developing a water bills projection model: integrated...

Developing a Water Bills Projection Model:

Integrated Final Report

Defra

March 2015

NERA Economic Consulting

CONFIDENTIALITY

We understand that the maintenance of confidentiality with respect to our clients’ plans and

data is critical to their interests. NERA Economic Consulting rigorously applies internal

confidentiality practices to protect the confidentiality of all client information.

Similarly, our approaches and insights are proprietary and so we look to our clients to protect

our interests in our proposals, presentations, methodologies and analytical techniques. Under

no circumstances should this material be shared with any third party without the prior written

consent of NERA Economic Consulting.

© NERA Economic Consulting

Integrated Final Report Contents


Contents

Executive Summary i

1. Background and Introduction 1

2. Development of the Water Bills Projection Model 2

2.1. Formulation of Model 2

2.2. Model Scope and Limitations 3

2.3. Conceptual Structure of the Model 5

2.4. Model Inputs 7

2.5. Assumptions 12

2.6. Model Operation and Outputs 19

3. Model Specification 21

3.1. Model Technical Structure 22

3.2. Baseline Model Variables and Scenario 30

3.3. Sources for Driver Variables 32

3.4. Policy Scenarios 44

3.5. Output Specification 59

4. Key Model Results 61

4.1. Introduction 61

4.2. Limitations 61

4.3. Inputs and Assumptions 62

4.4. Baseline Results and Exogenous Variable Sensitivities 68

4.5. Policy-Specific Results - using baseline exogenous variables 78

4.6. Non-Baseline Policies and Policy-Based Sensitivities 84

4.7. Intermediate Baseline Outputs 94

5. Possible Model Developments 101

5.1. Dataset Upgrades 102

5.2. Structural Upgrades 104

5.3. Policy Upgrades 106

5.4. Output Upgrades and Output Analysis Tools 108

Appendix A. Elasticity Effects 110

Appendix B. Specification Issues Agreed with TSG 114

Appendix C. Glossary 118

Integrated Final Report Executive Summary

NERA Economic Consulting i

Executive Summary

This report presents work by NERA to develop the Defra Water Bills Projection Model. The

model has been designed to assist decision making in the water sector by showing the impact

of policy, regulatory, and company investment choices on final customer bills in England &

Wales over the period from 2015-50. It allows users to project bills under a range of

scenarios about the sector-policy environment, and about relevant macroeconomic and

environmental factors including the drivers of sector-costs, reporting the associated typical

bills for classes of customer by company or by region. The projections depend completely on

the assumptions input by the user such as the projected investment profile, as well as on other

assumptions that are implicit in taking a modelling approach, including the assumption that

the projected situation can be financed and can be compliant with relevant future laws.

The model was designed in an iterative process with a technical steering group (TSG)

consisting of industry stakeholders. These included representatives from Defra, Ofwat, the

EA, and from five water companies. During the six months of the project, several iterations

of the draft model and memos setting out options have been presented to the TSG. Their

feedback has usefully been incorporated into the final model. Several water companies also

provided longer-term data which we used to generate longer term trends to populate the

model from 2020 and beyond.

Figure 1.1 sets out an overview of the model’s structure. The structure is based on the cost

and revenue characteristics that we observe in the industry. The impacts on bills are derived

from simplified regulatory profit and loss account building blocks for wholesale value chain

components and application of a margin for retail.

Figure 1.1

Conceptual Structure of the Model

Source: NERA illustration

Abstraction

Reform

WRMP

Demand

WRMP

Supply

Properties

Climate Change

and RSA

RPEs

Retail

Competition

Activity-

Based

Costing

EU Directives

and UK statute

Upstream

Competition

GDP

Population

Financials:

COD/COE

PAYG

Gearing

Tax

Leakage

Supply Pipe

Adoption

Inflation

Baseline

Variables

Modelled

Demand

PAYG

RCV

WACC

Runoff & New

Depreciation

Drivers

Variables

Metering

Policy

Scenarios

Interest Rates

Modelled

Supply

Internal Calculations

Modelled

Opex &

Capex

Cost Impact on Bills

Modelled

Finance Costs

Tax

NHH Real Bill

Impact

Compound

Cost Impacts

Regulatory

Mechanisms &

efficiency

effects

Greeness

HH PCC targets

Wholesale Costs

NHH Allowed Retail

Costs

Wholesale Costs

attributed to HH

HH Retail Cost to

Serve

Wholesale Costs

attributed to NHH

NHH Retail

Margins

HH Retail Margins

HH Real Bill

Impact

HH and NHH

Nominal Bill

Impact

Greater

Resilience


NERA Economic Consulting ii

Figure 1.2 displays the average household bill levels under a range of scenarios for the factors

that are thought to matter most to costs in the sector, hence to bills. In interpreting these

results it should be borne in mind that:

There is little information available about the more distant future. For many of our

external input variables, such as cost inflation, there are no long-run forecasts directly

suitable for use as data inputs. There is also considerable uncertainty about the likely

regulatory and policy environment, for example about the long-run statutory requirements

to improve wastewater discharges. Assumptions must be made by the model user - we try

to be transparent about those used in this report;

In making assumptions to form long-term data inputs, we face the difficulty that the future

may be different from the past. For some important input data series such as capital

maintenance expenditure requirements we use current average levels and short/medium-

term forecasts made by water companies as a basis for long-run forecasts, though this

relationship might change, for example capital maintenance needs might increase more

than expected as assets become older and change with the service-quality and climate

context. An unforeseen change in the underlying relationships could make our results

misleading, when hindsight can be applied;

Though the model is designed to reflect available data, it is also only a model, one also

designed to be within the computational capacity of Excel avoiding use of macros.

Consequently the most granular level of data, relationship, and result treated in the

model is the company value chain level and an annual time step; no effects at finer levels

are modelled; few feedbacks are covered within the model.

Although the model does have limitations, it nonetheless remains a comprehensive and robust

set of scenario forecasts based on the best available evidence and a degree of industry

expertise.

Figure 1.2 shows that by 2050, the range of national average annual household bills goes

from £237 in the lower scenario setting, to £553 in the upper scenario, in real terms. The

black line shows the estimate of real household bills produced under the “baseline” driver

assumptions and scenarios in the model, falling from £355 in 2015 to £343 by 2050.


NERA Economic Consulting iii

Figure 1.2

Baseline and Sensitivity Range for Average Household Bills – Real

Source: NERA

The dark blue area around this baseline projection shows the national household average bill

range – in real terms - from setting all of the driver variables (e.g. GDP, population growth,

construction cost inflation, etc.) and baseline policies (retail competition in 2017, upstream

reform in 2020, and regulatory mechanisms) to their low or high sensitivities. The high

sensitivity includes the WFD Scenario 3 cost estimates which includes non-cost beneficial

solutions (this scenario also accounts for the cost spike in 2015).

