COMMERCIAL IN CONFIDENCE FINAL REPORT

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F I N A L R E P O R T

ReviewofdemandforecastsforSAWater

PreparedforEssentialServicesCommissionofSouthAustralia

January2013

THE CENTRE FOR INTERNATIONAL ECONOMICS

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Review of demand forecasts for SA Water 1

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Contents

Executive summary 3

Demandforecastshouldsupportpriceregulation 3Technicalamendmentstoforecasts 4

1 Introduction 7

Theroleofdemandforecastsineconomicregulation 7RegulatoryapproachforSAWater 8Thisreport 9

2 Structure of demand forecasts 11

CurrentpricesforSAWaterservices 11StructureofSAWater’sforecasts 12SAWater’smodel 13

3 Historical analysis of demand drivers 15

Overviewofapproach 15Validityofhistoricaldata 16Reviewofmodelestimationandapplicationofmodelresults 19

4 Projecting forward demand 27

Forecastcustomernumbers 27Bounceback 33

5 Revised forecasts 38

A Forecast of annual water consumption billed 42

BOXES, CHARTS AND TABLES

1 Originalandamendedforecastofconsumptionbilledoverregulatoryperiod 62 Cumulativechangestototalconsumptionbilledoverregulatedperiod GL 61.1 OverviewofregulatoryapproachforSAWater 82.1 Forecastnumberofcustomersconnectedtopotablewaternetwork 132.2 Forecastpotablewaterusebycustomercategory 133.1 DemanddriversusedinACIL’sdemandforecasts 153.2 RevisedABShistoricalpopulationdata 163.3 Transitiontoquarterlybilling 173.4 Changestomodelresultswithamendmenttohistoricalwaterrestriction

data 173.5 Sensitivityofhistoricalwaterrestrictiondata 18

2 Review of demand forecasts for SA Water

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3.6 Fittedresidentialmodelwithadjustmenttohistoricalwaterrestrictions 183.7 Twonon‐stationaryseries 203.8 AnnualgrowthinpopulationandSAWatercustomers 213.9 Historicalcustomergrowthlesspopulationgrowth 213.10 Differenceinaverageannualgrowthratesforpopulation,dwellingand

customergrowth 223.11 AnnualgrowthrateofcommercialcustomersandGSP 233.12 Commercialaverageconsumptionmodel 254.1 Forecastofresidentialcustomers 284.2 Forecastofcommercialcustomers 294.3 RevisedGrossStateProductionforecasts 304.4 Reviewofadjustmenttoresidentialpriceelasticity 324.5 Totalwaterconsumptionforecastwithadjustedandunadjustedprice

elasticities 324.6 Originalandrevisedpricecoefficientsfromannualregressionmodels 334.7 Deviationofforecastsfromactualconsumptiondatain2011‐12 344.8 Bouncebackof50percentover2years 354.9 Bouncebackof75percentover3years 364.10 Bouncebackof100percentover4years 365.1 Originalandamendedforecastofcustomernumbers 385.2 Originalandamendedforecastofconsumptionbilledoverregulatoryperiod 395.3 Cumulativechangestototalconsumptionbilledoverregulatedperiod GL 405.4 Cumulativechangestoresidentialconsumptionbilledoverregulatedperiod

GL 405.5 Cumulativechangestocommercialconsumptionbilledoverregulated

period GL 415.6 Cumulativechangestonon‐residentialconsumptionbilledoverregulated

period GL 41A.1 Forecastofannualwaterconsumptionbilled GL 42


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Executivesummary

TheEssentialServicesCommissionofSouthAustralia ESCoSA iscurrentlydevelopingtheregulatorypricingapproachforSAWater.ESCoSAwillpublishitsfirstdeterminationforSAWaterinearly2013,coveringthethreeyearperiodfrom2013‐14to2015‐16.

ACILTasmanwascommissionedbySAWatertoforecastwaterdemandfortheregulatoryperiodfortheresidential,commercialandnon‐residentialsectors.

Subsequently,theCIEwasappointedbyESCoSAtoreviewACILTasman’sdemandforecasts.Ourapproachtothisreviewinvolvesthefollowingbroadsteps:

■ reviewthehistoricalanalysisofdemanddrivers,includingthevalidityofthehistoricaldataandreviewofACIL’smodelandapplicationofmodellingresults;

■ reviewofACILTasman’sindependentprojectionsanddemandforecastingmethodology,includingareviewofthemethodtoforecastcustomernumbersandaverageconsumptionperconnection;

■ providerecommendationswherenecessary;and

■ provideareviseddemandforecastbasedonamendmentsdiscussed.

InundertakingourreviewwefoundACIL’smodelandmethodologypaperprovidedahighleveloftransparencyinthedataandmodellingunderpinningtheforecast.Theapproachtoforecastingdemandwasgenerallysoundandourrecommendationsarelargelytoimproveonparticulartechnicalareasoftheforecastingapproachusedandupdatestothedataused.

ForthepurposesofpriceregulationofSAWater,wefoundtwocategoriesofissueswerecommendtobeaddressedintheforecasts.

1 Thedemandforecastsneedtobeabletosupporttherequirementsofpriceregulation.

2 Technicalamendmentstotheforecastingapproachandmethodologythatwilllikelyimproveonthecurrentforecasts.

DemandforecastshouldsupportpriceregulationThedemandforecastssubmittedbySAWatertoESCoSAaretoassistESCoSAinmakingrequiredregulatorydecisions.Akeypartofthisisthatforecastsprovideinformationoneachaspectofdemandthatischargedataseparateprice.Thismeansthatforecastsshouldincludecustomernumberswherethereisafixedchargepercustomerandtier1,2and3demandprojectionswherethereisadifferentpricechargedforeachtier oranothermethodologytoarriveatthis .


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Intheircurrentform,thedemandprojectionsdonotprovidesufficientinformationforESCoSAtomultiplypricesbydemandtoarriveatrevenue.Elementswhicharecurrentlymissinginclude:

■ aforecastofthenumberofnon‐residentialcustomerswhichisneededtoestimatefixedchargesfromthesecustomers;

■ forecastsofthetotalnumberofbilledcustomers;

– weunderstandthatthecustomernumberprojectionsrelatetocustomersusingwaterbutthatthereisalargersetofcustomersthatischargedforwater i.e.facingfixedbutnotusagecharges ;

■ forecastsofcustomernumbers orpropertyvaluestowhichchargesareapplied forsewerageservices,

– whichweunderstandtobedifferenttowaterservices;and

■ estimationofwaterdemandateachwaterpricingtierforresidentialusageoranalternativemethodtoallowfortheregulatortocalculaterevenuefromusageprojections.

Further,SAWaterseparatelydevelopedforecastsofsewerageconnections,valueofpropertyagainstwhichseweragechargesareleviedandquantityofseweragetreatmentdemanded.Thefirstandsecondwouldbothpotentiallyberelevantforrevenueprojections,howevertheseforecastshavenotbeenincludedinSAWater’ssubmission.

TechnicalamendmentstoforecastsBasedonourreviewofthedemandforecastswerecommendamendmentstothecurrentmodelassetoutbelow.Theserecommendationsincludeupdatestohistoricaldataandindependentprojections whererevisionhasoccurredsubsequenttoACIL’sanalysis aswellasamendmentstomodellinghistoricalrelationshipsandtheapplicationofempiricalresultstoforecastcustomernumbersandaverageconsumptionperconnection.

WehaveincorporatedtheserecommendationsintoACIL’sdemandforecastmodelandprovidedupdateddemandestimates.Thisallowsfordifferentpossible‘bounceback’scenariosasdiscussedbelow.

Theconsumptionoutcomefor2011–12suggeststhattherehasbeensomebouncebackinconsumptionbutthatconsumptionremainslowerthantheconsumptionexpectedgivencurrentconditionsandthereplacementofwaterrestrictionswithwaterwiserules.Itisdifficulttoknowhowmuchconsumptionwillcontinuetoincreasetothelevelsexpectedgivencurrentprices,waterwiserulesandotherfactorsthatimpactonconsumption.Consumptionmaytakealongtimetoreachthelevelthatwouldbeexpectedifrestrictionshaveledtopeopleinvestinginwatertanksorlowwatergardens.Itmayalsotaketimeforpeopletorevertbacktousingwaterforactivitiesthattheydidpre‐restrictions.


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RECOMMENDATIONS

1 HistoricalpopulationdatabeupdatedwithinACIL’sdemandforecastingmodel. 16

2 Annualbillingdatain2009‐10shouldbeadjustedtoreflectthatalargepartofnon‐residentialwaterconsumptionwasbilledquarterlypriorto2009‐10. 17

3 Thedemandforecastingmodelbeadjustedtoreflectthatbothlevel1and3waterrestrictionsappliedin2010‐11. 19

4 Analternativeapproachbeusedfordevelopingforecastsofcustomernumbers.Optionsaresetoutinchapter4. 20

5 Therestrictionthattheconstantiszeroforthecommercialusagemodelberemoved. 25

6 Themethodologyusedtoforecastresidentialandcommercialcustomernumbersbeamendedtoapplygrowthtothemostrecentdataoncustomernumbersratherthanforecastingthelevelofcustomernumbers. 28

7 UpdatedGSPforecastsshouldbeincorporatedintothedemandforecastingmodelusedtogenerateaverageconsumptionpercommercialcustomerandtotalannualnon‐residentialconsumption. 30

8 Theunadjustedpriceelasticity,asestimatedintheresidentialconsumptionmodel,shouldbeusedtoforecastaverageresidentialwaterconsumptionperconnection. 33

9 Anadjustmentshouldbemadetotheprojectionstoallowforwaterdemandtorecovergraduallytoexpectedconsumptionwhichalsoreflectsbouncebackofwaterconsumptionexperiencedin2011‐12. 37

Thepossibleoptionsforbouncebackcovertheextenttowhichconsumptionrevertsbacktolevelsexpectedforwaterwiserules givencurrentpricesandotherfactors andthetimingofthischange.Giventheuncertaintysurroundingtheoccurrenceofbouncebackofwaterconsumption,wehavemodelledtherevisedforecastoftotalconsumptionbilledovertheforecastperiodforthreebouncebackscenarios table1 .

