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7 Forecast water use

Forecast water sales and property numbers are key inputs to the price setting process. They are the ‘activity drivers’ used to determine the prices necessary to recover Sydney Water’s efficiently incurred costs. This chapter first summarises actual water use and the proposed forecasts. It then describes the method, key assumptions and risks associated with the forecasts of residential and non-residential water sales.

7.1 Water use to 30 June 2012 and for the next price determination As part of the portfolio approach to securing Sydney’s water supply, significant progress has been made in balancing water supply and demand through improved levels of water efficiency, reducing leakage from the water supply network and increasing water recycling. These impacts are ongoing. Figure 7.1 shows the pattern of total water use since 2001-02, including the forecast for 2011-12.

Figure 7.1 Total water use, 2001-02 to 2011-12, GL

Key points •••• Residents, businesses and government organisations have reduced their water use by

around 25% since early 2000, and have maintained this low level during the current price determination period.

•••• Water sales have been well below anticipated levels since 2005.

•••• Total water use is forecast to remain at about 490 GL per year over the next price determination period despite a growing population, due to:

•••• further recycled water use by industry and residents

•••• residents, businesses and government organisations improving on their current levels of water efficiency, and

•••• expected reductions in water use by BlueScope Steel’s Port Kembla steel works.

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Since early 2000, total water use has declined by over 25% or 130 billion litres (GL) since early 2000, and this level of water use has been maintained over the current price determination period. While total water use has declined, the population served by Sydney Water has increased from about 4 million to over 4.5 million over the last 10 years.

Sydney Water’s Operating Licence 2005-10 contained a ‘water saving target’, that total water use (including residential and non-residential water use and system losses), was to be below 329 litres per person per day (LPD) by June 2011. The Operating Licence 2010-15 requires water use to stay within this level. Current water use is currently well below this, at around 303 LPD.

Previous forecasts and actual outcomes

Total water use from the 2005 IPART price determination to 30 June 2011 has been consistently below forecast (Table 7.1). In the six years to 30 June 2011, total water use has been about 10% less than forecast.

Table 7.1 Past forecast and actual total water use, GL

This forecasting error occurred because of several assumptions:

•••• in the 2005 price determination it was assumed that drought restrictions would be gradually eased and lifted by January 2007. This did not occur and in fact the level of drought restrictions was increased to Level 3, which then remained in force over the 2005 price determination.

•••• in the 2008 price determination it was assumed that drought restrictions would be gradually lifted during the first year. In fact, drought restrictions were not lifted until late June 2009, when they were replaced by Water Wise Rules.

•••• in the 2008 price determination it was further assumed that after drought restrictions were replaced by Water Wise Rules, customers would increase their total water use by about 50 GL to 60 GL a year. However residents, business and institutionshave not increased their average level of water use since the lifting of drought restrictions.

Experience has shown the difficulties in accurately forecasting water use during and immediately after severe and sustained drought. The assumptions that must be made about the timing of lifting drought restrictions and the community’s subsequent response, have large impacts on forecast water use levels. However, limited information was available at the time on which to base these key assumptions.

Property numbers

Total property numbers are also below that forecast in the 2008 price determination. The total number of houses and units connected to Sydney Water’s networks by 30 June 2012 is expected to be around 50,000 less than forecast. This represents around 3% of total residential property numbers.

Generally, growth in ‘greenfield’ or new development areas was about half that forecast, except for the Rouse Hill area, which achieved around 75% of forecast growth. Part of this reduction in growth rates in 2008 and 2009 can be attributed to the impacts of the global financial crisis.

Prior determination period Current determination period

2005-06 2006-07 2007-08 2008-09 2009-10 2010-11

Forecast 559 575 590 519 560 544

Actual 528 510 482 492 506 496

Difference -31 -65 -108 -27 -54 -48


Forecast water use over the next five years

As Water Wise Rules have been in place for over two years, it is now possible to forecast water use, based on an extended period of observed outcomes, that can reasonably be expected to continue into the future. This compares to previous forecasts, which had to make assumptions about water use patterns that would occur after the lifting of drought restrictions.

Total water use is expected to remain at about 490 GL per year over the next price determination period (Table 7.2). This level of water use is consistent with 2008-09 water use levels, being the last year of drought restrictions.

Table 7.2 Forecast water use over the next price determination period, GL

1 Water sales include billed metered and billed unmetered. 2 Non-revenue water includes leakage, unauthorised water use, unbilled (eg fire fighting) and customer meter under-registration.

The main factors contributing to water use remaining at about 490 GL per year, even with a growing population, are:

•••• the Rosehill-Camellia recycling project, replacing about 4 GL per year of potable water use by industry, and the Kurnell recycling project replacing around 1.4 GL per year

•••• continuing improvements in the water efficiency of residential households consistent with a water usage price set at the long run marginal cost of water

•••• Sydney Water maintaining low levels of leakage from the water supply network, and

•••• the decision by BlueScope Steel to reduce its annual production from its Port Kembla steel works. Depending on actual production levels and recycled water volumes supplied, BlueScope’s unfiltered water use is expected to decline substantially.

With forecast total water use remaining at around 2008-09 levels, Sydney Water confidently expects to meet its operating licence target of keeping total water use at or below 329 LPD.

Forecasting approach

To develop the forecasts in Table 7.2, Sydney Water has undertaken a detailed analysis of the water use of residential and non-residential properties. Separate forecasts were developed for identified user groups (e.g houses and blocks of units and flats) within the two broad groups. The approach addresses the previous concerns raised by McLennan Magasanik Associates at the 2008 price determination, especially their preferred option of direct forecasts of residential and non-residential water use.

The analysis undertaken provides current estimates of the impact on water use from changes to water usage prices, participation in water efficiency programs and the move to Water Wise Rules. Identifying and quantifying the key drivers of water use established a sound basis for developing the forecasts contained in Table 7.2.

In its Issues Paper, IPART has sought comment on whether alternative approaches to forecasting water use, such as moving average models, would provide for more accurate forecasts. Sydney Water considers that its rigorous assessment of water use by user group and forward-looking models provide the most reliable basis for understanding and forecasting water use over the price determination period. Ultimately, actual consumer behaviour may prove different from that forecast

Current determination period Next determination period

2011-12 2012-13 2013-14 2014-15 2015-16

Water sales 1 436 435 433 435 438

Non-revenue water 2 53 53 53 53 53

Tota l water use 489 487 486 488 491


due to unpredictable risks and uncertainties (see section 7.4), which no model specification or forecasting approach can address.