The light blue areas at the top and bottom of the fan correspond to the “upper” and “lower”

modelled scenarios, which result from setting all of the drivers, policies and scenarios (e.g.

greater resilience ) to their high and low settings respectively. The impact of setting all the

policy drivers to their “low” settings is more muted, as for some policies the low setting is the

same as having them turned “off” in the baseline, and there is therefore no change from them

in the “lower” scenario. In contrast, the “upper” scenario measures include substantial extra

resilience expenditure from 2020 as well as maintaining the EA WFD Scenario 3 cost

estimates. For these “upper” and “lower” scenarios we emphasise that currently unforeseen

policies and/or extreme conditions could have impacts that are currently not captured by the

model.

Figure 1.3 shows the modelled evolution of baseline average household combined water and

sewerage bills in real terms in 2015, 2030 and 2050 for each of the WASC regions. South

West Water’s average bills are the highest, while Severn Trent Water’s are the lowest. There

is some convergence towards the industry average due to a greater decrease in the bills in

regions which initially have higher-than-average bills. The decline in bill levels from decade

to decade matches the declining baseline average bill level presented above in Figure 1.2.

This is the result of all the baseline assumptions in concert, principally through their effect on

sector cost levels.

200

250

300

350

400

450

500

550

600

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

2011

2013

2015

2017

2019

2021

2023

2025

2027

2029

2031

2033

2035

2037

2039

2041

2043

2045

2047

2049

£/P

rop

ert

y (2

01

2/1

3 p

rice

s)

High/Low Bill Scenarios Range Upper/Lower Scenarios Range Model Baseline

Historic Forecast

AMP6


NERA Economic Consulting iv

Figure 1.3

Baseline Water and Sewerage Household Average Bills

by WASC in 2015, 2030 and 2050 – Real

Source: NERA

Figure 1.4 sets out the totex projection by cost component. The opex component is roughly

the same size as the combined enhancement and maintenance capex spend. The figure shows

that the gradual decline in totex is largely due to a reduction in enhancement capex spending.

This reduction in capex roughly coincides with the 2027 conclusion of the river basin

management plan cycles in the current WFD. At present, no known large scale capex

programme is projected after the WFD, but it is possible that further quality or environmental

improvements will need to be implemented. As a result, the decline in totex should be taken

to represent a starting point from which additional options will be considered.

Figure 1.4

Projected Totex by Cost Component - Baseline Case

Source: NERA

Figure 1.5 shows the net changes to the RCV over the modelling horizon. The figure

displays the industry’s enhancement expenditure added to the share of CM that is added to

the RCV. The RCV is growing when this sum is greater than the depreciation series, which

is the case in the first ten years of the horizon. Following that point, the RCV stabilises in the


NERA Economic Consulting v

base case as the increasing levels of CM additions are offset by a gradual decline in

enhancement expenditure.

Figure 1.5

Net Changes to the RCV

Source: NERA

Figure 1.6 shows the key input assumptions underlying the baseline case. The model

produces an overview sheet summarising the key inputs (macroeconomic and long term cost

efficiency savings assumptions) as well as additional displaying the WFD inputs that feed

into the modelled scenario so that the user can check them for plausibility and internal

consistency. Note that the cost efficiency incentive rate is presented as a negative value to

represent its decreasing effect on costs.

Figure 1.6

Key Baseline Inputs

Source: NERA

-2.5%

-1.5%

-0.5%

0.5%

1.5%

2.5%

3.5%

4.5%

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

2031

2032

2033

2034

2035

2036

2037

2038

2039

2040

2041

2042

2043

2044

2045

2046

2047

2048

2049

Gro

wth

Rat

e %

GDP Growth RPIReal Price Effect: Opex Real Price Effect: CapexCost Efficiency Incentive Effect CPI (if applicable)Risk-free Rate

Integrated Final Report Background and Introduction

NERA Economic Consulting 1

1. Background and Introduction

This report presents work by NERA to develop a Water Bills Projection Model for Defra.

The project comes in the context of a number of challenges to the water sector in England

and Wales. In the short term, water companies will need to invest to meet the demands of

stricter environmental standards and growing populations, while regulatory and structured

changes such as retail competition for non-households and abstraction reform will affect the

way they do business. Over the longer term, the impacts of climate change are projected to

increase the need for resilience in both the supply of water and treatment of wastewater and

the network infrastructure that delivers it. The consequences of these and other challenges

for water and sewerage companies’ costs – and therefore customers’ bills – will depend on

interactions between them. To adequately assess the costs and benefits to the public of any

environmental or regulatory policy that affects the water sector, it is important to understand

this process of interaction and quantify its implications for customer bills, both now and in

the future. A major objective of this project is to provide Defra, Ofwat, and the EA with a

tool to help do this.

The Water Bills Projection Model has been designed to assist decision making in the water

sector by showing the impact of policy, regulatory, and company investment choices on final

customer bills in England & Wales over the period from 2015-50.1 The first five years

coincides with the AMP6 price review period where bills the regulatory nature of the industry

ensures that bills will be very similar to the levels set in the Ofwat Final Determinations. As

a result, the modelled baseline “projections” could be considered to begin in 2020.2 The

model allows users to project bills under a range of policy scenarios and assumptions about

relevant macroeconomic and environmental factors, reporting typical bills for classes of

customer by company or by region.

This report is structured as follows:

Chapter 2 describes the stages of formulation and the structure of the model, its scope and

limitations, and the inputs and assumptions underlying its results;

Chapter 3 sets out an abridged and updated version of the model’s technical specification

report;

Chapter 4 provides a comprehensive set of the model’s outputs;

Chapter 5 sets out potential model upgrades.

1 From this point onwards when describing the model we will always refer to the financial year beginning when we

reference years. For example, 2015 refers to the financial year 2015/16 beginning on 1 April 2015. The final year of

the horizon is therefore 2049/50 which ends on 31 March 2050 and which we will refer to as 2049.

2 Although this version of the model is based on the DDs and may therefore change when updated for the FDs (and any

subsequent CMA determinations that might override them, if they occur).

Integrated Final Report Development of the Water Bills Projection Model


2. Development of the Water Bills Projection Model

This chapter sets out the stages of formulation of the model. This model formulation process

is described through the data inputs and sources drawn upon and the main assumptions used.

The structure of this chapter is as follows:

Section 2.1 describes the process that NERA followed to develop the model;

Section 2.2 sets out the scope of the model as well as its limitations;

Section 2.3 provides a description of the model’s structure;

Section 2.4 briefly describes the inputs and the sources from which these were drawn;

Section 2.5 sets out the main assumptions underlying the model; and

Section 2.6 provides a high-level overview of the modelling operations and outputs.

2.1. Formulation of Model

In this section we describe the four major stages of model formulation in turn.

2.1.1. Technical specification process

The formulation of the model began with a specification phase where the model’s technical

aspects were agreed. This phase involved considerable participation by the Technical

Steering Group (TSG) including workshops and comments on various specification proposals

that were circulated by email. The model structure, policies, and variables to be included

were agreed at this point, as well as the data sources and sensitivity specifications. The

summary report on the model specification has been merged into this report, see Chapter 3.

2.1.2. Preliminary model

A preliminary version of the model was presented to the TSG in July 2014. The purpose of

the model presentation session was to demonstrate, at an early stage, a functional but

rudimentary version of the model. This allowed the TSG to see how the model would work

and to suggest structural or technical changes as appropriate.

