressionanalysis degreeofdifferencing

Dep artment of PublicWorksand Transporta tion tota lprojectescalationcost

errortermattime t escalationcostin timeintervali expec ted monetary va lue EngineeringNewsRecord expected utilit y value escalation factorfor time inte rvali probabilitydensity fun ctionofY integration







~EDO p P(AI PERT P, PS (~.)j

:\1..\ moving average :\1.,\' 1',£, meanabsolutepercentage error

numberofperiodsillseasonalt'y,'lt' :-'licrow ftCorporation

XaticnalEconomic De velopmen tOflio- orderof autoregressiveoperatio n

probabil ityofoccurre n ceof possibledollarprofitr\

Progr ammeEvaluat ion andRe vie w Teehniquo unescnlated expenditureinlim!'Intervali PrimaveraSystems,rnco

ratioofpricesofcommodityibetween thetimebaseperiod0and periodI

P(x;l probability of occurre nce of possible fut ureoutcomei PWBII Prefabrica ted Wooden BuildingInduslryIndex PWBIl{·lJthePWBll laggedImonth

q order ofmovingavera geopera tion

positive number andpar amet erofhctadistrib nrion positivenumberand pernmcrcreofhcta,listributiorr contractsum

smoothedseasonalind exat timeI SawmillandPlaningMillProductsIndex TimberlineSoftwareCorpora t ion smoothedtrend attimet utilityof possibledoll ar profitA

utilityvalueofpossib lefutureoutcome withmonet ary valuex;

UnionWage RateInd e x the UWRllagged Iimonths

weightorrelativeim p ortanceofcommodityi percentageof total time

costof possible future outcomej percenta geof total cost

cumulat ive monthly va lue of work executed observedvalueoftimeseriesattimet forecastforlead time mfrom time / forecaste dvalue of tim eseries at timet:

observedvalueoftimeseries at timet -i valueofdependent va ria bleat time t obser ved valueoftheill>.expla na toryvariableat timet

level smoothing parame ter coefficientofX;





h seasonalindex smoothing param et er h. adjustmentdt,eto escalation .:l totalunadjustedvalueofworkex ecuted .., trend smoothingparam e t e r r{r) gamma functionofr

l'( .~ ) gammafunct ionof~

¢, weightingcoefficientor theit~pre vious period 0, weightingcoefficient oftheql~pre v iousperiod /iy i\verageofcomplete dpro ject'scost


varianceofcomple ted projec t'scost



AIC (AkaikeInfor mati onCriterion)TheAlels11figureoftll!'; !~used in determiningBox-Jen kinsmodels.Basedon empiricalresearch .lilt' model withthe lowestAlewillgenerallyhetill'mos tart-urateIGnndrit"h andStellewagen,19(7)

ARI M A AnARIl\lAprocess is anautoregressive-integrate dmoving aver- age process,This wide classofprocessesprovidesa rangeof modele. stationaryandnon-st ationa ry, that adequatelyrepresentmany oftill' limeseriesmet in practice (Boxand Jenk ins,1!H 6).

Auto correlat ion The cor relatio nofa variableanditself afixednumb er

o r


AutoregressiveAn autoregressivemodelis astochasticmodelinwhichthe current value of a processis expressedas a finite,linearaggregateof previousvaluesof theprocessand ashockterm.

BIC (BayesInform at ion Crite r io n )TheBICisa figureof merit.used indeterminingBox-Jenkins models.Based onempiricalresearch,the model with thelowestBICwillgen erallybe themost accurate(Goodrich and Stellewagen, 1981).

DifferencingDifferencingisthe transf orma tionofatime seriesinvolving the replacementof everyvalue oftheseriesbyitsdifference fromthe previous value.



Esca lation~scalationin ronsrrucrlcncoshistheincreaseinthecosts of anyof theconstruct ionelementsrequiredfororiginal contractworks occurringduringccustrucricn.

Exp e cted monet ar y valu eTheexpected moneta ryvalueis a measure of

\'" III CtlUltcanI~used insituationswherethere arevariouspossible

futureoutcomeseachwith anattendantamount ofloss orgain.The expec tedmonetaryvalueisthesum oftheproduc t ofthe costofeach possiblefut ureoutcome wit hitsprobability.

Exp ectedutility valueThe expect edutilityvalue is a measure ofvalue thatcanbe usedin situatio nswherethere are variouspossiblefuture cutcc mcseachwith anattendant amount oflou or gain.The expected uti lityvalueisthesumof theproductof the utili tyvalueof eachpoe- siblcfutureoutcomewith itoprobability. Theutilityvalueofeach possible futureoutcomeis obtainedfromautilityfunction developed specificallyforthedecisionmaking party to represent thepartiespref- erenceforvaryingmonetar yamounts overthe entire ran geof possible fut ureoutcomes.

Homnace d aaf lcity Atimeseriesistermedhomoscedasticifitsvariance andcovari ancedonotchangewithtime.

Int egr a ti o nA timeseriesisintegrated withdegreedifd istheminimum degree of differencingthat renders thetimeseriesBtationary, Lag Lagisthe differencein timeunits ofa seriesvalueand a previousseries



MA PE(Mea nAb solut ePercent a geError )"'1,\PE i~a 1II1'i\~llrt'tlrllll"

accuracyor rore<:utsmadeorfut ure \"aluesorAtimeseries"To oUlain the"'tAP E,the differencebetweeneachrOfl,<,alll('f1vnlneofa linu'",·ri,.,.

and the ActUalobserved valuee isfirst eeleulated. The"'UPi-:illthen computedASthe averegeorthemagni t udesortll("S(".lilfcH·I\I".'"Wh"ll thesedifferen cesareexpressed as a percent...gc ofth,'aclnalo!>so·r\,..\


MultivariateAmultivariate met-hodisamet-hodinvolvingmore1.110111 Olll' variable atII.time ,

Risk The termrisk ,whenusedin the cont extofconstruct ioncost.escalat ion , means thepossibility offinanciallossarisingfromtheexecutionofa construction con tract.

Rob us tArobust st atisticalmethodisastatisticalmethodwhichisInsen- eitiveto mode ratedeviations fromunderlying stati:ttk a lassum ptions.

Univa ri a t eAunivariatemethod isamethodinvolv ins:onlyonevariableat atime.


