attachment e.1 demand forecasting methodology

Attachment E.1

Demand Forecasting Methodology

SA Water’s demand forecasting

A report on the development of Demand Forecasting

Methodology, Model and Supporting documentation

Prepared for SA Water

August 2012

ACIL Tasman Pty Ltd

ABN 68 102 652 148 Internet www.aciltasman.com.au

Melbourne (Head Office) Level 4, 114 William Street Melbourne VIC 3000

Telephone (+61 3) 9604 4400 Facsimile (+61 3) 9604 4455 Email [email protected]

Brisbane Level 15, 127 Creek Street Brisbane QLD 4000 GPO Box 32 Brisbane QLD 4001


Canberra Level 2, 33 Ainslie Place Canberra City ACT 2600 GPO Box 1322 Canberra ACT 2601


Perth Centa Building C2, 118 Railway Street West Perth WA 6005


Sydney GPO Box 4670 Sydney NSW 2001


For information on this report

Please contact:

Jeremy Tustin Telephone (03) 9604 4411 Mobile (0421) 053 240 email [email protected]

Contributing team members

Jim Diamantopoulos Sue Jaffer

http://www.aciltasman.com.au/

mailto:[email protected]







ii

Contents

Contents ii

Executive summary vi

1 Introduction 1

2 Forecasting principles 3

2.1 Principle E1: Freedom from statistical bias 4

2.2 Principle E6: Reflect the particular situation and the nature of the

market for services 5

3 Background – potable water supply in South Australia 7

3.1 SA Water’s customers – number and category 7

3.1.1 Customer numbers 7

3.1.2 Customer category 8

3.1.3 Customer numbers by category 10

3.2 Water demand data 11

3.2.1 Billed water sales drives SA Water’s revenue 12

3.2.2 Billed water sales can be disaggregated by customer class 13

3.2.3 Integrating demand forecasts with monthly budgeting process 13

3.2.4 The transition to quarterly billing 13

3.2.5 Summary – forecasting billed water sales 14

3.2.6 Overview of historical billed water sales 14

3.3 Water restrictions in South Australia 17

3.4 Water demand per customer 19

3.4.1 Residential 20

3.4.2 Commercial 21

3.4.3 Other non-residential 22

3.5 Summary – billed water sales 23

4 Economic and demographic drivers 24

4.1 Drivers included in the models 25

4.1.1 The level of economic activity 25

4.1.2 Population 26

4.1.3 The price of water 27

4.1.4 Weather 31

4.2 Drivers not included in the models 34

4.2.1 Other household demographics 34

4.2.2 Non revenue water - Meter replacement program 34

4.2.3 Rebates and other demand management activities 36


iii

5 Model specification 38

5.1 Key drivers 38

5.2 Model specification - annual billed water sales model 39

5.2.1 Residential customer numbers 39

5.2.2 Water usage by residential customer 40

5.2.3 Commercial customer numbers model 42

5.2.4 Commercial water usage model 43

5.2.5 Other non-residential water usage model 45

5.3 Model specification - bulk supply 46

6 Developing the forecasts 49

6.1 The level of economic activity 49

6.2 Population 50

6.3 Temperature 51

6.4 Rainfall and evaporation 53

6.5 Water price 54

6.6 Price elasticity of demand 55

6.6.1 Economic literature – residential demand 56

6.6.2 Relevance for SA Water 58

6.6.3 Economic literature on the elasticity of non residential

demand 59

6.6.4 The ‘bounce back’ effect 61

7 Billed water sales forecasts 63

7.1 Residential sector 63

7.1.1 Customer numbers 63

7.1.2 Residential water demand per customer 65

7.1.3 Total demand for water in the residential sector 67

7.1.4 Commercial sector 69

7.2 Other non-residential sector 75

7.3 Total demand for water 77

7.3.1 Forecasts 77

7.3.2 Sensitivities 79

7.4 Bulk water supply forecasts 80

8 Forecasting principles 82

8.1 Freedom from statistical bias 82

8.2 Drivers of demand, sound assumptions and sound accounts of

market conditions 82

8.3 Most recently available data 83

8.4 Model performance and consistency with other models 84

A Rebate sensitivity A-1


iv

List of figures

Figure ES 1 SA Water – billed water sales – historical and forecast (median weather) ix

Figure ES 2 Billed water sales with weather sensitivities xi

Figure 1 SA Water – historical customer numbers, 1996-97 to 2010-11 10

Figure 2 SA Water bulk supply and billed water sales, 1996-97 to 2010-11 12

Figure 3 SA Water. Billed water sales by customer class, 1996-97 to 2010-11 15

Figure 4 History of water restrictions in South Australia 18

Figure 5 SA Water, billed water sales per customer, 1996-97 to 2010-11 20

Figure 6 Billed water sales per customer - residential, 1996-97 to 2010-11 21

Figure 7 Billed water sales per customer – commercial, 1996-97 to 2010-11 22

Figure 8 Water demand per customer – other non-residential, 1996-97 to 2010-11 23

Figure 9 South Australian Gross State Product – 1996-97 to 2010-11 26

Figure 10 South Australian population, 1996-97 to 2010-11 27

Figure 11 Real water prices in South Australia 1996-97 to 2011-12 (2012 dollars) 29

Figure 12 Cooling Degree Days – Kent Town Weather Station – 1977-78 to 2010-11 32

Figure 13 Annual rainfall – Kent Town Weather Station – 1977-78 to 2010-11 33

Figure 14 Evaporation – Kent Town Weather Station – 1977-78 to 2010-11 (partial) 33

Figure 15 Non revenue water and meter replacements, 1996-97 to 2010-11 35

Figure 16 Water saving rebates issued July 2008 to December 2011 36

Figure 17 Water saving rebates and second tier water price 37

Figure 18 Residential customer numbers model 40

Figure 19 Residential water usage model 42

Figure 20 Commercial customer numbers model 43

Figure 21 Commercial water usage model 44

Figure 22 Water usage model – other non-residential customers 46

Figure 23 Bulk water model 48

Figure 24 GSP growth projections 49

Figure 25 South Australian population growth projections 50

Figure 26 CDD18 at Kent Town weather station -actual and median over several periods 52

Figure 27 Annual and median rainfall and evaporation – Kent Town 54

Figure 28 SA Water – residential customer numbers – historical and forecast 63

Figure 29 SA Water – residential water demand per customer – historical and forecast 65

Figure 30 SA Water – residential water demand – historical and forecast 67

Figure 31 SA Water – commercial customer numbers – historical and forecast 69

Figure 32 SA Water – commercial water demand per customer – historical and forecast 71

Figure 33 SA Water – commercial water demand – historical and forecast 73

Figure 34 SA Water – other non-residential water demand – historical and forecast 75

Figure 35 SA Water - total water demand – historical and forecast 77

Figure 36 Total water demand with weather sensitivities 80


v

Figure 37 Historical and forecast bulk supply to 2015-16 81

Figure 38 Estimated water savings arising from rebates, ML A-1

List of tables

Table ES 1 Drivers of component models viii

Table ES 2 SA Water – billed water sales – historical and forecast x

Table ES 3 Bulk supply model forecasts versus billed water sales model forecasts xii

Table 1 Forecasting principles 4

Table 2 SA Water total billed water sales, 1996-97 to 2010-11 16

Table 3 Annualised growth in billed water sales, by sector, per cent per annum to 2010-11 16

Table 4 The price of water in South Australia – 1996-97 to 2011-12 – residential customers 28

Table 5 Drivers of component models 39

Table 6 South Australian population projections – comparing South Australian Government and ABS 51

Table 7 Future water price changes 54

Table 8 Estimates of the price elasticity of residential demand for water 57

Table 9 Estimates of the price elasticity of non-residential demand for water 60

Table 10 SA Water – residential customer numbers – historical and forecast 64

Table 11 SA Water – residential water demand per customer – historical and forecast 66

Table 12 SA Water – residential water demand – historical and forecast 68

Table 13 SA Water – commercial customer numbers – historical and forecast 70

Table 14 SA Water – commercial water demand per customer – historical and forecast 72

Table 15 SA Water – commercial water demand – historical and forecast 74

Table 16 SA Water – other non-residential water demand – historical and forecast 76

Table 17 SA Water - total water demand – historical and forecast 78

Table 18 Total water demand with weather sensitivities 80

Table 19 Bulk water model forecasts versus billed water sales model forecasts 81


Executive summary vi

Executive summary

The South Australian Water Corporation (SA Water) is owned by the

Government of South Australia (“the Government”). It is an integrated water

and wastewater utility providing services to more than 1.5 million people

across South Australia.

Traditionally, the South Australian water industry has not been subject to

independent economic regulation. SA Water’s prices were determined annually

by the Government pursuant to the Waterworks Act (1932) (SA) and the Sewerage

Act (1929) (SA).

To date, SA Water has not been subject to independent economic regulation,

but this will change soon. In particular, the Essential Services Commission of

South Australia (ESCOSA) is expected to determine water prices to apply from

2013-14 until 2015-16.

While the details of ESCOSA’s approach have not been finalised, demand

forecasts will inevitably be an important input.

SA Water engaged ACIL Tasman to development a demand forecasting

methodology and a model and supporting documentation. This report

provides an overview of that methodology and the forecasts that ACIL

Tasman produced. It is accompanied by a forecasting model and a user’s

manual.

Demand forecasting methodology

This report presents demand forecasts based on two independent models. The

forecasts themselves are similar, though not identical.

The first model actually comprises five independent, regression models, each

with its own drivers:

1. a model of residential customer numbers

2. a model of average water demand by residential customers

3. a model of commercial customer numbers

4. a model of average water demand by commercial customers

5. a model of total water demand by other non residential customers

SA Water’s industrial customers are categorised with other non-residential

customers for data availability reasons.


Executive summary vii

These models were estimated using annual data pertaining to billed water sales

between 1996-97 and 2010-11. Due to billing lag, billed water sales in any given

year differ from physical water consumption that year.

Component forecasts are produced using projections of the drivers of each

component model. Total forecasts are produced by multiplying the customer

numbers forecasts by the corresponding average water use forecasts and

adding these to each other and to the total water demand forecasts for the

other non-residential sector.

The model produces forecasts of billed water sales on an annual basis for each

of three customer categories.

The second model is a single regression model based on monthly bulk supply

data, which pertains to the total quantity of water supplied to SA Water’s

network. It too performs well, explaining almost 90 per cent of variation in

historical data.

A key difference between the monthly and annual data is that the monthly data

is not attributed to customer categories. That is, this dataset captures the total

amount of water supplied each month, but not the category of customer to

which that water was supplied.

Drivers of demand

The drivers of both of the models described above are shown in Table ES 1.

The drivers were chosen empirically, based on the combination that provided

the best performing model. They are consistent with prior expectations, with

water demand driven by population, economic activity, weather, the price of

water and the level of water restrictions.

Other drivers were tested but robust relationships were not found. In some

cases this is likely to be due to data availability or the fact that some potential

drivers have occurred observed simultaneously with others. For example,

demand management activities have not been widely used except when prices

were rising. Where two variables move together, regression techniques are

unable to separate their effect.


Executive summary viii

Table ES 1 Drivers of component models

Residential

customer

numbers

Average

residenti

al usage

Commercial

customer

numbers

Average

commercial

usage

Total other

non

residential

usage

Total Bulk

water

supply

(monthly)

Population (%

annual growth) √

Economic activity

(Gross State

Product) √ √ √ √

Price of water

($/kL, second tier) √

√ √ √

Temperature (CDD

18) √

√ √ √

Water restrictions

(level) √

√ √ √

Rainfall (mm) √ Evaporation (mm) √

ACIL Tasman’s forecasts of water demand are based on projected drivers from

the South Australian Department of Treasury and Finance (economic activity),

the Australian Bureau of Statistics (population) and SA Water (water price and

restrictions). Weather is assumed to return to long term median levels.


Executive summary ix

Demand forecasts – billed water sales

ACIL Tasman’s forecasts of SA Water’s demand billed water sales, prepared

using the first model described in this report, and assuming median weather

conditions, are as shown in Figure ES 1 and Table ES 2.

Figure ES 1 SA Water – billed water sales – historical and forecast (median weather)

Source: ACIL Tasman modelling

0

50

100

150

200

250

3001

996

-97

19

97-9

8

19

98-9

9

19

99-0

0

20

00-0

1

20

01-0

2

20

02-0

3

20

03-0

4

20

04-0

5

20

05-0

6

20

06-0

7

20

07-0

8

20

08-0

9

20

09-1

0

20

10-1

1

20

11-1

2

20

12-1

3

20

13-1

4

20

14-1

5

20

15-1

6

20

16-1

7

20

17-1

8

20

18-1

9

20

19-2

0

20

20-2

1

GL

Historical Forecast


Executive summary x

Table ES 2 SA Water – billed water sales – historical and forecast

Year Water demand (ML)

Actual

1996-97 210,339

1997-98 213,117

1998-99 217,446

1999-00 221,610

2000-01 232,640

2001-02 218,032

2002-03 242,554

2003-04 216,033

2004-05 215,647

2005-06 216,888

2006-07 221,072

2007-08 192,254

2008-09 189,280

2009-10 185,630

2010-11 175,219

2011-12 184,313

2012-13 176,275

2013-14 178,850

2014-15 181,380

2015-16 183,784

2016-17 188,434

2017-18 190,664

2017-18 191,613

2018-19 192,697

2020-21 197,383


SA Water’s demand is forecast to continue to decline very slightly. Over the

period from 2011-12 to 2015-16, it is forecast to decline at an annualised rate

of 0.1 per cent per annum. As with the individual sectors, this is forecast to

include a decline with the 2012-13 price increase followed by growth after that.

Over the likely regulatory period from 2013-14 to 2015-16, total water demand

is forecast to grow at 1.4 per cent per annum.

Sensitivity to weather

The key uncertainty for these forecasts is future weather conditions. As is

common regulatory practice, these forecasts shown in Table ES 2 and Figure

ES 1 are based on the assumption that South Australia’s weather will return to

long term trend during the forecast period. While we consider this to be a


Executive summary xi

reasonable assumption, we note that it requires the weather to be considerably

cooler in the next few years than it has been recently.

To illustrate the sensitivity of the forecasts to this assumption, Figure ES 2

shows the same forecasts as presented in Figure ES 1 assuming 10th (hot) and

90th (cool) percentile weather conditions.1

Figure ES 2 Billed water sales with weather sensitivities

Data source: ACIL Tasman modelling

Under the tenth percentile weather assumption, SA Water’s total demand over

the likely regulatory period is 3.2 per cent above the base forecast.

Under the ninetieth percentile weather assumption, SA Water’s total demand

over the likely regulatory period it is 3.2 per cent below the base forecast.

1 50th percentile weather is the median outcome, that is, the number of CDD that separates

the top half of the sample from the bottom half. The weather in any given year is equally likely to have been above as below this number. 10th and 90th percentile weather conditions are defined as the numbers that separate the top ten and the top ninety per cent respectively. That is, a randomly selected historical year has one chance in ten (ninety) of being above the tenth (ninetieth) percentile value)

150,000

160,000

170,000

180,000

190,000

200,000

210,000

220,000

230,000

240,000

250,000

19

96-9

7

19

97-9

8

19

98-9

9

19

99-0

0

20

00-0

1

20

01-0

2

20

02-0

3

20

03-0

4

20

04-0

5

20

05-0

6

20

06-0

7

20

07-0

8

20

08-0

9

20

09-1

0

20

10-1

1

20

11-1

2

20

12-1

3

20

13-1

4

20

14-1

5

20

15-1

6

ML

Total water sales Base weather 10% POE 90% POE


Executive summary xii

Demand forecasts – bulk supply

Table ES 3 shows the forecasts of bulk supply derived from the bulk supply

model. It also includes a comparison against the forecasts derived from the

annual model (which were prepared independently as described above).