The presentation was also used as an opportunity to highlight some of the more technical

modelling issues and trade-offs faced by the modelling team and to seek stakeholder views on

how to resolve them. During the session several issues were clarified, and others were

addressed through follow-up memos which set out the potential options and suggested our

preferred modelling approach, leading to an agreed decision.

2.1.3. Draft model

The first draft model was a fully functional model delivered to Defra and capable of

projecting water bills under any combination of seven policy scenarios and any user user-

results driver variable series or sensitivity.3 The draft model was delivered alongside a draft

3 For detail on the policies and driver variables, please see section 2.4.2.



results report and draft model handbook. The draft (November 2014) version of the model

preceded final quality assurance stage and was circulated for comments prior to the

finalisation of the model’s development, the external quality assurance, and the subsequent

finalisation of this report.

2.1.4. Final model

The model was finalised following a final iteration of comments from stakeholders and

external quality assurance checks performed by Vivid Economics. The changes resulting

from stakeholder feedback and comments received from Vivid include:

The addition of an inputs tab where the model displays an overview of the key

(macroeconomic, WFD, and long term assumption) inputs feeding into the model to

check them for plausibility and internal consistency;

The addition of a WFD tabs where the user can view the incurred level of WFD for the

modelled scenario at the industry or the RB level respectively;

Changes to the cost efficiency savings assumptions (see section 3.3.4 for more details) as

well as to the long term opex and capex RPEs and the RPI input forecasts;

Adjustments to the definition of the baseline and preset high/low and upper/lower

scenarios;

Corrections to the magnitude of the PR14 regulatory mechanisms and incentives to

include the effects of moving to separate the HH and NHH retail controls from their

wholesale counterparts (which are additional to the effects from moving to NHH retail

competition);

The addition of a second Macro that resets all assumptions to their preset levels; and

Several small corrections to formulae and formatting as pointed out by Vivid economics.4

2.2. Model Scope and Limitations

The modelling horizon extends from 2015 to 2050 via annual time increments, over which

the available input data falls into one of three categories. Short-term data covers the period

from 2015 to 2020, and is mostly sourced from reputable forecasts and published PR14

documents. The longer-term data from 2020 to 2040 is based on WRMP data or forecasts

using long-run averages. From 2040 to 2050, all data is forecasted for this project as none of

the datasets available contain useable figures that go that far into the future.

The model draws on data covering a specific set of cost drivers under a range of states of the

world. While some drivers can be added by future users, the process requires making the

new variables feed through the model appropriately, which requires relatively advanced

modelling skills and an in-depth understanding of the model.

4 As an output from their external quality assurance of the model, Vivid provided a traffic light-based comments log for

their findings where green items were not a major concern, amber items needed attention as they may be cause for

confusion, and a small number of red items which were labelled as potentially serious issues. As the total number of

items was modest, NERA implemented all the changes suggested regardless of whether green, amber, or red.



The model’s granularity extends to each company’s region’s fourteen value chain elements –

seven for water and seven for sewerage as listed in Table 2.1 below. The model provides two

spare value chain elements to accommodate further value chain element disaggregation.5

Table 2.1

The Model Contains 14 Value Chain Elements

Water Sewerage

Water resources Sewerage network

Raw water distribution Sewerage treatment

Water treatment Sludge treatment

Treated water distribution Sludge disposal

Spare water value chain element Spare sewerage value chain element

Retail water households Retail sewerage households

Retail water non-households Retail sewerage non-households

Source: NERA analysis of Ofwat (2008) “Accounting Separation: Consultation on allocation of

activities between business units

These value-chain or cost-driver components, given for each company region, function as the

“atoms” from which the model constructs a revenue requirement and in turn customer bills.

It calculates total expenditures at this “atomic” level and then combines them into different

price controls, company-region representations, or, potentially, regional aggregations at the

river basin level. The model is unable to represent effects other than those that can be

constituted from these basic elements, such as within-company or within-company region

impacts.

The model has a pre-specified set of seven policy options, set out in more detail in section

2.4.2.2 The user can add up to three additional policy options relatively simply from a

technical point of view, as these feed through the model through a set of predetermined

channels in which policies must be defined prior to being added. The “Water Bills Projection

Model: User Guide” describes the process for performing these policy option additions.

The model faces three major limiting factors:

A lack of available information on the more distant future. For many variables, there

are no long-run forecasts of external factors such as cost inflation that are directly suitable

for use as data inputs, so there is a need to make assumptions about how these inputs

should be forecast. There is also considerable uncertainty surrounding the likely

regulatory and policy environments in the future, such as the need for wastewater

improvement, so even the available longer term company forecasts may be affected by

variations in company assumptions about the context.

Linked to the last factor, the reliance on a set of assumptions to generate input

forecasts. For the longer-term horizon of the model, the model’s projections are

5 The spare value chains are value chain elements (left blank in the model) where no costs are currently realised but

which may be used in future modelling work if desired.



dependent on a set of assumptions that may not accurately reflect future events. In

particular, for many of the long-run input series in the model such as capital maintenance

needs we adopt a long-run average approach – including the available forecasts - to

projecting these variables. We also make several key assumptions regarding the relative

ongoing stability of the sector and of the climate, each of which could make the model

misleading if their realisation was very different from our assumption.

The model is designed to reflect available data, and to be within the computational

capacity of Excel avoiding use of macros. Consequently the most granular level of data

and results in the model is the company value chain level and an annual time step; no

effects at finer levels are modelled; few feedback effects are covered.

For this reason many of our input factors have in-built sensitivity ranges (low, medium, high)

and so do many of our policy effects have in-built “strength of effect” ranges (low, central,

high). Accordingly, as a general matter we consider it better to present the resulting bill

projections as ranges covering the low and high scenarios (i.e. from £Y/year to £X/year) than

as point figures.

2.3. Conceptual Structure of the Model

The model is structured according to the cost and regulatory characteristics of each of twelve

value chain elements contained within water and sewerage services. These characteristics

shape the influence of quantity and quality changes on costs hence bills. In particular, value

chain components are each assumed to have their own fixed and variable proportions of costs.

For example, the water resources components, when flexed to increase supplies, will exhibit

increasing marginal costs – based on the cost solutions set out in the latest company Water

Resource Management Plans (WRMPs) and other company documents.The model structure

is based on the cost and revenue characteristics that we observe in the industry. The water

resources component has increasing unit costs, whereas the water and sewerage networks and

the treatment and disposal components have roughly constant incremental costs.6 The retail

components are characterised by constant average cost to serve (ACTS). The impacts on bills

are derived from simplified regulatory profit and loss account building blocks for upstream

components and application of a margin for retail. In addition to the value chain elements

that are already reported by companies, Table 2.1 shows that the model also contains one

additional spare wholesale element for each service.

Figure 2.1 sets out an overview of the water resources and treatment component’s structure.