■ Bouncebackscenario1—allowingfor50percentbouncebackovertwoyears.Thisleadstodemandbeing4.1percentlowerthanACIL’soriginalforecastovertheregulatoryperiod.

■ Bouncebackscenario2—allowingfor75percentbouncebackoverthreeyears.ThisgivessimilardemandprojectionsovertheregulatoryperiodasACIL’soriginalforecast.

■ Bouncebackscenario3—allowingfor100percentbouncebackoverfouryears.Underthisscenarioexpecteddemandis2.9percenthigherthanACIL’soriginalforecast.


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1 Original and amended forecast of consumption billed over regulatory period

Total consumption billed (ML) over regulatory period (2013-14 to 2015-16)

ACIL’s original forecast

CIE Amended forecast

Bounceback assumption 100% over 1 year

50% over 2 years

75% over 3 years

100% over 4 years

Residential 367 592 338 906 355 993 370 958

Commercial 28 716 28 495 29 676 30 703

Non residential 147 707 153 971 156 116 157 931

Total 544 014 521 373 541 785 559 592

Source: ACIL Tasman, 2012, SA Water’s Demand Forecasting. Prepared for SA Water. With amendments to model by the CIE.

Theimpactsofthechangesrecommendedandeachbouncebackscenarioonconsumptionforecastsareshowninchart2.

2 Cumulative changes to total consumption billed over regulated period (GL)

Data source: The CIE.

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1 Introduction

TheEssentialServicesCommissionofSouthAustralia ESCoSA hasrecentlybeenappointedastheeconomicregulatorofthewaterindustryinSouthAustralia.ESCoSAwillpublishitsfirstdeterminationforSAWaterinearly2013,coveringthethreeyearperiodfrom2013‐14to2015‐16.ESCoSAwillbemakingadeterminationofSAWater’sdrinkingwaterandseweragepricesfor2013‐14to2015‐16underPart3oftheESCAct,asauthorisedbysection35oftheWaterIndustryBill2011.Inordertodothis,ESCoSAisrequiredtoconsidertheexpecteddemandforSAWater’sservicesintothefuture.ThedemandforecastsincludeboththenumberofcustomersexpectedtobeconnectedtoSAWater’snetworkandtheexpectedlevelofusageovertheupcomingregulatoryperiod.

TheroleofdemandforecastsineconomicregulationDemandforecastsaretypicallyaprimaryinputintodecision‐makingbyregulatedutilities.Theycanhelptoinform:

■ pricingstructureswhichmaybeabletobechangedthroughouttheregulatoryperiod;

■ marketing—demandforecastingrequiresanunderstandingofthechoicescustomersandpotentialcustomersaremaking,whichisusefulinformationfortargetingofcustomers;

■ risksandriskmanagement—ifdemandforecastshaveastochasticcomponentratherthanbeingasingleforecast;and

■ capitalandoperatingexpenditureplanningdecisions.

Demandforecastsalsoformaprimaryinputintothedecisionsbyeconomicregulators.Demandforecasts,forexample,caninfluence:

■ thenotionalrevenueallowancethrough:

– operatingexpenditureprojections;and

– capitalexpenditureprojectionsandhencetheregulatoryassetbase,whichinturnimpactsondepreciationandthereturnoncapital.

■ prices underapricecapapproach aspricesaresetsothatdemandmultipliedbypricesisequaltothenotionalrevenueallowance.

Theroleofdemandforecastswilldependontheregulatoryapproachadopted.InAustralia,economicregulationtypicallytakestheformofpricecapsorrevenuecaps.

■ Underapricecapapproach,pricesaresetbytheeconomicregulatorbasedontheirassessmentofthedeterminedrevenueforthebusinessbyassessingeachofthecoststhatformthe‘buildingblock’.Inthisinstance,expecteddemandisusedto‘translate’


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therevenuerequirementintopricesthataredeterminedbytheregulator.Thebusinessbearsthedemandriskwhereactualdemanddiffersfromthatforecastedattheregulatorydetermination.Regulatorymechanismscanbedevelopedtominimiseabusiness’exposuretothedemandrisk.

■ Underarevenuecapapproachtoregulation,theregulatordeterminestherevenuerequirement.Inthisinstance,thebusinesshascontroloverprices.Forexample,businessescanraiseorlowerpricestoachievethedeterminedrevenuerequirement,takingintoaccountthedemandsidefactors.

RegulatoryapproachforSAWaterTheregulatoryapproachforSAWaterisstillbeingdevelopedandthereareelementsoftheapproachthatwillneedtobeexplicitlydeterminedbyESCoSA.Chart1.1providesanoverviewofthebroadregulatoryframeworkcurrentlybeingconsideredforSAWater.

1.1 Overview of regulatory approach for SA Water

Source: The CIE


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TheregulatoryapproachforSAWaterincludesthefollowing.

■ TheexpectedrevenueineachyearoftheregulatoryperiodthatSAWaterwillreceivewillbebasedon:

– pricessetbytheSouthAustralianGovernment.InitsMay2013RegulatoryStatement,theGovernmentwillsetthe‘pricepathforecast’for2013‐14to2015‐16.1

– ESCoSAwilldeterminetheexpectednumberofcustomersandwateruse.2

■ TheestimatedBuildingBlockRevenuerequirementineachyearoftheregulatoryperiodwillbebasedon:

– ESCoSA’sestimatesoftheefficientoperatingandappropriatecapitalallowance.Thelateritemwillrequireanassessmentoftheefficientlevelofcapitalexpenditure,theWeightedAverageCostofCapital,theappropriateassetdepreciationratesandusefullifeofassets.3

– theSouthAustralianGovernment’sestimateoftheopeningRegulatoryAssetBase.4TheopeningRABisexpectedtobe‘back‐solved’toensurethattheBuildingBlockRevenueoverthe3yearregulatoryperiodequatestotheexpectedrevenueoverthethreeyearperiod.

DemandforecastsfortheregulatoryperiodshouldbepresentedinawaythatallowsESCoSAtounderstandtherevenueimplicationsofparticularpricepathsorthepriceimplicationsofparticularrevenuepaths.ThisrequiresthesubmissionofseparatedemandforecastsforeachofthepriceelementsthatSAWaterlevies.

ThisreportThepurposeofthisprojectistoprovideESCoSAwithadviceonthereasonablenessofSAWater’sprojectionsandtoprovideanalternativedemandforecastwheregapshavebeenidentified.Thefocusoftheanalysisisonaveragedemandineachyearofthe

1 TheSouthAustralianGovernmentindicatedthatit“doesnotexpectwaterpricestogrow

significantlyfasterthaninflation.However,theactualchangeinwaterpriceswilldependontheinitialvalueoftheRegulatedAssetBase,whichtheTreasurerwillsetinMay2013”http://www.escosa.sa.gov.au/library/121015‐ClarifyWaterIssues‐MediaRelease.pdf

2 ThePricingOrderrequirestheCommissionto“includeamechanismwhichallowsfortheadjustmentofallowablerevenuetobederivedwheretheCommissiondeterminestheretobeamaterialvariationbetweenforecastandactualwaterconsumptionorsewerageconnections”.InitsregulatorysubmissionSAWaterhasproposeda‘bankingmechanism’whichwouldbecarriedforwardtoadjusttherevenuerequirementatthenextregulatorydetermination.

3 Theoperatingandcapitalexpenditurerequirementswillalsoneedtobeconsistentwiththeforecastcustomernumbersandwateruse,giventhatthesefactorsareoneofthedriversofexpenditure.

4 TheopeningRABwillthevalueasat1July2013.TheRABat1July2014andfutureyearswillbebasedonrollingforwardcapitalexpenditureincurredduringpreviousyearwithadjustmentssuchasdepreciation.


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regulatoryperiod,ratherthanpeakdemand.5Whiletheaveragedemandispresentedforeachyear,regulatorstypicallydonotseektocapturethepotentialyearonyeardemandvolatilityduetochangingrainfallandtemperaturepatterns.Inthissense,theannualpredictivecapacityofanydemandforecastingmodelislessimportantthanthemodelresultsbeingcorrectonaverageovertheregulatoryperiod.

Ourfinalreportprovidesourexpertviewonanumberofspecificaspectsincludingwhetherthedemandforecasts:

■ recogniseandreflectthekeydriversofdemand egdemographicchange,priceeffects,economicfactors,weatherrelatedfactors,policyfactors ;

■ arebasedonsoundassumptions,applybestpracticeprinciplesandutilisethebestavailableinformation;

■ areconsistentwithotheravailableforecastmethodologies;

■ arebasedonthemostrecentlyavailabledata;

■ arebasedonsoundandrobustaccountsofcurrentmarketconditionsandfutureprospects;

■ arebasedonvalidmethodologyandassumptions;

■ havebeensubjecttoappropriatetestingandvalidationexercises;

■ arefreefromstatisticalbias;and

■ containanyactualorpotentialweaknesses,uncertaintiesandrisks.

Thefindingshaverequired,wherepossible,testingtheassumptionsincludedinSAWater’smodellingbasedondatafromotherindependentsources.Ourefforthasbeenfocusedonthosefactorsthatmateriallyimpactontheresults.

