attachment e.1 demand forecasting methodology
TRANSCRIPT
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
Telephone (+61 7) 3009 8700 Facsimile (+61 7) 3009 8799 Email [email protected]
Canberra Level 2, 33 Ainslie Place Canberra City ACT 2600 GPO Box 1322 Canberra ACT 2601
Telephone (+61 2) 6103 8200 Facsimile (+61 2) 6103 8233 Email [email protected]
Perth Centa Building C2, 118 Railway Street West Perth WA 6005
Telephone (+61 8) 9449 9600 Facsimile (+61 8) 9322 3955 Email [email protected]
Sydney GPO Box 4670 Sydney NSW 2001
Telephone (+61 2) 9389 7842 Facsimile (+61 2) 8080 8142 Email [email protected]
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
SA Water’s demand forecasting
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
SA Water’s demand forecasting
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
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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
SA Water’s demand forecasting
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
SA Water’s demand forecasting
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.
SA Water’s demand forecasting
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.
SA Water’s demand forecasting
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.
SA Water’s demand forecasting
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
SA Water’s demand forecasting
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
Source: ACIL Tasman modelling
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
SA Water’s demand forecasting
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
SA Water’s demand forecasting
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.
SA Water’s demand forecasting
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.
SA Water’s demand forecasting
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.
SA Water’s demand forecasting
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
SA Water’s demand forecasting
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.
SA Water’s demand forecasting
Forecasting principles
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
SA Water’s demand forecasting
Forecasting principles
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.
SA Water’s demand forecasting
Forecasting principles
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.
SA Water’s demand forecasting
Forecasting principles
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.
SA Water’s demand forecasting
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.
SA Water’s demand forecasting
Background – potable water supply in South Australia
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.
SA Water’s demand forecasting
Background – potable water supply in South Australia
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).
SA Water’s demand forecasting
Background – potable water supply in South Australia
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.
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SA Water’s demand forecasting
Background – potable water supply in South Australia
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.
SA Water’s demand forecasting
Background – potable water supply in South Australia
12
Figure 2 SA Water bulk supply and billed water sales, 1996-97 to 2010-11
Data source: SA Water
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
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SA Water’s demand forecasting
Background – potable water supply in South Australia
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
SA Water’s demand forecasting
Background – potable water supply in South Australia
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.
SA Water’s demand forecasting
Background – potable water supply in South Australia
15
Figure 3 SA Water. Billed water sales by customer class, 1996-97 to 2010-11
Data source: SA Water
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.
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140,000
160,000
180,000
Residential Commercial Other non residential
SA Water’s demand forecasting
Background – potable water supply in South Australia
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
Data source: SA Water
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.
SA Water’s demand forecasting
Background – potable water supply in South Australia
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.
SA Water’s demand forecasting
Background – potable water supply in South Australia
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
SA Water’s demand forecasting
Background – potable water supply in South Australia
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.
SA Water’s demand forecasting
Background – potable water supply in South Australia
20
Figure 5 SA Water, billed water sales per customer, 1996-97 to 2010-11
Data source: ACIL Tasman calculations based on SA Water data
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.
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SA Water’s demand forecasting
Background – potable water supply in South Australia
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
Data source: ACIL Tasman calculations based on SA Water data
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.
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300
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SA Water’s demand forecasting
Background – potable water supply in South Australia
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
Data source: ACIL Tasman calculations based on SA Water data
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
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Commercial
SA Water’s demand forecasting
Background – potable water supply in South Australia
23
Figure 8 Water demand per customer – other non-residential, 1996-97 to 2010-11
Data source: ACIL Tasman calculations based on SA Water data
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
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SA Water’s demand forecasting
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
SA Water’s demand forecasting
Economic and demographic drivers
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.
SA Water’s demand forecasting
Economic and demographic drivers
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
SA Water’s demand forecasting
Economic and demographic drivers
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
SA Water’s demand forecasting
Economic and demographic drivers
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
Data source: SA Water
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.
SA Water’s demand forecasting
Economic and demographic drivers
29
Figure 11 Real water prices in South Australia 1996-97 to 2011-12 (2012 dollars)
Data source: SA Water
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
SA Water’s demand forecasting
Economic and demographic drivers
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).
SA Water’s demand forecasting
Economic and demographic drivers
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.
SA Water’s demand forecasting
Economic and demographic drivers
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
SA Water’s demand forecasting
Economic and demographic drivers
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
SA Water’s demand forecasting
Economic and demographic drivers
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
SA Water’s demand forecasting
Economic and demographic drivers
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
Data source: SA Water
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
SA Water’s demand forecasting
Economic and demographic drivers
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
Data source: SA Water
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)
SA Water’s demand forecasting
Economic and demographic drivers
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
Data source: SA Water
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
SA Water’s demand forecasting
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.
SA Water’s demand forecasting
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
SA Water’s demand forecasting
Model specification
40
Figure 18 Residential customer numbers model
Source: ACIL Tasman modelling
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
SA Water’s demand forecasting
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.