Moving average models can be expected to systematically under or over forecast likely water use over the price determination period depending on the specification chosen. This is because of the underlying structural changes occurring in residential and non-residential water use, as discussed in sections 7.2 and 7.3. They are also suitable for forecasting one period only (e.g. one year). Sydney Water has provided some preliminary outcomes from moving average models in Appendix 15.

Forecast property numbers

Forecast growth in residential and non-residential property numbers is shown in Table 7.3. Residential property forecasts are based on the NSW Department of Planning and Infrastructure’s (2008-09) Metropolitan Development Program. It is expected that over the next price determination, around 53,000 new residential properties will connect to Sydney Water’s water and wastewater networks.

Table 7.3 Forecast property numbers (including exempt properties) for the next price determination period

Forecast non-residential properties are based on the average development rates and lot sizes over the previous 10 years. The forecasts considered the remaining non-residential development potential in Sydney. It is expected that over the next price determination, around 5,000 new non-residential properties will connect to Sydney Water’s water and wastewater networks.

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

Water

Residential 1,696,423 1,714,223 1,729,193 1,744,273Non-residential 136,045 137,782 139,397 141,076

Water tota l 1,832,468 1,852,005 1,868,590 1,885,349

Wastew ater

Residential 1,658,921 1,676,371 1,690,451 1,704,521Non-residential 123,144 124,881 126,496 128,175

Wastewater tota l 1,782,065 1,801,252 1,816,947 1,832,696

Stormw ater

Residential 473,180 479,765 483,684 487,651Non-residential 50,313 51,214 52,295 53,475

Stormwater total 523,493 530,979 535,979 541,126


7.2 Residential water use Residents account for around 65% of Sydney’s total water use and over 70% of metered water use. Houses account for around 70% of residential water use, with units, flats, townhouses and dual occupancies accounting for the remaining 30%.

Prior to the imposition of drought restrictions, the water use of houses averaged around 290 kL per year. There was also a very pronounced seasonal pattern, with water use in summer around one quarter higher than winter (Figure 7.2).

Figure 7.2 Average water use of houses, kL per month

Since drought restrictions were implemented, the average water use of houses has fallen by around 25%. This reduction has occurred through both an underlying reduction in water use (evidenced through the lower water use during winter) and proportionally lower increases in water use during spring and summer.

Sydney Water’s research has found that the overall reduction in water use is attributable to:

•••• the extensive water efficiency programs implemented since early 2000

•••• the increases in water usage prices implemented since October 2005

•••• residents’ choices about their water use given severe and sustained drought

•••• mandatory restrictions on outdoor water use.

Sydney Water’s water efficiency programs, comprising subsidised water efficient appliances, rainwater tank rebates and advice on water efficient gardens, is estimated to have reduced residential water use by around 16 GL per year, by 2008-09.

Given the extensive residential water efficiency programs implemented since early 2000, there is little scope to achieve further large reductions in water use from these programs in the future. Instead, further improvements in water efficiency will occur though capital expenditure on the water supply network (such as renewals) to maintain low leakage rates, and mandated levels of water efficiency for new and renovated houses (the Building Sustainability Index or BASIX).

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From October 2005 to June 2009, nominal water usage prices were increased from around $1.00 per kL to $1.60 per kL for households that used less than 100 kL per quarter (Tier 1 price). The water usage price increases were greater for households using more than 400 kL per year, with the additional water use attracting a price up to $1.83 per kL (Tier 2 price). Sydney Water estimates that the increases in water usage prices have reduced residential water use by around 16 GL per year.1

The estimated reduction in residential water use associated with drought restrictions is around 70 GL per year. Based on Sydney Water’s analysis of water use during drought restrictions, it is likely this total amount is split approximately equally between indoor and outdoor water use.

Forecast residential water sales

To develop more reliable and transparent forecasts of water use, Sydney Water has developed new models to provide separate forecasts of residential and non-residential water use. These new models have incorporated the observed response by residents from replacing drought restrictions with Water Wise Rules in late June 2009.

The new forecasting method fitted econometric models to household-level water use data using sophisticated econometric estimation techniques. The average daily water use of a household (per quarter) was modelled against:

•••• the household’s previous water use (habit formation)

•••• the price of water

•••• whether drought restrictions or Water Wise Rules were in place

•••• participation in a water efficiency program

•••• the season

•••• weather conditions.

Key outputs were the estimated impact on residential water use from:

•••• changes in water usage prices ($ per kL)

•••• replacing drought restrictions with Water Wise Rules

•••• changes in the season and weather conditions during the year.

Detail on the new models used to forecast residential water use is provided in Appendix 15. The models were then used to forecast residential water use to 30 June 2016 based on the key assumptions described below.

Key forecasting assumptions

In forecasting residential water use over the next five years, the key forecasting assumptions are:

•••• residents have fully adjusted to Water Wise Rules

•••• water usage prices will gradually increase to Sydney Water’s estimate of the long run marginal cost (LRMC) of water over the next price determination (see Chapter 8)

•••• new residential properties will achieve the mandated levels of water efficiency as required under BASIX

•••• drought restrictions will not be reimposed during the price determination period.

1 For further detail on the impact of water usage prices on residential demand, see Abrams, B., Kumaradevan, S., Sarafidis, V. and Spaninks, F. (2011) The Residential Price Elasticity of Demand for Water, Joint Research Study. Available on Sydney Water’s website, sydneywater.com.au.


The most important assumption to the forecasts is that residents and businesses have fully adjusted to Water Wise Rules. This assumption is supported by the currently observed levels of water use, and the results of the econometric modelling, which found that households fully adjusted to Water Wise Rules after around one year (see Appendix 15).

Forecast residential water sales

Forecast residential water sales over the next five years are presented in Figure 7.3. Total water sales are expected to increase from 320 GL in 2011-12 to 327 GL (7 GL increase) by 2015-16. Existing residential properties are expected to reduce their water use by about 3 GL over the five years to 2015-16. New residential properties are forecast to increase water use by about 10 GL per year by 2015-16.

Figure 7.3 Forecast residential water sales over the next price determination period1, GL

1 Includes unmetered billed consumption.

320 320 316 316 317

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7.3 Non-residential water use Non-residential properties, including businesses, schools, hospitals and public organisations account for around 25% of Sydney’s total water use. There has been a steady decline in the average water use of non-residential properties over the past 20 years (Figure 7.4).

Figure 7.4 Average water use of non-residential properties1, kL per month

1 Excludes businesses where water use is a key component of its production processes.

Compared to the early 1990s, the average water use per non-residential property has halved. This means that even with property growth, total water use by non-residential properties has fallen by around 30%, from around 186 GL in 1989-90 to 125 GL in 2009-10.