Finally,thisreportfocusesontheforecastsrelatingtopotablewaterdemand.AtthisstagerecycledwaterisnotsubjecttoESCoSA’sregulatoryreviewandpriceswillcontinuetobesetbytheSouthAustralianGovernment.6

5 Theanalysisofpeakdemandismorerelevantforopex/capexreviews,giventhatpeak

demandisadriverofinvestmentdecisions.

6 Currentlythepriceofrecycledwaterforresidentialcustomersthatreceivedualreticulationservicesis75percentofthesecondtierresidentialpotablewaterusageprice.


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2 Structureofdemandforecasts

Inaregulatorysettingtheregulatorisrequiredtodetermineforecastsofallelementsthatarerequiredtobeset.Thatis,thereneedstobeaspecificquantitytoapplytothepriceinordertodeterminetherevenueimplications.

CurrentpricesforSAWaterservicesThepricescurrentlysetforSAWater’sservicesincludethefollowing7:

■ Forpotablewaterservices:

– Awatersupplychargeperpropertywithaccesstothepotablewaternetwork.8… Forresidentialcustomersafixedchargeapplies.In2012‐13thechargewas

$73.25perquarter.… Forcommercialcustomersthewatersupplychargeisbasedonthevalueof

theirproperty,subjecttoaminimumcharge $73.25perquarterin2012‐13 .9

… Forothernon‐residentialcustomersafixedchargeapplies $73.25perquarterin2012‐13 .

– Ausagechargebasedonthevolumeofwaterconsumedduringtheyear.Anincliningblockusagechargeappliesforbothresidentialandnon‐residentialcustomers.… Forresidentialcustomersdifferentpricesapplyto3blocksofusage 0to30

kL,30to130kLandabove130kLofquarterlyusage .Thelastblockonlyappliestosingledwellings.Formultipledwellings egblocksofflats onlythefirsttwotiersapply.

… Forcommercialandothernon‐residentialcustomersasingleusagechargeappliesirrespectiveofthelevelofusage.10

■ Forwastewaterservices,afixedsupplychargeappliestoeachpropertyconnectedtothewastewaternetwork.Thechargeisbasedonthecapitalvalueoftheproperty.Nousagechargecurrentlyapplies.

7 ThisinformationisdrawnfromSAWater’swebsite

http://sawater.com.au/SAWater/YourBusiness/YourAccountBillPaymentCharges/Pricing Information.htm

8 Thisincludesvacantlandthatisconnectedtothenetworkbutcurrentlydoesnotconsumewater.

9 ThecapitalvalueofthepropertyisassessedbytheValuer‐GeneralinSouthAustralia.

10In2011‐12andearlieryearsatwotieredusagechargewasapplied 0to30kL,above30kLofquarterlyusage forcommercialandothernon‐residentialcustomers.


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■ Fordualreticulationrecycledwatercustomers,ausagepriceof75percentofthesecondtierpriceforresidentialusageapplies.Noadditionalfixedsupplychargeapplies.

Thepricesfortheseservicesareapplieduniformlyonastate‐widebasis.ForthisdeterminationESCoSAisresponsibleforregulatingpotablewaterandwastewaterservicesdeliveredbySAWater.Asnotedearlier,recycledwaterservicesdeliveredbySAWaterwillcontinuetoberegulatedbytheSouthAustraliangovernment.

StructureofSAWater’sforecastsIndevelopingitsdemandforecastsforthisdeterminationSAWaterengagedtheservicesofACILTasmantodevelopthedemandforecastsforpotablewaterprices.11ACIL’sapproachinvolvedthreesteps.

■ Identifyingthekeydriversofcustomernumbersandtheaverageconsumptionpercustomer

■ EstimatingtheimpactofeachofthedriversusinghistoricalbillingdatafromtheRatingsAnalysisreportsextractedfromSAWater’squarterlybillingdatabase.12ACILalsousedmonthlybulkwatersupplyasacrosscheckoftheanalysis.Thebulkwatersupplydataalsoincludesleakageandunbilledcustomers.

■ Generatingprojectionsforcustomernumbersandaverageconsumptionpercustomer basedonprojectionsofthekeydriversandtheestimatedimpactsofeachofthedriversderivedfromthepreviousstep .

ACILTasman’sforecastsofcustomernumbersarepresentedintable2.1.Separateforecastsarepresentedforthetwocustomercategories,residentialandcommercial.Thisdivisionreflectstheviewthatthedriversofcustomernumbers andusage arelikelytodifferacrosstheresidentialandcommercialsectorsand,therefore,separateanalysisisrequired.ItshouldbenotedthatSAWater’sbillingsystemincludesafurtherdisaggregationofthecustomercategories.Forexample,theresidentialcategoryismadeupof10sub‐categorieswhichincludesingledwellings,homeunitsandflats.

11DemandprojectionsrelatingtowastewaterservicesweredevelopedseparatelybySAWater.

Thesedonotformpartofourreviewwhichisfocusedonpotablewaterdemand.

12SAWaterhasindicatedthatrecycledwaterusageandcustomernumbershavebeenremovedfromtheRatingsAnalysisreport.Billedrecycledwaterusagein2010‐11wasapproximately2.4GLwhichwasalmostdoubletheusagein2009‐10.Therewere7514billedrecycledwatercustomersin2010‐11.


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2.1 Forecast number of customers connected to potable water network

Customer type 2012-13 2013-14 2014-15 2015-16

‘000 customers ‘000 customers ‘000 customers ‘000 customers

Residential 655.7 665.2 (1.4)

674.7 (1.4)

684.2 (1.4)

Commercial 27.4 27.7 (1.1)

28.0 (1.1)

28.3 (1.1)

Note: Figures in parentheses present the growth compared to the prior year. We understand that customer numbers includes only customers that use water and that there are additional customers that are charged but that do not have any water usage. SA Water has not provided projections of customer numbers for the non-residential sector.

Source: SA Water Regulatory Business Proposal, table 5-7.ACIL Tasman (2012) tables 10 and 13.

SAWater’spotablewaterusageforecastsbycustomercategoryarepresentedintable2.2.Anadditionalcategoryofother‘non‐residential’customersisincluded.Theusageforecastsarepresentedasanaggregateforeachcustomercategory.

2.2 Forecast potable water use by customer category

Customer type 2012-13 2013-14 2014-15 2015-16

GL GL GL GL

Residential 119.0 120.8 (1.5)

122.5 (1.4)

124.3 (1.5)

Commercial 9.1 9.3 (2.2)

9.6 (3.2)

9.8 (2.1)

Other non-residential 48.2 48.7 (1.0)

49.3 (1.2)

49.7 (0.8)

Total 176.3 178.9 (1.5)

181.4 (1.4)

183.8 (1.3)

Note: Figures in parentheses present the growth compared to the prior year.

Source: SA Water Regulatory Business Proposal, table 5-7.

SAWater’smodelTocalculatethevalueofExpectedRevenueflowingfromregulatedprices,atariffmodelthatisabletolinkprices/tariffstochangingcustomernumbersandtotalwaterusageisusuallyemployed.CurrentlyadetailedtariffmodelisnotavailableforthecalculationofExpectedRevenueestimatesinSouthAustralia.

Atariffmodelwillberequiredinthefuturefollowing,ordevelopedinlinewith,thecorrectionofanumberofgapsinSAWater’sforecasts.Theinformationgapsare:

■ Residentialsector

– Theforecastshavebeenpreparedatanaggregatelevelandseparateforecastshavenotbeenprovidedforeachofthethreetiers.Itcouldbeexpectedthatwaterusageineachtierwouldgrowatadifferentrate.SAWaterhassubsequentlyprovidedanestimateofthemarginalpriceforallcustomers.Wehavenotverifiedwhetherthismarginalpriceisreasonable.


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– Customernumberforecastsarenotforchargedcustomersbutonlyforthosethatusewater.SAWateralsochargespropertieswithaccesstowaterbutwithnousage.Thesearenotincludedintheforecastsandtheexpectednumberofsuchcustomersisrequiredtocalculatethisrevenueitem.

■ Commercialsector

– Theaveragepropertyvalueshavenotbeenprovidedtoestimatetherevenuefromthewatersupplycharge;instead,onlycommercialcustomernumbershavebeenprojected.Thismaybeaproblemwherenewcommercialcustomershaveadifferentaveragepropertyvaluetoexistingcustomers.

■ Othernon‐residentialsector

– Forecastcustomernumbershavenotbeenprovided,whichmeansthatfixedsupplyrevenuefromthiscategorycannotbeprojected.

Noinformationhasbeenprovidedthatwouldallowwastewaterrevenuetobecalculated.


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3 Historicalanalysisofdemanddrivers

OverviewofapproachACILTasmanhasusedhistoricaldatatoidentifyrelationshipsbetweenkeydriversandbothcustomernumbersandaverageconsumptionperconnection.

Thehistoricaldataondriversofdemandintheresidential,commercialandnon‐residentialsectorsusedtoforecastcustomernumbersandtotalconsumptionofwaterbilledforeachsectoraredetailedintable3.1.

ACILTasmanexplainedinitsmethodologypaperthatotherpossibledemanddriverssuchasdemandmanagementactivities,wereexcludedbasedonalackofstatisticalsignificanceinthemodelling.

3.1 Demand drivers used in ACIL’s demand forecasts

Demand drivers Residential Commercial Non residential

Total Bulk water supply

Customer numbers

Average usage

Customer numbers

Average usage

Total usage (monthly)

Population (% annual growth)

Economic activity (Gross State Product)

Price of water ($/kL, second tier)

Temperature (CDD18)

Water restrictions (level)

Rainfall (mm)

Evaporation(mm)

Source: ACIL, 2012, SA Water’s demand forecasting: a report on the development of Demand Forecasting Methodology, Model and Supporting Documentation. Attachment E.1 Demand Forecasting Methodology.