Table ES 3 Bulk supply model forecasts versus billed water sales model forecasts

Forecast Bulk supply

Non revenue

water

(Assumed %)

Water

delivered (ex

non revenue)

Billed water

sales

forecasts

Deviation (%)

2011-12 210,970 12.6% 184,388 184,313 -0.04%

2012-13 209,149 12.6% 182,796 176,275 -3.57%

2013-14 211,076 12.6% 184,481 178,850 -3.05%

2014-15 212,876 12.6% 186,054 181,380 -2.51%

2015-16 214,367 12.6% 187,357 183,784 -1.91%

2016-17 217,649 12.6% 190,225 188,434 -0.94%

2017-18 219,008 12.6% 191,413 190,664 -0.39%

2018-19 219,373 12.6% 191,732 191,613 -0.06%

2019-20 219,865 12.6% 192,162 192,697 0.28%

2020-21 223,181 12.6% 195,060 197,383 1.19%

Data source: ACIL Tasman

Table ES 3 shows that the two sets of forecasts are reasonably close for most

of the forecast horizon. The models differ in that the monthly model

demonstrates less responsiveness to price shocks, but is also less responsive to

the variables that drive long term trend growth such as GSP.

There is insufficient data to disaggregate the monthly sales data to customer

category. Specifically, there is no data to reflect the seasonal characteristics of

different customer categories.

Forecasting principles

ESCOSA has indicated that demand forecasts should be:

1. free from statistical bias

2. recognise and reflect key drivers of demand

3. based on sound assumptions using the best available information

4. consistent with other available forecasts and methodologies

5. based upon the most recently available data

6. reflect the particular situation and the nature of the market for services

7. based upon sound and robust accounts of current market conditions and

future prospects.


Executive summary xiii

ACIL Tasman’s view is similar.

The demand forecasting methodology, model and forecasts discussed in this

report were prepared to satisfy these principles in the following way.

Freedom from statistical bias

ESCOSA’s first requirement is that the forecasts should be free from statistical

bias. It is in the nature of forecasting that actual outcomes will differ from the

forecast value. There will always be a forecast error.

A forecasting model is statistically biased if it has a tendency either to over or

under estimate outcomes, in other words a model is statistically biased if the

error is more likely to be either positive or negative. An unbiased model will be

no more likely to produce a positive error than a negative error.

The methodology used to prepare these forecasts is, subject to certain technical

assumptions, intrinsically free from statistical bias. The lack of statistical bias is

also shown by the comparison between the ‘fitted’ values of the model and

historical outcomes.

Drivers of demand, sound assumptions and sound accounts of

market conditions

The models presented here recognise and reflect the key drivers of demand, in

line with ESCOSA’s second requirement. They are based on sound

assumptions and use the most recent data and the best available information in

line with ESCOSA’s second, third, fifth and seventh requirements.

In particular, the forecasts presented here take account of the price of water,

economic activity and population, all of which are likely, based on economic

theory, to be drivers of water demand.

The calibrated models also account for variation in weather, both temperature

and rainfall (for the monthly model). The forecasts were produced on the

assumption of median weather conditions as is conventional in demand

forecasting.

The forecasts of water use are based on forecasts of the key drivers of demand.

Those driver forecasts were obtained from independent reputable sources,

namely:

• the South Australian Government (for economic growth)

• the Australian Bureau of Statistics (ABS) (for population).

Historical data used in calibrating the models was obtained from the ABS and

the Bureau of Meteorology.


Executive summary xiv

In addition to these data sources, the forecasts rely on an assumption regarding

water use behaviour now that water restrictions have been lifted and replaced

with water wise measures. These ongoing measures are similar to the

restrictions that were in place between 2003 and 2006. The forecasts are based

on the assumption that, if all else was equal, average water use behaviour in

future would be similar to what was observed under level 1 water restrictions.

Other factors, in particular water price, are accounted for separately. The other

key assumption made in preparing the forecasts relates to the future price of

water. The forecasts were based on the assumption that prices would be in line

with the Government’s announcement of 21 May 2012, which is the most

recently available information.

Model performance and consistency with other models

The models perform well. The multiplicative nature of the models means it is

not possible to provide a single statistic that summarises the performance of

the total forecasting model. However, individually, four of the five

components of the annual billed water sales model explain more than 90 per

cent of the variation in historical data. The fifth model explains slightly less

than 90 per cent.

Similarly, the monthly model explains approximately 90 per cent of the

variation in historical data.

The monthly and annual models were prepared independently of one another,

in a methodological sense, and rely on independent data. It is noteworthy that

the two models produce similar forecasts.


Introduction

1

1 Introduction

The South Australian Water Corporation (SA Water) is owned by the

Government of South Australia (“the Government”). It is an integrated water

and wastewater utility providing services to more than 1.5 million people

across South Australia.

Traditionally, the South Australian water industry has not been subject to

independent economic regulation. SA Water’s prices were determined annually

by the Government pursuant to the Waterworks Act (1932) (SA) and the Sewerage

Act (1929) (SA).

In recent years this price setting process was reviewed by the Essential Services

Commission of South Australia (ESCOSA). ESCOSA considered whether the

Government had the necessary information to determine prices in accordance

with applicable national policy guidelines in the National Water Initiative

(NWI) and relevant agreements of the Council of Australian Governments

(COAG).

This regulatory environment is set to change.

In July 2009, the Government adopted “Water for Good: A plan to ensure our water

future to 2050”, (“Water for Good”). Among other things, Water for Good

contained a commitment to introduce a regime of independent economic

regulation of the South Australian water industry. ESCOSA will be the

regulator.

The Government introduced the Water Industry Bill to Parliament in July 2011.

It was passed into law on 5 April 2012.

The Water Industry Act declares the water and wastewater industries to be

regulated industries for the purposes of the Essential Services Commission Act

2002 (“ESC Act”). This declaration makes the water industry subject to

ESCOSA’s general powers as described in the ESC Act.

To provide greater certainty as to the scope and form that regulation will

ultimately take, the Government sought ESCOSA’s advice on a number of

relevant matters in September 2010.2

2 For detail see a letter from the then Treasurer of South Australia, Hon Kevin Foley MP to

ESCOSA dated 27 September 2010 reproduced in ESCOSA, “Economic Regulation of the South Australian Water Industry Draft Advice – Public Version”, August 2011, available at www.escosa.sa.gov.au


Introduction

2

In preparing that advice, ESCOSA published a Statement of Issues in

December 2010 (Statement of Issues) and draft advice to the Treasurer in

November 2011 (Draft Advice).

In the Draft Advice, ESCOSA proposed a regulatory regime for the South

Australian water industry, with ESCOSA responsible for regulating water and

wastewater prices from 1 July 2013.3

The particular form of that regime has not yet been determined. Regardless of

the detail, demand forecasts will be an important input into the process.

Therefore, SA Water engaged ACIL Tasman to:

• develop a robust demand forecasting model and provide related advice

• generate appropriate demand forecasts and supporting documentation to

support SA Water’s regulatory business proposal for 1 July 2013 to

30 June 2016

This report presents ACIL Tasman’s model and advice to SA Water. It is

structured as follows.

Chapter 2 provides a discussion of the principles that guided the preparation of

these forecasts and how those principles were satisfied.

Chapter 3 provides an overview of SA Water’s business, its customers and

historical billed water sales. It includes a discussion of water restrictions and

policy matters.

Chapter 4 provides a discussion of the key drivers of water demand and the

available data.

Chapter 5 covers model specification. It presents a set of calibrated models and

assesses these models based on goodness of fit and statistical significance.

Chapter 6 provides a description of the forecast inputs.

Chapter 7 develops a set of forecasts based on the calibrated models.

3 This report relates to water prices only. Waste water prices are based on property values,

not demand.



3

2 Forecasting principles

Forecasts of the amount of water that will be sold in South Australia are

important for two key reasons:

• to inform SA Water’s forward planning and budgeting

• to determine the efficient price of water

Given that the water industry will be subject to economic regulation by

ESCOSA from 2012-13, our approach to preparing these forecasts was guided

by its likely requirements for the forthcoming water price review in addition to

our own understanding of best practice.

As ESCOSA noted in the Statement of Issues, “there is no specific guidance

under the ESC Act, Water Industry Bill or NWI as to what form of forecasting

methodology should be employed by a water business to develop demand

forecasts.”4

In the absence of this guidance, ESCOSA expressed the view that forecasts

should be developed based on a set of best practice principles. Among other

things, they should:

E1. be free from statistical bias

E2. recognise and reflect key drivers of demand

E3. be based on sound assumptions using the best available information

E4. be consistent with other available forecasts and methodologies

E5. be based upon the most recently available data

E6. reflect the particular situation and the nature of the market for

services

E7. be based upon sound and robust accounts of current market

conditions and future prospects.

ACIL Tasman’s view is similar. We consider that a best practice water demand

forecasting methodology would possess the following features:

AT1. It would incorporate forecasts of each of the key drivers of water

demand, including demographic, economic, price and weather related

factors

4 ESCOSA, “Economic Regulation of the South Australian Water Industry Statement of

Issues”, December 2010, p. 80, www.escosa.sa.gov.au



4

AT2. Its accuracy would have been assessed, i.e. its ability to predict actual

demand would have been compared against alternative models, and

found to be superior

AT3. The forecasts would have been compared with other independently

produced forecasts if these are available. Differences would have been

understood and explained

AT4. It would produce forecasts that are free from bias

AT5. The calibrated model would have been subjected to diagnostic

checking and statistical validation procedures to ensure that it isn’t

mis-specified

AT6. The modelling process would be transparent, repeatable and well

documented

AT7. The forecasting process would be cost-effective, with the forecasts

achieving a suitable level of reliability at minimum cost

ACIL Tasman’s principles AT6 and AT7 are distinct from ESCOSA’s

principles. They underpin our approach to structuring the data and the model,

and the level of detail we provide in this report and supporting documentation

and models.

Two of ESCOSA’s principles (E1 and E6) are concerned with analytical

technique and its application. These are discussed directly below. The

remainder of ESCOSA’s principles are concerned with detail regarding the data

and analysis. These are covered in the body of the report, as outlined in Table

1.

Table 1 Forecasting principles

ESCOSA principle number ACIL Tasman principle number Report reference

E1 AT4 Section 2.1

E2 AT1 Chapter 4

E3 AT1 Chapter 4

E4 AT3 Chapter 5

E5 AT1 Chapter 4

E6 N/A Section 2.2

E7 AT1 Chapter 4

2.1 Principle E1: Freedom from statistical bias

In this context bias means statistical bias. Specifically, a model is free from bias

if it is no more likely to overestimate than underestimate. More technically, it is

free from bias if the expected value of the sample parameter being estimated is

the population parameter. In this case, a model is biased if it is more likely to

either overestimate or underestimate billed water sales.



5

As discussed in more detail in chapter 5 of this report, the forecasts presented

here were prepared using regression equations using the ordinary least squares

method of estimation (OLS).

OLS is a popular, powerful and widely used method of estimation which has

been proved, subject to certain assumptions, to be the best linear unbiased

estimator.5 Therefore, subject to those same assumptions, for which diagnostic

tests were conducted on the final model specifications, the models presented

here are free from statistical bias.

2.2 Principle E6: Reflect the particular situation and

the nature of the market for services

The market for SA Water’s services is characterised by a small number of

homogeneous products supplied by an integrated monopoly. This simplifies

the estimation process, as it is not necessary to identify and model competition

from substitutes.

Modelling requires consistent data, which serves to limit the period of

estimation. Further, the need to identify different categories of demand (to

reflect different demand drivers) limits the choice of data to sales rather than

bulk supply. This is discussed in section 3.2.

It is also important to recognise that regression analysis can only identify the

contribution made by past behaviour/events for which quantifiable data exists.

Where influences are subject to change in future, these need to be taken into

account outside the regression model as we have done in relation to the price

elasticity of demand for residential customers, which is discussed in section 6.5.

Further, we note that the forecasts presented here reflect a number of very

recent and significant changes. The recent past was characterised by more

intensive water restrictions and demand management than South Australia had

experienced before. These were accompanied by price rises more rapid than

previously experienced.

For the most part these changes are completed, except that prices will increase

significantly once more in 2012-13.

Therefore, some uncertainty about future water demand will remain until the

longer term impact of these changes is revealed by customers’ behaviour. In

particular, the extent to which demand will ‘bounce back’ from the restricted

5 The assumptions in question are technical, and are widely discussed in Econometrics texts.

See, for example, Gujarati, Damodar N. “Basic Econometrics”, second edition, 1988, p63.



6

levels of recent years is as yet unknown. As discussed below, the recent data

provide some indication of the extent of bounce-back since the removal of

temporary restrictions in December 2010. However the relatively short period

since ‘water wise’ measures have come into effect has restricted our ability to

test for differences between the effect of water wise measures and level 1

restrictions. This lack of data means we have had to impose the assumption

that the impact of the current water wise measures is the same as the original

level 1 restrictions and that there will be no permanent effect from the higher

level restrictions.


Background – potable water supply in South Australia

7

3 Background – potable water supply in South Australia

The following sections provide an overview of potable water supply in South

Australia since 1996-97, when the available data series commenced.

Section 3.1 provides an overview of SA Water’s customer numbers, the

available data and the implication for preparing forecasts.

Section 3.2 describes SA Water’s recent billed water sales and discusses the

available data and the implications for preparing forecasts.

Section 3.3 provides an overview of the water restrictions implemented in

South Australia in between 2007 and 2010. Those restrictions had a significant

impact on billed water sales during that time.

Section 3.4 brings sections 3.1 and 3.2 together to provide an overview of

changes in water usage by average customer.

Section 3.5 provides a summary.

3.1 SA Water’s customers – number and category

A key driver of water demand is the number of customers supplied. Another

key driver is the ‘type’ of customer. These two issues are discussed in sections

3.1.1 and 3.1.2 respectively. Section 3.1.3 provides an overview of SA Water’s

customer base by category since 1996-97.

3.1.1 Customer numbers

The source of customer numbers data for this report was SA Water’s billing

system, CSIS.

More specifically, we were provided with ‘Rating Analysis’ reports drawn from

CSIS for the period from 1996-97 to 2010-11.

Those reports contain two fields relevant to the total number of customers SA

Water supplies, though neither was entirely suitable for present purposes.

Three adjustments were made to the numbers in CSIS.

First, SA Water charges all land owners whose land abuts the water network a

fixed charge each year. This is known as rating on abuttal. To enable this, CSIS

includes accounts for a number of customers who demand no water.



8

Our view is that these customers should be disregarded when forecasting the

demand for water.6 If they continue to demand no water, they need not be

considered. If they begin to demand water they should be captured through the

projection of new customer numbers. Including these customers in the

forecasting model would tend to understate the average use per customer and

may have unintended consequences.

Second, a number of SA Water’s customers obtain water under common

supply arrangements, meaning that multiple customers are supplied through a

single meter. For billing purposes, dummy accounts are maintained for each

group of common supply customers. Individual accounts are also maintained

for each customer in the group.

These issues mean that neither the water accounts, nor the water demand

accounts, field contains an accurate measure of the number of customers to

whom SA Water supplies water. SA Water advised that the best way to

estimate the number of customers to which it supplies water from the rating

analysis reports was to sum:

1. The number of water accounts with land use codes other than vacant land,

and

2. The number of water use accounts with land use codes equal to vacant land7

Third, the way that SA Water bills for recycled water means that customers

who receive it typically appear twice in SA Water’s billing system. Therefore, to

avoid double counting, the number of recycled water customers was subtracted

from the total number of customers.