Chapte r 1


1.1 Back g r o u n d

Wcbslers dictionarydefines to escalateas:"to gradually increase..;to raise and go up ..."Escalationin constructioncosts is the increaseinthecosh of any ofthe constructionelementsrequired for originalcontract works oc- curringduring construction.Theamountincludedin any construct ioncost estimate orconstructioncost breakdownto account foresca lationin con- slruction costs is an importan tcomponent of total conetructloncosts. This amountdeservesthoroughconsiderationand rational treatmentthroughout theentire construction process.

A substantial part oCthecost ofmany constructi onprojectsis attributable to escalation in construction costs. For example,in afeasibility study(pre- paredinFebruary1974) of deliveringpower Irom GullIsland Hydro-electric site to Newfoundlandusing land typecablesin tunnel,theamount ofeeca- lat ion to January 1979 was estimated at$13,420,000as showninTable1.1 (Teshmont ,1974).This was almost equalto theofcost ofcableprocurement andinstallation estim a ted tobe $14,905,000and was a majorcomponentof


Table 1.1:Costestima tefor landtypecablesilltunnel(from Tcshmout, 1974)

Cable Procutementand Installation Tunnel Construction logist icsand ConstructionSupport

Subtotal Engineeringand OwnersAdministration Contingency

Total1974Cost EscalationtoJanuary1,1979 I.D.CApril1974tojanuary1979


S14,905,000 37,725,000 13890000 66,520,000 5,440,000 7,370,000 S79,330,000 13,420,000 17,980,000 S110730000

The financialsuccess of constructi onprojec tscan be uncerta inandat risk due to thepossibility of drastica llychangingescalatio n rates duringCOIl- struction. At thebeginningof any givenproject ,therecan he anumber of different possiblefutu re escala t ion rates. Useof an erroneousescalat ion ratewhencsti mating cons truct ioncosts canhaveadverse elTeets011economic decision makingfor bothowner andcon tr act or . As an exa mple,Figure ).1 illus tra testheimpactof cha ngingesca lati on ratesonahypothetical construe- tjcnproject wit h anuneecalatcdcost of$40,000,000. The costflow profileof theprojectis expected toform the prede terminedScehapedcurves shownin FigureLl(Tham m , 1980).The projec t is tobe const ructedover aperiodof 3 year s starting 3monthsfromthe dateoftender. figure 1.1indicates thatif anannualesca lat ion rateof 10% is experie nced during const ructioninstead ofapriorestimate drateof 3%,aloss of$6,000,000wouldbe incurr ed.The partythat bear s the escalation riskcan bedevas ta ted by thisloss.


Cumulat ive pro ject expenditure

50 cu m uI 40 a

I I 0

30 -




~ 10 ns J

35 30 20 25

15 10 5

O'--l!l"""""--'---'---'---'---'--_.l--l o

Monlhs fro m date 01renee-

No escalation


Esca latio n of 7%

--f- Escalationof 3%


Escalation of 10%


Escalationrisk isshared bytilt'use ofescalat ionc1au!K'l!Iillcourract.1()("11- ments. ThereMeconflicting opinionsontheuseofescalauondi\IISt~in lix' '11 pricecont racts(Ei\R, 1980).InXortbAmerica. fixedorunitpricl' ronlrarb arcthe st.andardin const ruction,and most eenstruct tcncourrnctsIHWCIll) cost fluctuationorescalationclauses(Fellows.198·1).InNewfouudl..lll.l•.111 cont racts handled bythe Depar t men t of PublicWorkxandTtans pcrtetiou do not have escalationclauses(Brophey,1990).Fellows(llItH):o<latl'!l 1111\1 in otherparts ofthe world, eepeeially intheUnited Kingdom ,building"011-

tractsofferacontinu umof possibleescalationreimbursementmet hods ,and thereis a generaltrendtowardsusingform ula escalatio nformajorconstrue- tioncontracts.Thereare nohard andIastrulesastoWII(' 11('lIcalalioli dnllSl':O<

shouldbeused,although it is known that in somesituat ionstheII~ofthese clausestosha rethe riskofescalat ionisrequ ired.

1.2 Expected b enefi t and object ive.

This thesis aimsatexemini ng constructioncostescalAtioncovering the avail·

able analy t icaltechn iquestoquantify iteimpact.Theexpected benefiti~

the resultanttreatisethatcanbeofimmed iateusein theconst ruct ion in- dus try.To produce adocument covering theavailable analyti cal techniques toqua.nti£ythe impactof escalatio n ,theobjectivesofthi,researchcanIw summarised&!£ollows:

1.To examinethecauses of escalati onandits cffe'Cl on the various types ofcontracts.

2.Torecom mendamathem ati cal modeltouseinforec as tingesce- lation.

3.To provideguide lines forthe use ofescalation clause s in construe- tion contracts.

4.Torecommend a ratio nalmeth od forcomputingescalati oncoste when escalat ionclausesareused.


1.3 Methodol o gy

Theexaminatio n of constru ctioncost esca lat ioncoveringtheavailableana- lyt icaltechniquestoquantifyitsimpact isacheivedbyfollowingthe steps shownin figure1.2and discussedbelow.

Till)causes ofescalat ionand its effect onvari oustypes ofcontract sarc first exam inedFroma reviewof availableliterature.Fromthisreview,tee- ommendat ions of constru ction con tracts provision stha tminimizetheeffect ofescalat ionarcmade.

Aforecast oftheamountof escala tion in const ructioncosts is required forbudgetaryand bidd ingpurposes. Varlous forecastingmethodsavailable andin uscineconome tr ics, busines sand construction arcre viewed. From the review,recommendations are made ofthose methodswhichwouldbe appropriate for forecasting the amountof escalation in constructioncost.

To forecast the amount of escalation in thecostsof anyconstruction project,it is necessaryto forecasttheescala tionrate and applythisrare totheestimatedexpenditure cashflow. Theescalation rateis measured by means of costindices. Examplesaregivenfor the use ofrecommended forecastingmet hods inforecast ing a selectedconst ructioncostindex obtained from StatisticsCanada.Theexamples includea discussion of theusefulness and limitations of the forecastingmethods.

Methodsofapplyingthe forecasted escalationrateto a construction projectscashflow are examined.A modelis recommendedforestimating a construction projectscash flow foruse in forecasting theamountofeSCA- lat ion inconstructioncosts .