WereviewedthedataandmethodologyusedinACILTasman’sdemandforecastandfoundthat:

■ thehistoricaldatausedisreasonablewiththeexceptionofthetreatmentofwaterrestrictionsin2010‐11andtheadjustmentforquarterlybillingin2009‐10;

■ thedatausedwasuptodateatthetimeofthedevelopmentoftheforecastsbutthereisnowimprovedinformationonhistoricalpopulationgrowthfromthe2011census;and

■ thebroadmethodofordinaryleastsquaresregressionissoundbutthattherearemethodologicalissuesregardingtheuseofthistechniqueinsomeinstances.

Theareaswhereweconsiderthatthereareissueswiththeforecastsaresetoutbelow.Notwithstandingthese,inlargepartthemethodologyusedisconsideredreasonable.


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Validityofhistoricaldata

Historicalpopulationdata

TheAustralianBureauofStatisticsreviseditspublishedEstimatedResidentPopulationdataforSouthAustraliasubsequenttoACIL’sanalysis.RevisionsweremadetotheSouthAustralianseriesoftheABSEstimatedResidentPopulationdataforeachyearbetween2006‐07and2010‐11 table3.2 .

3.2 Revised ABS historical population data

Year Original data used in ACIL’s modelling

Updated data Difference

2007 1 585 794 1 582 559 - 3 235

2008 1 603 985 1 597 343 - 6 642

2009 1 624 512 1 614 375 - 10 137

2010 1 644 152 1 629 434 - 14 718

2011 1 657 001 1 638 232 - 18 769

Source: ABS, Australia Demographic Statistics. Table 4: Estimated resident population, states and territories. Cat. No. 3101.0.

NotethattheuseoftheserevisedprojectionsmakesasubstantialdifferencetotheresidentialcustomernumbermodelusedbyACIL.13

RECOMMENDATION

1 Historical population data be updated within ACIL’s demand forecasting model.

Adjustmentforquarterlybillingdata

SAWaterchangeditsbillingmethodin2009‐10fromsixmonthlytoquarterlybilling.ACILadjustedtheannualconsumptiondatatoavoidthe2009‐10dataoverstatingthebilledwatersales.Thisadjustmentwasmadeacrossthethreecustomerclassesresidential,commercialandnon‐residential .However,asnotedinACIL’sreport,thelargenon‐residentialcustomerswerebilledquarterlypriorto2009‐10andthesecustomersconstitutethemajorityofwaterconsumptionforthissector.Therefore,ACIL’sadjustmentmethodologyleadstowaterconsumptionforresidentialandcommercialcustomerstobeoverstatedandnon‐residentialwaterconsumptiontobeunderstatedfor2009‐10.

Wehaveadjustedtheannualbillingsdatain2009‐10toreflectthatthemajorityofnon‐residentialwaterconsumptionwasalreadybilledquarterly table3.3 .Thismakesnoadjustmenttonon‐residentialdata.Ifthereisinformationontheshareofnon‐

13WeupdatedACIL’sresidentialcustomernumbersmodelwiththerevisedABShistorical

populationdata.Thecoefficientonthesingleexplanatoryvariableincreasedfrom0.59estimatedinACIL’soriginalmodel to0.65.Theestimateonthecoefficientimpliesthatforagivenincreaseinthepopulationof100people,SAWater’scustomerbasewillincreaseby65customers.


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residentialconsumptionthatmovedfromsixmonthlytoquarterlybillingthenthissharecouldinsteadbeused.

3.3 Transition to quarterly billing

Annual consumption billings ACIL’s amendment to quarterly billing data

CIE’s amendment to quarterly billing data

2009-10 (ML) (ML)

Residential 123 267 117 703

Commercial 9 908 9 461

Non residential 52 455 58 466

Total 185 630 185 630

Source: ACIL, 2012, SA Water’s Demand Forecasting. Prepared for SA Water. With amendments to model by the CIE.

RECOMMENDATION

2 Annual billing data in 2009-10 should be adjusted to reflect that a large part of non-residential water consumption was billed quarterly prior to 2009-10.

Aggregationofwaterrestrictions

Inthehistoricaldata,eachyearisallocatedtoeithernorestrictions,orlevel1,2or3restrictionsbasedonthelevelofrestrictionexperiencedforthemajorityofthefinancialyear.Thisapproachisproblematicwhenmultiplewaterrestrictionslevelsappliedinagivenfinancialyear.Forexample,in2010‐11level3waterrestrictionsappliedfromJulyuntilDecember,whichwererelaxedtolevel1waterrestrictionsfromDecemberuntilJune withtheexceptionoftheEyrePeninsula .

InitsmodellingACILassumedlevel1waterrestrictionsappliedfortheentiretyof2010‐11.ThisdoesnotcapturethefivemonthperiodbetweenJulyandDecemberwherecustomersfacedlevel3waterrestrictions.

WetestedthesensitivityofACIL’sassumptionandre‐estimatedthewaterconsumptionmodelsforresidential,commercialandnon‐residentialwiththeassumptionthatbothlevel1andlevel3waterrestrictionseachappliedforhalfof2010‐11 table3.4 14.Theparameterswereverysensitivetothisassumption.

3.4 Changes to model results with amendment to historical water restriction data

Demand drivers Residential Commercial Non-residential

Original Amended Original Amended Original Amended

Intercept 4.57 4.65 0.00 0.00 6.46 6.41

GSP na na 0.47 0.48 0.36 0.37

Cooling degree days (CDD 18) 0.15 0.14 0.12 0.11 0.10 0.09

Real price residential -0.38 -0.27 -0.37 -0.27 -0.32 -0.28

(Continued on next page)

14Resultsintable3.4arefromamendinghistoricalwaterrestrictiondatain2010‐11,holdingall

elseconstant.


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Demand drivers Residential Commercial Non-residential

Original Amended Original Amended Original Amended

Level 1 -0.11 -0.10 -0.11 -0.10 -0.12 -0.12

Level 2 -0.15 -0.14 -0.15 -0.15 -0.14 -0.14

Level 3 -0.27 -0.29 -0.25 -0.27 -0.19 -0.19

Note: na not applicable because demand driver not included in model

Source: ACIL, 2012, SA Water’s Demand Forecasting. Prepared for SA Water. With amendments to model by the CIE.

Theestimatesforbothsalesperresidentialconnectionandtotalresidentialconsumptionbilledincreasebyapproximately7.6percentineachforecastedyearwhenlevel3restrictionsareincludedinthehistoricaldataforhalfof2010‐11 table3.5 .

3.5 Sensitivity of historical water restriction data

Assumed level 1 restrictions in place in 2010-11a

Assumed level 1 and level 3 restrictions both applied in 2010-11b

Changes to estimated residential sales per connection

2013-14 181.57 195.32

2014-15 181.61 195.35

2015-16 181.65 195.39

Changes to estimated total residential consumption billed (ML)

2013-14 120 775 129 919

2014-15 122 530 131 801

2015-16 124 286 133 684

a Original estimates from ACIL’s modelling. b Estimates from ACIL’s model with an amendment to historical water restrictions in 2010-11.

Source: The CIE.

Themodelfittoactualhistoricaldataimprovesslightlywhenlevel1andlevel3waterrestrictionsareappliedequallyin2010‐11 chart3.6 .

3.6 Fitted residential model with adjustment to historical water restrictions

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Data source: ACIL Tasman, 2012, SA Water’s Demand Forecasting. Prepared for SA Water. With amendments to model by the CIE.

ThisissuehasbeenraisedwithSAWaterandSAWatersupportsACIL’sassumption.SAWaterindicatedthattheSouthAustralianGovernmenthadsignalleditsintentiontomovetolevel1restrictionsapproximately3monthspriortoDecember2011.SAWaterbelievedthatitisreasonabletoexpectthatconsumerschangedtheirwaterusebehaviourfromthedatewhichtheGovernmentsignalleditsintention.SAWateralsoindicatedthatwaterrestrictionspredominantlyimpactwateruseinthemonthsofJanuaryandFebruary.Therefore,itdidnotconsiderwhetherlevel1orlevel3waterrestrictionswereappliedintheJulytoDecember2010wouldmateriallyimpacttheresults.SAWaterhasnot,however,providedanyempiricalanalysistosupportitsviews.

Giventhisandtheimprovementintheempiricalfitofthemodelweconsiderthatanadjustmentshouldbemadetoallowforlevel3restrictionstoapplyforpartof2010‐11.

RECOMMENDATION

3 The demand forecasting model be adjusted to reflect that both level 1 and 3 water restrictions applied in 2010-11.

Reviewofmodelestimationandapplicationofmodelresults

Estimatingcoefficientsforcustomernumbersmodel

Modellingnon‐stationaryseries

ACILestimatedtherelationshipbetweenhistoricalresidentialcustomernumbersandpopulationgrowth,andsimilarlytherelationshipbetweenhistoricalcommercialcustomernumbersandgrossstateproduct.

Inboththeresidentialandcommercialcustomernumbersmodels,thedependentvariable customernumbers andthesingleindependentvariable populationintheresidentialmodelandgrossstateproductinthecommercialmodel areclearlynon‐stationaryseries chart3.7 .15

Inthiscasetheseriesarenon‐stationarybecausethereisapersistentlongruntrendofthecustomernumbers,populationandgrossstateproductovertime. Notrendhasbeenallowedforintheregressionanalysis. Modellingtwonon‐stationaryseriesashasbeendonecanestimateaspuriousrelationshipbecausethetwoseriesaretrendingovertime.Wherethisisthecasetherelationshipandgoodness‐of‐fitresultsrepresentedbytheR‐squaredwillbemisleading.16

15SAWaterhasindicatedthelowpowerofunitroottests.Customernumberseries,population

andGSPareclearlynon‐stationaryintheabsenceofanyallowancefortrends,asusedinACIL’sregressions,andthereisnoneedtorefertounitroottests.