3.1.2 Customer category

As discussed in chapter 2, ESCOSA holds the view (and we agree) that

demand forecasts would preferably be disaggregated to reflect the different

factors that drive water demand from different ‘types’ of customer. Ideally,

forecasting would be based on classes of customer that are close to

homogeneous. That is, customers in the categories would ideally respond

similarly to the relevant drivers of water demand.

In practice, this can only be done to the extent allowed by the available data.

6 If the existing tariff structure is maintained, with a fixed charge payable on abuttal, they

would need to be considered for revenue purposes.

7 As discussed below, ‘vacant land’ customers were also reallocated to the ‘residential’ category.



9

The billed water sales data reflect the fact that SA Water places its customers

into three categories, namely residential, commercial and other non-residential.

The residential class is largely self-explanatory. It includes all customers on

residential land.

The commercial class includes wholesale and retail trade, professional services

and other businesses.

The other non-residential category is a “catch-all” classification, and includes

primary producers, miners and other users. It included vacant land up until

1997-98.8

It would have been preferable to disaggregate the consumption data to a more

‘granular’ level. In particular, it would be preferable to distinguish ‘industrial’

customers from other commercial customers.

However, the available data were not sufficiently detailed to allow this to be

done with confidence.

We understand that some of SA Water’s customers are identified as ‘industrial’

customers on the basis that they occupy land classified by the Valuer-General

as industrial. However, SA Water has advised that this land use classification

does not apply to non-metropolitan areas. Therefore, in CSIS, the category

“industrial customers” exists only in urban areas.

SA Water has also advised that it has several major customers that are

industrial in nature and have no assigned land use in CSIS. These customers

are categorised as ‘other non-residential’.

The fact that the ‘industrial’ category reflects only a part of the broader

industrial sector in South Australia prevents us from being able to treat it

separately for forecasting purposes. It would have been possible to place

industrial customers in urban areas in the ‘commercial’ category or to treat

them separately, but this would have meant treating ‘urban industrial’

customers differently than ‘non urban industrial’ customers.

Rather than do this, we aggregated all ‘industrial’ and ‘other non-residential’

customers together for forecasting purposes. Thus references in this report to

‘other non-residential’ customers include customers in the ‘industrial’ category.

8 Vacant land was transferred to the residential category in 1997/98. For consistency, we

transferred it for the whole period for which data was available (from 1996/97).



10

3.1.3 Customer numbers by category

As at July 2011, SA Water supplied water to approximately 700,000 customers.

As Figure 1 shows, the vast majority of those are residential customers.

Figure 1 SA Water – historical customer numbers, 1996-97 to 2010-11

Data source: SA Water

In 1996-97 and 1997-98 there was a reallocation of vacant allotments from the

other non-residential to residential classifications.9 For forecasting purposes we

have assigned all vacant land customers to the residential customer class

throughout the period to avoid distorting the apparent growth in other non-

residential customers.

Given this adjustment, the number of residential customers supplied by SA

Water grew from approximately 527,000 in 1996-97 to approximately 632,000

in 2010-11. This equates to annualised growth of approximately 1.3 per cent.

Between 2005-06 and 2010-11 residential customer numbers grew slightly

faster, at 1.4 per cent per annum.

The number of commercial customers SA Water supplies increased from

almost 24,000 in 1996/97 to slightly more than 27,000 in 2010-11. This equates

9 Unlike the 59,000 allotments subject to rating on abuttal, we understand that these

properties do have water meters and are able to take water from SA Water’s network. In these two years the number of ‘vacant land residential’ customers increased from approximately 5,000 to approximately 14,000. We understand that most of this growth was due to the reallocation.

-

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000N

um

be

r o

f co

nn

ect

ion

s

Commercial Other non-residential Residential



11

to an annualised growth rate of 1.0 per cent. In the five years from 2006-07 to

2010-11 the number of commercial customers grew by 1.1 per cent per annum.

The number of other non-residential customers SA Water supplies grew from

approximately 39,000 in 1996-97 to almost 42,000 in 2010-11. This equates to

an annualised growth rate of 0.5 per cent. In the five years from 2006-07 to

2010-11, growth in the number of other non-residential customers was similar

at 0.4 per cent per annum.

3.2 Water demand data

The water that SA Water supplies to its customers originates from a number of dams, the Murray River and now the Adelaide Desalination Plant (ADP). It proceeds through a number of treatment plants to a network of pipes and, eventually, to the end user.

ACIL Tasman was supplied with the following two data series:

1. bulk supply – measuring the quantity of water ‘sent out’ from treatment plants into the network. Bulk supply is metered frequently and monthly reports were supplied from 1995/96

2. billed water sales – measuring the quantity of water billed to customers in each financial year, based on the date the bills were raised. Bills are raised on a rolling basis shortly after meters are read. Reports were supplied from 1996/9710

In a perfect world, bulk supply and billed water sales would be the same.

However, in practice they differ due to leakage and other “unpaid” water such

as that used for fire fighting.

Another difference between the two data series we received is due to timing

between billed water sales and physical supply. Billed water sales are metered

periodically,11 whereas bulk supply is metered monthly. Therefore, the billed

water sales data ‘lag’ the bulk supply data.

Figure 2 shows bulk supply and billed water sales from 1996-97 to 2010-11.

10 Before 1996/97 the corresponding data were captured by a different billing system. There

are some inconsistencies in the way customers are categorised between the two systems.

11 These data are quarterly from 2009/10 and biannual before then.



12

Figure 2 SA Water bulk supply and billed water sales, 1996-97 to 2010-11


To prepare forecasts of water demand, it is necessary to decide which of these

two ‘levels’ to forecast. There were four issues to consider in reaching this

decision, each of which is discussed below:

1. billed water sales drives SA Water’s revenue, while bulk supply drives its

costs

2. forecasting at the billed water sales level enables forecasts to be separated

between different customer classes (consistent with the terms of reference

for this report and our view of best practice)

3. forecasting at the bulk supply level would allow a longer time series to be

used, thus potentially producing a more powerful econometric model. It

would also have the added, albeit minor, benefit of allowing the model to

be used in monthly budget updates

4. the billed water sales data was disrupted by the change to quarterly billing

in July 2009 and contains a billing lag.

3.2.1 Billed water sales drives SA Water’s revenue

A key purpose for these forecasts is SA Water’s upcoming price determination.

While SA Water’s costs depend on the amount of water it treats, i.e. bulk

supplies, its revenue depends on the amount it supplies to customers. In our

view it is preferable to make a direct forecast of the parameter that drives the

revenue.

0

50000

100000

150000

200000

250000

300000

Bulk Supply Water Sales



13

3.2.2 Billed water sales can be disaggregated by customer class

In our view it is much preferable to forecast the quantity of water demanded

by different classes of customer separately. This is due to the fact that the

explanatory factors influence the demand for water differently for different

customer groups. For example, population is a much more direct driver of the

residential water demand than it is for commercial and industrial water

demand.

3.2.3 Integrating demand forecasts with monthly budgeting

process

We understand that SA Water reviews billed water sales on a monthly basis for

budgeting reasons and that it would be helpful for the model developed in this

report to be capable of providing monthly forecasts for that purpose.

3.2.4 The transition to quarterly billing

Prior to July 2009, SA Water’s customers were billed once every six months for

their water usage. While they received bills every quarter, two of the quarterly

bills were for fixed charges only. Only the remaining two included charges

related to water usage.

From 1 July 2009, all of SA Waters customers have been billed for water usage

on a quarterly basis.

One implication of the change from biannual to quarterly billing, which is

critical for these forecasts, arises from the transition.

SA Water reads its customers’ meters progressively. Meters are read every day,

but any given customer has a meter reading every three months (or every six

months prior to July 2009).

All of SA Water’s customers had a meter reading in the September quarter of

2009. At that time, some customers had not had a meter reading since January

2009 so their January 2009 ‘quarterly bill’ related to more than three months’

water usage. The effect is that the billed water sales data for 2009-10 are

overstated.

If not addressed, this overstatement would distort the apparent growth in

billed water sales during a period when prices were increasing rapidly. This

would tend to understate price elasticity, with lasting implications for the

forecasts.

To address this issue, SA Water provided us with revised data for 2009-10

which adjusted for the change in billing frequency. This data was based on the



14

amount of water billed at 2009-10 prices, rather than the quantity of water

billed in 2009-10. The data was only available in total. We apportioned that

total amount of water to each customer class based on the proportions

observed in other years.

Another implication of this change is that it reduced ‘billing lag’. That is, the

delay between when a customer uses water and when they are asked to pay for

it.

The implication is that the reduced ‘billing lag’ may have had an impact on

customer behaviour and therefore water consumption. However, the available

data are not sufficient to confirm or quantify this effect.12

3.2.5 Summary – forecasting billed water sales

In summary, there are two arguments for using billed water sales data for

forecasting rather than bulk supply data. These are that billed water sales drive

revenue and that these data can be disaggregated by customer class.

Offsetting these are three arguments for forecasting bulk billed water sales,

namely the length of the time series, the interruption in the billed water sales

data due to the transition to quarterly billing and the fact that SA Water would

benefit from a monthly forecast of bulk supply volumes.

In this report, an annual model of billed water sales and a monthly model of

bulk supply are both presented. As shown in section 7.4 the results of the two

models are similar, but not the same.

3.2.6 Overview of historical billed water sales

Billed water sales data were available for the period from 1996-97 to 2010-11.

Total billed water sales to each of these three customer classes discussed in

section 3.1.2 are shown in Figure 3 below.

The largest water using sector is the residential sector, with total billed water

sales of approximately 115,000 ML in 2010-11. The commercial and other

non-residential sectors used approximately 10,000 and 50,000 ML respectively

that year.

12 The key issue is the fact that this change happened almost simultaneously with substantial

price increases and a significant rebate program. The available data are not sufficient to identify the impact of these effects separately.



15

Figure 3 SA Water. Billed water sales by customer class, 1996-97 to 2010-11


As is discussed in 3.3 below, the peak in billed water sales in 2002-03 precedes

the introduction of Level 2 water restrictions at the end of the 2002-03

financial year. The next discrete downward shift in billed water sales takes

place in 2007-08, corresponding to the first full year under Level 3 restrictions

after their introduction in January 2007. In 2007-08 total billed water sales was

192 GL, compared to 221 GL in the preceding year (see Table 2). Total billed

water sales in 2008-09, the second full year in which Level 3 water restrictions

were in force, was 189 GL.

-

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

Residential Commercial Other non residential



16

Table 2 SA Water total billed water sales, 1996-97 to 2010-11

Billed water sales by

customer class (ML)

Residential Commercial Other non-

residential

Total

1996-97 137,870 9,973 62,495 210,339

1997-98 139,143 10,574 63,400 213,117

1998-99 142,194 10,505 64,747 217,446

1999-00 146,282 10,877 64,451 221,610

2000-01 154,947 11,026 66,667 232,640

2001-02 144,585 10,851 62,595 218,032

2002-03 161,258 11,829 69,467 242,554

2003-04 143,380 11,359 61,295 216,033

2004-05 143,886 10,880 60,881 215,647

2005-06 143,720 10,996 62,172 216,888

2006-07 147,104 11,245 62,723 221,072

2007-08 122,444 10,154 59,656 192,254

2008-09 121,298 9,693 58,289 189,280

2009-10 123,267 9,908 52,455 185,630

2010-11 114,041 9,387 51,792 175,219


The annualised rate of growth in billed water sales strongly reflects the

introduction of water restrictions at the end of the 2002-03 financial year and

their upgrade to Level 3 in 2006-07 (see Table 3).

Table 3 Annualised growth in billed water sales, by sector, per cent per annum to 2010-11

Sector 1 year 3 years 4 years 5 years 10 years Pre restrictions

4 years to 2002-03

Residential -7.5% -2.3% -6.2% -4.5% -3.0% 3.2%

Commercial -5.3% -2.6% -4.4% -3.1% -1.6% 3.0%

Other non

residential

-1.3% -4.6% -4.7% -3.6% -2.5% 1.8%

Total -5.6% -3.0% -5.6% -4.2% -2.8% 2.8%

Data source: ACIL Tasman calculations based on SA Water data

In the four year period preceding the introduction of water restrictions, i.e.

between 1998-99 and 2002-03, total billed water sales grew at a robust pace for

most customer categories. Over this period, total billed water sales to all

customer classes grew by 2.8 per cent per annum. 13

The annual growth rates shown in Table 3 should be treated with care as the

data upon which they are based has not been adjusted for the impact of

13 As discussed in 4.2.2, this growth might be overstated due to past under-recording by

meters that were replaced during this period.



17

changes in weather. Therefore, a simple comparison of these data is unlikely to

compare ‘like with like’. This issue is discussed further in 4.1.4.

3.3 Water restrictions in South Australia

Water use in South Australia has been restricted in one way or another since

2002. The level and detail of restrictions has changed more than 20 times since

then.

Restrictions were applied differently in different parts of South Australia. For

example, the Eyre Peninsula region has had relatively stringent restrictions

since December 2002, while restrictions in the greater Adelaide region were

relatively modest until late 2006.

The restrictions that were imposed on the Greater Adelaide region can be

summarised as follows:14

1. Level 2 restrictions were imposed from late June 2003 to October 2003

2. Water wise measures were imposed from October 2003 coinciding with the

lifting of the level 2 restrictions15

3. Level 2 restrictions were imposed in October 2006

4. After October 2006 water restrictions were tightened gradually reaching

Level 3 in December 2006 and Enhanced level 3 in June 2007

5. Water restrictions were lifted in December 2010. Water wise measures

remain in place.

This is shown graphically in Figure 4.

14 At various times the base level of restrictions has been known as Level 1 restrictions,

permanent water conservation measures and water wise measures. Regardless of the name, the restrictions that were applied were substantially the same. For simplicity we demand the name ‘water wise measures’ in this report although they were not referred to by that name at all times.

15 These were referred to as permanent water conservation measures at that time. More recently their name was changed to water wise measures. The measures were refined to apply from December 2010 coinciding with the lifting of Enhanced level 3 restrictions and their name was changed to water wise measures.



18

Figure 4 History of water restrictions in South Australia

Source: SA Water

Since 1 December 2010, no temporary water restrictions have been in place.

However, certain water using activities are now prohibited permanently under

‘water wise measures’. In summary, 16 water wise measures:

• prohibit the use of overhead sprinklers between 10:00am and 5:00pm

• require that cars and boats can only be washed using a hose with trigger

nozzle, a bucket or a high pressure low volume water cleaner

• permit external paved areas to be hosed down only in limited

circumstances

• require proof of purchase of an approved pool cover before issue of a

permit to fill new swimming pools

During the time that Level 3 water restrictions were in place they were varied a

number of times. In summary:

• use of sprinkler systems for watering outdoor trees, shrubs, plants and

lawns was prohibited

16 This is a summary only. For further detail please refer to:

http://www.sawater.com.au/SAWater/Environment/WWM/WWM_Overview.htm

•No restrictions Before December 2002

•Restrictions limited to Eyre Peninsula December 2002 to June

2003

•Level 2 restrictions July 2003 to October

2003

•Water wise measures October 2003 to

October 2006

•Level 2 restrictions followed by level 3 and then level 3 (amended) restrictions ie tightened

October 2006 to June 2007

•Gradual tightening from level 2 to level 3 restrictions October 2006 to June

2007

•Enhanced level 3 restrictions July 2007 to November

2010

•Water wise measures (Enhanced level 3 continues for Eyre Peninsula)

December 2010 to present



19

• gardens could be watered anytime with a hand held bucket or watering can

and for a limited number of hours using a hose fitted with a trigger nozzle.