Reviewcauses of escalation

Examineeffect ofesca lati on on various ty pes of contracts

Exa minemeth odsof


Review use and typesof fore ca sting esca lation availabl eescalationclauses


Recommendmethodof priceadjustm ent when escalationclausesare used

Demonstrate methodsof Rec ommendmethod of forecastingescalationrates estimatinga projects

cash flowfor escalation computationpurposes

Figure 1.2,Flow diagramof steps to be laken In examiningconstructioncost escalation


art' examlned. Thi~ls done in orderto establishguidelinesas to thecir- cumstanccsthatshould existbefore escalat ion clauses areincor porated in ronsrrucfioncontracts. Theexaminationincludes areview of methodsof price adjustmentused influctuat ion orescala tion clauses and a recomrnen- '!;It ioll ofI\.methodforprice adjust mentto compensateforescalation.

1.4 Scope

There arcmany Acids in construction, andalthoughthereare nodear cut lines separatingthe variousfields,theycanhe roughly dividedinto residen- tial,building-commercial, industrial,highway-heavy and speciality(Peurifoy awl Ledbet ter,1985). Necessary inputsand construct ionm...!hodsdifferfrom Addto field.Variati onsin design withinanyonegiven field necessit ate differ- entinputsin differingproport ions.Consequently, it is notpossible tospecify amodel for estimat inga cont rac t's cashflowforescalation purposes thatis directlyapplica blenotonly toall fieldsbut eventoall designs in agivenfield ofconstruct ion.Nonet heless,the basicprinciples thatapply to anymodel used inone field shouldapplyto otherfields.Thisthesis will quant it atively applya recom mendedmodel toselectedexamplesin thebuilding-commercial field.

Thereexistsaplethoraof forecast ingmethods in useinvar ious disci- plines some ofwhichareveryesoteric.As such, it is notpossible to review all existingforecasti ngmet hods andanalyzetheapplicationof all possible appropriateforecasting methodstoconstruct ion. Mostforecas tin gmethods, however. are modificati ons ofa number ofcommonly applied foreca.sting methodsorramiliesofforecasting models.Thisthesiswill examin etheuse-


familiesofforecasti ng models.

A lotof datawhich would havebeenusefulin this research is propt-ietnrv and cannot be accessed. Case studydatilwilltherdoft,he 1ls<"1110 11" I1IUI1- stratethe applicationof suggested orrecommended methods.Whitl,tlll'1l~' of case studydata is helpful.insomecases itlacks the hrl'al!t hre-quire d tomake gen eralizatio nsapplicableto the entirecons tr uctionil1(lusl ry. 'I'll"

methodologiesdiscussedinthisthes isare.however,applicable to all cscnln- tionscenarios.

One methodof reducingthe amount ofescalatio nis thelise of prepay- ment sorrnobillaatlonpayments .Pre paymen ts,however,rt'sllltil'an incn-ase in the coot ofinterestduringconst ruction.The balancingoftheamount of prepayments necessary to minimiz ethecombinedcost of escalationand in- terest duri ngcons tructionis notwith in thescopeofthis research andwill not be addressedherein.

1.5 Thesis organization

Thisthesis is divided into sevenchapters. Thefirst chapter,whichforthe most part isalread y presented, introduces theresearch,outlines theresearch objectives, andpresents thescope,methodology,andorganization of thethe- sis.The next chapte r,Chapte r 2.outlinesthe cause s of escalation.discusses theeffectofescalation on varioustypesof contracts, andrecommendsmeans ofmeasuri ngtheeffectofesca lat ion (or cont ractu alriskallocation purposes.

Chapte r 3 examin es theanalyticaltechniqu esavailableto forecast the reteof escala tion.Chap te r 4discusses theapplicat ion of theforecas ted rat e ofesca.

lation to theesti mate dcash flow expen ditu reof agi ven constructio n project andevaluates methods of forecas ting a.proj ect 's cashflow expenditurefor


eseela uc n computat ionpurposes.Theuse of~scalationclauses is discussed iii Chapter5 in whicha method of priceadjustment when escalationc1au s.es arc usedi~recom mended.Chapter 6 specifically addressestheuse ofcost indicesin quantifyingescalationboth forprice adjustmentand forecasting Imrp<JSCS.Finally,summaryand conclusionsaregivenin Chapter 7.


Chapter 2


In orde rtorecommend alternat emeth ods for usc in theI7llnslr!u:linllirulustf}' to treatescalat ionin constr uc tioncosts , one mustfirst understand its causes, Thischapteroutlines thecauses ofescalation, discussestbCf'ffl'cl


l'scillat.ion onvarious typesofcont racts and recomm endsmeansofmeasu ring thedfed ofescalationforcontract ual riskallocat ionpurposes.

2.1 Causes of escalation

The caus es ofescalationdifferfromprojectto projectbecause ofllll'dl- vcrsity ofrequired constructioncostelements anddifferingconditionsand method s ofconstructi on. Nonetheless, the principle causesofesealatlnnill mostconstr ucti onprojectsarealldepict edinFigure 2.1 andoutlinedbelow:

L InRation: TheEconomicsDictionary (Moffat, 1976)states that thereare many definitionsof inflation,but for mostpracticalpur- posesinfl.ation can be consideredas the" decreaseinthepurchas- ingpowerofthe nat ionsmoney."The cost ofconstruct ionelemen ts increaseswith infl.a tion and thusinflatio n causes constructi on cost escalation.











2.Market conditions: Shortages ofany givenmaterial or labour and any increase in thf' levelofconstructionaclidlif'st'all"i n~

increased demand of constructionelements willcause('~c"l;\t inll or constructioncosts.

3.Taxes: Any taxes or duties adjusted or imposedOilconstrucuo n elementsduring theperiodof construction will have a direct. lm- pact on the cost of construction.

4,Other Governmentalaction:Governmentrequirements,for ex- amplethosewhich relate to safety,labour,or cnvironuu-ntnlslan- derda, may be alteredduring theperiod of constructionandlend

tocost increases. •

Quasi-government bodieslikethe Organization ofPetroleum Ex- portingCountriesdictateprices whichmay changeduriugron- struction. Governmental actionisnot limitedto a givencontrac- tor ordirect supplier but toanynecessary stringof suppliers. Any party handling a constructionelementfromit s original locet iou orcondit ioncanbe affected. This wouldulti mately affeeltill' constr uction price(Padrnos 1981).