We focus a disproportionate amount of time on setting out this component of the model due

to its complexity relative to the other elements. As shown in the figure, the baseline and

driver input variables interact with user-selected policy scenarios to calculate projected

supply and demand requirements, which then feed through to modelled costs. These costs are

computed at the value-chain level according to each of the revenue building blocks. These

are then added together to determine real bills at the household metered, household

unmetered, weighted average household (metered & unmetered), and non-household level for

each company.

6 The assumptions of increasing marginal costs in water resources and roughly constant incremental costs in the networks,

treatment and disposal compoenents was agreed with the project’s technical steering group.



Figure 2.1

Conceptual Structure of the Model


The dashed lines in the figure split this component of the model into five distinct segments:

baseline variables, driver variables, policy scenarios, internal calculations, and cost impacts

on bills. The baseline variables form the projections which are the foundation of the model

and are based on the company WRMPs and Draft determinations (intended to be final when

available). The driver variables are a mix of company-specific or macroeconomic variables

that allow the user to test sensitivities around the baseline. The policy scenarios are switches

that affect the model’s drivers or cost functions, and allow exploration of the impacts of

policy options.

Abstraction

Reform

WRMP

Demand

WRMP

Supply

Properties

Climate Change

and RSA

RPEs

Retail

Competition

Activity-

Based

Costing

EU Directives

and UK statute

Upstream

Competition

GDP

Population

Financials:

COD/COE

PAYG

Gearing

Tax

Leakage

Supply Pipe

Adoption

Inflation

Baseline

Variables

Modelled

Demand

PAYG

RCV

WACC

Runoff & New

Depreciation

Drivers

Variables

Metering

Policy

Scenarios

Interest Rates

Modelled

Supply

Internal Calculations

Modelled

Opex &

Capex

Cost Impact on Bills

Modelled

Finance Costs

Tax

NHH Real Bill

Impact

Compound

Cost Impacts

Regulatory

Mechanisms &

efficiency

effects

Greeness

HH PCC targets

Wholesale Costs

NHH Allowed Retail

Costs

Wholesale Costs

attributed to HH

HH Retail Cost to

Serve

Wholesale Costs

attributed to NHH

NHH Retail

Margins

HH Retail Margins

HH Real Bill

Impact

HH and NHH

Nominal Bill

Impact

Greater

Resilience



2.4. Model Inputs

This section describes the dataset used in the model as well as the baseline and sensitivity

inputs. Section 2.4.1 describes the construction of the model dataset by combining data from

a wide range of public and institutional sources, and forecasting it out to 2050 whenever data

is absent. Section 2.4.2 then describes the cost drivers and policy options that determine the

supply-demand balance which underlie the model’s bill projections.

2.4.1. Constructing the Dataset

The following subsection briefly describes the data on non-cost variables that NERA

compiled from public sources. When this data was absent or when the available data series

came to an end, we forecast the remaining values according to the approaches set out in

Section 2.4.1.2.

2.4.1.1. Data provided by companies and agencies

We compiled data from various publically available sources. The bulk of the data came from

the available draft determinations, WRMP data tables, and August Submissions (all 2014). A

summary of the data obtained is displayed in Table 2.2.

Table 2.2

Variables and Sources

Draft Determinations

WRMP Tables August Submissions

W & WW Water Water Wastewater

W & WW RCV Allocation Population growth rate Addressing low pressure Sewer Flooding

Risk-free Rate Metering Meeting Lead Standards Private Sewers

Notional Gearing Leakage

Ecological Improvements at Abstractions

Sludge treatment and disposal

Effective tax rate Water Available for Use Improving Taste / Odour / Colour WFD Compliance

PAYG PCC New Development and growth

Depreciation rate and method

RSA Revocations and Modifications

Additional Environmental Capex

RCV run-off rate Target Headroom Capital Maintenance

Cost to serve (CTS) Distribution Input SEMD

SEMD Properties Resilience

Source: NERA

In addition to the above sources, we also compiled data from reputable national sources such

as the ONS, OBR, the Bank of England, and DECC. More details on the variables used can

be found in section 3.3.

2.4.1.2. Forecasting input data series

We allow the user to select from a series of approaches to forecast any data needed that are

absent from the input datasets. The model detects absent data (opposed to zero entries)

through the use of periods “.” in the data input cells. Where data is absent, the user can select

from the following five options to forecast each variable:



Roll over the latest available data cell to maintain the future values at a constant level;

Roll forward the future values at a constant level equal to the average of the 2015-2020

values;

Take the compound annual growth rate (CAGR) of the past “x” years, where “x” can be

specified to be any number of years for which data is available. The CAGR is then

applied to forecast future values at a steady compounding rate; or

Specify an alternative variable whose growth rate the forecast series tracks.

Alternatively, the user can input a custom series of privately held data, or public data that is

not already available in the model, as a “custom” sensitivity and choose to use this in the user

controls. The user manual describes the approach to using custom data in more detail.

2.4.2. Defining the Baseline and Sensitivities

2.4.2.1. Cost Drivers

This section summarises the data sources for the generic cost drivers in the model’s scenarios

sheet. In some cases, a data source is consistent throughout the short-term and long-term

modelling horizon. In other cases, we use different sources for the short-term and long-term.

Table 2.3 lists the data sources and assumptions used in the model’s baseline as well as the

sources for the high and low sensitivities for the short and long term periods.



Table 2.3

Short- and Long-Term Driver Sources

Driver Short-Term Baseline

Source Period Low7 High

GDP Growth PWC PR14 Risk Analysis 2015-2020 PWC Low Case PWC High Case

RPI PWC PR14 Risk Analysis 2015-2020 PWC Low Case PWC High Case

Energy Prices DECC E&E - ref prices 2015-2030 DECC E&E - Low price DECC E&E - High price

RPE: Opex8 Assumption: 0%

Assumption: 0.5%

2015-2020

2020-2025

0%

0%

0%

1%

RPE: Capex9 Assumption: 0%

Assumption: 0.5%

2015-2020

2020-2025

0%

0%

0%

1%

Cost Efficiency Incentive Assumption: 0%

Assumption: 1%

2015-2020

2020-2025

0%

2%

0%

0.5%

CPI BOE CPI Forecast 2015-2017 Same increase factor as

RPI10

Same increase factor as

RPI

Driver Long-Term Baseline

Source Period Low High

GDP Growth OBR long run average:

2.3% 2021-2049 2% 2.7%

RPI OBR long run average:

3% 2021-2049 2.5% 4.5%

Energy Prices DECC E&E- ref Price

with CAGR 2031-2049

DECC E&E-low Price

with CAGR

DECC E&E- High Price

with CAGR

RPE: Opex Assumption: 0.5% 2025-2049 0% 1%

RPE:Capex Assumption: 0.5% 2025-2049 0% 1%

Cost Efficiency Incentive Assumption: 0.5% 2025-2049 1% 0%

CPI BOE CPI target: 2% 2018-2049 1.7% 3%

Source: NERA

7 The cost efficiency incentive is put in place to account for technological change and improvements in management over

time. The low and high sensitivities were defined in terms of their effects on bills. For example, the low cost efficiency

incentive has a larger magnitude (and therefore results in lower bills) than the high cost efficiency incentive.