Hose watering was only permitted at certain times of the day

• external paved areas could only be hosed down in limited circumstances

• topping up of fountains and ponds was limited

• existing pools and spas that were empty could not be refilled and new

pools could only be filled if they were fitted with an approved cover. There

were limitations on topping up the level of all pools

• hoses were not permitted to be used in washing cars or boats other than

for specified circumstances

• there were a number of restrictions on the commercial use of water for

dust suppression and in nurseries, garden centres and farms

Level 2 water restrictions were less stringent. They were as follows:

• hand held hoses, watering cans and buckets could be used to water gardens

at any time be used at any time but sprinkler systems could only be used

twice a week from 8pm to 8am

• drip irrigation systems could be used at any time

• cars could be washed using buckets and sponges for washing and a trigger

hose or high pressure cleaner were permitted for rinsing

• cleaning paved areas was prohibited at all times except for fire and

emergencies

• a permit was required to fill a new pool or outdoor spa and a permit was

required to refill an existing pool or outdoor spa

• fountains or ponds that did not recycle water could not be operated or

topped up. The water in fountains or ponds that recycled water could only

be topped up with water from a hand held hose or bucket.

3.4 Water demand per customer

The previous sections showed that, at least until the imposition of water

restrictions, SA Water enjoyed growth in both customer numbers and total

water demand. In this section we turn to the (average) behaviour of individual

water users by removing the impact of increasing customer numbers over time

from the aggregate water demand time series.



20

Figure 5 SA Water, billed water sales per customer, 1996-97 to 2010-11


Figure 5 shows that billed water demand per customer exhibited an upward

trend from 1996-97 to 2002-03, before declining from 2003-04. Allowing for a

lag between consumption and billing this corresponds approximately with the

introduction of Level 1 water restrictions.

Average consumption peaked at 384.4 kL per customer in 2002-03. Its

minimum (recent) level was in 2010-11, when it was 250.2 kL per customer.

The decline in average water demand per customer occurred across all

customer classes for SA Water. However, it was most pronounced in the

residential sector. The following sections disaggregate consumption per

connection by customer class.

3.4.1 Residential

Before water restrictions were imposed, average residential water demand per

customer showed an upward trend. It rose from 261.7 kL per customer in

1996-97 to 280.7 kl per customer in 2000-01. It then dipped to 259.0 kl per

customer in 2001-02.17 Annualised growth in residential water usage billed per

customer was 1.4 per cent per annum between 1996-97 and 2002-03. Since that

time, average residential water demand per customer declined by 5.5 per cent

per annum.

17 It rose further in 2002/03, the first year of restricted water demand.

-

50

100

150

200

250

300

350

400

450

Total



21

In 2010-11, average residential water demand per customer was 180.5 kL per

customer. As can be seen in Figure 6, this was its lowest level since (at least)

1996-97.

Figure 6 Billed water sales per customer - residential, 1996-97 to 2010-11


3.4.2 Commercial

The time series of commercial water demand per customer is shown in Figure

7 below. There is a similar pattern to that observed in the residential sector.

Consumption per connection increased steadily until the introduction of water

restrictions and then declined. However, the decline was less pronounced in

the commercial sector than the residential sector.

-

50

100

150

200

250

300

Residential



22

Commercial water demand per customer grew at 1.9 per cent per annum from

1996-97 to 2002-03 when it peaked. It then declined at 3.8 per cent per annum

to 2010-11. Over the five years to 2010-11, commercial water demand per

customer declined at 4.2 per cent per annum.

Figure 7 Billed water sales per customer – commercial, 1996-97 to 2010-11


3.4.3 Other non-residential

The pattern in the average demand per other non-residential customer, which

is shown in Figure 8, is again similar to other customer classes.

In the pre-restrictions period, water demand per other non-residential

customer grew at 1.1 per cent per annum. In the last five years, it declined at

4.0 per cent per annum.

0

50

100

150

200

250

300

350

400

450

500

Commercial



23

Figure 8 Water demand per customer – other non-residential, 1996-97 to 2010-11


3.5 Summary – billed water sales

In summary, SA Water enjoyed a period of rising billed water sales from 1996-

97 until 2002-03. This rise was driven partly by increasing customer numbers

and partly by increasing water demand per customer.

Beginning shortly after water restrictions were introduced, billed water sales

declined in both total and per customer terms. The decline was apparent in all

customer classes.

It is notable that billed water sales did not increase in 2010-11, even though

restrictions were lifted in December 2010. Importantly, the restrictions that

applied during the 2010-11 summer were significantly ‘lighter’ than the

previous summer. This makes the continued decline in growth somewhat

counter-intuitive given that water restrictions were lifted at this time. As shown

in section 4.1.1, this also coincides with a period of reduced economic growth,

and ongoing price increases which appear to have contributed to the low billed

water sales.

Weather is also likely to have contributed to the low growth. The 2009-10

summer was significantly hotter and drier than 2010-11, as shown in section

4.1.4.

0

200

400

600

800

1,000

1,200

1,400

1,600

1,800

2,000

Other non-residential


Economic and demographic drivers

24

4 Economic and demographic drivers

A best practice demand forecasting model will incorporate the drivers of

demand for each relevant sector independently to account for the different

nature of demand, and responsiveness to drivers, in different sectors.

In our view, water demand forecasts should be split by customer category to

reflect differences in the nature of demand exhibited by different categories of

customer.

Forecasting demand separately for different customer classes separately allows

for drivers that may differ across the various customer classes. For example,

population growth was found to be important for domestic water demand,

while economic activity was more important for commercial demand.

By treating different customer segments independently, it is possible to

incorporate the different drivers for each customer class as well as allowing

differing sensitivities for the drivers across customer classes.

The billed water sales data allowed us to separate demand in the residential and

commercial sectors. All other demand was categorised as ‘other non-

residential’ for the reasons discussed in section 3.1.2.

The main drivers of billed water sales were found to be:

• the level of economic activity

• the price of water

• South Australia’s population

• temperature

• water restrictions

These drivers are discussed in section 4.1.

The bulk supply data did not allow demand to be split by customer category.

However, it had the advantage of being available on a monthly basis. This

allowed the effect of other factors to be identified.

The main drivers of bulk supply were found to be

• the level of economic activity

• the price of water

• rainfall

• temperature

• pan evaporation



25

• water restrictions.

These drivers are also discussed in section 4.1.

A number of other potential drivers were examined but excluded from both

models. These drivers, which are discussed in section 4.2, were:

• The rate of meter replacement

• Demand management activities

It is not possible to make definitive comments as to why these variables were

not found to be significant in the model. However, it does not necessarily

follow that they had no effect on water demand. In some cases the lack of

significance is likely to be because the impact of these factors cannot be

distinguished from the impact of other factors that were present at the same

time. Where two variables move together, regression techniques are unable to

separate their effect.

4.1 Drivers included in the models

4.1.1 The level of economic activity

Higher levels of economic growth, and the increased employment and

disposable incomes that come with them, are likely to be significant drivers of

water demand in all sectors. However, the responsiveness shown by each

sector is likely to vary. ACIL Tasman considers their inclusion in a well

specified model of water demand to be desirable. Figure 9 shows the level of

economic activity, measured as Gross State Product (GSP), in South Australia

for from 1996-97 to 2010-11.



26

Figure 9 South Australian Gross State Product – 1996-97 to 2010-11

Data source: historic data – Australian Bureau of Statistics

Between 1996-97 and 2010-11, South Australian GSP growth varied between

1.2 per cent (in 2010) and 5.9 per cent (in 2008). Average annualised GSP

growth over this period was 2.8 per cent per annum.

4.1.2 Population

Demographic factors will affect all of residential, commercial and industrial

water demand to some extent. The most important demographic driver of

water demand is population growth. If all else was constant, an increase in

population would lead to an increase in the amount of water used in all sectors.

However, as populations have grown in recent years, other factors have also

changed.

South Australia’s population since 1997 is shown in Figure 10.

-

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

100,000

$ m

illio

n



27

Figure 10 South Australian population, 1996-97 to 2010-11

Data source: Australian Bureau of Statistics

Between 1997 and 2011 South Australia’s population grew at an annualised

rate of 0.8 per cent per annum. Year on year growth was quite steady, moving

between 0.4 per cent (in 2000/01) and 1.3 per cent (in 2008-09) per annum.

4.1.3 The price of water

There are two components to the price of water sold by SA Water:

• Fixed (or access) charge

• Several ‘tiers’ of volumetric charges (prices)

Since 2008-09 there have been three ‘tiers’ in SA Water’s pricing structure.

Before that there were two from 1998/98 to 1997/08 (inclusive). There were

three tiers in 1996/97.

The third tier applies only to single occupancy residential dwellings, i.e. it

would apply to a house but not to a business premises or a block of flats on a

single title that share a water meter.

1,350,000

1,400,000

1,450,000

1,500,000

1,550,000

1,600,000

1,650,000

1,700,000

Nu

mb

er



28

Table 4 summarises SA Water’s residential customer prices from 1996/97 to

2011-12. It also shows the annual water bill for a representative householder

using 205 kL18 per annum (subject to the third tier where applicable).

Table 4 The price of water in South Australia – 1996-97 to 2011-12 – residential customers

Year Supply

charge

Tier 1 Tier 2 Tier 3 205 KL bill

$ per

annum

$ per kL $ per kL $ per kL $

1996-97 $118 $0.22 $0.89 $0.91 $217

1997-98 $131 $0.25 $0.90 $0.92 $234

1998-99 $119 $0.35 $0.89 $234

1999-00 $123 $0.36 $0.92 $242

2000-01 $121 $0.36 $0.91 $239

2001-02 $125 $0.38 $0.94 $248

2002-03 $130 $0.40 $0.97 $258

2003-04 $135 $0.42 $1.00 $268

2004-05 $141 $0.44 $1.03 $278

2005-06 $145 $0.46 $1.06 $287

2006-07 $148 $0.47 $1.09 $294

2007-08 $157.40 $0.50 $1.16 $313

2008-09 $157.40 $0.71 $1.38 $1.65 $360

2009-10 $137.60 $0.97 $1.88 $2.26 $414

2010-11 $142.40 $1.28 $2.48 $2.98 $507

2011-12 $234.60 $1.93 $2.75 $2.98 $700


The prices shown in Table 4 are nominal residential prices. To allow

comparison over time, Figure 11 shows the marginal water price in real terms

(2012 dollars) since 1996-97.19

18 This is the average residential demand per customer over the period from 2006/07 to

2010/11.

19 For residential customers this is the second tier price. For others, it is the volumetric price payable after any allowance that may have been applicable at the time.



29

Figure 11 Real water prices in South Australia 1996-97 to 2011-12 (2012 dollars)


It is very clear from both Table 4 and Figure 11 that the price of water

increased rapidly in South Australia in recent years. Before 2008-09, the price

of water had been fairly stable since 1996-97, with some decline in real terms.

Another characteristic of water prices in South Australia that is not seen in

Figure 11 is that certain customers received water allowances until 2002/03.

SA Water’s commercial customers pay fixed (annual) supply charges based on

the value of their properties.

Until 2002/03 those customers could use an amount of water equal to the

value of that fixed charge without paying extra. In effect, they paid for a certain

amount of water regardless of whether they used it or not. The amount was

determined by dividing their fixed charge by the water price of the day (second

tier when applicable).

These water allowances were phased out between 2002-03 and 2005-06.

Customers who had previously received them began to pay for all the water

they used, although, initially, the price they paid was less than the applicable

water price. As a result, commercial customers faced increasing water bills

through that period regardless of their water use. In other words, a commercial

customer that used the same amount of water in 2006-07 as they did in 2002-

03 would face an increase in the bill because more of their usage became

‘exposed’ to the water price.

$-

$0.50

$1.00

$1.50

$2.00

$2.50

$3.00

Commercial Other non-residential Residential



30

Economic theory suggests that the relevant price for these purposes is the

marginal price. That is the price the customer paid for the last unit of

consumption (or the price they would pay for the next unit).

With water allowances, some commercial customers would not have been

exposed to the marginal price of water. In other words, whether they increased

their usage or not, they would pay the same amount for water. Other

customers would have used more water than their allowance and would have

been exposed to the marginal price.

The available data are not sufficient to identify the extent of customers who

were, or were not, exposed to the marginal price for their consumption.

To some extent the removal of water allowances would have increased the

number of commercial customers who were ‘exposed’ to the marginal price for

their consumption. In effect, those customers faced a price rise for water

between 2002-03 and 2006-07, when ‘list’ prices were relatively stable.

There is a possibility that using only the ‘list’ price of water would mask the

responsiveness of these customers to the effective change in water price they

faced between 2002-03 and 2006-07. This possibility was examined by

including a variable based on the rate at which the water allowances were

phased out. The results of that test were that including this variable had only a

marginal impact on the model. This lends some support to the preferred

model, but does not provide sufficient reason to include this variable in the

core model.

A related issue is that the second tier price is likely to be the marginal price for

most of the water used by SA Water’s customers. Therefore, economic theory

suggests that it is the appropriate measure of price for use in the analysis.

However, the second tier is not the only measure of price. We also considered

the possibility that the amount people pay for their water bill may be more

effective in explaining the variation in water demand. Thus, in developing the

models presented in chapter 5, we also tested the relationship between a

representative water bill and consumption. For these purposes we constructed

the representative bill based on a residential customer using 205 kL water per

annum spread equally over all four quarters of the year.20

20 Note that the average usage each year could not be used without concealing the effect of

price on average consumption (i.e. price elasticity could not be estimated if changing average use was taken into account in the price measure itself).



31

Applying the representative bill in place of the second tier price resulted in:

• a poorer fit on historical data (i.e. inferior explanatory power)

• an implausibly high estimate of the price elasticity of demand

• implausibly low forecasts.

Due to its superior explanatory power, we prefer the model based on the

second tier price.

4.1.4 Weather

Demand for water is likely to be influenced by several weather variables. In

particular, demand may be influenced by the degree of hot weather over a

season and the extent of rainfall and evaporation.

Therefore, it is important that any forecasting methodology accounts for

variations in demand that arise from differences in weather conditions in the

past. Failing to account for the impact of variation in weather properly can lead

to biased forecasts.

For example, if the most recent year was associated with extremely warm

summer days and low rainfall while the preceding year was wet and mild, then

failure to account for this explicitly in the forecasting process will make

underlying demand appear to be growing faster than it actually is. In this

example a forecasting methodology based on historical trends will over predict

future water demand as weather conditions revert to normal in the forecast

period.

Weather drivers should either be incorporated into a forecasting model as

explanatory variables or the historical data should be adjusted (normalised) for

weather variations before any analysis based on that data is conducted.

In this case, we included three weather variables in the modelling as

explanatory variables, namely:

1. temperature

2. rainfall

3. evaporation

The temperature variable used was Cooling Degree Days 18 (CDD 18). The

number of CDD 18 in any given year is calculated by calculating the mean of

the daily maximum and minimum temperatures each day and, where this is

greater than 18, subtracting 18.21

21 We also tested CDD21, which is calculated the same way using 21 as the reference point.



32

Rainfall was measured in absolute terms, i.e. the amount of rain (in mm) that

was measured at various weather stations.

Evaporation is measured in millimetres.