This steady long-term reduction in water use can be attributed to numerous factors. First, pricing reforms in the early 1990s, with greater emphasis on usage-based charges is likely to have encouraged greater levels of water efficiency. Second, the gradual move away from manufacturing to a service-based economy in Sydney has also reduced the average water use of non-residential properties.

The decline in water use from early 2000 mainly reflects:

1. Sydney Water’s targeted water efficiency programs aimed at businesses that use relatively large amounts of water as part of their production processes

2. the increases in water usage prices implemented since October 2005

3. the impact of drought restrictions and the efforts by many businesses to reduce their water use given severe and sustained drought.

It is clear from Figure 7.4 that the rate of decline in non-residential water use is slowing, especially in recent years. While the forecasts incorporate some significant further reductions in potable water use from anticipated recycling projects and stated reductions in output by industry, the rate of decline in non-residential water use is expected to be modest over the next price determination period.

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Forecast non-residential water sales

Sydney Water has undertaken a detailed analysis of the water use patterns of identified groups of non-residential properties. The groups cover large industrial users down to small ‘strata units’ such as business parks (see Appendix 15).

For each identified group, econometric models were fitted to match and forecast the long-term trend in observed water use. Some large water users have stated water efficiency targets, and these targets have been included in the forecasting process.

Key forecasting assumptions

In forecasting non-residential water sales over the next price determination, the key forecasting assumptions are:

•••• non-residential properties will continue to gradually reduce their average levels of water use consistent with their long-term trends

•••• the Rosehill-Camellia and Kurnell recycling projects will replace around 4 GL and 1.4 GL of potable water use by 2015-16, respectively

•••• the decision by BlueScope Steel to reduce its annual production from its Port Kembla steel works is expected to substantially reduce its unfiltered water use

•••• non-residential properties have fully adjusted to Water Wise Rules

•••• businesses will achieve stated water usage efficiency targets.

It is also important to note that the vast majority of non-residential property growth over the last 20 years has been strata units, such as business and industrial parks (Figure 7.5). There has been virtually no growth in new non-residential businesses on individual lots of land.

Figure 7.5 Growth in non-residential property numbers, thousands

Note: This data is the monthly stock of properties each year.

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The average water use of strata units is very low, at around 150 kL per unit in 2009-10. This amount of water use is similar to the annual water use of a residential unit or flat.

Sydney Water has assumed that the growth in new non-residential properties will continue to be strata units with low levels of average water use. The increase in water use attributable to new non-residential properties is less than 1 GL per year by 2015-16.

Forecast water sales

Forecast non-residential water sales are presented in Figure 7.6. Based on the forecasting assumptions described earlier, water sales are expected to decrease from around 116 GL in 2011-12 to 111 GL in 2015-16.

Figure 7.6 Forecast non-residential water sales for the next price determination period1, GL

1 Includes unmetered billed consumption.

The majority of the reduction in water use is expected to occur in 2012-13. This is due to the timing of expected recycling projects (for example the privately operated sewer mining project at Kurnell that will supply recycled water to Caltex) and the forecast reduction in water use by BlueScope Steel.

7.4 Key risks and uncertainties Sydney Water has sought to take a balanced approach to forecasting residential and non-residential water use. In doing so, it should be recognised that the downside risks to the forecasts are likely to exceed upside risks.

One downside risk for residential water use is the reaction of households to anticipated increases in overall utility prices, especially energy bills. It could be that the expected increases in energy bills will mean households become even more careful over their water use, especially hot water. The outcome would be a sustained reduction in water use below that forecast. The increase in energy prices and Sydney Water’s proposed increases in the water usage price also mean that the prospect of a sustained underlying increase in residential water use is low.

Another risk is any unexpected downturn to the Sydney economy over the next price determination. Unexpected downturns can generate significant reductions in water use if production levels at key businesses are scaled back, as previously observed during the global financial crisis. In addition, the high Australian dollar can reduce the competitiveness of some

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businesses, including manufacturing and tourism. Output reductions in such sectors will reduce their demand for water. The recent decision by BlueScope Steel to reduce its production of steel highlights the ongoing structural change that is reducing water use by the non-residential sector.

It is unlikely that unexpected growth in the Sydney economy would lead to proportional increases in water use, given the output capacities of industries that use water as part of their production processes.

While considered unlikely over the next price determination, a return to severe and sustained drought may require the re-imposition of mandatory restrictions on outdoor water use. The analysis of water use during Water Wise Rules indicates that the re-imposition of Level 3 drought restrictions would reduce residential water use by around 4% (see Appendix 15).

Weather uncertainties

Water use in any particular year is also dependent on the weather. The forecasts presented earlier represent ‘average’ water use outcomes given observed weather conditions since the late 1950s.

Variation in weather conditions can reduce (or increase) residential metered water sales by around 14 GL (18 GL) per year given past observed extremes in weather conditions (Figure 7.7). These fluctuations in water sales mean actual water sales in any particular year can differ significantly from that used to set prices.

Figure 7.7 Variation in annual residential water use due to weather conditions, GL

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Sydney Water – Submission to IPART 2012 pricing determination Appendix 15 Page | 308

Appendix 15 Forecasting water use

15.1 Sydney Water’s previous and new approach to forecasting water use Previous approach

Since 2003, Sydney Water used a ‘top down’ approach to forecasting water use. The starting point was total ‘baseline demand’, with assumed water use of 426 litres per person per day (LPD). This baseline level of water use was then reduced by ‘water conservation activities’ (water efficiency, leak reduction and recycling) and drought restrictions to obtain annual forecasts.

At the 2008 price determination, IPART commissioned McLennan Magasanik Associates (MMA) to review Sydney Water’s water use forecasts. MMA considered that the ‘top down’ approach had several limitations, and that directly forecasting residential and non-residential water use was preferable.1 MMA’s main concerns are summarised in Box 15.1.

Sydney Water’s new approach

In preparing the water use forecast over the next five years, Sydney Water has undertaken a detailed analysis of water use by identified ‘user groups’ of residential and non-residential properties. The detailed analysis addresses the concerns raised by MMA. Importantly, the analysis has sought to identify and quantify the trends in water use, which MMA identified as an important limitation of the previous approach to forecasting water use.

Key points •••• Sydney Water has undertaken a detailed analysis of residential and non-residential water

use in developing its water use forecasts for the next price determination period.

•••• This appendix provides further detail on the method and outcomes of the residential and non-residential econometric models.