16 GrangerCWJandNewboldP 1974 ,Spuriousregressionsineconometrics,JournalofEconometrics,2,pp111‐120.


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3.7 Two non-stationary series

Data source: ABS, Australia Demographic Statistics. Table 4: Estimated resident population, states and territories. Cat. No. 3101.0 and SA Water.

RECOMMENDATION

4 An alternative approach be used for developing forecasts of customer numbers. Options are set out in chapter 4.

Residentialcustomernumberandpopulationgrowth

TheannualgrowthofSAWater’scustomerbasehasexceededannualpopulationgrowthoverthepastdecade chart3.8 .Overthepastdecade,theaverageannualgrowthrateincustomernumbersisalmostdoublethepopulationgrowthrate.However,thisdifferenceingrowthrateshasbeenslowingoverthepastdecade chart3.9 .

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3.8 Annual growth in population and SA Water customers

Note: Population series based on most recent ABS data for Estimated residential population.

Data source: ABS, Australia Demographic Statistics. Table 4: Estimated resident population, states and territories. Cat. No. 3101.0 and SA Water.

3.9 Historical customer growth less population growth


Itisimportanttounderstandthereasoningbehindthesedifferentgrowthpatternsfordemandforecasting.Possibleexplanationsinclude:

■ SAWaterhasexpandedintonewgrowthareas;

■ theaverageoccupancyrateperhouseholdhasdeclinedinSouthAustralia;and/or

■ watercustomerspreviouslynotwithSAWaterhavemigratedtotheSAWaternetwork.

Wehavereviewedavailabledatatobetterunderstandwhatisdrivingthehigherannualgrowthrateincustomersrelativetoannualpopulationgrowth chart3.10 .Fromthis,itappearsthatthegrowthincustomernumbersreflectsgrowthinunoccupieddwellingssuchasbeachhouses,secondhousesetc .Customernumbergrowthhasbeenfar

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higherthangrowthinoccupieddwellings.ThiswasthelikelyreasonputforwardbyACIL. Theadditionofseconddwellingsmayhaveimplicationsforpercustomerusagethatarenotfurtherconsideredhere.

Therecentnarrowingofthegapbetweencustomergrowthandpopulationgrowthsuggeststhatgrowthinunoccupieddwellingsmayhaveslowed.Whetherornotthiswillcontinueisbeyondthescopeofthiswork.However,itwouldappearreasonabletoexpectalongperiod,suchasthelastdecade,wouldprovidetoooptimisticaviewontheadditionalgrowthtocustomernumbersthatwouldoccurfromseconddwellingsoverthecomingregulatoryperiod.

3.10 Difference in average annual growth rates for population, dwelling and customer growth

Data source: ABS, 3101.0 Australian Demographic Statistics. Table 4: Estimated resident population, states and territories. ABS Australian Social Trends, cat. No. 4102.0, SA Department of Planning, Transport and Infrastructure. SA Water.

CommercialcustomernumbersandGSP

ThecommercialcustomernumbersmodelisbasedonaregressionofhistoricalcommercialcustomernumbersandGSP.Asnotedearlier,thereareconcernsregardingtheapproachtomodellinghistoricalgrowthincommercialcustomernumbersfortwotrendingseries.Chart3.11showsthatthereisnorealrelationshipbetweenannualgrowthofcommercialcustomersandGSP.Thatis,periodsofhigherGSPgrowthhaveonlyaveryweak andstatisticallyinsignificant relationshiptoperiodsofhighgrowthincommercialcustomernumbers.Forexample,in2007‐08GSPgrewatcloseto6percentcomparedtoagrowthinthenumberofcommercialcustomersof1percent.Inthesubsequentyear,thegrowthrateincustomernumberswasalsocloseto1.5percent,despiteGSPalsogrowingatonly1.5percent.

SAWaterhassuggestedthat:

Theadvantageofourapproachwouldbeseenif,forexample,asignificantchangeineconomicactivitywasforecast.Consideracasewherearecessionwasforecast oraboom .Ourmodelwouldprojectaslowingincustomernumbergrowth oranacceleration .Atrendbased

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approachwouldmissthatslowingandthenhavegrowthslowaftertherecession oraccelerateaftertheboom.

InourviewthisispreciselythedisadvantageofSAWater’sapproach.Historysuggeststhatwhenthereisaboomorrecessionthisdoesnotimpactoncustomernumbergrowth.Hencetoallowforthisisforecastsisnotreasonable.

3.11 Annual growth rate of commercial customers and GSP

Data source: ACIL Tasman, 2012, SA Water’s Demand Forecasting. Prepared for SA Water.

Estimatingthecoefficientsforaverageconsumptionpercustomermodel

Climate

Theconsumptionmodelsforthethreesectorsestimatedtheimpactoftemperatureonwaterdemandusingcoolingdegreedays CCD18 sourcedfromtheBureauofMeteorology’sannualweatherdata.

ACILTasmanfoundastrongrelationshipbetweenwaterdemandandtemperature.However,therelationshipsbetweenwaterdemandandeitherrainfallorevaporationwerenotasstrong.

ACIL’sincorporationofCDD18asaclimatevariabletomodelaverageconsumptionpercustomerisvalid.

Priceelasticity

Theaverageconsumptionpercustomerisinfluencedbytheassumptionsregardinghowconsumersrespondtofutureprices.Thepriceelasticitiesusedtoforecastcommercialandnon‐residentialwaterusagewereestimatedasthecoefficientonthepricevariableintherespectiveannualregressionmodelsforeachsector,notedintheprevioussection.

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Thepriceelasticityusedtoforecastresidentialwaterusagewasestimatedasthemidpointbetweenthecoefficientonthepricevariableestimatedintheregressionmodelandtheresidentialpriceelasticityintheliterature.ACILprovidedanexplanationforthisapproachinthemethodologyreport.Weconsiderthistobeareasonableapproachbecauseitreflectsthesensitivityofelasticitiestodifferentmodelassumptions.

Itisimportanttonotehowever,thatchangestotheunderlyingdatathathavebeenrecommendedinthisreportwouldsubstantiallychangetheestimatesofpriceelasticity.Thisisexpendedoninchapter4ofthisreport.

Waterrestrictions

ACILestimatedtherelationshipbetweenlevel1,2and3historicalwaterrestrictionsonwaterdemandinallthreesectors.Inallcasestherewasastrongrelationshipbetweenwaterdemandandwaterrestrictions.Theestimatedcoefficientsforwaterrestrictionswereusedtoestimateaverageconsumptionperconnectionforresidential,commercialandnon‐residentialcustomers.

Thisapproachtoestimatetheinfluenceofwaterrestrictionsonwaterdemandisvalid.WhilewedonotsuggestchangestoACIL’sapproach,wenotethatthestrongandsignificantimpactofwaterrestrictionsonconsumptionamongstcommercialandothernon‐residentialcustomersisunusual.Commercialandothernon‐residentialcustomersarenotexpectedtohaveasignificantcomponentofoutdoorwaterusage theexceptionbeingmunicipalparklands andwouldnotbeexpectedtobeimpactedbywaterrestrictionstothesameextentasresidentialcustomers.OvertheforthcomingregulatoryperiodwewouldrecommendthatSAWaterconductfurtheranalysistobetterunderstandtheusagepatternsofthesecustomerclasses.Thiswouldenablefurthertestingofthepotentialimpactofwaterrestrictionsonthesecustomers.

Interceptincommercialmodel

InestimatingtheaveragecommercialconsumptionmodelACILsettheintercepttozero.

ACILdonotprovideevidencetoexplainthisassumptioninthereport.Informationhassubsequentlybeenprovidedtoindicatethattheinterceptwasdroppedbecauseitwasnotstatisticallysignificant.

Thisisarestrictiveassumptionwhichisgenerallyusedonlyinrareoccasionsrequiringthefittedlinetopassthroughtheorigin.Someregressionstatistics suchasR2 canbeinterpretedonlywherethereisaconstant ornon‐zerointercept inthemodel.Giventhesubstantialimpactonparametervaluesfromrestrictingtheconstanttobezero,werecommendthatthisassumptionberelaxed.Incorporatingthisamendmentaltersthecoefficientsonthekeydriversofcommercialdemand GSP,priceofwater,temperatureandwaterrestrictions table3.12 .17

17Resultsintable3.12arefromamendmenttointerceptinthecommercialmodel,holdingall

elseconstant.


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Notethatwhenaninterceptisallowedforinthemodelandtheadjustmentismadeto2009‐10data,wefindthatanumberofthedrivervariablesbecomestatisticallyinsignificant.Thisisnotthecasewhentherestrictionsareallowedtobepartlylevel1andpartlylevel3for2010‐11.

3.12 Commercial average consumption model

Independent variables Commercial average consumption model

Intercept set to origin Intercept not set to origin

Intercept 0 2.48

GSP 0.47 0.26

Cooling degree days (CDD 18) 0.12 0.10

Real price commercial -0.37 -0.33

Level 1 -0.11 -0.08

Level 2 -0.15 -0.11

Level 3 -0.25 -0.20

Adjusted R Square 0.91 0.87


RECOMMENDATION

5 The restriction that the constant is zero for the commercial usage model be removed.

Excludedvariables

ThereareadditionaldriversthatmayinfluencewaterdemandthathavenotbeenincludedinACIL’sdemandforecast.Theseinclude:

■ income;

■ householdsizeandcomposition;

■ typeofdwelling detached,units,townhouses ;and

■ quantityanduseofrecycledwater.