These variables were tested using data from various weather stations in South

Australia. The modelling showed that the weather at the Kent Town data

station offered more explanatory power than the other stations that were

tested.22

Figures 12 to 14 show the temperature, rainfall and evaporation data at the

Kent Town weather station.

Figure 12 Cooling Degree Days – Kent Town Weather Station – 1977-78 to 2010-11

Source: ACIL Tasman calculations based on Bureau of Meteorology data

22 Other weather stations tested were Ceduna, Mt Gambier and Woomera.

0

200

400

600

800

1000

1200

CD

D



33

Figure 13 Annual rainfall – Kent Town Weather Station – 1977-78 to 2010-11

Source: Bureau of Meteorology

Figure 14 Evaporation – Kent Town Weather Station – 1977-78 to 2010-11 (partial)

Source: Bureau of Meteorology

Our modelling showed that there is a strong relationship between the demand

for water and temperature. The relationships between the demand for water

and either rainfall or evaporation were not as strong.

Note that this does not mean that our modelling suggests that rainfall and

evaporation are not relevant to water demand in South Australia. A more likely

explanation is that rainfall, temperature and evaporation are correlated with

one another. Regression techniques cannot distinguish the impact of different

0

100

200

300

400

500

600

700

800

900

An

nu

al r

ain

fall

(mm

)

Annual rainfall

0

200

400

600

800

1000

1200

1400

1600

1800

Evap

ora

tio

n (

mm

)

Annual Evaporation



34

variables when they are too highly correlated. It is notable that in the monthly

data, when there is more freedom for rainfall and temperature to move

independently, both are statistically significant.

4.2 Drivers not included in the models

4.2.1 Other household demographics

Other factors that are likely to affect residential demand for water include

household size and the size of new residential lots as well as the occupancy

rates across residential dwellings and commercial buildings. These changes

would ideally be accounted for in the water forecasting methodology.

However, we are not aware of reliable data projecting them. Therefore, they

have been omitted.

The modelling implicitly assumes that household size will continue to change

according to the trend observed in the period from 1996-97 to 2010-11.

4.2.2 Non revenue water - Meter replacement program

As discussed in section 3.2, the amount of water SA Water supplies can be

measured at the treatment station (bulk supply) or the customer’s meter (billed

water sales). Ideally these two measurements would be the same but in practice

they are not (as shown in Figure 2). SA Water refers to the difference between

these two measures as ‘non revenue water’.

SA Water has limited information about where non-revenue water goes.

Some of it is lost either through pipe burst incidents or because the water

network is not perfectly sealed. Some is used for unmetered purposes such as

fire fighting and some is probably stolen.

These effects cannot be forecast meaningfully. We have assumed that, on

average, the extent of non-revenue water due to these effects has been

constant, in percentage terms, in history and that it will continue at the same

rate in future.

However, it is possible that some water does reach the end user but is ‘non-

revenue’ water anyway. This is because it is not ‘counted’ by the relevant meter.

To an extent this is due to inevitable technical failure, but SA Water has

advised that it is also inherent to certain older style meters. SA Water estimates



35

that the domestic meters in demand in the 1990s tended to ‘under-read’ the

volume of water passing through them by approximately 5 to 7 per cent.23

In 1999-00, SA Water began a widespread program of upgrading these meters

with more accurate replacements. By 2010-11, SA Water estimated that it had

replaced almost 400,000 domestic meters and approximately 13,000 non-

residential meters.

Logically, this program should have reduced the amount of non revenue water,

although the amount cannot be determined readily. Figure 15, which compares

the changing ratio of non-revenue water over time with the meter replacement

program, suggests that the meter replacement program was effective.

Figure 15 Non revenue water and meter replacements, 1996-97 to 2010-11


Figure 15 also shows that, regardless of the meter replacement program, non-

revenue water varies year to year. In addition it increased after 2006-07.

In the modelling, we explored the possibility that including the meter

replacement data would improve the model. In practice, though, they did not,

so this driver was omitted from the model.

23 SA Water, “SA Water metering strategies for the next decade 1996/2006”, 1 July 2006,

supplied.

0

10000

20000

30000

40000

50000

60000

0

10000

20000

30000

40000

50000

60000

70000

80000

90000

100000

Meters replaced Non revenue water



36

4.2.3 Rebates and other demand management activities

While water restrictions were in place and the recent drought was in effect, the

South Australian Government implemented various demand management

measures intended to reduce water use. Some of these activities were focussed

on the residential sector, with others focussed on the other sectors.

The demand for water per customer declined in all three customer categories

after 2002-03. This would have been due to a number of factors including

these demand management activities. Where those activities resulted in

permanent changes to the way customers use water, it is important to account

for those changes in forecasting water demand.

To do this, we explored the possibility of incorporating data regarding the

number of rebates paid for water saving devices into the models of water

demand per customer. While the demand management initiatives were broader

than the rebate program, the number of rebates issued in a given year should

provide a reasonable measure of the intensity of the broader program.

The number of rebates issued between July 2008 and December 2011 is shown

(in cumulative terms) in Figure 16.

Figure 16 Water saving rebates issued July 2008 to December 2011


In practice, including the number of rebates issued did not improve the

models.24 This is likely to be due to the very close correlation between the

24 See Appendix A for details

-

50,000

100,000

150,000

200,000

250,000

2008-09 2009-10 2010-11 2011-12 (halfyear)

reb

ate

s (n

um

be

r)

Rebates paid (number, cumulative)



37

number of rebates issued and the price of water over the same period,

illustrated in Figure 17. It is also likely to be due to the fact that a large number

of the rebates that were received would have been motivated by the rising

prices. In these circumstances regression models cannot distinguish between

the two effects.

Figure 17 Water saving rebates and second tier water price


The extent to which rebates have influenced the reduction in water

consumption in SA Water’s network since their introduction was of key

interest to SA Water.

An alternative approach to identifying the effect of rebates and prices was

attempted. That approach is discussed in Appendix A.

$-

$0.50

$1.00

$1.50

$2.00

$2.50

$3.00

-

50,000

100,000

150,000

200,000

250,000

2008-09 2009-10 2010-11 2011-12 (halfyear)

seco

nd

tie

r p

rice

($

/KL)

reb

ate

s (n

um

be

r)

Rebates paid (number, cumulative) Second tier price


Model specification

38

5 Model specification

To develop forecasting models for SA Water’s demand, we tested numerous

specifications. We chose between those models based on the ‘goodness of fit’

with historical data and ‘sense checks’ with the model coefficients and other

sources of information.

The specification that was chosen for the annual model of billed water sales

comprises five component models. A separate model was estimated for each

of:

• Residential customer numbers

• Average demand per residential customer

• Commercial customer numbers

• Average demand per commercial customer

• Total demand by other non residential customers

These component models are combined to forecast demand for each customer

class as follows:

• Residential demand is forecast as residential customer numbers times

average demand per residential customer

• Commercial demand is forecast as commercial customer numbers times

average demand per commercial customer

• Total demand by other non residential customers as above

Forecasts of total demand are the sum of these three customer class forecasts.

The billed water sales forecasts are on an annual basis, reflecting the periodicity

of the billed water sales data.

The monthly model of bulk supply is a single regression that forecasts total

volume directly.

5.1 Key drivers

Our modelling indicates that the key drivers of billed water for SA Water are as

shown in Table 5.


Model specification

39

Table 5 Drivers of component models

Residential

customer

numbers

Average

residenti

al usage

Commercial

customer

numbers

Average

commercial

usage

Total other

non

residential

usage

Total Bulk

supply

(monthly)

Population (%

annual growth) √

Economic activity

(Gross State

Product) √ √ √ √

Price of water

($/kL, second tier) √

√ √ √

Temperature (CDD

18) √

√ √ √

Water restrictions

(level) √

√ √ √

Rainfall (mm) √ Evaporation (mm) √

For these purposes we measure the price of water using the marginal price of

water. For residential customers this is the second tier of SA Water’s inclining

block tariff structure. For other customers in past years it is the usage charge

that applied after any allowance had been used or, when no allowances were in

place, the second tier usage charge. See section 4.1.3 for details.

5.2 Model specification - annual billed water sales

model

This section provides an overview of the specification of the annual billed

water sales model and its components.

5.2.1 Residential customer numbers

The residential customer numbers model is a linear model with a constant

term. The model’s fit with historical data is shown in Figure 18. The coefficient

of determination for the model (adjusted R-squared) is 0.99.

The sole independent variable is population growth. The coefficient is

approximately 0.59, implying that the number of residential customers SA

Water supplies grows at 59 per cent of the rate of population growth or that,

for every 100 new persons in South Australia, SA Water’s customer numbers

increase by 59.25 This coefficient is statistically significant at well in excess of

the 99 per cent confidence level.

25 It also implies that, on average, SA Water’s customers are 1.7 person households. This is

lower than might be expected for average household size in South Australia as reported by


Model specification

40

Figure 18 Residential customer numbers model


5.2.2 Water usage by residential customer

The water usage model for residential customers is specified as a log-log

model, i.e. the model is based on the (natural) logarithm of the variables, not

the values themselves. This specification allows the regression coefficients to

be interpreted as elasticities of demand. The regression coefficients show the

responsiveness of demand for water to a one per cent change in each driver

(independent variable) assuming that all else is constant.

This specification also assumes that, unlike a linear demand curve, elasticity is

constant at all price levels. 26

the ABS, which is just over 2 persons per household. The difference is likely due to the fact that some of SA Water’s customers are ‘no person’ households, such as holiday homes, but these are not treated as households by the ABS.

26 An elasticity describes the way that one variable changes in response to a change in another. The price elasticity of demand describes the amount, in percentage terms, by which demand decreases in response to a one per cent increase in price. A linear demand curve has a price elastic portion where the change in demand is greater than the change in price (with both measured in percentage terms). It also has a price inelastic portion, where the change in quantity demanded is less than the change in price (with both measured in percentage terms).The price elastic portion of a linear demand curve is the upper left portion, where a small percentage change in price is accompanied by a large percentage change in quantity demanded. The price inelastic portion is the bottom right, where the reverse is true. On a linear demand curve, elasticity is different at all points on the curve. By contrast, in a log-log model such as the model used here, elasticity is constant at all price levels.

0

100000

200000

300000

400000

500000

600000

700000

Nu

mb

er

Fitted Actual


Model specification

41

The drivers (independent variables) in this model and their coefficients are:

• Temperature (CDD 18) – 0.15

• Water price -0.38

• Dummy variables for the three different levels of water restrictions

− Level 1 - -0.11

− Level 2 - -0.15

− Level 3 - -0.27

Therefore, the model implies that for a one per cent increase in:

• CDD, the quantity of water demanded by the average residential customer

will increase by 0.15 per cent

• Water price, quantity of water demanded by the average residential

customer will decrease by 0.38 per cent

The dummy variables for water restrictions are not additive. They imply that,

all else being equal, the quantity of water demanded by the average residential

customer reduced by approximately:

• 11 per cent under level one restrictions

• 15 per cent under level 2 restrictions

• 27 per cent under level 3 restrictions.

Each of these coefficients except CDD is statistically significant at the 99 per

cent confidence level. The CDD coefficient is significant at the 95 per cent

confidence level.

Figure 19 shows the relationship between the fitted model and historical data.

The coefficient of determination (adjusted R-squared) is 0.94.


Model specification

42

Figure 19 Residential water usage model


5.2.3 Commercial customer numbers model

The commercial customer numbers model is a linear model with a constant

term. Its fit with historical data is shown in Figure 20. The coefficient of

determination for this model (adjusted R-squared) is 0.98.

The sole independent variable is growth in GSP. The coefficient is

approximately 0.12, implying that SA Water receives a new commercial

customer when South Australia’s GSP increases by approximately $8.5

million.27 That coefficient is statistically significant at well in excess of the 99

per cent confidence level.

27 Alternatively, for each increase in GSP of $1 million, SA Water receives 0.12 more

commercial customers.

4.90

5.00

5.10

5.20

5.30

5.40

5.50

5.60

5.70

ln(k

L/co

nn

ect

ion

)

Fitted Actual


Model specification

43

Figure 20 Commercial customer numbers model


The model indicates that SA Water’s commercial customer numbers increase at

approximately 0.12 per cent of the rate of GSP growth.

5.2.4 Commercial water usage model

The water usage model for commercial customers is specified as a log-log

model. As discussed in section 5.2.2 above, this means that the coefficients can

be interpreted as elasticities, i.e. the ratio of percentage changes in the variable

in question and the quantity of water demanded by the average commercial

customer.

The drivers (independent variables) in this model, and their coefficients, are:

• GSP– 0.4828


• Water price - -0.37


− Level 1 - -0.11

− Level 2 - -0.15

28 Note that GSP is a driver in both commercial customer numbers and average water demand

per customer. This implies that, as GSP rises, new customers arrive and existing customers demand more water.

21000

22000

23000

24000

25000

26000

27000

28000

Nu

mb

er

Fitted Actual


Model specification

44

− Level 3 - -0.25

Each of these variables except CDD is statistically significant at well in excess

of the 99 per cent confidence level. CDD is statistically significant at the 95 per

cent confidence level.



Figure 21 Commercial water usage model


The model indicates that water usage by the average commercial customer will:

• Increase by 0.48 per cent for a 1 per cent increase in GSP growth

• Increase by 0.12 per cent for a 1 per cent increase in CDD

• Decrease by 0.37 per cent for a 1 per cent increase in price (i.e. the price

elasticity of demand is -0.37)

Similarly, the model implies that, all else being equal, commercial customers

demanded:

• 11 per cent less water under level 1 restrictions

• 15 per cent less water under level 2 restrictions

• 25 per cent les water under level three water restrictions

5.65

5.7

5.75

5.8

5.85

5.9

5.95

6

6.05

6.1

6.15

6.2

ln((

Nu

mb

er)

Fitted Actual


Model specification

45

The restrictions in question did not apply in the same way to commercial

customers as they did to residential customers. However, when the restrictions

were in place, the availability, and security, of water in South Australia was a

widely discussed topic and significant effort was made to reduce water use. The

model cannot distinguish between the response to this more general effort and

the restrictions themselves.

This result suggests that, notwithstanding that some aspects of the water

restrictions were not directly applicable to commercial customers, their water

demand was reduced anyway.29

5.2.5 Other non-residential water usage model

Unlike the commercial and residential sectors, the model that performed best

for the other non-residential sector was a single, log-log model for total billed

water sales.

The drivers (independent variables) in this model and their coefficients are:

• GSP – 0.36


• Water price - -0.32


− Level 1 - -0.12

− Level 2 - -0.14

− Level 3 - -0.19

Each of these variables is statistically significant at (at least) the 98 per cent

confidence level.



29 Some aspects of the water restrictions were applicable to commercial customers as well.


Model specification

46

Figure 22 Water usage model – other non-residential customers


The model indicates that water usage by the average other non-residential

customer will:

• Increase by 0.36 per cent for a 1 per cent increase in GSP

• Increase by 0.10 per cent for a 1 per cent increase in CDD

• Decrease by 0.32 per cent for a 1 per cent increase in price (i.e. the price

elasticity of demand is -0.32)

Similarly, the model implies that, all else being equal, commercial customers

demanded:

• 12 per cent less water under level one restrictions

• 14 per cent less water under level two restrictions

• 19 per cent les water under level three water restrictions

Water restrictions applied differently to other non-residential customers than

to residential customers. Water restrictions were in place for irrigators and local

councils were subject to the irrigated public open space scheme to reduce their

water use.

5.3 Model specification - bulk supply

ACIL Tasman has also estimated a monthly model using monthly bulk supply

data.