Box 15.1 MMA’s view of Sydney Water’s top down approach to forecasting total water use

In MMA’s view, there were a number of limitations with Sydney Water’s previous approach to forecasting, which restricted the model’s usefulness. These limitations included:

•••• estimating monthly population growth is difficult, which leads to error in the LPD calculated for each month

•••• the approach masks a large number of underlying trends, such as reductions in industrial demand

•••• estimates of the trend variable are not robust and are likely to change over time as the interplay of underlying trends change

•••• assuming the average consumption (per person) remains constant at 426 LPD implies that water consumption patterns remain constant relative to the population

•••• the LPD model did not account for changes in the structure of industry resulting in the growth of service industries offset by the decline of manufacturing.


The new approach to forecasting water use involved fitting econometric models to the identified user groups. The models quantified the impact of identified variables (e.g. price, weather conditions, water efficiency programs and long-term trends) on water use patterns. Forecasts of the explanatory variables were then used to develop forecasts of water use.

The models were also supplemented by additional information, such as the stated water use targets of individual industrial water users, where available. To support the approach and development of the models and to undertake a formal peer review, Sydney Water engaged the services of Sapere Research Group.

The residential and non-residential water use econometric models are described below.

15.2 Residential water use Residents account for around 65% of Sydney’s total water use and over 70% of metered water use. Houses account for around 70% of residential water use, with units, flats, townhouses and dual occupancies accounting for the remaining 30%.

Sydney Water has undertaken panel data econometric modelling of residential water use. Panel data econometrics makes use of both cross sectional and time series information available at a household level.

A key advantage of panel data analysis is the ability to obtain more accurate estimates of the various influences on water use (price, drought water restrictions, water efficiency programs etc.) than is possible under either cross-sectional or time series analysis separately. It can also be used to establish dynamic relationships between individual explanatory variables and water use over time (eg the impact of changing water usage prices on water use over time), and control for omitted variables not available at the household level.

Developing reliable econometric models using panel data requires sophisticated estimation methods together with experience in modelling and analysing large panel datasets. To that end, Sydney Water engaged Dr Vasilis Sarafidis, Lecturer in Econometrics, University of Sydney, to apply the panel data econometrics to the datasets developed within Sydney Water.

Sample size, user groups and clusters

The residential econometric models are based on a sample of around 130,000 individual houses, 6,800 blocks of units and 21,000 townhouses from June 2004 to December 2010. This sample represents more than 10% of total residential properties.

Models were estimated separately for different user groups of:

1. owner occupied houses, no participation in a water efficiency program

2. owner occupied houses, participation in a water efficiency program

3. tenanted houses, no participation in a water efficiency program

4. tenanted houses, participation in a water efficiency program

5. townhouses, owner occupied

6. townhouses, tenanted

7. blocks of housing units

Owner occupied and tenanted houses were further disaggregated through clustering analysis. Property size was used as the basis for clustering. Clustering analysis allows the water use of each clustered group of households to have its own response to changes in the explanatory variables. For example, households that occupy a large property – and are likely to undertake more outdoor watering – are anticipated to be more sensitive to weather conditions and the season compared to households living on small blocks of land. Separate models for each cluster allow this ‘heterogeneity’ in responses to be examined. Together, 65 econometric models were estimated from all the sampled households (Table 15.1).


Table 15.1 User groups and clusters

Model specification and estimation technique

The model specification is as follows:

,1 ,

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+⋅+⋅+⋅+⋅++⋅+

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δγ

γγβα

itiit

j ititjjj itjjitj jitjj jitj

j jitjititj jitjitit

u

uWWRseasonLseasonRLdiyKwmrI

waterfixHevapdevraindevpricecc

Where:

lncit denotes the natural logarithm of average daily consumption (ADC) of household i at time period t

priceit denotes the average water usage price calculated based on daily water use

raindevit, and evapdevit denote the deviation from the average value of rainfall and evaporation

waterfixit, wmachineit and diyit denote participation in the WaterFix, washing machine rebate and DIY Kit water efficiency programs, respectively

L2Rit capture the time when Level 2 drought restrictions were in force

seasonit is a set of variables that captures the effect of the season

WWRseasonit is a set of variables that captures the effect of the move to Water Wise Rules (WWRs) by season.

The error term is composite and consists of i, which allows for unobserved household-specific effects that may be correlated with the explanatory variables, such as geographical location, size of the property, household size etc, and it, which is the usual random noise component. The variables are calculated in ‘first differences’. This means that the model estimates the change in water use given a change in the explanatory variables. A constant is then calculated to estimate the level of water use.

The estimation technique chosen was the Generalised Method of Moments (GMM). GMM is a common choice when the explanatory variables are endogenous. In the present context, the lagged dependent variable (water use) is endogenous by construction, since taking firstdifferences induces contemporaneous correlation between lncit-1 and it. The average water usage price is also endogenous because its value depends on water usage when a two-tier pricing structure for water use was in place.

User group Number of households Number of clusters

Ow ner occupied houses

No water efficiency participation 46,631 17Water efficiency participation 41,496 31

Tenanted houses

No water efficiency participation 10,334 7Water efficiency participation 5,296 7

TownhousesNo water efficiency participation 10,267 1Water efficiency participation 11,327 1

Units and flats (blocks) 6,832 1

Total 132,183 65


To calculate the average outcome for each user group, individual outcomes were weighted by the number of households in each cluster. As the models were calculated in first differences, a constant term was estimated based on the difference between the observed and predicted average daily consumption (in logs).

Further explanation and justification of the parameters included in the econometric models and estimation technique is provided in Abrams, B., Kumaradevan, S., Sarafidis, V and Spaninks, F. (2011) The Residential Price Elasticity of Demand for Water, Joint Research Study, Sydney, February.

Model outcomes

The parameter estimates for units and flats are shown in Table 15.2 below. The parameter estimates for houses and townhouses are shown in Table 15.3 on the following page. A detailed description of one user group is also provided.

Table 15.2 Model outputs for units and flats

Detailed description – owner occupied houses that had participated in a water efficiency program

The coefficient values for owner occupied houses that had participated in a water efficiency program are interpreted as follows.

The coefficient for the lag of water use (ADC-1) gives the expected rate at which households adjust to permanent changes in the explanatory variables. A value of approximately 0.23 means that after three months (one meter-reading period) households have adjusted around 77% (one minus the coefficient for ADC-1) of the total estimated response to a permanent change in an explanatory variable. Over 98% of the adjustment occurs after nine months.