Exclusionofsomedriverscanresultinomittedvariablebias.Thiscanleadtoestimatesofotherparameters,suchaspriceelasticities,beinghigherorlowerthantheirtruelevelbecauseofrelationshipsbetweenomittedvariablesandincludedvariables.

Wehavetestedwhetherincomewasasignificantdriverofusage,throughusinggrossstateproductasaproxyforincome.Thiswasnotsignificantinthemodelandmayhavebeenexcludedforthisreason.

Inclusionofotherdriversatthehouseholdlevelwouldgenerallyrequiremoredetailedanalysisthanwasundertaken.Thedriversmentionedabovemayleadtolongertermdeviationsinwateruseperdwelling.Assuch,themodelmayfinditdifficulttoaccommodatestructuralchangesrelatingtodwellingcomposition,waterrecyclingandhouseholdcomposition.ItisnotpossibletoknowtheimpactthatthiscouldhaveontheforecastsintheabsenceofanunderstandingoftheimportanceofthesefactorsinSouthAustralianwaterdemand.


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Itislikelythattheimpactsinthe3yearsoftheregulatoryperiodwillberelativelysmall,althoughtheycouldleadtopersistenterrorsinforecasting.Giventhis,weconsiderACIL’sapproachtobereasonable.Thatsaid,theremaybescopetoincludeadditionalvariablesinfuturedeterminationswhereitisfoundthattheseareimportant.

Themodellingconducteddoesnotincludeanyconsiderationoflagsinconsumptionandlagsforrestrictions.Weconsiderthistobereasonableinlightoftheavailabledata.However,inclusionoflagsislikelytobecomemoreimportantinfuturemodellinginordertoconsidertheextenttowhichbouncebackofdemandhasoccurred.Amodelthatdoesnotincludelagswillleadtobiasedestimatesoftheimpactofrestrictionsandwaterwiserulesifthereisasubstantialpartofthedatainthepost‐restrictionsperiod.


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4 Projectingforwarddemand

IntheprevioussectionwediscussedACIL’sapproachtousinghistoricaldatatoestimatethecoefficientsforeachoftheunderlyingdriversofcustomernumbersandaverageconsumptionpercustomer.Driversincludedprice,GSP,weatherandwaterrestrictions.Thecoefficientsestimatetherelationshipsbetweenkeydriversandbothcustomernumbersandaverageconsumptionperconnection.

ACILapplytheestimatedcoefficientstoprojectionsofthekeydrivers e.g.GSPandclimatedata toforecastcustomernumbersandaverageconsumptionpercustomer.InthissectionwereviewACIL’sapproachtoapplyingthisinformationtodevelopfuturedemandforecasts.

Forecastcustomernumbers

Forecastappliedaschangeratherthanappliedtothelevel

Therearetwoalternativemethodstoapplyestimatedcoefficientstoprojecteddriverstoforecastcustomernumbersandaverageconsumptionperconnection.Thefirstistoapplytheestimatedcoefficientstothe‘level’oftherelevantvariables,forexample,therealpriceofwaterinagivenyear,togiveanexpectedlevelofwaterconsumption.Thesecondistoapplytheestimatedcoefficientstothe‘change’oftherelevantvariable,forexample,thechangeintherealpriceofwaterrelativetothepreviousyear,toderiveanexpectedchangeinwaterconsumption.Alevelapproachwillperformbetterwheredatatendstorevertquicklytosomemeanlevel intechnicaltermsitisstationary andwherethereislittlelikelihoodofstructuralchange.Achangeapproachwillperformbetterwheredatadoesnotreverttosomemeanlevel isnon‐stationary andwherestructuralchangeismorelikely.

ACILusesalevelapproachforallitsanalysis.Thisisconsideredtobeareasonableapproachforforecastsoftheusagepercustomer,althoughthereisariskthatthereisstructuralchangenotbeingfactoredin suchasrelatedtopostwater‐restrictionconsumptionbehaviour .Forcustomernumbersthisapproachisnotconsideredtobereasonable,asdiscussedbelow.

Alevelsapproachtocustomernumberscanbebestunderstoodthroughasimpleexample,asfollows.

■ Thereare100customersin2012

■ Amodelindicatesthatonaveragea1percenthigherlevelofpopulationisassociatedwith1percenthighernumberofcustomers.


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■ Thelevelmodelhasaforecastof105customersfor2012,whichishigherthanactual.

■ Populationgrowthin2013isexpectedtobe1percent.

Undertheaboveexample,alevelmodelwouldprojectthatcustomernumbersin2013wouldbe105*1.01 106customers.Thatis,itwillbebasedofftheprojectionfor2012.Incomparison,applyingasachangewouldsuggestcustomernumbersof100*1.01 101customersfor2013andisbasedoffactualcustomernumbersfor2012.

ThisisexactlywhathappensinACIL’sprojections chart4.1andchart4.2 .

Intechnicalterms,thelevelmodelincludestheerrorinthemostrecentforecastintoprojections.Thisisokayforstationaryseriesbecausetheerrorinoneyeardoesnotimpact orimpactsless onfutureyears.Itdoesnotmakesensefornon‐stationaryseriesbecauseerrorsarepermanent.

Thetypicalapproachtoforecastingfornon‐stationaryseriesistoapplygrowthprojectionstothemostrecentyear. Thismethodisalsoillustratedinchart4.1and4.2asacontinuationofthehistoricalseries. ThisistheapproachthatwerecommendapplyingforSAWater’scustomernumbers.

RECOMMENDATION

6 The methodology used to forecast residential and commercial customer numbers be amended to apply growth to the most recent data on customer numbers rather than forecasting the level of customer numbers.

4.1 Forecast of residential customers


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4.2 Forecast of commercial customers


Alternativeapproaches

Asnotedintheprevioussectionwehaveconcernsregardingtheapproachtoestimatingthekeydriversofcustomernumbersbyregressingtwonon‐stationaryseries.Giventhis,webelievethatanalternativemethodforgeneratingestimatesoffuturecustomernumbers,residentialandcommercial,shouldbeused.

Itisbeyondthescopeofthisprojecttoconsideralloptionsforalternativeforecastingapproaches.Weconsiderthatapproachesthatwouldbereasonablecouldinclude:

■ applyingatrendinhistoricalgrowthincustomernumbers,sayoverthepast5years;or

■ applyingABS’sforecastedpopulationgrowthwithanadjustmentfactortoaccountforthedifferencebetweenannualcustomergrowthandpopulationgrowthoverthepastfiveyears.

Ofthesethesecondisconsideredpreferablebecauseitincorporatesinformationonpopulationprojections.Weprovideestimatesofcustomernumbersanddemandusingthisapproach.

Inthecaseofcommercialcustomers,asnotedearliertheredoesnotappeartobearelationshipbetweenGSPgrowthandgrowthincommercialcustomernumbers.Oneapproachthatwewouldconsidertobereasonablewouldbetouseanaveragegrowthforanhistoricalperiod,suchasthelast5years.Weshowprojectionsusingthisapproachinchapter5.

GrossStateProductforecasts

SubsequenttoACIL’sanalysis,theGSPforecastsproducedbytheSouthAustraliangovernmentwererevisedfortheperiodbetween2011‐12and2015‐16 table4.3 .

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4.3 Revised Gross State Production forecasts

Financial year Original GSP forecast used in ACIL’s modelling a

Revised GSP forecast b

% %

2011-12 2.25 1.50

2012-13 3.00 2.75

2013-14 3.25 3.00

2014-15 3.00 3.00

2015-16 2.45 3.25

a Sourced from South Australian Government 2011-12 Mid-Year Budget Review. b Sourced from South Australian Government, 2012, 2012-13 Budget Statement, http://www.statebudget.sa.gov.au/budget_papers.html. c Forecast for 2015-16 was not included in original GSP forecast, ACIL estimated 2.45 based on the historical average.

ACIL’sanalysisusestheGSPforecastsforforecastingcommercialcustomernumbersandforecastingaveragecommercialconsumptionpercustomerandannualnon‐residentialconsumption.Asnotedearlier,theCIEdoesnotsupportACIL’smodellingtoforecastcommercialcustomernumbers.

TheupdatedGSPforecastsshouldbeincorporatedintoACIL’smodeltoforecastaverageconsumptionpercommercialcustomerandtotalannualnon‐residentialconsumption.IncorporatingtheupdatedGSPforecastsdecreasesthetotalcommercialandnon‐residentialconsumptionbilledovertheforecastingperiodbyapproximately0.4percentrelativetoACIL’sforecast.

RECOMMENDATION

7 Updated GSP forecasts should be incorporated into the demand forecasting model used to generate average consumption per commercial customer and total annual non-residential consumption.

Climate

ACILforecastedcoolingdegreedays CDD18 basedontheannualmedianCDD18overtheperiod1977‐78to2010‐11.TheforecastedannualCDD18was682daysperyear.TheforecastedCDD18wasappliedintheresidentialandcommercialaverageconsumptionperconnectionmodelsandthenon‐residentialtotalannualconsumptionmodel.

Intheabsenceofotherclimateforecasts,ACIL’sapproachtoforecastCCD18isvalid.Weconsiderthatthemeanshouldbeusedinsteadofthemedian,astheforecastsareseekingtomeasureexpecteddemand,notmediandemand.Thisisbecausearegulatorisseekingtoensurethattheexpectedrevenuesofabusinessmatchitsexpectedcosts,ratherthanthatitsmedianrevenuesmatchitsexpectedcost.However,thedifferencebetweenthemedianandmeanforCDD18wasnotmaterialenoughtowarrantamendment.