10.650

10.700

10.750

10.800

10.850

10.900

10.950

11.000

11.050

11.100

11.150

11.200

ln((

ML)

Fitted Actual


Model specification

47

The model was calibrated using monthly data commencing from 1995-96, 198

observations in total. The dependent variable is the natural logarithm of

monthly bulk water supplied.

The drivers (independent variables) and there coefficients are:30

• The natural logarithm of Gross State Product (GSP) – 0.28

• Rainfall - -0.001

• Cooling degree days 18 – 0.002

• Pan evaporation 0.003

• The natural logarithm of the real marginal price of water (residential) – 0.20

• Water restrictions

− Level 1 - -0.13

− Level 2 - -0.23

− Level 3 - -0.26

The fitted model has an R2 of 0.88.

The model coefficients can be interpreted as follows:

• a 1% increase in the level of GSP leads to a 0.28% increase in bulk supply

• a 1% increase in the real price of water leads to a 0.2% reduction in bulk

supply

• an increase in cooling degree days by 100 leads to a 0.1 ML increase in bulk

supply per month

• an increase in rainfall of 100mm leads to a reduction in bulk supply of 0.2

ML per month

• an increase in pan evaporation of 100mm leads to an increase in bulk

supply of 0.3 ML per month

• water use is 12.6% less on average during Level 1 restrictions

• water use is 22.5% less on average during Level 2 restrictions

• water use is 26.4 % less on average during Level 3 restrictions.

Figure 23 shows the predicted values against the actual monthly levels of bulk

supply.

30 The model is based on the level, not the logarithm, of the variable unless otherwise

specified.


Model specification

48

Figure 23 Bulk water model


0

10000

20000

30000

40000

50000

1/0

7/19

95

1/0

7/19

96

1/0

7/19

97

1/0

7/19

98

1/0

7/19

99

1/0

7/20

00

1/0

7/20

01

1/0

7/20

02

1/0

7/20

03

1/0

7/20

04

1/0

7/20

05

1/0

7/20

06

1/0

7/20

07

1/0

7/20

08

1/0

7/20

09

1/0

7/20

10

1/0

7/20

11

ML

Fitted Actual


Developing the forecasts

49

6 Developing the forecasts

Using the models described above to develop forecasts requires forecasts of

the drivers. The following sections describe the sources from which these were

obtained. Consistent with ESCOSA’s best practice principles and our own, we

believe that the forecasts of explanatory variables should be obtained from an

independent, reputable source where possible.

6.1 The level of economic activity

In preparing SA Water’s demand forecasts we used the GSP growth forecasts

prepared by the South Australian Department of Treasury and Finance and

published in the 2011 Mid Year Budget Review. These were the most recent

projections available from the Government at the time of writing. Growth in

those forecasts is shown in Figure 24.

Figure 24 GSP growth projections

Data source: to 2014-15, Government of South Australia, "Mid Year Budget Review 2011-12", p.22,

http://www.statebudget.sa.gov.au/; beyond 2014-15, ACIL Tasman modelling

The Government’s GSP growth forecasts do not go beyond 2014-15. After

that time we determine a medium annual growth rate for GSP based on the

average growth rate of GSP over the period 1991 to 2011. High and low

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

4.5%

5.0%

Gro

wth

pa

GSP - Medium forecast GSP - High forecast GSP - Low forecast

http://www.statebudget.sa.gov.au/



50

forecast growth rates for GSP are derived by taking the 75th and 25th percentiles

of past GSP growth over the 1991 to 2011 period.

6.2 Population

Two sources of population growth projections were considered. The first was

the Australian Bureau of Statistics, the second was the South Australian

Department of Planning and Local Government.

Both projections include low, medium and high growth scenarios. These six

projections are shown in Figure 25.

Figure 25 South Australian population growth projections

Data source: ABS and South Australian Department of Planning and Local Government

ACIL Tasman does not have an independent view regarding South Australian

population growth. However, we note that the ABS projections are more

closely aligned to recent growth in the (estimated) resident population South

Australia, which was 0.9 per cent per annum between 2000-01 and 2010-11.

The ABS medium case (B series) projections are approximately consistent with

this. However, the South Australian Government projections are for

population growth to exceed recent history by between approximately 20 and

40 per cent (see Table 6 growth rates in the medium series). Given the

0.0%

0.2%

0.4%

0.6%

0.8%

1.0%

1.2%

1.4%

1.6%

Gro

wth

pa

ABS High ABS Medium ABS Low

SA Gov high SA gov medium SA Gov low



51

economic conditions that have prevailed since the South Australian

Government projections were made (in December 2010) we believe it is

appropriate to adopt the ABS projections in our forecasts.

Table 6 South Australian population projections – comparing South Australian Government and ABS

SA Government (medium) ABS (B series)

2011 to 2012 1.32% 0.98%

2011 to 2016 1.22% 0.96%

2011 to 2021 1.1% 0.93%

Data source: South Australian Department for Planning and Local Government and ABS

6.3 Temperature

The weather variable used in the models is CDD 18.

As we understand it, there are is no reliable way to project annual weather over

the time frame, and in the detail, necessary for these purposes. Weather

forecasting typically has a forecast horizon of a few weeks, making it unsuitable

for medium term forecasting purposes.

While long term climate projections do exist, these tend to relate to the level of

average temperatures many years in the future. They may include conclusions

that weather will become increasingly variable due to the effects of climate

change, but they do not make predictions about whether particular years will

be hotter, or cooler than usual, as would be required to forecast of billed water

sales.

This issue applies to price regulation in other regulated industries as well,

notably electricity. The general approach that regulators have taken is to

assume, for regulatory purposes, that median weather will occur throughout

the regulatory period.31 This has the advantage of simplicity and that, in the

absence of a more accurate forecasting methodology, the median is the

outcome that is most likely to be observed. It also has the advantage that, over

time, outcomes will tend to ‘balance out’ to the median.

ESCOSA has not yet committed itself to an approach regarding weather, but

we note that it previously applied the median approach in regulating ETSA

Utilities. In the absence of guidance to the contrary, we have assumed that

ESCOSA will apply the same approach to regulating SA Water.

31 This is referred to as the 50% probability of exceedence level



52

It remains to determine the period over which the median should be

calculated.

Figure 26 shows CDD 18 for Kent Town over the period from 1977-78 to

2010-11 (reproducing Figure 12). It also shows medians calculated over a

variety of periods.

Figure 26 CDD18 at Kent Town weather station -actual and median over several periods

Data source: Bureau of Meteorology

Figure 26 illustrates South Australia’s temperature variability and also shows

that temperatures observed in recent years were unusually high by historical

standards.

The median CDD over the period from 1977-78 to 2010-11 is approximately

682 CDD per year. By contrast, the median over the last ten years is

approximately 713, almost 5 per cent higher. The five year median is 891, 30

per cent above the longer term figure and 25 per cent above the ten year figure.

For the purposes of preparing these forecasts, we have assumed that weather

conditions will return to the long term average. Therefore, we have estimated

water demand based on the 1977-78 to 2010-11 median of CDDs.

0

200

400

600

800

1000

12001

97

7-7

8

19

79

-80

19

81

-82

19

83

-84

19

85

-86

19

87

-88

19

89

-90

19

91

-92

19

93

-94

19

95

-96

19

97

-98

19

99

-00

20

01

-02

20

03

-04

20

05

-06

20

07

-08

20

09

-10

CD

D 1

8 (

Ke

nt

Tow

n, n

um

be

r)

Actual CDD18 Median - 77/78 to 10/11

Median 01/02 to 10/11 Median - 06/07 to 10/11



53

6.4 Rainfall and evaporation

Similarly to temperature, the bulk water supply forecasts are based on the

assumption that rainfall and evaporation will return to long term median levels.

These are shown in Figure 27 below.32

32 If the monthly bulk water model is to be used for forecasting and, in particular, if it is to be

used for forecasting sensitivities at more extreme weather conditions, it should be noted that there is a much smaller than 10 per cent chance that 10 POE rainfall, evaporation and temperature would all be observed in the same year. If these were independent events, the probability of all three occurring would be one in one thousand (i.e. a 0.1 POE event). In practice, though, they are unlikely to be independent, for example, if average annual temperatures are higher than average, evaporation and rainfall are both likely to be above average as well.



54

Figure 27 Annual and median rainfall and evaporation – Kent Town

Rainfall

Evaporation

6.5 Water price

During the forthcoming regulatory period, average water prices are expected to

increase by the amounts shown in Table 7 as approved by the Government on

21 May 2012.

Table 7 Future water price changes

12-13 13-14 14-15 15-16

Real 22.0% -0.1% -0.1% -0.1%

Nominal 25.0% 2.4% 2.4% 2.4%

0

100

200

300

400

500

600

700

800

900

An

nu

al r

ain

fall

(mm

)

Annual rainfall Median

0

200

400

600

800

1000

1200

1400

1600

1800

Evap

ora

tio

n (

mm

)

Annual Evaporation Median



55


Given that the models rely on the second tier price, it was necessary to make

an assumption regarding the price structure that would be chosen. We assumed

the second tier would increase by the amount shown in Table 7 each year.

When the most recent price path was announced, the Government also

announced that it would provide a rebate, of either $45 or $75, to certain

customers. This rebate will reduce the total cost of water for those customers.

However, as discussed in section 4.1.3, the models presented here are based on

the marginal price of water, which is unaffected by the rebate.

6.6 Price elasticity of demand

Another important consideration in forecasting the demand for water is to

assess the extent to which price changes affect water demand. This is measured

by the price elasticity of demand.

The price of water will increase by 25 per cent (in nominal terms) in 2012-13.

The forecast changes in the other key drivers of water demand are very small

by comparison. Therefore, the relationship between price and demand for

water will be important for SA Water’s demand forecasts for 2012-13.

As discussed in section 5.2.2, our modelling shows that holding all else

constant, residential water usage decreased by 0.38 per cent for each 1 per cent

increase in the (second tier) price of water. In other words, our model suggests

that residential sector’s price elasticity of demand for water is 0.38.

However, our model of the residential sector’s demand for water excludes any

measure of the Government, and SA Water’s, demand management activity.

These variables were omitted because data were limited and, when tested, they

were not found to be statistically significant.33

In particular, the models do not contain data regarding the number of rebates

that were distributed to customers for water saving equipment or the effort put

into promoting the water efficiency message. In the residential sector these

activities were pursued vigorously while prices were increasingly rapidly.

The fact that these two activities were pursued at the same time as prices were

rising and not at other times presents a challenge for the model. As noted

above, regression techniques are unable to distinguish between the effects of

two different variables unless they can be observed independently.

33 See Appendix A for details.



56

Therefore, the price elasticity estimate produced by the model for the

residential sector should be interpreted as an estimate of the combined impact

of rising prices, widespread rebates and active promotion of the water

conservation message.

By contrast to the recent past, we understand that in 2012-13 water price will

increase significantly but the level of effort put into promoting the water

efficiency message and the rebate program will be significantly reduced.

In our view, the reduction in these two activities will lead to a smaller response

to the price increase than was observed in the recent past. We consider that the

elasticity of demand estimate for the residential sector should be revised

downwards in the forecast period to account for this.

Therefore, we consider that the elasticity estimated by the model is too high (in

absolute terms) for application to demand during the forecast period.

Our review of the literature, which is presented below, indicates that the value

should be above -0.18 (again in absolute terms). Our estimate of -0.38 forms

an upper bound on the likely elasticity. Drawing these bounds together, we use

the mid-point of the range, -0.28, as our estimate of the price elasticity of

demand for residential customers for forecasting purposes. This is the basis on

which we have prepared the forecasts presented in this report.

As discussed below, we see nothing in the literature to suggest that our

estimated price elasticities for commercial and non-residential customers is

inappropriate. Therefore we have not changed those from the values estimated

in the models.

As discussed in 4.2.3, our analysis of the price elasticity of demand in the

commercial sector indicates that it was similar during the period when free

water allowances were being ‘transitioned out’ and in the more recent period of

high price rises. This, together with the fact that the recent rebate activity was

focussed on the residential sector more than commercial, leads us to conclude

that the commercial and other non-residential elasticity estimates from the

models should be left unchanged.

6.6.1 Economic literature – residential demand

A large number of studies have attempted to measure price elasticities for

water demand for both residential and non-residential demand. Many of these

studies were conducted overseas, with some conducted in Australia.

Table 8 shows the estimates derived by Australian studies for residential

customers. The majority of these studies find water demand to be relatively



57

price inelastic, with elasticities between -0.1 and -0.50. However some studies

have found demand to be more price elastic in the longer term and/or for

outdoor usage (-0.77 in the long run by Dandy, Nguyen and Davies, -1.12 to -

1.44 in the long run by Hoffman, Worthington and Higgs and -0.70 to -1.30 by

Xayavong et al).

Table 8 Estimates of the price elasticity of residential demand for water

Authors Year Area Price Elasticity

Australian studies

Thomas and

Syme

1988 Perth -0.20 to -0.22

Barkatullah 1996 Sydney -0.21

Warner 1996 Sydney -0.12 to -0.13

Graham and Scott 1997 ACT -0.15 to -0.39

Dandy, Nguyen

and Davies

1997 Adelaide -0.28 short run

-0.77 long run

Grafton and

Kompas

2007 Sydney -0.352 nominal short run

-0.418 real short run

Hoffman,

Worthington and

Higgs

2006 Brisbane -0.51 to -0.59 in short run

-1.12 to -1.44 in long run

Xayavong et al 2008 Perth Indoor -0.70 to -0.94

Outdoor -1.30 to -1.45

Grafton and Ward 2008 Sydney -0.17

Abrams et al 2011 Sydney -0.09 in short run, at $2.00/kL

-0.18 in long run, at $2.00/kL

Similar variability in the results from international studies can be seen in the

review published by Worthington and Hoffman (2007). In a total of 32

international studies, estimates of the price elasticity of residential demand

ranged between -0.03 to -1.63.

The studies vary in the nature of the data set used (i.e. whether it comprises

household level data or average billed water sales or production per household)

and whether it uses cross-section, time series or panel data. The model

specification also varies, with researchers choosing between linear, semi log or

log log demand functions.34

For example, using a semi-log form Abrams et al (2011) estimated elasticities

which increased with the level of price. Kenney et al (2008) and Loaiciga and

Renehan (1997) found very sizeable demand responses to large price rises

34 A linear specification is rarely used because it assumes that there is a price at which no water

would be consumed at all. The semi-log form assumes that customers become more sensitive to price as the price rises. The log log form assumes a constant elasticity of demand at all prices.



58

which were implemented during periods of water shortages. However, in these

earlier studies the demand responses were also influenced by extensive media

coverage of the drought and the need for conservation, so it is difficult to

isolate the price effect. As noted above, in our view our results here are

affected in the same way.

The studies suggest that demand is more responsive over the longer run, when

there is greater opportunity for customers to adjust water using appliances.

Olmstead and Stavins (2008) suggest that long run price elasticities can be

nearly double short run estimated elasticities, with Abrams et al (2011) reaching

a similar conclusion. However Abrams et al (2011) found that it took under

one year for customers to adjust to their long term position, with 97 per cent

of the adjustment taking place within 12 months, which suggests that the “long

term” is in fact a relatively short period.

The demand for water for indoor uses has been found to be inelastic, with

some price elasticity estimates not significantly different from zero (such as

Mansur and Olmstead (2008)). Outdoor usage is generally more discretionary

in nature and exhibits greater price responsiveness. For example, Xayavong

(2008) found outdoor usage to be 50 to 90 per cent more price elastic than

indoor usage.