8 Assumption was cross-checked against a long term observed Opex RPE trend based on three elements: energy, labour,

and materials cost. The sources used for the cross check are for Energy: DECC E&E - ref prices; for Labour: ASHE

Long Run Average; for Materials: ONS long run average. We use a 10.3% weight on energy prices, a 12.4% weight on

labour prices, and a 6.0% weight on materials prices based on the opex expenditure in each of these categories for a

sample of the larger companies (we used Anglian, Yorkshire, Thames, Severn Trent, Southern) during 2010/11 (JR

data). RPEs are defined as relative to RPI, so the model’s RPE figures are consistent even after any changes to the RPI

assumptions used in the model.

9 Assumption was cross-checked against a long term observed trend of the BIS COPI (All work) index. We assumed

0.5% (compounding) based on the observation that the 25 year (1987-2011) average arithmetic average growth rate was

0.65%. We assumed the range of the high and low sensitivity values following a review of the periods with the highest

and lowest 10, 15, or 20 year rates observed within the 25 year window.

10 We quantify the relative CPI high vs CPI base case scenario by using the same proportional factor as the RPI high vs

RPI base case. The same applies to the CPI low case. For example, if RPI base is 3%, RPI high is 4%, and CPI base is

2% then we set CPI high as (4/3)*2%.



2.4.2.2. Policy Scenarios and Impacts

Table 2.4 sets out the model’s policy options/ scenarios and features. The first two columns

list and briefly describe the options. The option that is specified in the model’s baseline is

listed in the third column – items listed as “off” are not included in the baseline scenario.

The fourth column lists the scenarios that can be selected by the user, as well as some of the

additional features available for each measure .

Table 2.4

Policy Options and Features

Policy option/scenario Description Baseline Option Alternative Options and

Features

Retail Competition

Adoption of non-domestic retail competition in England (all) &

Wales (>50Ml/D) with voluntary separation for NHH retail

WSL Reforms and Voluntary

Separation

WSL reforms only option (without exit)

Regulatory Mechanisms This measure shows the effect of

alternative incentives

Regulatory Mechanisms

(PR14)

Low (PR09) or high (2*PR14) and

customisable cost of capital impact

Upstream Competition Policy Switch for upstream

competition reforms Upstream

Reforms option

Timing of implementation. Varying levels of

competition and CoC

WFD Cost of meeting WFD compliance Based on DDs and Company

Long-Term data

EA Sc3: All Feasible Measures; EA Sc4: Cost-

Beneficial Measures; and Strength Options

Private Supply Pipes Adoption of private supply pipes Off Strength options only

PCC targeting Reductions to the volume of HH

water demand Off Strength options only

Abstraction Reform

Policy Switch for abstraction reform (midway option is

average of water shares and system plus)

Off Water Shares; System

Plus; or Midway options

Greater-Resilience

Switch for significant increase of industry resilience, through

higher headroom and resilience costs

Off

Strength options with customisable Ml/d

target and cost adjustments

Source: NERA, often based on policy Impact Assessment studies

Note: All policies have low/central/high strength options, and all but the regulatory

mechanisms and WFD costs are able to be switched off by the user

The model is designed such that policies can affect the model’s projections through the

following four impact categories by value-chain element:

Botex effects: percentage changes to capital maintenance and operating expenditure

costs;

Enhancement effects: percentage changes to the cost of new capital expenditure;

Volume effects: percentage changes to the level of water supplied or demanded; and



WACC effects: percentage point adjustments to the weighted average cost of capital (NB

– these are input as percentage point adjustments in contrast to the other effects).

Table 2.5 displays the impacts that correspond to each of the seven policies/scenarios in the

model, and a brief description of these impacts.

Table 2.5

Modelled Policy/Scenario Impacts11

Policy/scenario Cause of Impact

Retail Competition

Botex costs: Regulation fees, acquisition & retention, settlement & switching

Botex benefits: Wholesale efficiency, retail efficiency savings, bundling benefits

Volume Reductions: Demand reductions through customer demand management

Regulatory Mechanisms Botex benefits: Totex cost assessment, menu regulation, water trading, separate retail HH and NHH controls

Upstream Competition

Botex costs: Regulation fees, increased scrutiny costs

Botex benefits: Efficiency catch-up and ongoing improvements

Enhancement benefits: Efficiency catch-up and ongoing improvements

WACC Increases: Higher borrowing costs to reflect greater risk

Private Supply Pipes Botex costs: HH & NHH Repair and replacement costs, HH & NHH administration costs

Per Capita Consumption Targeting

Volume Reductions: Household demand reductions resulting from water saving technology or increased importance of water conservation

Abstraction Reform

Botex costs: Transition costs

Botex benefits: Government and business admin cost savings, improved gross margins

Volume Increases: Increased water supply availability at current abstraction sites

Greater-Resilience

Volume Increases: Increased target headroom requiring additional supply capacity

Botex costs: Additional costs for resilience (doubling of pipes, etc.)

Enhancement costs: Additional costs for resilience in water and sewerage (doubling of pipes, installations of larger sewers to prevent overflows at bottleneck locations, etc.)

Source: NERA

Many of the quantitative policy effects in the model were obtained by NERA from analysis

of policy impact assessments from Defra or others (e.g. for retail competition, abstraction

reform, upstream competition, private supply pipe adoption, and regulatory mechanisms).

Additional scenarios such as greater resilience and PCC targeting are based on assumptions,

developed with the TSG , defined around existing company costs/volumes, and contain

sensitivities around the base case.

11 Note that the WFD costs feed through thte model like any other cost variable (e.g. as would, say, sewer flooding

expenditure) hence they do not feed through the model’s policy channels. Instead, because the model considers the

WFD costs as a driver variable (which allows sensitivities / alternative levels of WFD costs), it feeds through the model

as a cost rather than as an impact on costs. As a result, WFD scenarios are not included in this table.



2.5. Assumptions

2.5.1. Allocation of Initial RCV across Value Chain Elements

We used two approaches to allocate the initial RCV across the different value chain elements:

a focused and an unfocused approach. Both approaches are based on an allocation of each

company’s net modern equivalent asset values (MEAV), and are described in more detail

below. The unfocused approach is used in the baseline, but the user is able to switch to the

focused approach if desired.

The unfocused approach entails calculating the value-chain proportions of the net MEAVs,

and allocating the RCV to each value chain element using those proportions. This approach

is very simple and transparent. However, if competition is introduced, entrants to the

competitive parts of the supply chain will need to earn a return on the full net MEAV in order

to enter the market. For this reason, it may not be a suitable approach to RCV allocation in

modelled scenarios where competition is introduced.

Under the focused approach, the distribution of the RCV to each value-chain element is

carried out in a two-stage process. The first stage applies the full net MEAV for each

contestable element of the value chain (that is, for the upstream competition policy, resources

and treatment). The second stage then allocates the remaining share of RCV to the non-

contestable elements: treated and untreated water distribution and sewerage networks.