Parameter Value

ADC (t-1) 0.50610

Price 0.00011

Price (t-1) -0.00012

Price (t-2) 0.00045

Price (t-3) -0.00064

Price (t-4) -0.00009

Raindev -0.00101

Tempdev 0.00627

Waterfix -0.10452

Summer 0.33154

Autumn 0.31647

Winter 0.26652

Spring 0.31676

WWRsummer 0.01111

WWRautumn 0.01606

WWRwinter 0.03216

WWRspring 0.02546

Vacancy rate -0.00004

Constant 0.44476

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ney

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Tab

le 1

5.3

Mo

del

ou

tpu

ts f

or

ho

use

s an

d t

ow

nh

ou

ses,

wei

gh

ted

ave

rag

e o

utc

om

es f

or

ow

ner

occ

up

ied

an

d t

enan

ted

ho

use

s

Pa

ram

ete

r N

o w

ate

r e

ffic

ien

cy

Wa

ter

eff

icie

nc

yN

o w

ate

r e

ffic

ien

cy

Wa

ter

eff

icie

nc

yO

wn

er

oc

cu

pie

dT

en

an

ted

pa

rtic

ipa

tio

np

art

icip

ati

on

pa

rtic

ipa

tio

np

art

icip

ati

on

AD

C (

t-1

)0

.34

80

20

.22

93

60

.42

28

40

.42

78

90

.31

94

70

.43

62

9P

ric

e-0

.00

01

8-0

.00

02

9-0

.00

03

2-0

.00

02

50

.00

06

70

.00

07

0P

ric

e (

t-1

)0

.00

13

30

.00

16

00

.00

11

50

.00

14

80

.00

02

30

.00

00

7P

ric

e (

t-2

)-0

.00

41

5-0

.00

06

4-0

.00

03

0-0

.00

03

9-0

.00

05

10

.00

01

4P

ric

e (

t-3

)0

.00

11

3-0

.00

16

8-0

.00

01

5-0

.00

00

6-0

.00

03

0-0

.00

12

8P

ric

e (

t-4

)0

.00

06

5-0

.00

10

10

.00

00

1-0

.00

13

1P

ric

e (

t-5

)-0

.00

13

5R

ain

de

v-0

.00

89

8-0

.00

95

0-0

.00

52

0-0

.00

74

4-0

.00

71

2-0

.00

41

2E

vap

de

v0

.08

65

70

.05

42

00

.03

60

80

.04

27

50

.02

69

80

.01

00

8W

ate

rfix

-0.1

09

65

-0.1

38

22

-0.1

01

11

-0.1

36

08

Wa

terf

ix (

t-1

)0

.02

21

90

.06

43

00

.01

18

70

.05

84

2W

MR

-0.0

74

07

-0.0

33

53

-0.0

70

50

0.0

03

23

WM

R (

t-1

)0

.00

68

60

.01

86

6-0

.01

02

90

.01

47

1D

IY-0

.01

39

9-0

.01

08

8-0

.02

54

5-0

.02

47

9D

IY (

t-1

)-0

.00

23

1-0

.00

28

30

.00

06

6-0

.01

21

0L

2R

0.0

02

03

0.0

11

40

0.0

21

49

0.0

06

04

-0.0

02

30

0.0

06

81

Su

mm

er

-0.1

59

78

-0.5

82

47

-0.2

34

44

-0.1

18

99

-0.1

37

10

0.0

14

93

Au

tum

n-0

.23

96

3-0

.66

09

7-0

.28

22

7-0

.15

42

6-0

.18

17

80

.01

66

1W

inte

r-0

.27

20

0-0

.67

38

1-0

.29

47

6-0

.17

42

5-0

.18

55

30

.00

43

6S

pri

ng

-0.1

79

73

-0.5

97

56

-0.2

48

69

-0.1

27

57

-0.1

43

68

0.0

27

42

WW

Rs

um

me

r0

.05

02

50

.00

97

70

.02

14

90

.02

07

90

.02

17

8-0

.00

45

0W

WR

au

tum

n0

.03

57

30

.01

73

60

.00

12

10

.01

36

40

.02

92

40

.01

76

2W

WR

win

ter

0.0

16

10

0.0

26

03

0.0

14

66

0.0

03

17

0.0

31

28

0.0

14

29

WW

Rs

pri

ng

0.0

48

10

0.0

45

95

0.0

39

37

0.0

47

29

0.0

50

81

0.0

21

37

Co

ns

tan

t0

.07

37

00

.40

03

80

.06

43

2-0

.06

42

8-0

.25

50

6-0

.39

50

6

Ow

ne

r o

ccu

pie

d h

ou

ses

Te

na

nte

d h

ou

ses

To

wn

ho

use

s


The coefficient for price in the immediate period is -0.00029. Under the semi-log functional form, this value is scaled by the level of price (in cents per kL) to give the short-run real price elasticity. At $2.00 per kL, the short run price elasticity is -0.06. This means a 10% increase in price (from $2.00 to $2.20 per kL) could be expected to immediately reduce water use by around 0.06%.

The long-run price elasticity is calculated by summing the coefficient for price and dividing it by one minus the parameter value for ADC-1. Based on the weighted average long-run price elasticities of the individual clusters, the value is equal to -0.0013. At $2.00 per kL, the long run price elasticity is -0.26. This means a 10% increase in price (from $2.00 to $2.20 per kL) could be expected to reduce water use by around 2.6% in the long run.The values for rainfall and evaporation are as expected, being negative and positive, respectively. The values are interpreted in percentage terms. The values mean that a one millimetre increase in average rainfall (evaporation) levels can be expected to reduce (increase) water use by around 1% (5.4%) in the quarter that the changes occur.

The impact of the three water efficiency programs (WaterFix, washing machine rebates and DIY Kits) have variables for both the period the household participated in the program and one lagged period. Given that the impact of the programs is instantaneous (eg a new showerhead), the purpose of including the lagged period is to allow for similar reductions in water use in both the short and long run. Table 15.4 shows the estimated short and long run percentage reduction in water use for an individual household from the three water efficiency programs.

Table 15.4 Short and long run reduction in water use from water efficiency programs, %

The value when Level 2 drought restrictions were in place is as expected, indicating that water use was around 1% higher compared to Level 3 drought restrictions.

The coefficients of the seasonal variables are interpretable in relative terms. The difference between the coefficient of winter and summer is about -0.055. The model has found that moving from summer to winter results in an average reduction of about 5.5% in average daily water use, all else constant.

The coefficients of the WWRs seasonal variables represent the additional water use associated with replacing Level 3 drought restrictions with WWRs by season. As the move to WWRs represents a permanent change, it is meaningful to calculate the long run impact of WWRs using the short run coefficient values divided by one minus the parameter value for ADC-1. The estimated long run increase in water use associated with WWRs by season is shown in Table 15.5.