5.Schedulealterations:Scheduleextensionmay be necessary ifallY requiredconstruction elements are delayedforunforeseenreasons.

Schedule extension canbecaused by a multitudeofotherfactors such aschangeorders,abnormalweath er conditions,strikes, and poor management.Anyextension ofcontractschedule ultimately increases construct ioncosts.

6. Alloca tion of risk:Levittetal (1980)saythat allocc,tion ofUIL-

cont rollable riskby cenrrecttothe contractor causesescalation.


costlydelays andlegal action.

7,Majorevents : Major nat ionaland internationalevents like war canhave an impact on const ruction andcausecostescalation.

Theaboveoutli ned causesof escalat ion arcnot exhaustive.Many other factorscan causeescalat ionofconstruct ioncosts. Manyof the causesof esca- lation atebytheir very nature unpredictable. For instance,theoccurrence of maj orevents likewar or the actions of quasi-governmentbodies. As a result, escalation contrib utestotheuncer tainty inthefinal costofa construc tion projectat any time beforetheproje ct is complete.



2.2 Effect ofescalatio n

Till'!maineffectofescalationis thatitint roduces risk and uncertaintyre- gilnliu g aprojec t'scost before and duringa project'sexecution (\Varza wski.

IVS2a).Hardie(19SL)statesthat"t he placement of financialrisk is the de- cldingFa-torinselection ofthetypeof contract(or a particularproject ,"

Indeed.theim p a ct ofthe risk causedbyescalationofconst ructioncostson the partiesto anycontractvaries accordingtothe typeof contra ct.

In orderto assess the effectof escalation,itisnecessary toclassify con- tractsaccording to the mannerin whichresponsibility for escalationcosts isinco rporated intoco nt ra cts. As Oyamadaand Yokoyama(198 6)putit,

"project riskisincorporatedinto the contractconditions orsummed up into theestim atesvalueasa contingency." Ibbs et al(1981) reiterate "contract s arcdefinedas some derivativeofeithercost reimb ur sable or fixedpricecon- tracts," In discussingthe elfectofescala tion ,cont ractswillherein be clas- lIific'i as eitherfixedprice contractsor contracts with a compensationor escalationclause.

2.2.1 Effe ctin fixedpricecon t racts

In fixedprice co nt racts,whether lumpsumor unit price,escalationcan cause the contractor10"gointo thered" (Oyamadaand Yokoyama,1986).

The risk borne by the contrac tor manifest s itself primarily in theamountof moneyincluded inthe contractsumtocoverescalati on.Unlessthecontr actor included a sufficientamou ntto cover escalationin his bid,thenumberof contractualdisputeswill significan tlyincreaseasthecontra ctorseeks reason


"the ownermust accept an offer of a prudent contractor with a high risk factor reflectedin price or cope with a contractor who underestimated costs and later tries to recuperate his losses through constant claims and poor work quality."

2.2 .2 Effectin co n t racts witha com pe nsat io n ores- calat io n claus e

In contracts with a compensationor escalation clause the effect of escalation to the contractor is significantly reduced or non-existent. Administrative costs and uncertainty to the owner, however, increase. Such contracts re- quire carefully structured compensation or escalation clauses. Claims by the contractor for payment to reimburse escalation of costs have to be verified during execution of the contract. This increases administrative costs to the owner (Erickson etal,1978). The owner hastoinclude an amount in his budget, over and above the contract sum, to cover escalation. Levitt et al (1980) state;

"increased risk associated with final contract price ... will reduce the projects worth to the owner ... some public owners find it very difficult (for funding and political reasons) to accept uncertainty in contract price."

2.3 Construction cost risk

The effect of escalation as determined above is risk, and the value of the amount paid by the owner for escalation depends on the attitudes towards risk of thevarious parties to any contract.In orderto recommend methods to minimize the amount paid by the owner for escalation, it is therefore necessary to examine the concept of risk due to cost escalation.

The term, "risk" herein implies the possibility of financial loss or gain arising from the executionof a construction contract (Carr, 1977). The



party lllalhears therisk isla rgely determinedhy theterms of the contract.

TIll'mannerin whichtherisk is shared or bornewillsignificantly affectthe contrac tsum and the finalconstructioncosts (Levittetai,1980).The owne r ultimatelypaystheprice of constructionincludinginherentrisks,since.even infixed price cont racts, thecontractor's price includ esa.nam ou ntto cover escalatio ncosu(ASC E,1979).The amountpaid,howe ver,may beopti mized by judicio usalloca tio n ofesca lation risk.Todothis,itisessentialtomea- surethevalue the conlract orortheownersseocleteswithvaryingamounh of possiblefuture escelat lcncosts, each withsomeeatlmetedprobabilit yof

2.4 M easures of value em b od y in g risk

There aretwo measuresofval ue thatcanbeused insit uationswherethere arevariouspossiblefutu reoutcom es,eachwith an attendan tamountof 10$S or gainnam d y:

I.Expected monetary value 2.Expectedutility value.

2.4.1 Exp ect ed monetaryvalue

The expectedmonetary value isthesum oftheprodu ct of the costof each possiblefutureoutcom ewith itsprobability.The expected monetaryvalue canbe mathematica lly represented as (Carr 1977):

EM V=f,r;P( x;) Where:



X; = Costofpossible future outcome i,



Probability ofoccurrence ofpossiblefutureomcomci.

2.4.2 Exp ect edutilit yvalu e

Theconcept ofexpectedutility valuewasdevelopedto takeinto accountlil t' fact tha tthe valueanindividual placesonagivenmoncteryamountdl']lI'lld :<

ontheparticular circumstances ofthe individual.Theexpected utilityvnlu, can bemathematica llyrepresentedas(Carr1977):



~t,=iU{X;lP(Zil (1.1)


EUV=Expectedutility value,

U(Xi)= Utilityvalue ofpossiblefutureoutcomewithmonetaryvaluer, P(Xi)=Probabilityof occurrenceof possible futureoutcomei.

The utilityvalue ofeachpossible futureoutcomeis obtainedfroma util- ityfunct ion whichhas beendeveloped specifically for the party makingtile decision,Thisut ility functionrepresents the parties preferenceforvarious monetaryamounts overtheentire rangeof possiblefuture outcomes.