2.5.2. Allocation of TOTEX across Value Chain Elements

We allocate TOTEX across value-chain elements in different ways for different cost

components. For opex and capital maintenance (CM), we allocate costs according to

companies’ allocation of opex and CM across the value chains in their 2013/14 regulatory

accounts. For enhancement cost lines, we allocate each item to the value chains on a line-by-

line basis. The allocation proportions have been sense checked by the TSG including the

water company representatives.

2.5.3. Modelling Water Supply-Demand Balance

Many of the policies’ and variables’ cost effects lead to a shift in either supply or demand,

which the model must reconcile. Increases in demand that cannot be met by a company’s

supply during a given year automatically trigger a supply investment such that the demand is

met (and the target level of headroom is maintained). As a simplification, the model assumes

that these supply additions can be brought online during the year in which they are required.

In contrast, excess supply capacity is assumed to be “sunk”, in the sense that supply capacity

cannot be reversed. The effect of having excess supply capacity is that it defers the need for

further investment until all of the excess capacity “slack” becomes used up. The model does

not attribute operating expenditure costs to “non-utilised” capacity, but the corresponding

capex does depreciate at the same rates as utilised capex.

2.5.4. Supply and Supply Curves

The model makes the following general assumptions about company supply:



Regardless of demand patterns, baseline existing water and sewerage capacity is always

retained and assumed to be included in company cost figures.

Whenever increases in demand require companies to meet supply headroom targets, they

install additional capacity based on the lowest whole-life cost option from their respective

WRMPs (see below). This capacity is never subsequently retired.

Leakage reductions are modelled as supply-increasing measures, with all other non-

revenue water fixed as projected in the WRMP.

The cost of any measures to increase capacity – reflected by the company “supply curves” –

is estimated as follows:

Supply curves are approximated by company average incremental cost (AIC) curves,

which give the per-unit cost of available measures to increase supply, on the assumption

that these are installed in ascending unit-cost order. Thus, for example, if a water

company has two available programmes, one that increases its supply by 7 Ml/day for a

cost of £700m and another that raises supply by 5 Ml/day for £1bn, the company’s AIC is

£100m per Ml/day for the first 7Ml/day it installs above its existing capacity and £200m

per Ml/day for the next 5Ml/day.

For those companies that include water supply enhancement programmes in their

WRMPs, the AIC of additional water capacity is calculated by adding the capital and

operating costs of the programmes from Table 3 of the WRMP (e.g. WRP3).

For other companies the AIC of additional capacity is estimated using the cost curves

from neighbouring or similar companies. These cost curve mappings are displayed in

Table 2.6.

Table 2.6

Additional Water Capacity Supply Curve Mappings

Mapped To Mapped From

South West Water United Utilities

Welsh Water United Utilities

Yorkshire Water South East Water

Cambridge Water & South Staffs Portsmouth Water

Sembcorp Bournemouth Portsmouth Water

Dee Valley Water Portsmouth Water

Northumbrian Water Wessex Water

Source: NERA assumption

2.5.5. Demand Elasticities and Metering

Volumes demanded are driven by population growth and the baseline trends in per capita

consumption (PCC) in the model, which are to a large extent determined by the user-

specified scenarios. However, the following aspects are imposed on the model when the

corresponding sensitivities are selected:

The demand response to a change in water or energy price is lagged by a year – so if

water bills rise by 10% in one year, customers reduce their demand in response to this in



the following year. This year-long lag reduces processing time, as the model would

otherwise need to solve equilibrium supply and demand responses simultaneously.

Demand responses to water and energy prices can be turned off and on in the model

controls – they are off in the baseline;

The price elasticities of demand for water for household and non-household customers are

taken from a 2003 UKWIR report prepared by NERA12

, which found the domestic price

elasticity estimate in England & Wales to be -0.14.13

Changes to water consumption also

result in equivalent changes in sewerage service demand. The model uses this figure as

the elasticity for both the household and non-household sectors;

The cross-price elasticity of demand for water with respect to energy prices is based on a

study of Danish water demand14

and the relative proportions of water heated by electricity

in Denmark and the UK. The value we use from that report is -0.17;

The policy that reduces PCC is assumed to be costless – representing for example a shift

in consumer views about water conservation due to climate events;

Any new metering is assumed to lead to a 12.5% reduction in consumption by newly

metered households,15

and for WaSCs, the growth rate of measured sewerage customers’

volume is assumed to be the same as that of water customers’ volume.

The costs of metering comprise the capital costs of installation and maintaining new

meters (attributed to water wholesale), and the operational cost of reading them

(attributed to water retail). These costs are based on a recent metering report published

by Ofwat. 16

2.5.6. Capital Maintenance

The cost of maintaining companies’ existing capital stocks are rolled forward from the most

recent levels in the model’s baseline. Capital maintenance associated with any additional

enhancement generated by different scenarios modelled is assumed to occur based on a

renewal cycle of 10 to 100 years depending on the value chain element.17

This additional

CM is assumed to be incurred smoothly in each year after the asset is built, in order to

incorporate the longer term cost effects smoothly over the horizon rather than as lumpy

renewals at set intervals, some of which might be outside of the modelled horizon. The asset

life assumptions used for additional CM by value chain are displayed in Table 2.7.

12 Baker et al., “The Impact of Household Metering on Consumption: Further Analysis”, UKWIR 2003, page 81.

13 The interpretation of this is that a price increase of 1% in the current year leads to a decrease in demand by 0.1412% in

the following year.

14 Hansen (1996), “Water and Energy Price Impacts on Residential Water Demand in Copenhagen”, Land Economics,

p.66.

15 Ofwat (2011) "Exploring the costs and benefits of faster, more systematic water metering in England and Wales", p.8

16 Ofwat (2011) "Exploring the costs and benefits of faster, more systematic water metering in England and Wales", p.26-

28

17 Any CM on additional assets resulting from the modelled scenarios are therefore based on the assumed asset life for the

value chain and hence not based on an assessment of the actual cost of maintenance required.



Table 2.7

Asset Lives for Additional Capital Maintenance by Value Chain

Water Value Chain Asset Life (years) Sewer Value Chain Asset Life (years)

Water Resources 80 Sewage Network 100

Raw Water Distribution 80 Sewage Treatment 20

Water Treatment 20 Sludge Treatment 20

Treated Water Distribution 80 Sludge Disposal 10

Source: NERA

The additional CM expenditure is calibrated to be equal to the NPV from incurring the full

cost of each additional item again at the end of the item’s asset life (with no CM in the

meantime).18

For example, the capital maintenance expenditure associated with a £100m

enhancement project with a 20 year asset life leads to additional CM expenditure of £6.57m

per year beginning in the year after the asset is built and lasting in perpetuity. The NPV of an

£6.57m annuity is equal to a single renewal of £100m twenty years in the future. This

additional CM includes renewal costs, so the additional CM is incurred in perpetuity on the

basis that the additional asset is maintained in a “like-new” state. This smoothed approach

makes changes to bill levels easier to interpret, as renewal expenditure is not incurred during

a single year.

2.5.7. Financial Parameters

The model uses financial parameters to calculate the weighted average cost of capital

(WACC). The model applies the WACC to the regulated capital value (RCV) to generate the

return on capital (e.g. WACC x RCV) component of the wholesale building block for each

wholesale value chain element.