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Itisinterestingandsurprisingthatrainfalldidnotappearassignificantintheforecastingmodel.Itmaybepossiblethatvariationsinclimaticvariablesortheirinteractionmightshowaninfluencefromrainfall.

Timeinwaterrestrictions

Thefutureprojectionsofaverageconsumptionpercustomerassumethatlevel1waterrestrictionsremainedinplaceduringtheforecastperiod.Itisdifficulttoforecastthelikelihoodofwaterrestrictions,however,itisimportanttoassessthesensitivityoftheforecasttokeyassumptions.Forinstance,ifwaterrestrictionsdonotapplyintheforecastperiodtheforecastedtotalwaterconsumptionwouldincreasebyapproximately12percent.Conversely,iflevel3waterrestrictionsapplytheforecastedtotalwaterconsumptionwoulddecreasebyapproximately12.5percent.

Notethatdemanddoesnotbouncebacktotheexpectedlevelofconsumptionatlevel1restrictionspriortolevel3restrictionsbeinginplacethentheimpactsofthetimeindifferentrestrictionperiodswouldbesmaller.

TheCIEbelievesthatACIL’sassumptionregardingthetimeinrestrictionsisreasonable,giventherelativelyhighstoragelevels,theoperationoftheAdelaideDesalinationPlantandtherecentinvestmentsinrecycledwaterfacilities.

Priceelasticities

Adjustingmodelledresidentialpriceelasticity

ACILhasassumedthatthepriceelasticityusedtoforecastresidentialwaterusagewasthemidpointbetween‐0.18 estimatedfromtheliterature and‐0.38 estimatedcoefficientfromACIL’sresidentialannualregressionmodel .

TherecanbegoodreasonstochangemodelparametersasACILhasdone,asstatisticalanalysiswillalwaysrequireadegreeofjudgement.Adjustmentshavetobedonecarefullyinordertoensurethatthemodelremainsrobust.Weconsiderthatadjustmentstoparameterscouldbemade,iftherearegoodreasonsandthemodelisseekingtoapplygrowthratestoexistingusagefigures.InthecaseofACIL’sforecastingmethod,whichmodelsthelevelofwaterusage,weconsiderthatmodelparametersshouldnotbeadjusted.

Adjustmentstothemodel’sparametersinalevelmodelchangethefitofthemodelhistoricallyandmaylockinadifferentlevelofconsumption.Forexample,inchart4.4weshowtheperformanceoftheACILmodelforresidentialconsumptionpercustomeranditsperformanceoncetheadjustmenthasbeenmadetothepriceelasticity.Byadjustingthepriceelasticityfigure,themodelsignificantlyoverestimatesrecentconsumption.Weunderstandthatthisisnottheintentionoftheadjustmentmade,withACILnotingthattherearefactorsnotincludedinthemodelthathavelikelymeantthatthehistoricalrelationshipbetweenpriceandconsumptionisoverstated.ACIL’sintentionistoapplytheadjustmentonlytofuturepricechangesratherthantoaltertheimpactofpastpricechanges.Thiswouldhavetobedoneusingtwoelasticity


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parametersonetotheexistingpriceandonetosubsequentpricechangesorthroughusingamodelofthechangeinconsumption.

4.4 Review of adjustment to residential price elasticity

Data source: ACIL Tasman, 2012, SA Water’s Demand Forecasting. Prepared for SA Water. With amendments to model and actual by the CIE.

ACIL’soriginalforecastfortotalwaterconsumptionin2011‐12,usinganadjustedpriceelasticityof‐0.28andadjustedtoreflectactualweatheroutcomesforthisyear,was188.0GL.Thisis6.2percentaboveactualwaterconsumedin2011‐12.TheforecastusinganunadjustedresidentialpriceelasticityinACIL’soriginalmodel,providesacloserfit,2.4percentaboveactualwaterconsumedin2011‐12 table4.5 .Whentheadjustmenttotheelasticityisremovedalargerreductioninwaterusageisimpliedfor2012‐13,reflectingthepriceincreaseinthatyear.

4.5 Total water consumption forecast with adjusted and unadjusted price elasticities

Year ACIL’s original model

Adjusted price elasticitya Unadjusted price elasticity

2011-12 actual 177.0 177.0

2011-12 forecast 188.0 181.2

deviation from actual in 2011-12 6.2% 2.4%

2012-13 176.3 167.7

2013-14 178.9 170.2

2014-15 181.4 172.6

2015-16 183.8 174.9

a ACIL’s original forecast for total water consumption

Note: ACIL’s original forecast for 2011 12 has been adjusted for weather.


RECOMMENDATION

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8 The unadjusted price elasticity, as estimated in the residential consumption model, should be used to forecast average residential water consumption per connection.

Revisiontopriceelasticities

Thechangestohistoricaldataandmethodimpactonthemeasuredpriceelasticities.Thehistoricalchangestodataandmethodrecommendedare:

■ updateofABShistoricalpopulationdataandamendmentofACIL’sadjustmentforquarterlybillingin2009‐10;

■ inclusionofbothlevel1and3waterrestrictionsinthehistoricaldatafor2010‐11;and

■ allowingthecommercialaverageconsumptionmodeltoestimatetheinterceptratherthanapplyingACIL’sassumptionofsettingtheintercepttozero.

Thechangetothepriceelasticitiesresultingfromtheseamendmentsaredetailedintable4.6.Thechangesinestimatedelasticitiesaresubstantialandshouldbereflectedindemandprojections.WenotethattheremovaloftheadjustmenttotheresidentialpriceelasticityandtheotheradjustmentsabovegenerateapriceelasticityclosetothatoriginallyusedbyACIL.

4.6 Original and revised price coefficients from annual regression models

Sector Estimated coefficient on price

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intercept

Residential (unadjusted price coefficient) -0.38 -0.40 -0.29 -0.29

Residential (adjusted price coefficient) -0.28 -0.29 -0.23 -0.23

Commercial -0.37 -0.39 -0.29 -0.28 a

Non-residential -0.32 -0.26 -0.23 -0.23

a Amendment to commercial model to include an intercept only changes price coefficient in commercial sector.


BouncebackACILTasmanhasassumed100percentbouncebackfromlevel3waterrestrictionsovertheforecastperiodtolevel1waterrestrictions,whichareconsideredtobesimilartowaterwiserestrictionsnowinplace.However,indiscussionswithSAWateritindicatedthat100percentbouncebackdoesnotappeartohaveoccurredsincelevel3waterrestrictionswerereplacedwithwaterwisemeasuresandsuggestedthatmanyofthechangesinwaterconsumptionarisingfromlevel3restrictionshavebeenlockedintosomeextent.

Itisdifficulttodirectlyobservetheextentofany‘bounceback’inconsumptionasclimateconditionsandotherexternalfactorsalsovaryandthetimeperiodsincelevel3restrictionswereremovedhasbeenshort.Deloittereviewedexperiencesofbounceback


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inwaterusageacrossAustralianjurisdictions,18howeverthelimitedtimeseriesdataavailabledidnotprovidecleartrendsofbouncebackandtheanalysisconducteddidnotallowforconsumptiontograduallychangeaftertheremovalofrestrictions.Theextentofbouncebackinotherjurisdictionshasbeen:

■ SydneyWater—recentsubmissiontotheProductivityCommissionSydneyWaternotedtherewasnoobservedbouncebackoverthefirstthreemonthsof2010‐11,followingreplacementoflevel3restrictionswithwaterwiserulesinJune2009.19

■ Victorianwaterutilities—Victorianurbanwaterutilitieshaveassumedsomelevelofbouncebackbetween70percentand90percentofpre‐restrictionlevels.20Atthisstage,wearenotawareofanyanalysisthathasbeenundertakentoconfirmtherobustnessoftheVictorianwaterutilities’assumptions.

■ ActewAGL—soughttotesttheextentofanybounceback,althoughithasbeenlimitedbyinsufficientobservedhistoricaldatafollowingtheliftingofwaterrestrictions.

BasedondiscussionswithSAWaterregardingbouncebackandconsumptionoutcomesin2011‐12,weconsiderthattheassumptionofimmediatebouncebacktousageatlevel1restrictionsisunreasonableandthatthisadjustmentwilltakesometime.

ACIL’stotalconsumptionforecastassumedfullbouncebacktowaterwiseconsumptionlevelsinthefirstyear.Usingthisbouncebackassumption,ACIL’sforecastwas188GL,or6.2percentaboveactualconsumptiondatain201112,afteradjustingforweather.Giventheconsumptionoutcomefor2011‐12,itappearslikelythatconsumptionbouncebackhasbeenabout25percenttowardsthelevelsexpectedforwaterwiserulestable4.7 .

4.7 Deviation of forecasts from actual consumption data in 2011-12

2011-12 Total water consumption Deviation to actual

GL Per cent

Actual 177.0

ACIL forecast 188.0 6.2

25 per cent bounceback 175.9 -0.6

Note: Total water consumption forecasts in 2011 12 were adjusted for weather.


18 Deloitte,2012,Measuring’bounceback’inwaterusage.PresentationtotheESCDemandForecastingWorkshop.http://www.esc.vic.gov.au/getattachment/8d0e817a‐ee72‐4c79‐829d‐d20b4491c95f/Deloitte‐Presentation‐Demand‐forecasts‐workshop‐Wa.pdf

19SydneyWater2010,Australia’sUrbanWatersector,AsubmissiontotheProductivityCommission.November,p.34.

20PricewaterhouseCoopers PwC 2009,WaterPriceReview2009:Demand,ReportforEssentialServicesCommission,p.13http://www.esc.vic.gov.au/NR/rdonlyres/E19898BE‐3012‐4619‐999E‐880367FD0615/0/RPTPWCWaterBusinessesdemandreport120090417.pdfAccessed2011.