Table 15.5 Long run increase in water use from replacing drought restrictions with WWRs, %

It is interesting to note that the change in water use due to the transition to WWRs by households that have participated in a water efficiency program with WWRs is modest. This indicates that the water use decisions of these households are determined by other motivations, such as a desire to conserve water, rather than the regulatory regime in place. It may also be that the improved water

Short run Long run

WaterFix -11.0% -11.4%

Washing machine rebates -7.4% -8.6%

DIY Kits -1.4% -2.2%

Summer Autumn Winter Spring

Long run impact on water use 1.1% 2.3% 3.7% 6.2%


efficiency of indoor appliances has lessened the increase in water use, given around half the overall reduction in water use during drought restrictions was indoor.

Given the estimated rate of adjustment (based on one minus the parameter value for ADC-1), the available evidence indicates that these households have fully adjusted to replacing drought restrictions with WWRs.

The ‘in sample’ performance of the weighted average model for owner occupied houses that had participated in a water efficiency program is shown in Figure 15.1.

Figure 15.1 Owner occupied houses, participation in a water efficiency program

Water Wise Rules and increases in water usage prices

Using the parameter estimates from the econometric models, it is possible to quantify the effects on residential water use of replacing Level 3 drought restrictions with WWRs and increasing water usage prices.

Across all households, residential water use is estimated to have increased by 3.4% to 4.4% as a result of replacing Level 3 drought restrictions with WWRs (Table 15.6). However, increasing the water usage price from $1.60 per kL to $2.00 per kL (25% real increase) is estimated to have more than offset the move to WWRs for houses and townhouses. The impact of the price increases on units and flats is the smallest, reflecting the lack of price signal they receive given they are served by common meters. The net impact across all user groups is a small reduction in overall residential water use.

Table 15.6 Estimated impact of WWRs and water usage price on residential water use, %

Drought restrictions Drought restrictions

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

Mar

-04

Jun-

04

Sep-0

4

Dec-0

4

Mar

-05

Jun-

05

Sep-0

5

Dec-0

5

Mar

-06

Jun-

06

Sep-0

6

Dec-0

6

Mar

-07

Jun-

07

Sep-0

7

Dec-0

7

Mar

-08

Jun-

08

Sep-0

8

Dec-0

8

Mar

-09

Jun-

09

Sep-0

9

Dec-0

9

Mar

-10

Jun-

10

Sep-1

0

Wat

er u

se (

kL/p

rope

rty/

day)

Actual Prediction

Drought restrictions Water Wise Rules

Houses Townhouses Units and fla ts

Increase with Water W ise Rules 4.4% 3.4% 4.4%

Decrease from price increses -4.7% -3.6% -1.9%


As discussed earlier, a further output from the econometric models is the rate at which households adjust to price changes and the replacement of drought restrictions with WWRs. With an average parameter value ADC-1 of about 0.35, this indicates that households fully adjusted (greater than 99%) to WWRs after about 15 months.

Annual forecasts by user group

Figure 15.2 shows the annual forecasts (kL/property/year) for the different residential user groups from 2007-08. In all cases, the combined impact of improvements in water efficiency (through either subsidised programs and/or increases in water usage prices) more than offset the long-run impact of the move to WWRs. However, the overall reduction is modest.

Figure 15.2 Forecast water use by user group, kL/property/year

15.3 Non-residential econometric models Non-residential properties, including businesses, schools, hospitals and public organisations account for around 25% of Sydney’s total water use. The non-residential sector is characterised by a strong downward trend in average water use (see chapter 7). Time series regression was used to quantify the trend in average water use, correcting for short-term fluctuations due to weather and season and any underlying increase in water use following the replacement of drought restrictions with WWRs in late June 2009.

Identified segments and the dataset

Analysis was carried out for sub-sectors of non-residential properties, referred to as segments. These segments were based on non-residential property type codes, which were combined to create a limited number of segments with distinct characteristics, such as different average water use levels, distinct trends in average water use, or different trends in the number of properties over time.

Properties that had participated in Sydney Water’s water efficiency programs were separated and are referred to as Every Drop Counts (EDC) participants. The resulting segments for econometric modelling were:

0

50

100

150

200

250

2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16

Wat

er u

se (

kL/p

rope

rty/

year

)

Owner occupied houses, no water efficiency program participationOwner occupied houses, water efficiency program participation

Tenanted houses, no water efficiency program participationTenanted houses, water efficiency program participation

Owner occupied townhousesTenanted townhouses

Units and flats


•••• EDC participants

•••• industrial

•••• commercial

•••• agricultural

•••• industrial and commercial strata units

•••• government, institutional and other.

In addition to the segments identified above, separate models were developed for a limited number of larger users, based on historical demands and stated savings targets. This segment includes BlueScope Steel. Its forecast water use has been significantly reduced due to its decision to exit the export market for steel, reducing output from its Port Kembla Steel works by around 50%.

Models are based on Sydney Water’s apportioned monthly metered consumption (‘monthly consumption’). This is calculated on a property level as follows:

1. calculate the average daily water use between two quarterly meter reads; then

2. roll up the average daily water use into an estimate of monthly water use.

Adding up the monthly consumption of all properties in a particular segment gives total monthly consumption for that segment. Formally, we denote the total monthly consumption by segment i in month m of year y by ),( ymiq where:

i = EDC participants, industrial, commercial, ….

m = 1,2,…12 = January, February,…, December.

For example, )2007,2(EDCq is total consumption by EDC participants in February 2007.

Total monthly consumption per property, ),(, ymiq , is total monthly consumption divided by the

number of properties and days in the month:

ym

ymi

ymi

ymi days

propsq

q,

),(

),(

),( = .

For ease of reference, ),( ymiq will simply be referred to as ‘average consumption’. In the remainder

of this appendix, the subscript i for the segment is omitted except where it may cause confusion.

Model specification and estimation technique

A model for average consumption was estimated for each segment using a combination of seasonal decomposition and time series regression analysis. The former is used to model the seasonal component of water use and the latter is used to model the variation in ‘deseasonalised’ water use over time.

The seasonality index calculated is multiplicative, ie:

mymym sqq ×=*

),(),( .

That is, average water use in some month equals deseasonalised demand, denoted by *),( ymq ,

multiplied by the seasonal factor for that month, denoted by sm. The seasonal factors were estimated using the ratio-to-moving-average method.