2.5 Choice of m ea sure of value

Theappropriate measure ofvalue to usein opt imizingriskallocationisex- pectedu'Jlity value ratherthan expected monetary value(Erickson ct al., 1978), Thisisbecause use of theexpected monetaryvaluePff~UPPlJSCS indifferencetothe magnitudesof theamountsdue tothe variouspossible



fut ureoutcomes as longas theexpectedmoneta ryvalue isthesame.For instance,a.contrac tormay beindifferent to a 50-50chanceof loss orgain of say$L,OOO,but may notbe indifferentwhenthe magnitude ofloss orgain i~say, $1,000,000 even though theexpected monetary valuein both cases i~~f~ro . Further more,use of the expected monetary value isbasedon the assumptio nthatthe riskyprocesswill be repea tedwit haeufflcientlyhigh frequen cy such that varying profitsand lossesin theshort runwill average outto the expectedmonetary value inthe longrun,This isnot the casefor rlakainvolved in escalat ionof construction costs.The averaging out in the longru nwill not occurif the amountofloss is so great that a givenparty goesout of business. Inthiscase, the assumptionoffrequency,onwhichthe liseof the expectedmonetary valueisbased, doesnot hold.The conceptof expected utilityvalue,rather than the concept of expectedmoneta ryvalue, will thereforebe usedin optimizingriska'locarion.

2.6 Establishing a party's utility function

Touse theexpectedutilityvalueconcept itis necessaryto developthe parties utilityfunction.A party'sutilityfunctio ncan be developed by interviewing the p...rty.This processbeginsbyarbit rarily assigning utilityvalueson some arbitraryscaleto anytwo monetarysumssubjectto thecondition thatthe la rger themoneta rysum the larger theut ility. After establishingthese two points,theutili ty valuetorallothersums canbe uniquelydete rmined.

The mannerof determinationis demonstratedbythe followingexample (Eriksonand O'Connor,1979).Let A andCrepresent two possibledollar profitswith probab ilitiesof occurrenceofPtA)and P(C).Let U(A) and


betweenadefinite profitofBand thochance ofgettingprofitsAandC. tho utilityof Bcan bedeter m ined as follows:

EUV=UIA ) , P(." ) +UIC) , P(C)= 1"(11) . I'(lJ)

where: P(B )



SolvingforU(B)providesathird pointonthe ill.lividllal >lutilityFunction.

Thisprocedur e canbe repeatedtodeter minelUImany pointsa:ldesired til define the indivi dualsut ilityfunction. Theindividualsriskprcfcn-n«- is classifiedbytheform of theresultantutility funct ion.

2.7 Cla ssification of individualrisk prefer- ences

2.7.1 Gener alcla ssifi c ation

Theexpect edutilityvalueof a givenrisk situat ionto a given party depends onthe partiesriskprefer ences. Foranyset ofcondit ions , Erickson dal (19i8)identifythreedis t inctclasses ofindividualrisk preferencesna mely:

1.Risk averse: Where apartyplacesa highervalue tothe ri\lkthan the expectedmonetaryvalue.

2. Riskneutral:Wherea partyplacesa valueto therisk equal to the expectedmonetaryvalue.

3. Risk takeror gambler:Where a partyplacesa lowervaluetothe riskthanthe expectedmonetaryvalue .

The characteristicformsof the utilityfunctions fortheabove classesof riskpreference are depictedin Table 2.1. In Table2,1,theprofit in dol- larsis plott edonthex-ax ie agll.inst theexpectedutilityvalue measured on an arbitr aryscale called utilesontbc y-axia.Increasingurilcs wit hprofil



T~ble2.1:Classification ofind iv idualrisk preference (fromEriksonand oCOl~nor,I!J7?)Approved forpublic release;distr ibutionunlimitedbyCon- serucucn EngineeringResearchLaborator y (CER L)

~ I







~ S'L ~:~ ~!lL

~ s


., """''' . , ... " .,



:~>0 :~>0 :~>0








~<o ~.O ~>o

~ ~



lUSHl(IHl YTKANTHI: Ttll:SAMt:ASTill .\101I(HIOttl.y ntAHTIIt '~tVIOUSOOlUROf' PRl'o'lI)J' OOI..I..AlIO' ,Rtvloo,OOllAR0'

r~orlT PJtOl IT 'RO"l'




AUlIIoICJtISlt [ll I'OS UfiI [XPOSUlItI'fie AmME RlSlltxPOSulll






ntE~lSK VAWlOf TtlE lllU WilU£CW~IIlIS"


depictedforallth ree broad classifications(IlIIclassificationshavr-positive firstderivat ivesof utileswith profit)indicatethatallindividuals prefermort' profitto less, Thesecond derivativeindica tes thesubject.ivemarll;inlll ulilily an ind ividua lplaces on additionalprofit.The Interpretat ion of tilt'S!,t'un '{'s withregardsto riskexposureis given in Table2.1.

As Willenbrock(1973) asserts,the utilityfunctionsdepicted art;' cnrdi- nal rather than ord inal.lt thereforecannot heconcludedthatan individun]

prefers a dollar amount B six times asmuch as anamountA simplybecause theutilitiesof A and D as obta inedfromtheindividua l's ntilityfunction nrr- 10and 60 respectively,Theutility scale isarbitraryandanotherscalecould just as well havebeenusedfor whichthe utility of A is 10 andthatofIIb 600.Nonetheless,therelativemagrritudeaofdiffeeeneeabetween utilitynum- bersaremeaningfu l in that they do not ch a nge underlineartransformatlcn.

Becauseof thischaracteris tic,the general shape ofthe utilityfunct ionisnot dependa ntontheorigin andscalechosen.

2.1.2 Classification ofcont ractors riskpreference The contractorsrisk preferen ceswilldependonhisfinancialposition,fi·

nanclallystablecontractors aretypicallyriskaversebecause asWarszaw~ki (19S2b)attests"the assets of most contractorsarelowincomparison wit h their cont ractvolumes,and they maybehighlyvulnerable to losses."