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Wehavemodelledthreebouncebackscenarioswhichvarytheextentoftotalbouncebackandthelengthofthebouncebackperiod.The2011‐12totalwaterconsumptiondatarecentlyreleasedwasusedasabenchmarktoderivethethreebouncebackscenarios,settinganannualbouncebackof25percentineachyearofthebouncebackperiod.Thethreebouncebackscenariosare:

■ 50percentover2years chart4.8 :duringtheregulatoryperiod,CIE’sforecastoftotalwaterconsumptionisapproximately4percentlowerineachyearthanACIL’soriginalforecast.

■ 75percentover3years chart4.9 :duringtheregulatoryperiod,CIE’sforecastoftotalwaterconsumptionisapproximately0.4percentlowerineachyearthanACIL’soriginalforecast.

■ 100percentover4years chart4.10 :duringtheregulatoryperiod,CIE’sforecastoftotalwaterconsumptionisbetween1.5percenthigherinthefirstyear,increasingto3.6percenthigherattheendoftheregulatoryperiodcomparedtoACIL’soriginalforecast.

4.8 Bounceback of 50 per cent over 2 years


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Intheabsenceofadditionaldataonbounceback,thethreebouncebackscenariosprovidearangeforforecastsoftotalwaterconsumption.

Choosingbetweenbouncebackscenariosisdifficult,giventhattherehasnotbeensubstantialexperiencewithbouncebackinSouthAustraliatodrawon.Itmaybepossibletobetterunderstandbouncebackthroughmodellingofmonthlydatatoseeiftherehasbeenanyconvergencebetweentheactualfiguresandamodelwith100percentbouncebackoverthetwoyearssincelevel3restrictionswereremoved.Itmayalsobepossibletouseotherjurisdictionsasanexample,wherethesejurisdictionshavehadalongertimeoutofrestrictions.HistoricaldataforHunterWatermightbeoneoptionforthis.However,itisnotclearthatalljurisdictionswillbethesame.Theamountofbouncebackmayalsodependonotherfactorssuchasthedurationofrestrictions.

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Consumptiondataavailableforthemostrecenttwoyearssincewaterrestrictionswerereplacedsuggeststhattherehasbeensomelevelofbounceback,likelyaround25percentperyear.Itisnotclearwhetherconsumptionwillbouncebackinfulltothelevelexpectedbymodellinggiventoday’spricesandlevel1restrictions.

RECOMMENDATION

9 An adjustment should be made to the projections to allow for water demand to recover gradually to expected consumption which also reflects bounceback of water consumption experienced in 2011-12.


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5 Revisedforecasts

InpreviouschapterswerecommendedupdatesandamendmentstothedataandmodellingspecificationusedinACIL’sdemandforecasts,includingdiscussionofthechangetotheforecastofamendingeachinturn,holdingallelseconstant.

InthischapterwestepthroughthesuggestedupdatesandamendmentstodataandmodellingandreportthecumulativechangetothetotalforecastedwaterusagerelativetoACIL’soriginalforecastoftotalwaterconsumptionbilledfortheforecastperiodbetween2013‐14and2015‐16.

Ourrevisedforecastsforcustomernumbersandconsumptionbilledovertheregulatoryperiodareprovidedintable5.1and5.2.Giventheuncertaintysurroundingtheoccurrenceofbouncebackofwaterconsumption,wehavemodelledtherevisedforecastoftotalconsumptionbilledovertheforecastperiodforthreebouncebackscenarios.Forthethreescenarios,theCIE’srevisedforecastis:

■ Bouncebackscenario1:demandapproximately4.1percentlowerthanACIL’soriginalforecastwith50percentbouncebackover2years.

■ Bouncebackscenario2:demandapproximately0.4percentlowerthanACIL’soriginalforecastwith75percentbouncebackover3years;and

■ Bouncebackscenario3:demandapproximately2.9percenthigherthanACIL’soriginalforecastwith100percentbouncebackover4years table5.2 .

Annualforecastsoftotal,residential,non‐residentialandcommercialwaterconsumptionbilledforthethreebouncebackscenariosbetween2011‐12and2015‐16areprovidedisAppendixA.1.

5.1 Original and amended forecast of customer numbers

Year Residential customers Commercial customers

ACIL CIE ACIL CIE

2011-12 646 143 641 149 27 052 27 364

2012-13 655 659 650 660 27 365 27 675

2013-14 665 173 660 252 27 715 27 990

2014-15 674 689 669 926 28 048 28 309

2015-16 684 207 679 683 28 329 28 631



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5.2 Original and amended forecast of consumption billed over regulatory period

Total consumption billed (ML) over regulatory period

ACIL’s original forecast

CIE Amended forecast

Bounceback assumption 100% over 1 year 50% over 2 years 75% over 3 years 100% over 4 years

Residential 367 592 338 906 355 993 370 958

Commercial 28 716 28 495 29 676 30 703

Non residential 147 707 153 971 156 116 157 931

Total 544 014 521 373 541 785 559 592


Theextentoftheamendmentstoforecastedtotal,residential,commercialandnon‐residentialwaterconsumptionbilledisillustratedinchart5.3,5.4,5.5and5.6.

■ Populationdataandquarterlybilling—updatetheABShistoricalpopulationdataandamendtheestimationofwaterconsumptionbilledin2009‐10.

■ Disaggregationofhistoricalwaterrestrictions—revisethehistoricalwaterrestrictiondatatoincludelevel1andlevel3waterrestrictionsin2010‐11.

■ Forecastcustomernumbers—theprojectionofresidentialcustomernumbersisbasedontheABSpopulationforecastadaptedtoincludethefiveyearaveragehistoricaldifferenceingrowthratesofcustomersandpopulation chart3.8 .Commercialcustomernumbersareprojectedbasedonanaveragehistoricalgrowthincustomernumbersoverthemostrecentfiveyears.

■ Incorporateinterceptincommercialmodel—theassumptionthattheinterceptissettozerointhecommercialmodelwasrelaxed.

■ Unadjustedresidentialpriceelasticity—themodelledresidentialpriceelasticityisusedtoforecastaverageconsumptionperconnectioninsteadofanadjustedpriceelasticityasusedbyACIL.

■ UpdateGSPindependentforecast—updatedGSPindependentforecastswereincorporatedintomodel.

■ Bounceback—wehavemodelledthreebouncebackscenarios,50percentover2years,75percentover3yearsand100percentover4years.

Adjustingthehistoricalwaterrestrictiondatatoincorporatebothlevel1and3waterrestrictionsin2010‐11causedthelargestchangetotheforecast.Thisisbecausethisamendmentsubstantiallychangedtheestimatedcoefficientonthepricevariableforresidential,commercialandnon‐residentialdemand.Forexample,thecoefficientonthepricevariableintheresidentialmodelchangedfrom‐0.38to‐0.27 table3.4 ,whichresultedinthepriceelasticityusedintheresidentialmodelchangingfrom‐0.28to‐0.23.21Theamendmentmakesdemandrelativelymoreinelasticandthereforelessresponsivetopriceincreasesovertheforecastperiod.

21BecauseoftheACIL’sapproachtoestimatetheresidentialpriceelasticityasthemidpoint

between‐0.18andtheestimatedcoefficientonthepricevariable,theadjustedpriceelasticitywhenthehistoricalwaterrestrictiondatawasamendedwas‐0.23.


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Residentialwaterconsumptiondecreasedwhentheunadjustedresidentialpriceelasticitywasappliedintothemodel.TheunadjustedpriceelasticityismoreelasticthantheadjustedpriceelasticityusedbyACILwhichmakesdemandmoreresponsivetotheforecastedincreaseinpricein2011‐12and2012‐13.

ForecastedwaterconsumptiondecreasesacrossallthreesectorswhenwelimitbouncebackrelativetoACIL’sassumptionoffullbounceback.Thescaleofdeclineinconsumptionisrelativetothebouncebackassumption.

5.3 Cumulative changes to total consumption billed over regulated period (GL)

Note: Cumulative change presented for the three bounceback scenarios


5.4 Cumulative changes to residential consumption billed over regulated period (GL)



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5.5 Cumulative changes to commercial consumption billed over regulated period (GL)



5.6 Cumulative changes to non-residential consumption billed over regulated period (GL)



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A Forecastofannualwaterconsumptionbilled

A.1 Forecast of annual water consumption billed (GL)

Year ACIL’s original a CIE

Bounceback scenario 100% over 1 year 50% over 2 years 75% over 3 years 100% over 4 years

Total water consumption billed (GL)

2011-12 188.0 175.9 175.9 175.9 2012-13 176.3 169.1 172.3 172.3 2013-14 178.9 171.4 178.1 181.6 2014-15 181.4 173.8 180.6 187.7 2015-16 183.8 176.2 183.1 190.3 Residential water consumption (GL)

2011-12 126.8 113.7 113.7 113.7 2012-13 119.0 109.7 112.4 112.4 2013-14 120.8 111.3 116.9 119.8 2014-15 122.5 113.0 118.7 124.6 2015-16 124.3 114.6 120.4 126.5 Non-residential water consumption (GL)

2011-12 51.5 52.9 52.9 52.9 2012-13 48.2 50.3 50.7 50.7 2013-14 48.7 50.8 51.5 51.9 2014-15 49.3 51.3 52.0 52.7 2015-16 49.7 51.9 52.6 53.3 Commercial water consumption (GL)

2011-12 9.7 9.3 9.3 9.3 2012-13 9.1 9.1 9.3 9.3 2013-14 9.3 9.3 9.7 9.9 2014-15 9.6 9.5 9.9 10.3 2015-16 9.8 9.7 10.1 10.5

a ACIL’s forecast in 2011-12 has been adjusted for weather.



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