Abrams et al (2011) found that owner occupied houses were more price

sensitive than housing units. Tenanted houses were closer to owner occupied

houses in terms of price response. The lower price elasticity for units is

consistent with the fact that housing units are largely unable to pass on

volumetric water charges. It may also reflect the greater proportion of indoor

usage by units.

In their Sydney based study, Grafton and Ward (2008) found that price

elasticities were not significantly different for periods with and without price

restrictions.

Abrams et al (2011) found that the long term price elasticity is significantly

lower for households participating in a water efficiency program. This reflects

the fact that the purchase of water efficient appliances is in fact part of the

response that households are likely to make to higher prices over the longer

term. Once such purchases have been made, however, the scope for further

reductions in water demand become more limited.

6.6.2 Relevance for SA Water

In drawing conclusions on the relevance of these studies for SA Water we

observe that:



59

• recent price rises in South Australia, and consequent price levels, are high

relative to the traditionally low levels of price that will have been

incorporated in the data used by past academic studies. Hence the forward

price elasticity is likely to be higher than recent past estimates, such as the

Grafton and Ward result of -0.17 for Sydney.

• South Australia has a higher a proportion of outdoor usage and stand alone

houses than NSW. Thus price elasticities estimated for the Sydney region,

such as the Abrams estimate of -0.18 and the Grafton and Ward estimate

of -0.17, are likely to underestimate the price elasticities applicable for SA

Water.

• The Abrams (2011) result of lower price elasticities (by around half) for

households that have implemented water savings measures suggests that

the future price elasticities are likely to decline, given the past and

continuing program of permanent water saving measures in South

Australia.

For these reasons we regard 0.18 as a lower bound on the plausible elasticity

and the estimate form our model, 0.38, an upper bound.

6.6.3 Economic literature on the elasticity of non residential

demand

Relatively little attention has been given to formal modelling of non-residential

water demand. Published studies have tended to focus on water intensive

industrial uses, as opposed to commercial demand, using sectoral production

functions. Estimated price elasticities vary widely, as shown by Table 9.



60

Table 9 Estimates of the price elasticity of non-residential demand for water

Authors Year Area Price Elasticity

Grebenstein and Field 1979 USA -0.80 to -0.33

Babin, Willis and Allen 1982 USA -0.66 to +0.14

Ziegler and Bell 1984 USA (Arkansas) -0.08

Williams and Suh 1986 USA -0.97 to -0.44

Renzetti 1988 Canada (British

Columbia)

-0.54 to -0.12

Schneider and Whitlatch 1991 USA (Columbus, Ohio) -1.16

Renzetti 1992 Canada -0.59 to -0.15

Wang and Lall 1999 China -1.0

Dupont and Renzetti 2001 Canada -0.77

Onjala 2001 Kenya -0.6 to +0.37

Féres and Reynaud 2003 Brazil -1.08 on average

Goldar 2003 India -0.4 to +0.64

Reynaud 2003 France -0.79 to -0.10

Kumar 2004 India -1.11 on average

Source: ACIL Tasman (2007), Pricing for Water Conservation in the Non-Residential Urban Sector, Prepared for the

Steering Committee of the Smart Water Fund

Analysis undertaken by ACIL Tasman (2007) used a sample of non residential

customers in Melbourne. The study found price elasticities of around -0.60 for

smaller users and around -1.1 for larger users. The study also found that

manufacturing and water intensive users tended to be more price responsive,

and that customers with Water Management Plans averaged slightly lower price

elasticities.

In preparing demand forecasts for the ESC’s 2009 Price Review, the three

Melbourne metropolitan retailers proposed elasticities of -0.185 to -0.20 for

non residential demand. These were assessed on the basis of an elasticity of -

0.80 adjusted for estimated waterMAP savings (which were accounted for

separately in the demand forecasts). However the ESC was concerned that

applying an elasticity to customers that were in the waterMAP demand

management program would amount to double counting, so no price elasticity

was applied for those customers.

While the range of elasticities estimated for non residential customers is

relatively large, this reflects the wide heterogeneity of industrial and

commercial uses for water. On balance, and unlike residential demand, we see

nothing in the literature to suggest that our estimated price elasticities for

commercial and other non-residential customers is inappropriate.



61

6.6.4 The ‘bounce back’ effect

Another factor that has been widely discussed is the so called ‘bounce back

effect’ or the extent to which water usage may remain below ‘pre-restrictions’

levels after restrictions have been removed.

In 2009 PWC reviewed the demand forecasts submitted by the Victorian

metropolitan water businesses to the Essential Services Commission. PWC

considered that ‘bounce back’ after the lifting of restrictions should be in the

range of 70 to 90 per cent, i.e. consumption might remain between 10 and 30

per cent below pre-restrictions levels after restrictions were lifted. PWC was

satisfied that the demand forecasts submitted by the water businesses, which

were based on end-use modelling, were consistent with this range.

We have not made an assumption regarding the level of bounce back explicitly.

Rather, in the models presented here we removed the impact of the dummy

variable for level 2 restrictions when those restrictions were lifted (and

permanent water conservation measures were introduced). The dummy for

level 1 restrictions was kept in place throughout the forecast period to

recognise the effect of ongoing permanent water restrictions.

This approach produces results that are broadly consistent with PWC’s 2009

findings for Victoria. The estimated effect of level 1 restrictions is

approximately 10 per cent in each of the three ‘usage’ components of the billed

water sales model and in the monthly sales model. These coefficients imply

that, if all else was equal, consumption would return to approximately 90 per

cent of pre restrictions levels when restrictions were lifted. In practice, we

anticipate that average sales per customer will be significantly less than 90 per

cent of pre-restrictions levels, though, due to the effect of other variables,

especially price.

The findings are also broadly consistent with the impacts estimated for the

impact of permanent water savings/demand management activities and of level

1 restrictions identified in other jurisdictions. ACIL Tasman’s previous review

of the literature35 found that the impact of permanent water savings and

demand management activities varied between 2.3% for Melbourne and 14%

for Sydney (with Sydney having on average the most aggressive demand

management policies). The impact of stage 1 water restrictions was found to

be 7.8% in Melbourne and 13% in Sydney and ACT.

35 ACIL Tasman, 2011, SA Water demand forecasts: a review of the methodology used to

prepare SA Water’s 2011/12 demand forecast



62

If more data had been available, we would have tested for any structural

change in the Level 1 restriction dummy over time. In particular, it would be

useful to test whether the impact of the level 1 dummy is different before and

after the imposition and subsequent lifting of higher level restrictions.

However this is not currently possible, given that there is only one data point

for the later period.


Billed water sales forecasts

63

7 Billed water sales forecasts

As discussed in chapter 5, our model for forecasting billed water sales actually

comprises five component models. In sections 7.1 and section 7.2 we present

each of these five forecasts separately. Then, in section 7.3, we present the total

forecast of SA Water’s billed water sales and sensitivities.

In section 7.4 we present forecasts from the independent bulk water model

and compare them to the monthly forecasts of (total) billed water sales from

the annual model.

7.1 Residential sector

7.1.1 Customer numbers

Figure 28 and Table 10 show historical and forecast residential customer

numbers.

Figure 28 SA Water – residential customer numbers – historical and forecast


400,000

450,000

500,000

550,000

600,000

650,000

700,000

750,000

Cu

sto

me

rs (

Nu

mb

er)

Historical Forecast



64

Table 10 SA Water – residential customer numbers – historical and forecast

Year Customers

Actual

1996-97 526,767

1997-98 533,304

1998-99 539,833

1999-00 546,279

2000-01 552,052

2001-02 558,270

2002-03 565,736

2003-04 572,247

2004-05 580,243

2005-06 589,293

2006-07 598,152

2007-08 606,135

2008-09 615,662

2009-10 625,565

2010-11 631,712

Forecasts

2011-12 646,143

2012-13 655,659

2013-14 665,173

2014-15 674,689

2015-16 684,207

2016-17 693,688

2017-18 703,124

2018-19 712,510

2019-20 721,831

2020-21 731,076


From 1996-97 to 2010-11 customer numbers grew at an annualised rate of 1.3

per cent per annum. Growth was faster in more recent years. Between 2006-

07 and 2010-11, residential customer numbers grew at an annualised rate of 1.4

per cent per annum.

Forecast growth is consistent with historical trends. Over the likely initial

regulatory period from 2011/12 to 2015/16, we forecast customer numbers to

grow at an annualised rate of 1.4 per cent per annum. Over the longer term

2011-12 to 2020-21, forecast growth is at the same rate.36

36 The growth rates are similar, but not the same although they appear so due to rounding.



65

7.1.2 Residential water demand per customer

Figure 29 and Table 11 show the forecast growth in residential water demand

per customer. While the number of residential customer is forecast to continue

to grow, demand per customer is forecast to remain roughly steady.

Figure 29 SA Water – residential water demand per customer – historical and forecast


0.0

50.0

100.0

150.0

200.0

250.0

300.0

Sale

s p

er

con

ne

ctio

n (

kL)

Historical Forecast



66

Table 11 SA Water – residential water demand per customer – historical and forecast

Year Water demand per customer (kL/customer/year)

Actual

1996-97 261.7

1997-98 260.9

1998-99 263.4

1999-00 267.8

2000-01 280.7

2001-02 259.0

2002-03 285.0

2003-04 250.6

2004-05 248.0

2005-06 243.9

2006-07 245.9

2007-08 202.0

2008-09 197.0

2009-10 197.0

2010-11 180.5

Forecasts

2011-12 191.90

2012-13 181.53

2013-14 181.57

2014-15 181.61

2015-16 181.65

2016-17 183.76

2017-18 183.61

2018-19 182.31

2019-20 181.16

2020-21 183.21


From 1996-97 to 2010-11 residential water demand per customer declined at

an annualised rate of 2.6 per cent per annum. However, this masks the fact that

it grew by 1.4 per cent per annum from 1996-97 to 2002-03, when it peaked. It

then declined at 5.5 per cent per annum to 2010-11.

Between 2006-07 and 2008-09, when water restrictions were at their height,

residential water demand per customer declined at 10.5 per cent per annum.

Residential water demand per customer is forecast to continue to decline,

although at a slower rate than has been observed recently. Over the period

from 2011-12 to 2015-16, residential demand per customer is forecast to

decline at an annualised rate of 1.4 per cent per annum. However, this masks



67

underlying variability driven by price changes. In 2011-12, the model indicates

that it will recover some of the decline of 2010-11 with the lifting of

restrictions. This recovery is forecast to be lost in 2012-13 following another

significant price increase. After 2012-13, residential water demand per

customer is forecast to be flat for the regulatory period.

7.1.3 Total demand for water in the residential sector

Figure 30 and Table 12 show the residential water demand, both historical and

forecast.

Figure 30 SA Water – residential water demand – historical and forecast


0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

180,000

ML

Historical Forecast



68

Table 12 SA Water – residential water demand – historical and forecast


Actual

1996-97 137,870

1997-98 139,143

1998-99 142,194

1999-00 146,282

2000-01 154,947

2001-02 144,585

2002-03 161,258

2003-04 143,380

2004-05 143,886

2005-06 143,720

2006-07 147,104

2007-08 122,444

2008-09 121,298

2009-10 123,267

2010-11 114,041

Forecasts

2011-12 123,996

2012-13 119,022

2013-14 120,775

2014-15 122,530

2015-16 124,286

2016-17 127,474

2017-18 129,102

2018-19 129,897

2019-20 130,766

2020-21 133,939


Residential water demand is forecast as the product of residential customer

numbers and average demand per customer. Both history and forecast are

dominated by the fluctuations in average demand per customer, albeit

moderated by steady growth in customer numbers.

From 1996-97 to 2010-11 residential water demand declined at an annualised

rate of 1.3 per cent per annum. However, as with average demand per

customer, this masks the fact that it grew by 2.6 per cent per annum from

1996-97 to 2002-03, when it peaked. It then declined at 4.2 per cent per annum

to 2010-11.


residential water demand declined at 9.2 per cent per annum.



69

Residential water demand per customer is forecast to recover slightly. Between

2011-12 and 2015-16, residential water demand is forecast to grow at an

annualised rate of 0.1 per cent per annum. However, this masks underlying

variability driven by price changes. In 2012-13, the model indicates that

residential water demand will fall by 4.0 per cent before recovering at 1.5 per

cent per annum over the initial regulatory period.

7.1.4 Commercial sector

Customer numbers

Figure 31 and Table 13 show historical and forecast commercial customer

numbers.

Figure 31 SA Water – commercial customer numbers – historical and forecast


10,000

15,000

20,000

25,000

30,000

35,000

Cu

sto

me

rs (

Nu

mb

er)

Historical Forecast



70

Table 13 SA Water – commercial customer numbers – historical and forecast

Year Customers

Actual

1996-97 23,526

1997-98 24,064

1998-99 24,228

1999-00 24,345

2000-01 24,507

2001-02 24,650

2002-03 24,919

2003-04 25,058

2004-05 25,325

2005-06 25,568

2006-07 25,796

2007-08 26,055

2008-09 26,466

2009-10 26,744

2010-11 27,056

Forecasts

2011-12 27,052

2012-13 27,365

2013-14 27,715

2014-15 28,048

2015-16 28,329

2016-17 28,616

2017-18 28,910

2018-19 29,212

2019-20 29,520

2020-21 29,837


Growth in commercial customer numbers is forecast to be slightly stronger

than over the historical period. From 1996-97 to 2010-11 customer numbers

grew at an annualised rate of 1.0 per cent per annum. They are forecast to

grow at 1.1 per cent per annum from 2011-12 to 2020-21.

Between 2011-12 and 2015-16, commercial customer numbers are forecast to

grow at an annualised rate of 1.1 per cent per annum.

Commercial water demand per customer

Figure 32 and Table 14 show the forecast growth in commercial water demand

per customer. While the number of commercial customers is forecast to



71

continue to grow at a similar rate to history, demand per customer is forecast

to grow much more slowly.

Figure 32 SA Water – commercial water demand per customer – historical and forecast


0.0

50.0

100.0

150.0

200.0

250.0

300.0

350.0

400.0

450.0

500.0Sa

les

pe

r co

nn

ect

ion

(kL

)

Historical Forecast



72

Table 14 SA Water – commercial water demand per customer – historical and forecast

Year Water demand per customer (kL/customer/year)

Actual

1996-97 423.9

1997-98 439.4

1998-99 433.6

1999-00 446.8

2000-01 449.9

2001-02 440.2

2002-03 474.7

2003-04 453.3

2004-05 429.6

2005-06 430.1

2006-07 435.9

2007-08 389.7

2008-09 366.2

2009-10 370.5

2010-11 346.9

2011-12 351.8

2012-13 331.7

2013-14 336.9

2014-15 341.7

2015-16 345.8

2016-17 355.1

2017-18 358.8

2018-19 359.6

2019-20 360.7

2020-21 370.3


From 1996-97 to 2010-11 commercial water demand per customer declined at

an annualised rate of 1.4 per cent per annum. However, this masks the fact that

it grew by 1.9 per cent per annum from 1996-97 to 2002-03, when it peaked. It

then declined at 3.8 per cent per annum to 2010-11.


commercial water demand per customer declined at 8.3 per cent per annum.

Commercial water demand per customer is forecast to recover slowly. Between

2011-12 and 2015-16, commercial water demand per customer is forecast to

decline at an annualised rate of 0.4 per cent per annum. However, this masks

underlying variability driven by price changes. In 2011-12, the model indicates



73

that it will decline by 5.7 per cent. After 2012-13, commercial water demand

per customer is forecast to return to 1.2 per cent annual growth for the

remainder of the regulatory period.