When the contractorisfinanciallyinsecureheis alreadyat risk ofhf~in,l!;

out of business,Therefore,additionalcontractriskmay not carrylUImuch weight to the contractoras thepossibilityof financia lgainwhicha flnancially insecure contractormay desperatelyrequire.The contracto rthus takes on a moregambling or risk takingat titude (Carr,1977).Willenbrock(1973)



aptly describ ed ariskta kingcon tr actoras

" a gambler,apla yerof lo ngshots,a man whofee ls thateven a la rgeloss couldnot makethingsmuchworse tha n theyarenow

;;~~~~i:n~ ~hf:~~~~r~~~:;~~~~ ~h~s~~~:i~~ai~~r~~~a~~~';~i~~


Whendraft ingcont r actsto be signed by a con tractor yet tobedetermined it ismo st appropriatetoassume that the contracto rwillbe riskaversethere- fore allowing foroptim a l riskallocation .This isbecau se financially sta ble contractors are morelikely toberiskaverse andcontr actsare bestmad e with fin anciallysecure contractors.

2.7.3 Classificationofownersrisk pre ference To theowner, theriskinvolved inconstruction costsisonly a portionof the lot alriskin projectscosts . This is becaus ethe ownersviewof the totalproject cos tsinclud ethe cost of thevariouscompone ntsin theprojects lifecycl e (such asoperationand mainten ancecosts,expected rev enueand salvage value),eachwiththeir attendantrisk.In additio n,some ownerslike government, arelargeand areengagedin anum b erofconstr ucti onprojects.

Unlikethecontr actor,theowners assets arelikel ytobe high in comparison tothe contract costs,andthus the owne rwillnot beas vulnera b le as the contract ortolosses duetovariation incontractprice.Ownersaretherefore morelikelytobe riskneutral th o ughea chindivid ual owner'sriskpreference should be exami ned, Ingeneral,as Carr (1977)points out," the owners perception or risk...isbroaderthanthe cont ractors."


2.8 Risk allocation bas edon utility valuecon- sid e ra t io ns

From11.purd yexpec ted utilitypoint of \' LCW,itcnutllt'fl'f\>r"I,.."a idIhal arisk averse contractorwillInclude in hisprice11.higheramount.thantill' expectedmonetary value andthu s1\high er amount than a fb\;.ncutra lowm-r wouldbe willing topay for the same risk(Er icksonct;\1.,l!lil'1). A~('lI rf (1977)stat es: "itwillusu allypay theow ner to accepta.s muchasposs ibll'of any risk."

In general, a large owner likegovernmentwill pa ylessforescnlat.l onifhr- assumes theriskof cos t escalation in fixed price cont racts.Ti l LSisIW r;\ II SI'lilt' amount a contractorincludes to cover costescalationin hisbid willhe moro than the valueat which the large ownerassessesthe risk. ,\ 11 f'x1llllpleofhow the utilityvaluetheory can beused to model the cost effects of allo cation of escalationriskisgivenin AppendixA.

The determinati onoftheexact form of a party's utilityIuncrio n canhe quitedifficult inpractice and dependingonthe format ofthequcstiounire may varyfrom interviewerto interviewer.However, the cx.,d formof the utilityfunction isnotrequired in ordertodecide how to allocateconst ruction escalationrisk.What needsto bedeterminedis whetheragiven part.yi.~risk averse,risk neutral,or a risktaker andthis doesnotvary with inter-viewer or form atofquestionaireinnormalcircums tance s.Ifthe conrectorisrisk averse and theowneris risk neutralor arisk taker thentheriskofcost escalation would best be borneby the owner.

The expec ted utilityvalue ofthe amount included in a.hi,l is, however, nottheonlyfactorto considerinmaking adecisiononwhetheror notto lise



escalaticnc1amcsloassign therisk of cost escalation totheowner. Other considerations arc discussedinChapter.5.


Chapter 3


3 . 1 Introducti on

Itis necess ary,for budget ar yand bid d ingpur poses, torOf{'ca.~Ltill'amountill mo n et arytermsofescalation coststhatwillbeincurredduring the execut ion of aeonst r uctlcnpro ject.To forecas t theamo un t ofcost esca lation ,one ruu forecast an applicableesca lat ionra teand apply this rate to the estimal('d cash Row. Forecastingtheapplicableescala tion rate can heachieved hy fore castingthe futurevalueof an appropriate costindex.Costindices arc indicators of theamountof costescalation. Indicesdescribehowthe cost of a particular const ruct ion unit changes withtime.Theformulationand structureof appropriatecost indexes arcdiscussed in Chapter 6. Cost indlcus are timeseriesbecause theyare generallyproducedatregulartimeintnrvals.

Met hodsfor analyzingand forecasting timeseriescantherefore be used to fore casttherate of escalationof a givenconstructionproject.

A numberof methodsareavailableforforecastingtimeseries. Many of these metho dsrequire asubstantial degreeofmathematicaldexterityand can be timeconsuming.In thepast,the part ies to acont ract were oftenad-



vised to hirea consultantto applythese techniques(Ste venson1984).The current availabilityol userfriendlycom p uter forecasting softwarepackages, such asFORE CAST PRO (BFS,1988), havenow reducedthe amount of mathematical manipula tion necessaryfor a practitlcner, Thekeyrequire- mentsill applying the variousforecastingmethods usingthesepackages are the abilityto interpretthecomputer outputandan understan dingof the limitationsof thetechniquesused.

This chapte rexaminesthe analyticaltechniques availahletofo recast the rate of escalation of constructioncosts by forecastingthe valuesof an ap- propriatecostindex. A brier outlineor thetheory underlyingthe vari- ous forecastingmethodsapplicableto construct ioncostforecasting is given.

Forecas tin g the valuesof a cost indexpublished by StatisticsCanada using FORE C AST PRO isused as anexamp leofthe application of eachofthe applicableforecasting methods.Fromtheseexamples,and from theoutline of theunderlyin g theory,thebenefitsand limita ti ons of each methodaredis- cussed andageneralstrategy for choosing betweenvariousmethodsis give n.

Finally,the usefulnessof thevariousfor e casting methodstothe owner an d contractorof agivenconstruction project isevalua ted.Part icular attention is given to whet herornot these methods offcrec es ttngfuture valuesof cos t indices significantly reduce therisk offin anciallossdue to costescalation.

Method s ofapplying theforecastedvaluesof an appropriatecostindexto obtain an estimateofthe amount or escalationin a constru ction project are discussed in the nextchap ter.