Dividing average water use by the relevant seasonal factor gives deseasonalised water use. Deseasonalised water use is modelled using regression analysis. The specification of the regression model is:


),(),(5),(4),(3),(2),(1*

),( ymymymymymymym WWRtRETq +×+×+×+×+×+= .

TΔ , EΔ and RΔ are measures of average daily maximum temperature, average daily pan evaporation and average daily rainfall, respectively. The Δ symbol indicates that these variables are measured in terms of their deviation from their seasonal average value. For example, the value of RΔ in, say, February 2005, 2005,2RΔ , is the average daily rainfall in February 2005 minus the average daily rainfall for all February included in the sample.

The time trend is captured by the variable t, which measures time in months. The models were estimated using data from October 2003 to September 2010 (see below). Therefore, t=1 for October 2003, t=2 for November 2003, etc. The dummy variable WWR is used to estimate the effect of WWRs on average water use; WWR=1 when WWRs apply and 0 otherwise.

α, β1 to β5 are regression coefficients and ε is the disturbance term. The regression coefficients were estimated using Ordinary Least Squares.2

For some segments, the natural logarithm of deseasonalised average demand, *),(ln ymq , was used

as the dependent variable. Also, in some cases the natural logarithm of time, lnt, was used for the time trend.

The models were estimated using data covering the seven-year period from October 2003, when Level 1 drought restrictions were introduced, to September 2010. This period excludes pre-restrictions data but includes 15 months of data following the lifting of drought restrictions and introduction of WWRs. Therefore, while the models do not quantify the impact of restrictions on water use, they do quantify the extent of any change in water use following the lifting of drought restrictions.3

Model outcomes

The parameter estimates for each non-residential segment is shown in Table 15.7 on the following page. A detailed description of the outputs for industrial properties is provided below.

Detailed description – industrial properties

The first step of the modelling was to estimate the seasonal factors. The seasonal factors for the industrial segment varied from 0.96 to 1.04.

Next, the seasonal factors were used to calculate deseasonalised water use for each month. The regression model was then applied to the deseasonalised data. The coefficient values for the regression model for industrial water use properties are shown in Table 15.7 and are interpreted as follows.

Temperature and rainfall have the expected positive and negative signs, respectively. The positive coefficient for temperature deviation means that above-average temperatures will increase demand and below-average temperatures will decrease demand. Similarly, the negative coefficient for rainfall means that above-average rainfall will reduce demand while below-average rainfall will increase demand. The coefficient for evaporation deviation was not statistically significant and therefore not included in the final model specification.

Because the regression model is for deseasonalised water use, the impact on water use of above or below-average temperature and rainfall in a particular month cannot be determined directly from the estimated coefficients. To calculate the impact in a particular month the impact on

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Tab

le 1

5.7

Mo

del

ou

tpu

ts f

or

the

no

n-r

esid

enti

al s

egm

ents

Not

es

(a):

Dep

ende

nt v

aria

ble

is th

e na

tura

l log

arith

m o

f (de

seas

onal

ised

) av

erag

e de

man

d.

(b):

The

mod

el fo

r th

e in

dust

rial s

egm

ent w

as e

stim

ated

usi

ng d

ata

from

Jul

y 20

05 to

Sep

tem

ber

2010

inst

ead

of O

ctob

er 2

003

to S

epte

mbe

r 20

10. T

his

is b

ecau

se a

s di

stin

ct tr

end

occu

rred

for

this

seg

men

t aro

und

July

200

5 (t

rend

bec

ame

less

ste

ep).

The

refo

re, f

or th

e in

dust

rial s

egm

ent,

t=1

for

Ju

ly 2

005

inst

ead

of O

ctob

er 2

003

for

the

othe

r se

gmen

ts.

(c):

For

the

indu

stria

l and

com

mer

cial

uni

ts th

e co

effic

ient

for

WW

Rs

cann

ot b

e in

terp

rete

d in

the

usua

l way

, ie

as th

e ch

ange

in w

ater

use

follo

win

g th

e in

trod

uctio

n of

W

WR

s. P

relim

inar

y an

alys

is s

how

ed th

at fo

llow

ing

an in

itial

dow

nwar

d tr

end,

wat

er u

se in

thes

e se

gmen

ts h

as b

een

larg

ely

cons

tant

sin

ce 2

005.

The

WW

Rs

varia

ble

has

been

use

d to

qua

ntify

the

redu

ctio

n in

wat

er u

se fr

om th

e in

itial

val

ue in

Oct

ober

200

3 (a

s m

easu

red

by th

e co

nsta

nt)

to th

e lo

wer

val

ue a

t whi

ch it

has

sta

bilis

ed.

For

exa

mpl

e, a

vera

ge w

ater

use

by

indu

stria

l uni

ts h

as s

tabi

lised

at 0

.363

2 (v

alue

of t

he C

onst

ant)

– 0

.063

5 =

0.2

997

or a

bout

300

litr

es p

er u

nit p

er d

ay.

(d):

Inte

ract

ion

term

bet

wee

n (la

g of

) ev

apor

atio

n de

viat

ion

and

the

pres

ence

of W

WR

s.

Pa

ram

ete

r In

du

str

iala

Co

mm

erc

ial

ED

CG

ov.

, In

st.

A

gri

cu

ltu

ral

Ind

. un

its

Co

m.

un

its

an

d O

the

ra

Co

ns

tan

t1

.17

32

3.2

88

21

27

.62

33

1.4

91

65

.25

64

0.3

63

20

.56

24

Te

mp

era

ture

de

via

tio

n0

.00

31

0.0

07

60

.74

38

Ra

infa

ll d

evi

ati

on

-0.0

03

8-0

.00

55

-0.4

07

3-0

.00

45

-0.1

08

5

Eva

po

rati

on

de

via

tio

n0

.01

10

0.6

08

70

.03

69

0.3

02

30

.00

23

Tim

e t

ren

d -

lin

ea

r (t

)-0

.00

13

b-0

.39

07

Tim

e t

ren

d -

lo

g (

lnt)

-0.0

70

7-0

.05

10

L1

Re

str

icti

on

s2

.93

09

L2

Re

str

icti

on

s1

.77

17

Wa

ter

Wis

e R

ule

s0

.01

69

4.6

07

70

.05

83

0.8

29

3-0

.06

35

c-0

.09

16c

La

g (

1)

of

Eva

po

rati

on

de

via

tio

n0

.02

58

Eva

p d

evi

ati

on

x W

WR

d0

.02

20

La

g (

1)

Eva

p d

evi

ati

on

x W

WR

b0

.04

08


deseasonalised water use, as estimated by the regression model, needs to be multiplied by the seasonal factor for that particular month. In the case of the industrial and commercial segments, where the dependent variable is the natural logarithm of deseasonalised water use, the estimated impact on deseasonalised water use needs to be converted to levels before multiplying by the appropriate seasonal factor.