The financial parameters used by the model include the asset beta, the cost of debt premium,

the notional level of gearing, the total market return and the risk-free rate. These parameters,

with the exception of the risk-free rate, are held fixed at the levels provided by Ofwat in the

Risk and Reward Guidance for PR14 (to be updated to final determination levels when

known). In effect, the model assumes that the level of investment risk associated with

investing in the water sector is constant relative to the market. Changes to the risk free rate

over time drive the bulk of the variability of the WACC, and a small amount of additional

variation occurs due to some policy scenarios’ assumed impacts on investors’ perception of

investment risk in the sector.

The risk-free rate for 2015-20 is held at the level provided by Ofwat in the Risk and Reward

Guidance for PR14 of 1.25%. After AMP6, the model reverts back to the long-run average

18 We use a discount rate of 3.5% to compute the NPV cost.



risk-free rate for the UK from the Dimson, Marsh and Staunton database of 2.30%.19

The

model combines the risk-free rate with the debt premium to compute the cost of debt, and

applies the risk-free rate with the other Capital Asset Pricing Model (CAPM) parameters to

compute the cost of equity.

2.5.8. Driver Interdependencies

In this section, we discuss the key interdependency relationships that are integrated into the

model. In some cases, one variable has a causal effect on another without being affected by it

in return. In other cases a pair or group of variables have an impact on one another.

In order to establish which driver interdependencies are necessary we considered each

combination of drivers. We then set out for the TSG our view on all the non-negligible

relationships between drivers, which we provide for reference in the bulleted list below. We

do not discuss those where we have determined no relationship between drivers, except

where this warrants explanation.

Macroeconomic drivers are likely to have most impact on: water companies’ financing,

as was seen for example during the recent credit crisis, which led to favourable financing

costs in the industry affecting companies’ WACCs; on property growth as there is

empirical evidence that higher economic growth supports household formation and

reduces the size of average households; and on willingness to pay for environmental

goods. Macroeconomic drivers may also affect quality levels, as higher GDP per capita

may make customers more willing to pay for service improvements. Although

macroeconomic drivers can also impact population; meter penetration, the elasticity of

water demand; and regulation, by affordability concerns increasing regulatory scrutiny.

Macroeconomic conditions are also likely to affect NHH demand..

Demography will have the greatest impact on the demand for water and correspondingly

on leakage; and on companies’ physical assets, as new capacity is required in response to

changes in demand. Demography could also affect, although to a lesser extent, climate

change, as larger population leads to larger GHG emissions; and regulation, as for

example Ofwat cite future population growth as a core regulatory challenge. We note

that demography would have an effect on macroeconomic drivers as, for example,

increases in the young population generally increase GDP. However, we would expect

this effect to be sufficiently lagged in comparison with population’s effect on water use,

since it takes a long-time horizon for new births to become productive, that this does not

need representation in the model.

Regulation may have an effect on all core industry specific categories, since Ofwat and

the EA’s regulatory purview includes ensuring the general well-functioning of the system.

Quality levels would be expected to have its main impacts on regulation, as failing

quality standards would force Ofwat or the EA to change incentives or otherwise in their

regulatory arrangements; and company physical assets, as changing quality standards

would require investment in new assets and technology.

19 DMS (February 2014), “Credit Suisse Global Investment Returns Yearbook 2014”



Financing may have a modest effect on regulation; and company physical assets, as

financial constraints may limit companies’ ability to invest in new assets and

infrastructure. It is possible that financing constraints may also impact on success on

quality levels and compliance. However we expect this to be covered by financing’s

effect on company assets, which in turn, as described below, affects quality levels.

Climate change has the potential to have a broad range of effects, many of which are

uncertain and difficult to quantify given the range of probabilities over various climate

change scenarios. We would expect the UK CP09 projections or later updates to be

useful in coordinating these relationships. In general, we would expect climate change to

affect water availability and demand, as environmental factors make existing sources

more vulnerable; macroeconomic drivers, if carbon abatement slows economic growth;

water use, if climate change campaigns convince people to reduce water consumption and

drive companies to reduce leakage; regulation, as Ofwat and the EA evolve their

regulatory approaches to deal with climate change; quality levels, as increased climate

volatility may make it more challenging to meet quality standards; and company physical

assets, if climate change requires companies to invest more in flood defences or storage

capacity to cope with droughts. There may be some harder to discern financial risks

affecting financing that result from climate change.

Implementing a built in dependency relationship between certain driver variables can make

the model more realistic and internally consistent. However, this internal consistency comes

at the cost of model tractability and complexity without necessarily adding significant

benefits. As a result, following TSG views especially from the specification workshop

meeting, this section sets out our model structure based on introducing the minimum degree

of complexity required to determine a projection of water bills.

Table 2.8 outlines our judgements on the modelled inter-dependencies. We incorporate the

interdependencies on a year by year basis without a lag.

Table 2.8

Interdependencies Imposed Between Model Drivers

Source: NERA

Of these four model drivers, the properties and greenness variable are modelled as fully

functional interdependencies. They are different from the other two variables in the sense

GDP

Industrial Demand

Affecting

Driver

Risk Free Interest Rates

Allowed Rate of Return

Greeness

Affected

Aspect Reasoning

Economic growth leads to higher rates of household formation

Properties

As incomes increase the demand for environmental goods increases

E conomic growth leads to increased production

Interest rates are an important component of the weighted average cost of capital



that they are driver variables in themselves, and can be subject to their own sensitivities

independently from those that are associated with the variable with which they are linked. In

contrast, industrial demand is not a variable in the model that can be run at a higher or lower

sensitivity other than through GDP, which it assumed to track. The same can be said for the

allowed rate of return – it is conditional on the risk free rates (and also to a smaller extent on

certain policy effects) but cannot have sensitivities tested on it through any other channel.

2.5.8.1. GDP Impact on Property Growth

The modelled interaction between GDP and property growth is taken from the Department

for Communities and Local Government’s report, “Estimating Housing Need”. The report

estimates the elasticity of household formation with respect to individual real incomes to be

0.088, and we use GDP as a proxy for real incomes.

2.5.8.2. GDP Impact on Greenness

Forecasting future desire for environmental goods and services requires the underlying

determinants of environmental valuation to be identified. However, few studies have

investigated the specific effects of increases in income on water-related environment

improvements. A study by Pearce (2003)20

collates evidence on the income elasticity of

willingness to pay for improvement in environmental attributes. The evidence shows positive

income elasticity, suggesting that willingness to pay for environmental goods and services

rises with income, but the magnitude is inconclusive. The empirical evidence presented in

the paper states a range of 0.3-0.7 as the income elasticity of WTP for various types of

environmental improvements.

Another study, by Hökby and Söderqvist (2002) on the willingness to pay for various

environmental services in Sweden, provides a median elasticity of demand estimate of 0.46.21

This aligns with the Pearce findings, as well as those from Kahn and Matsusaka (1995)22

and

Kriström and Riera (1996)23

that this parameter is generally less than unity. We rely on the

Hökby and Söderqvist estimate of 0.46 with a range of +/-0.10 as the magnitude for the high

and low scenario sensitivities. We use +/-0.10 as this is equivalent to half of the range from

the Pearce study, which corresponded to a greater spectrum of environmental goods and

services.