3.2 Fore casti ng Methods

Empirical stud ies haveshownthatthereis no singlebf'l;tforecasting1I11'I holi applicableto allsituations (Goo drich1989). To decide011whichforecasting method isbestfor a givensituation,itisnecessary tocritirnllyI'X;111I11I' -

the availabledata.This,and anunderst anding ofthe fundnmentalsoftil..

variousIorecasuingpro ced ures are prer equi s itesfor 01lt1l1111111; goodftlr<"·;I HI H.

Forecastingmethodscan becllnifit:-1intothree categories. TIlt' calc- gories are:subjectivemethods,univariate methods and multivariau-methods (Chatfie ld,1 975 ).

3.2.1 Subjective Methods

Subject ive met hodsarebasedon humanjudgement ofthe variousrad o n;

thatma yhavean impactontherequired forecast(Firth, 1977). These methodsmayrange fromint uitiveand subjective decisions madehytlw decisionmakers(Nelson,1973) to highlyrefined rating schemesthltt turn qualitat iveinformation into quantitativeestimates.

Subjectiveforecastsarebased onjudgement, intuition,commercialkuowl- edgeandany otherinformationtheforecaster deems relevant.A wide range offactorsmay betakenintoaccount dependingon the knowledgeand the experience of the forecaster.This makessubject ive forccaete uniqueto the individu al forecas terandthereforenot reproducible.

Subjective methodsand int uitiveest imates are widelyusedill construc- tion est imatingand arcmoduseful when thereis insufficlcnthistorical dat a.

ontheappropri ate costindex.Mat hematic al met hods cannotgenerallybe used to makelongrangeforecasts,thatis, forecastsofdurationover2years



(Firth, 1!J77).Forsuch forecasts, subjective methods hn"c10he used.

'1'111'f()Tl'ca~t':rsintui tionmayoften pro veto bemorereliab lethan any mathema ti calmethod (Ch atfield,1!l75).As such,suh jcctiv e metho dscan b(' us,-.Ia~a haslsofjudging the accuracyof other methods bycomparing the [urecast sobtained usingmathematicalmethods withthefor ecastersintuitive ,,~tilllatt'S. Sincesubjective for ecast sarenot reproducible theywill notbe ILnaJ~-j(ellandcompared to othermethods discussed herein .

3.2,2 Uni variateme thods

Univariatemethods arebased on fittingamodelto the historicaldata ofa giventhne series andextrapo lating toobtainforecast s.There are manyunl- variatemethod savailable.Theseunivariatemethod sincludeamong others, extrapolationoftrendcurves.averaging, expcne, smoothing.Box-Jenkins method , ste pwise autoregressionand adapt ivefilteri ng. Amongthese,the most popula rareexponentialsmoothing and theBox-Jenkinsmethod (Lye, I!lDO).

Exponen t ialsmo othin g

Themost commonly usedexponential smoot hing met hods are theHolts- Winter sfam ily ofmodels(Goo drich,1989).Thesemodeltimeseriesusing up to threecompo ne ntsrepresent inglevel, trendand season alinfluences. Recu r- siveequ alions are used toobta in smoothedvaluesforthe model comp on ents.

Eachsmoothed valueofanymodelcomponen t isa weight ed averageofcur- rent andpast datawith theweight s decreasingexponentia lly.Holts-Winters familyofexpo nentialsmoothing modelscan beclassified int othreeclasses


\Vintersthree-parametersmoothing (Goodrich ami Stcllwagt-u.]!lSi).

Simple exponentialsmoothinguses anoquntio n to modeltlw h,\,,'1tlf1Ill' series ofthe form:




the level smoothing parameter }~=observedvalueoftimeseriesat timet L1=smoothed levelat timet This equationreduces tothe recursiveform:

The forecastingequa tionis:







forecastforleadtime mfromtime/


Holttwo-paramete r smoothingusestwro equationsto modellevel1l11l1

trend. Thesearegiven intheir recursiveform by:



(1-0)(L '_1






thesmoothedtrendattime/ 28

(:1041 1:1·'1


1'" trendsmoothi ngparamet erand otherparamete rsare asprevlc ualy de- fill'.'d.

Till:fo rc.'Ca.~tingequatio nis :


Winters three-para mete rsmoothinginvolvesthreesmoot hing parame ters fnrlevel.trend andseasona leffects. Thesmoothingequati ons arc of the form:



S~ , +




1(L1- L'_I+(l-1)T,_1]

Sl ""6iI'



(3.7) (3.8) (3.9)

5, ""smoothed seasonalinde x attimeI, n=thenumberof periodsin theseasonalcycle,



seasonalindexsmoothing parameterandether parametersareas previ- ouslydefined.

Theforecasting equat ionisofthefonn:


Simpleexponentialsmool hingisappropriatefordatawhichfluct uates around aconstant orhasaslowlychangingleveland is neitherseasona.!nor hM anytrend. Usc of theHoltstwo-parameter modelis appropriatefor d.lta whichfluctuatesabouta levelthat changes wit hsomenearly consta nt linea rtrend.Wintersthr ee-parametermodel is usedfor datawith trend


adjusted to represent data that has a damped exponentialrather thnn lincar trend (Goodrich.HlS9),

All exponentialsmoothingequations give more-weight\0moren-o-nt values of data,The largerthe values of the sntocthlngJmrilllwl,'rsIllO'mm,' emphasis011recentobservations and less onthepast. This is illlllili\'I'ly appealing for forecastingapplications.

Thesmoothingparameters are normallyobtainedbyeit.hr rusingite-rn- fiveleast squaresor a gridsearch forthe parametersthll!. givetlu-minimum squarederror over the historicaldata. Thiscalculatio npron'~"n'lllIirt·sa great numberof computations whichare normally iurorporn tcdintoa com- puter program .

Exponent ialsmoot hingmodelsarerobustin thattheyarcinsensitiveto changesin thedatastatisticalstruct ure(Goodrich, 1989 ). No assumptlona about thestatisti caldistribut ionof data are madein exponential smoothing andther eistherefore no need to analyzediagnost icstatistics givenwith most comput er program s.

Oneofthemain advantagesofusingexponent ial smoothinghithatnnrc~

the smoothi ng paramet ers have beenest imated.only the previous{oreca'lt andthe mostrecent observationhave to bestoredor are necessary to mak"it.

new forecast .This makesthe calculationof a new forecast computationally veryconvenient .

Box-Jen kinsMet hod

Box-Je nkinsmethod (Boxand Jenkins,1976) modelstime serit:.'!by mak- ing strongand explicitdistr ib utio nalassump tionsabout the underlyingdata generati ng process.The methodusesacombinationofautoregressive(AR).