For example, the positive coefficient of 0.0031 for temperature means that if the average daily maximum temperature in a particular month is one degree above average, this will increase the natural logarithm of deseasonalised average water use by 0.0031 which is equivalent to about 0.01 kilolitre per property per day or about 300 litres per property for the month (assuming 30 days).

As noted above, the seasonal factors for the industrial segment vary from 0.96 to 1.04. Therefore, depending on the particular month, an average daily maximum temperature of one degree above average will result in an increase in monthly demand of about 288 litres to 324 litres per property. The value of the trend variable coefficient is –0.0013. Because the dependent variable is in log form, the value of the coefficient can be interpreted as a percentage decrease. That is, the estimated coefficient is equivalent to a negative trend in average water use of about 0.1% per month.

Figure 15.3 shows the ‘in sample’ performance of the model for industrial water use properties.

Figure 15.3 In sample performance, industrial water use properties, kL/property/month

Annual forecasts by user group

The forecasts assume that the trend for each segment will continue over the price determination period. It is further assumed that long-term average weather conditions will apply (defined as the average value over the last 30 years). For EDC participants, it is assumed that the estimated downward trend is wholly due to the EDC Program and that the downward trend will not continue beyond June 2011. This is because, from 2011-12, the aim of the program is to maintain the reductions in water use by EDC properties.

To convert average water use to total water use, the forecast average water use is multiplied by the forecast number of properties. For most segments this forecast is based on the average monthly growth in the past 10 to 15 years. This approach is considered sufficient given the very low growth rates in these segments.

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The only non-residential segment showing any significant property growth is non-residential units (see Chapter 7). For this segment, the number of properties was forecast by fitting 3rd order polynomial curves to the historical property counts. This resulted in a very close fit.

The resulting forecast for each segment is shown in Figure 15.4.

Figure 15.4 Annual forecast by segment, ML per year

15.4 Moving average models In its Issues Paper, IPART has raised the possibility of using a moving average of past water use as a way of increasing the accuracy of water use forecasts.

A moving average simply uses the mean of previous values as the fitted value at each time. The selection of past periods is subjective. A moving average with a short length (few past periods) will respond to rapid changes in a time series, while a moving average with a longer length (many past periods) will respond more slowly to any rapid changes.

One important limitation of moving average models is that they are suitable only to forecast one period (eg one year) into the future. As described by Sharpe, De Veaux and Velleman (2010):

Of course, this method [moving average] can forecast only one period into the future, for Time = t+1. (You can repeat that value as a forecast beyond period t+1, but unless the time series is essentially unstructured and horizontal, it won’t be a very good forecast.)4

The water use patterns of residential and non-residential properties cannot be considered unstructured and horizontal. As described in Chapter 7 and this appendix, both residential and non-residential water use have undergone significant long-term structural changes. It is these changes that have limited the effectiveness of Sydney Water’s previous approach to forecasting total water use.

Moving average models are therefore not well suited to four to five year forecasts of total water use. While total water use over the past five years has been relatively stable, this represents a coincidence rather than an underlying characteristic. For example, if water usage prices had not

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been significantly increased since October 2005, it is likely total water use would be higher than currently observed.

Given these underlying trends, moving average models can be expected to under or over-forecast likely water use depending on the model specification chosen. As shown below, the forecast can vary markedly depending on the somewhat arbitrary choices made over the model specification.

Some simple moving average models

Figure 15.5 shows the five and 10-year moving averages (MA (5) and MA (10)) as applied to total water use since 1992-93. Figure 15.6 compares the indicative forecasts from the MA (5) and MA (10) models compared to that proposed by Sydney Water.

Figure 15.5 Moving averages, total water use, GL

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Figure 15.6 Water use forecasts, MA (10), MA (5) and Sydney Water’s proposal

Both the MA (10) and MA (5) models are likely to overstate future water use. This is because they implicitly understate the gradual impact on future water use of the increases in water usage prices, improvements in water efficiency, and reduction in water use by BlueScope Steel. By implicitly assuming the average past outcome will continue into the future (e.g. average past steel production by BlueScope Steel), the model fails to take account of the underlying trends in water use.

Figure 15.7 shows the five and 10-year moving averages as applied to the first difference or change in total water use since 1992-93. Figure 15.8 compares the indicative forecasts from the MA (5) and MA (10) models compared to that proposed by Sydney Water.

Figure 15.7 Moving averages, change total water use, ML

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Figure 15.8 Water use forecasts, first difference MA (10), MA (5) and Sydney Water’s proposal

In first differences, both the MA (10) and MA (5) models are likely to understate future water use. This is because the models are incorporating the same historical long-term reduction in water use in future water use from 2010-11 water use levels. Sydney Water’s models, while incorporating improvements in underlying water efficiency, also incorporate the move to WWRs and the growth in property numbers in the forecasts.

Always ensure cost recovery?

It is questionable whether moving average models can achieve IPART’s stated objective of ensuring a utility will always recover its costs, albeit with a lag. This would only occur if water use could be considered a ‘stationary process’. However, in practice, water use is subject to long term increases and decreases given factors such as drought restrictions and water usage prices. This in turn can generate permanent levels of under or over-recovery over time.

A simple example highlights the underlying problem with moving average models given water use cannot be considered a stationary process.

If total water use prior to a new determination had been constant at 500 GL per year for five years, then applying a MA (5) model would generate a forecast of 500 GL per year.

However, there may be a permanent decrease in water use during the forecast period, say to 450 GL per year, after which water use then remains stable at 450 GL per year into the future.

Applying a moving average model would result in a loss of revenue over the determination period given the lower water sales. Critically, however, the moving average process will not correct for this under-recovery in the next price determination period. The MA (5) model will forecast water use in the next determination period at 450 GL per year. While this would prove accurate for the period, it will not address the previous under-recovery. Increasing the length of the moving average process will only increase the level of under-recovery. This is because the next forecast will include the influence of times when demand was 500 GL per year.

This situation highlights the fact that moving average models are designed to forecast one period into the future only, assuming a stationary process. Using them for longer periods requires many underlying assumptions to hold (ie nothing ever changes and water use can be considered a stationary process), which is unlikely to be the case in practice.

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IPART - Independent Pricing and Regulatory Tribunal Home · non-residential water sales. 7.1 Water...

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