Demand for water in the commercial sector

Figure 33 and Table 15 show the commercial water demand, both historical

and forecast.

Figure 33 SA Water – commercial water demand – historical and forecast


0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

ML

Historical Forecast



74

Table 15 SA Water – commercial water demand – historical and forecast


Actual

1996-97 9,973

1997-98 10,574

1998-99 10,505

1999-00 10,877

2000-01 11,026

2001-02 10,851

2002-03 11,829

2003-04 11,359

2004-05 10,880

2005-06 10,996

2006-07 11,245

2007-08 10,154

2008-09 9,693

2009-10 9,908

2010-11 9,387

Forecast

2011-12 9,516

2012-13 9,077

2013-14 9,336

2014-15 9,585

2015-16 9,795

2016-17 10,161

2017-18 10,373

2018-19 10,504

2019-20 10,649

2020-21 11,049


Commercial water demand is forecast as the product of commercial customer

numbers and average demand per customer. Both history and forecast are

dominated by the fluctuations in average demand per customer, albeit

moderated by steady growth in customer numbers.

From 1996-97 to 2010-11 commercial water demand declined at an annualised

rate of 0.4 per cent per annum. However, as with average demand per

customer, this masks the fact that it grew by 2.9 per cent per annum from

1996-97 to 2002-03, when it peaked. It then declined at 2.8 per cent per annum

to 2010-11.


commercial water demand declined at 7.2 per cent per annum.



75

Commercial water demand is forecast to recover slightly. Between 2011-12 and

2015-16, commercial water demand is forecast to grow at an annualised rate of

0.7 per cent per annum. As with residential demand, commercial demand is

forecast to decline significantly in 2012-13, with a 4.6 per cent decline in that

year. It is then forecast to recover at 2.6 per cent per annum through the

regulatory period.

7.2 Other non-residential sector

Unlike the residential and commercial sectors, we forecast total water demand

in the other non-residential sector directly.

Figure 34 and Table 16 show forecast demand for water in the other non-

residential sector.

Figure 34 SA Water – other non-residential water demand – historical and forecast


0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

ML

Historical Forecast



76

Table 16 SA Water – other non-residential water demand – historical and forecast


Actual

1996-97 62,495

1997-98 63,400

1998-99 64,747

1999-00 64,451

2000-01 66,667

2001-02 62,595

2002-03 69,467

2003-04 61,295

2004-05 60,881

2005-06 62,172

2006-07 62,723

2007-08 59,656

2008-09 58,289

2009-10 52,455

2010-11 51,792

2011-12 50,801

2012-13 48,176

2013-14 48,739

2014-15 49,265

2015-16 49,703

2016-17 50,799

2017-18 51,189

2018-19 51,213

2019-20 51,282

2020-21 52,395


From 1996-97 to 2010-11 other non-residential water demand declined at an

annualised rate of 1.3 per cent per annum. However, as with the other sectors,

this masks the fact that it grew by 1.8 per cent per annum from 1996-97 to

2002-03, when it peaked. It then declined at 3.6 per cent per annum to 2010-

11.


other non-residential water demand declined at 3.6 per cent per annum.

Other non-residential demand is forecast to recover slightly. Between 2011-12

and 2015-16, other non-residential water demand is forecast to decline at an

annualised rate of 0.5per cent per annum. As with the other sectors, this is



77

forecast to include a decline of 5.2 per cent with the 2012-13 price increase

followed by growth at 1.0 per cent per annum over the regulatory period.

7.3 Total demand for water

7.3.1 Forecasts

Based on the methodology and drivers described here, our best estimate of SA

Water’s demand is presented in Figure 35 and Table 17 below.

Figure 35 SA Water - total water demand – historical and forecast


-

50,000

100,000

150,000

200,000

250,000

300,000

ML

Historical Forecast



78

Table 17 SA Water - total water demand – historical and forecast


Actual

1996-97 210,339

1997-98 213,117

1998-99 217,446

1999-00 221,610

2000-01 232,640

2001-02 218,032

2002-03 242,554

2003-04 216,033

2004-05 215,647

2005-06 216,888

2006-07 221,072

2007-08 192,254

2008-09 189,280

2009-10 185,630

2010-11 175,219

2011-12 184,313

2012-13 176,275

2013-14 178,850

2014-15 181,380

2015-16 183,784

2016-17 188,434

2017-18 190,664

2018-19 191,613

2019-20 192,697

2020-21 197,383


From 1996-97 to 2010-11 SA Water’s demand declined at an annualised rate of

1.3 per cent per annum. However, as with the individual sectors, this masks the

fact that it grew by 2.4 per cent per annum from 1996-97 to 2002-03, when it

peaked. It then declined at 4.0 per cent per annum to 2010-11.

Between 2006-07 and 2008-09, when water restrictions were at their height, SA

Water’s demand declined at 7.5 per cent per annum.

SA Water’s demand is forecast to continue to decline very slightly. Over the

period from 2011-12 to 2015-16, it is forecast to decline at an annualised rate

of 0.1 per cent per annum. As with the individual sectors, this is forecast to

include a decline with the 2012-13 price increase followed by growth after that.



79

Over the likely regulatory period from 2013-14 to 2015-16, total water demand

is forecast to grow at 1.4 per cent per annum from a base that is low by

historical standards. This is slower than the growth observed before the

drought, which is unsurprising given that economic growth is forecast to be

relatively flat and water prices significantly higher than they were.

7.3.2 Sensitivities

The key uncertainty for these forecasts is future weather conditions. As

discussed in section 6.3, we have assumed that South Australia’s weather will

return to long term trend during the forecast period. While we consider this to

be a reasonable assumption, we note that it requires the weather to be

considerably cooler in the next few years than it has been recently.

To illustrate the sensitivity of the forecasts to this assumption, Figure 36 and

Table 18 show the same forecasts as presented in Figure 35, with different

assumptions regarding the weather. Specifically, we have assumed 10th and 90th

percentile weather conditions (equivalent to a hot and cool year respectively). 37

Under the tenth percentile weather assumption (hot year), SA Water’s total

demand over the likely regulatory period is 3.2 per cent above the base

forecast.

Under the ninetieth percentile weather assumption (cool year), SA Water’s total

demand over the likely regulatory period it is 3.2 per cent below the base

forecast.

37 For reference, note that 2010/11 was close to a median (50th percentile) year, with 679

CDD (median is 682). Similarly, 2007/08 was close to a 10th percentile (hot) year, with 891 CDD (10th percentile is 866). A 90th percentile year has not been observed for some time. The most recent was 1995/96, when 539 CDD were observed (90th percentile is 534).



80

Figure 36 Total water demand with weather sensitivities


Table 18 Total water demand with weather sensitivities

2011-12 2012-13 2013-14 2014-15 2015-16

Median weather 184,313 176,275 178,850 181,380 183,784

10th percentile 190,161 181,873 184,531 187,143 189,625

90th percentile 178,499 170,709 173,202 175,651 177,977


7.4 Bulk water supply forecasts

Table 19 shows the forecasts of bulk water supply that are derived from the

long term monthly model. It also includes a comparison against the forecasts

derived from the annual models (which were prepared independently as

described above).

150000

160000

170000

180000

190000

200000

210000

220000

230000

240000

250000

19

96-9

7

19

97-9

8

19

98-9

9

19

99-0

0

20

00-0

1

20

01-0

2

20

02-0

3

20

03-0

4

20

04-0

5

20

05-0

6

20

06-0

7

20

07-0

8

20

08-0

9

20

09-1

0

20

10-1

1

20

11-1

2

20

12-1

3

20

13-1

4

20

14-1

5

20

15-1

6

ML

Total water sales Median

10th percentile 90th percentile



81

Table 19 Bulk water model forecasts versus billed water sales model forecasts

Forecast Bulk water Non revenue

%

Water

delivered (ex

non revenue)

Billed water

sales

forecasts

Deviation (%)

2011-12 210,970 12.6% 184,388 184,313 -0.04%

2012-13 209,149 12.6% 182,796 176,275 -3.57%

2013-14 211,076 12.6% 184,481 178,850 -3.05%

2014-15 212,876 12.6% 186,054 181,380 -2.51%

2015-16 214,367 12.6% 187,357 183,784 -1.91%

2016-17 217,649 12.6% 190,225 188,434 -0.94%

2017-18 219,008 12.6% 191,413 190,664 -0.39%

2018-19 219,373 12.6% 191,732 191,613 -0.06%

2019-20 219,865 12.6% 192,162 192,697 0.28%

2020-21 223,181 12.6% 195,060 197,383 1.19%


The results show that the two sets of forecasts are reasonably close for most of

the forecast horizon. The models differ in that the monthly model

demonstrates less responsiveness to price shocks, but is also less responsive to

the variables that drive long term trend growth such as GSP. By contrast, it is

more sensitive to changes in weather. It would produce a significantly higher

forecast than the annual model under 10th percentile weather conditions.

Figure 37 shows the forecasts derived from the bulk supply model on a

monthly basis to 2015-16

Figure 37 Historical and forecast bulk supply to 2015-16

0

5000

10000

15000

20000

25000

30000

35000

40000

45000

50000

19

95

-96

19

95

-96

19

95

-96

19

96

-97

19

96

-97

19

97

-98

19

97

-98

19

97

-98

19

98

-99

19

98

-99

19

99

-00

19

99

-00

20

00

-01

20

00

-01

20

00

-01

20

01

-02

20

01

-02

20

02

-03

20

02

-03

20

02

-03

20

03

-04

20

03

-04

20

04

-05

20

04

-05

20

05

-06

20

05

-06

20

05

-06

20

06

-07

20

06

-07

20

07

-08

20

07

-08

20

07

-08

20

08

-09

20

08

-09

20

09

-10

20

09

-10

20

10

-11

20

10

-11

20

10

-11

20

11

-12

20

11

-12

20

12

-13

20

12

-13

20

12

-13

20

13

-14

20

13

-14

20

14

-15

20

14

-15

20

15

-16

20

15

-16

20

15

-16

ML

Actual Forecast



82

8 Forecasting principles

ESCOSA has indicated that demand forecasts should be:

1. free from statistical bias

2. recognise and reflect key drivers of demand

3. based on sound assumptions using the best available information

4. consistent with other available forecasts and methodologies

5. based upon the most recently available data

6. reflect the particular situation and the nature of the market for services

7. based upon sound and robust accounts of current market conditions and

future prospects.

ACIL Tasman’s view is similar.

The demand forecasting methodology, model and forecasts discussed in this

report were prepared to satisfy these principles in the following way.

8.1 Freedom from statistical bias

ESCOSA’s first requirement is that the forecasts should be free from statistical

bias. It is in the nature of forecasting that actual outcomes will differ from the

forecast value. There will always be a forecast error.

A forecasting model is statistically biased if it has a tendency either to over or

under estimate outcomes, in other words a model is statistically biased if the

error is more likely to be either positive or negative. An unbiased model will be

no more likely to produce a positive error than a negative error.

The methodology used to prepare these forecasts is, subject to certain technical

assumptions, intrinsically free from statistical bias. The lack of statistical bias is

also shown by the comparison between the ‘fitted’ values of the model and

historical outcomes.

8.2 Drivers of demand, sound assumptions and

sound accounts of market conditions

The models presented here recognise and reflect the key drivers of demand, in

line with ESCOSA’s second requirement. They are based on sound

assumptions and use the most recent data and the best available information in

line with ESCOSA’s second, third, fifth and seventh requirements.



83

In particular, the forecasts presented here take account of the price of water,

economic activity and population, all of which are likely, based on economic

theory, to be drivers of water demand.

The calibrated models also account for variation in weather, both temperature

and rainfall (for the monthly model). The forecasts were produced on the

assumption of median weather conditions as is conventional in demand

forecasting.

The forecasts of water use are based on forecasts of the key drivers of demand.

Those driver forecasts were obtained from independent reputable sources,

namely:

• the South Australian Government (for economic growth)

• the Australian Bureau of Statistics (ABS) (for population).

Historical data used in calibrating the models was obtained from the ABS and

the Bureau of Meteorology.

In addition to these data sources, the forecasts rely on an assumption regarding

water use behaviour now that water restrictions have been lifted and replaced

with water wise measures. These ongoing measures are similar to the

restrictions that were in place between 2003 and 2006. The forecasts are based

on the assumption that, if all else was equal, average water use behaviour in

future would be similar to what was observed under level 1 water restrictions.

Other factors, in particular water price, are accounted for separately. The other

key assumption made in preparing the forecasts relates to the future price of

water. The forecasts were based on the assumption that prices would be in line

with the Government’s announcement of 21 May 2012, which is the most

recently available information.

8.3 Most recently available data

The models presented in this report were based on the most recently available

data as at early 2012, when they were estimated.

As with any forecasting project, there are some areas in which the data are not

ideal.

None of these data problems are sufficiently serious to impair the forecasts

unreasonably, though they will probably contribute to the errors in them. Nor

are issues of this type uncommon in regulated (network) businesses.



84

8.4 Model performance and consistency with other

models

The models perform well. The multiplicative nature of the models means it is

not possible to provide a single statistic that summarises the performance of

the total forecasting model. However, individually, four of the five

components of the annual billed water sales model explain more than 90 per

cent of the variation in historical data. The fifth model explains slightly less

than 90 per cent.

Similarly, the monthly model explains approximately 90 per cent of the

variation in historical data.

The monthly and annual models were prepared independently of one another,

in a methodological sense, and rely on independent data. It is noteworthy that

the two models produce similar forecasts, though the uncertainty regarding the

total difference between the two data series, which is due to water that is lost

or otherwise not paid for, makes a detailed comparison troublesome.


Rebate sensitivity A-1

A Rebate sensitivity

As discussed in section 4.2.3, including the number of rebates issued did not

improve the models.

This does not necessarily suggest that the rebates that were issued had no

effect on water usage. Rather, it is likely due to the very close correlation

between the number of rebates issued and the price of water over the same

period. It is also likely to be due to the fact that a large number of the rebates

that were received would have been motivated by the rising prices. In these

circumstances regression models cannot distinguish between the two effects.

Nonetheless, the extent to which rebates have influenced the reduction in

water consumption in SA Water’s network since their introduction was of key

interest to SA Water. Therefore, an alternative approach to identifying the

effect of rebates and prices was attempted.

SA Water estimated the impact of the total water saved through rebates. These

estimates were based on an ‘appliance model’ approach, where the number of

devices for which rebates were paid was multiplied by the amount those

devices could be expected to save. The estimates are shown in Figure 38

below. According to SA Water’s calculations, water rebates saved 2,657 ML in

2010-11.

Figure 38 Estimated water savings arising from rebates, ML


To attempt to separate out the impact of water rebates from general price

effects, ACIL Tasman added back SA Water’s estimates of the water savings

-

1,159

1,423

2,657

-

500

1,000

1,500

2,000

2,500

3,000

2007-08 2008-09 2009-10 2010-11

ML


Rebate sensitivity A-2

from rebates to total residential water consumption and re-estimated the

average residential use per customer model. If a large part of the water

reduction in recent years can be attributed to rebates rather than price then we

would expect a substantial reduction in the absolute value of the price elasticity

coefficient compared to the original estimate of -0.38.

The estimated price elasticity from the regression with the rebate related

volumes removed is -0.35. This shows that while rebates have had a significant

impact on the reduction in water consumption in recent years, they do not

account for the majority of that reduction. Even after accounting for the

impact of the rebates, there was a significant reduction in water usage by

commercial customers, which appears to have been driven by the large real

price rises that took place in the last few years.

attachment e.1 demand forecasting methodology

Documents