Inte gration(I)and moving average(~[A)operationsinthegeneral Autcre-

~rc'ssi\'('IutegratedMoving Average(A R I~fA)modelto representthe corre- li~t io ll ;tlstructure ofaunivariat etimeseries.

1\ 11autoregressive operat ionof orderpdevelopsa Iorecestbased ona lilll~arwI:igh t cuslimofprevious dat a representedby:

whe re:

fl=forecastedvalueof series attime I,

~l_i= observedvalue oftime series attimeI- i . t/!;= weightingcoefficientofthejlhprevious period,

CI=error term at time I.

Thecoefficients arefound byminimizing the sum ofsquared errors usually using anonlinear regressionrout ine.

A movingaverageoperationof orderqdevelopsa forecast whichis a function or thepreviousforecast erroreusing an equation ofthe form:




weighting coefficientfortheqthpreviousperiod.

The restofthe terms are as previouslydefined.

The autoregressiveand moving average operationscan only be applied to stat ionary timeseries, Thatis.theycan only beapplied to data which has aconstantmean value with time. If a timeseriesisnon-stationaryithasto


operations can be performed,Forecast values11"\'1' to Ill'tnl\\"foTllll"\h,wk totheoriginal non-stationary state by the Intogratlon. (I).o]wrtllion.

A three stepprocedureofide ntificat ion. cst.imatio uandIliap;no"tirclm-k- ing was originallyproposedby00)(andJenkins(Box<111\1Jenkins.I!lilil to select a modelfrom thegeneral class of ARIMA models.This ill·ra li\"<·

process is depletedin Figure:U.The identlflcatlonprocess is,lp('i,liup;til<' bestARIMA(pdq) modelto fitthe da ta.Thismea ns identi fyingIIll'fl"gr",' ofdifferencin g d, the ARorderp andthe MAorderq.Theesf.ima tiou pro- cess involvesstatisticallyestimat ing themodel para mete rs.Thediagllostk step involvesexaminationofthe residu alsto ensure thatthe AHI MAmod- elling assum ptionsof indepe nde nce, homosced ast icity, andnorm alityof 1I11' residualsare not violat ed.

To useBox-Jenkinsmethod , the datamusthave a strong correlatio nal behaviou r, and thereshouldbe sufficientdatatopermitreasona blyaccurate estimatesoftheparameters.Itis suggestedthatthereshouldbe at leas t50 observations forgoodesti ma tes(Box-Jenkins,1976),

The selected Box-Jenkinsmodelwhichsatisfiesthediagnosticr.hf'rks will generally fitthehistoricaldatawellandthe parametersestimat eddescribe thedataonwhichtheyare estimated, These para meters areest;maws"f unknown parameters. Therefore whenthe forecastsusingthemodelare comp ared withfuturedata ncrused in estimatingthemodel paramctnrs,th«

fit maynotbe asgood(Ahraha mand Lcdolter,1983).

Oth eruniva ri atemethods

Otherunivariate methodsincludeextr apolationortrend curves, avereglng, stepwiseautoregressionand adaptive filtering.Extrapolationoftrendisin-



Figure 3.1:Stages in theiterative approachtomodelbuilding (£romBox and Jenki ns,1976).


herent in alltheotherunivar-iat emethods.1~:xJl<lll('nlilll~11l0'ltl ti ll~em-om- passes R\"eraging and is comparable toadapth"1' filte ring(('1i1~"f1l1l1111'aUtI Sulivan . 1977). Stepwiseauto regression ranhe rq;lIrd"dilSsouu' form of Box-Jenkins method(GrangerandNewbold.19;;).For l1ws('reasons .all exami nationoftheuse of Box-J enkins methodand expoucuria! smulilltin gttl forecas tconstructioncostindicessho uldtorevealthe bene fitsandlillliialiolls of usingunivari atemethod s toforecastconstruct ioncostescnlatiou.

3.2.3 Mu lti variateMetho d s Choiceor typeor multiva riatemethod

Multivariatemethods forecast agiventime seriestakingintoaccount obser- vations of oth ervariables. Generall y,these models usc equations ,1('vd op"11 byregression to representtherelations hipbet ween thedependen tOfen- dogenousvariableandthe exogeno us or expla natory variables. l\llllt ivilriatl' methodsareeithersingleequatio nmodelsOfsimu lt aneousequatlo u models.

Insingleequationmode ls,the valuesof theexplanat ory var-iables,I(~­

termi ne the value of thede pendent variabl e andthe explanato ryva riables arenot influencedby thevalues ofthedepend ent variabl e.Sirnult alll'l)1ls equationmodelstakeintoaccount thesimulta neousdepend ency bctwI'('1I the dependent andexplanatoryvariab les.

Theconstruct ioncostsor anygivensinglenorma l sized constructio n projectseldo m have anysigni ficantinfluenceonthemarketforces which causechanges incost. As suc h,singleequationmodels aremostapplicable to constructio n.Therefore, inthistreat ise,onlysingle equ at ionmodels will bedisc ussed.

Singleequationmodels can be of eithernon- linearorlinearspecification .



Alinear specificationmeansthatthedependentvariableorsometransfer- marionof thl'depe ndentvariable can be expr essedasalinearfunctionof the explana to ryvnriahlcor some transformationof theexplanatoryvariable.A moue!withItlinearspecificationis the mostapprop riat e tousewith con- et ruc tloncost indic esbecaus e con st ructioncostcom ponen tsare generally addluvc.

Thl'mulrlvarla tomethodmost applicable to constructio ncostsisthere- forethe single equationlinearregressionmodel.This regressionmodelisof the form(Pindyc k andRub infe1d, 1976):


\'011=the dependent variab le at time I, Pi=coefficientofXii,

Xi != the observed valueof theil l<explanatory variable attimet, C,=theerror term at timet .

Requ ir ement sfor useof regressionmodel s

Thesingleequation linear regressionmodelassumes thattheresidualsare nor mally distribu ted randomvariables with ameanof zero andaconstant variance.Itisalso requiredthat theexplanatory variab les arelinea dyrelated to thedependent variable (orcanbetransformedint o some linearrelation), andthatexplana tor y variables arenot collinear. In usingregressionmodels itis furtherassumedthatthefitted regression model canbe used beyond the




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