SA Water’s demand forecasting
Model specification
42
Figure 19 Residential water usage model
Source: ACIL Tasman modelling
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
SA Water’s demand forecasting
Model specification
43
Figure 20 Commercial customer numbers model
Source: ACIL Tasman modelling
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
• Temperature (CDD 18) – 0.12
• Water price - -0.37
• Dummy variables for the three different levels of water restrictions
− 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
SA Water’s demand forecasting
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 19 shows the relationship between the fitted model and historical data.
The coefficient of determination (adjusted R-squared) is 0.89.
Figure 21 Commercial water usage model
Source: ACIL Tasman modelling
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
SA Water’s demand forecasting
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
• Temperature (CDD 18) – 0.10
• Water price - -0.32
• Dummy variables for the three different levels of water restrictions
− 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.
Figure 22 shows the relationship between the fitted model and historical data.
The coefficient of determination (adjusted R-squared) is 0.95.
29 Some aspects of the water restrictions were applicable to commercial customers as well.
SA Water’s demand forecasting
Model specification
46
Figure 22 Water usage model – other non-residential customers
Source: ACIL Tasman modelling
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
SA Water’s demand forecasting
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.
SA Water’s demand forecasting
Model specification
48
Figure 23 Bulk water model
Data source: ACIL Tasman
0
10000
20000
30000
40000
50000
1/0
7/19
95
1/0
7/19
96
1/0
7/19
97
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98
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ML
Fitted Actual
SA Water’s demand forecasting
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
SA Water’s demand forecasting
Developing the forecasts
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
SA Water’s demand forecasting
Developing the forecasts
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
SA Water’s demand forecasting
Developing the forecasts
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
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83
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85
-86
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87
-88
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-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
SA Water’s demand forecasting
Developing the forecasts
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.
SA Water’s demand forecasting
Developing the forecasts
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
SA Water’s demand forecasting
Developing the forecasts
55
Data source: SA Water
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.
SA Water’s demand forecasting
Developing the forecasts
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
SA Water’s demand forecasting
Developing the forecasts
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.
SA Water’s demand forecasting
Developing the forecasts
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:
SA Water’s demand forecasting
Developing the forecasts
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.
SA Water’s demand forecasting
Developing the forecasts
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.
SA Water’s demand forecasting
Developing the forecasts
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
SA Water’s demand forecasting
Developing the forecasts
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.
SA Water’s demand forecasting
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
Source: ACIL Tasman modelling
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
SA Water’s demand forecasting
Billed water sales forecasts
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
Source: ACIL Tasman modelling
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.
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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
Source: ACIL Tasman modelling
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
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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
Source: ACIL Tasman modelling
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
SA Water’s demand forecasting
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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
Source: ACIL Tasman modelling
0
20,000
40,000
60,000
80,000
100,000
120,000
140,000
160,000
180,000
ML
Historical Forecast
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Table 12 SA Water – residential water demand – historical and forecast
Year Water demand (ML)
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
Source: ACIL Tasman modelling
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.
Between 2006-07 and 2008-09, when water restrictions were at their height,
residential water demand declined at 9.2 per cent per annum.
SA Water’s demand forecasting
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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
Source: ACIL Tasman modelling
10,000
15,000
20,000
25,000
30,000
35,000
Cu
sto
me
rs (
Nu
mb
er)
Historical Forecast
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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
Source: ACIL Tasman modelling
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
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Billed water sales forecasts
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
Source: ACIL Tasman modelling
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
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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
Source: ACIL Tasman modelling
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.
Between 2006-07 and 2008-09, when water restrictions were at their height,
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
SA Water’s demand forecasting
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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
Source: ACIL Tasman modelling
0
2,000
4,000
6,000
8,000
10,000
12,000
14,000
ML
Historical Forecast
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Table 15 SA Water – commercial water demand – historical and forecast
Year Water demand (ML)
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
Source: ACIL Tasman modelling
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.
Between 2006-07 and 2008-09, when water restrictions were at their height,
commercial water demand declined at 7.2 per cent per annum.
SA Water’s demand forecasting
Billed water sales forecasts
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
Source: ACIL Tasman modelling
0
10,000
20,000
30,000
40,000
50,000
60,000
70,000
80,000
ML
Historical Forecast
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76
Table 16 SA Water – other non-residential water demand – historical and forecast
Year Water demand (ML)
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
Source: ACIL Tasman modelling
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.
Between 2006-07 and 2008-09, when water restrictions were at their height,
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
SA Water’s demand forecasting
Billed water sales forecasts
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
Source: ACIL Tasman modelling
-
50,000
100,000
150,000
200,000
250,000
300,000
ML
Historical Forecast
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78
Table 17 SA Water - total water demand – 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
2018-19 191,613
2019-20 192,697
2020-21 197,383
Source: ACIL Tasman modelling
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.
SA Water’s demand forecasting
Billed water sales forecasts
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).
SA Water’s demand forecasting
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80
Figure 36 Total water demand with weather sensitivities
Data source: ACIL Tasman modelling
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
Data source: ACIL Tasman modelling
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
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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%
Data source: ACIL Tasman
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
SA Water’s demand forecasting
Forecasting principles
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.
SA Water’s demand forecasting
Forecasting principles
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.
SA Water’s demand forecasting
Forecasting principles
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.
SA Water’s demand forecasting
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
Data source: SA Water
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
SA Water’s demand forecasting
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.