20 Pearce (2003), “Conceptual Framework for Analysing the Distributive Impacts of Environmental Policies”

21 Hökby, S and Söderqvist, T (2002). “Elasticities of Demand and Willingness to Pay for Environmental Services in

Sweden, Paper to 11th Annual Conference of the European Association of Environmental and Resource Economists,

Southampton, 2001. The study was based on 21 elasticities for various environmental issues such as deterioration of

angling due to eutrophication, opportunities for moose hunting, access to forest areas, and preventing the extinction of a

type woodpecker in Sweden. Of the 21 cases, one was negative and four were greater than 1, while the remaining 16

fell within a range of 0.20 to 0.91. The median was 0.46 and the mean was 0.68, but we prefer the median as the mean

was biased upwardly by the four positive outliers, the highest of which was 2.68.

22 Kahn and Matusaka (1995), “Demand for Environmental Goods: Evidence from Voting Patterns on California

Initiatives”

23 Kriström, B. and Riera, P. (1996), “Is the Income Elasticity of Environmental Improvements Less Than One?”.

Environmental and Resource Economics 7, 45-55.



We implement the greenness link through a relationship in the growth in income (i.e. GDP)

to demand for environmental improvement and hence company expenditure. There is an

underlying assumption that increased environmental demand effectively translates to higher

environmental spending through elicitation of customer views, for example using WTP

studies. Hence, an increase in income of 1% leads to an increase in companies’

environmental spending by 0.46% in the base case. This spending relates to water

expenditure on ecological improvements at abstractions sites and additional environmental

capex, and to wastewater expenditure on odour, WFD compliance, and additional

environmental capex. We include this effect in the baseline and provide the functionality for

the user to switch off this effect if desired.

2.5.8.3. GDP Impact on Industrial Demand

The relationship between the growth rates of GDP growth and industrial demand is assumed

to be 1:1 – that is, all else being equal, a 1% increase in GDP leads to a 1% increase in

industrial demand for water and sewerage.

2.6. Model Operation and Outputs

The model generates two sets of results based on the two scenario combinations selected by

the user in the Scenario Manager. For each scenario combination, the model retains:

A complete set of the input baseline data based on the datasets and data forecasting

methods as specified by the user in the Dataset Manager. The spreadsheet flags any data

items that have been overwritten by the user;

A complete set of the input scenario data after the model has applied the effects of

policy scenarios and cost driver sensitivities for the current scenario combination

selection;

RCV account for each company region disaggregated to the level of individual wholesale

and retail value chain elements;

Revenue requirements for each company disaggregated to the level of individual

wholesale and retail value chain elements; and

Average levels of bills for each of the three customer groups of metered household

customers, unmetered household customers and non-household customers.

The model uses these five output elements to create a set of standard graphical results that

compare modelling outputs from the two scenario combinations. The user can create

additional graphical outputs on the basis of the five output elements using standard Excel

tools.

In the subsections that follow, we describe the disaggregated modelling results in more detail.

2.6.1. Results

Results by company regions are available for the following categories of outputs:



Average water and sewerage bill figures in nominal terms for the household (HH)

measured, HH unmeasured, and non-household (NHH) customer groups;

Average water and sewerage bill figures in real terms for each customer group, broken

down by value chain element or by service;

Expenditure forecasts including TOTEX by wholesale cost component or by value chain;

Average year RCV broken down across each of the wholesale value chains;

Wholesale revenue requirements; and

The proportion of population whose water, sewerage, or combined bills exceed a set

percentage of their income.

The results by river basins use an apportionment of company-regions across each river basin

based on data provide by the Environment Agency. Results are available for the following

categories of outputs:

Average water and sewerage bill figures in nominal terms for each customer group; and


down by service.

Aggregated results for England and Wales are available for the following categories of

outputs:

Average water and sewerage bill figures in nominal terms for each customer group;


down by service;

Expenditure forecasts including TOTEX by wholesale cost component or by value chain;

Average year RCV broken down across each of the wholesale value chains;

Wholesale revenue requirements;

The proportion of population whose water, sewerage, or combined bills exceed a set

percentage of their income;24

Industry-wide depreciation compared to Enhancement and CM additions to RCV;

Industry distribution input (DI) (i.e. the volume of water demanded); and

Industry water available for use (WAFU) (i.e. the volume of available water supply).

A complete set of output results are presented in Section 4.

24 Income forecasts by region are taken from the Family Resources Survey Almanac 2012-13. Used directly for the

period to 2020, after which an average regional growth rate is used. These forecasts are roughly consistent with the

baseline GDP growth used in the model. Forecasts are available at:

https://www.gov.uk/government/publications/family-resources-survey-2012-to-2013

https://www.gov.uk/government/publications/family-resources-survey-2012-to-2013

Integrated Final Report Model Specification


3. Model Specification

This model specification chapter provides details on the model’s technical structure,

including data sources and policy scenarios, and the selected model options that were chosen,

after consultation with the TSG.

This chapter is structured as follows:

Section 3.1 describes the model’s technical structure;

Section 3.2 describes the baseline variables and scenarios to be used in the model;

Section 3.3 lists and discusses data sources for the drivers;

Section 3.4 sets out the policy scenarios that will be able to be chosen in the model;

Section 3.5 describes the output specifications.



3.1. Model Technical Structure

This section describes the technical structure including a general view of model

functionalities and relationships between model elements.

Figure 3.1 displays the technical structure of the spreadsheet model in terms of the five types

of worksheets. These include the user control interface (displayed in orange), the data input

sheets (in green), the input processing tabs (in purple), the internal computation sheets (in

light blue), and the outputs (in dark blue).

Figure 3.1

Technical Structure of the Model


Scenario Manager

PR14 DDsPR14 FDs

August Submissions

Final WRMPs

DataManager

Supply Curve

Draft WRMPs

Current Co Data

Trend Data

Macro Scenarios

Scenario Shock

Current Policy

Drivers and Policies

RCV Calculation

Regulatory P&L

Average Bills

Summary Outputs

Company Bills by VC

Bills -Company and

National

Dist Effects -National

Rev Building Blocks

Wholesale

Policy Effect Costs

Policy Effect Benefits

Combined Shocks

Forecast Co Data

Override

ScenarioInputs

Water and WW Totex

SDB Returns

Dist. Input and WAFU

Policy Effect Net

Assumptions

Baked in Policy

VC Depreciation

Bills - RiverBasin

Dist Effects -Company

Input Summary

RB Mapping

River BasinWFD Cost

WFD Costs



A high-level description of the five sheet categories is:

User control interface – containing a switch for each driver variable setting and policy

option setting. There is a scenario selector with a database of initial plus user-defined

scenarios containing a complete set of switch identifiers to define each scenario;

Raw data inputs – providing an interface for generic data input and company-specific or

geography-specific data input. The generic data input are for data that are common across

all companies while the company data input are for data that are company specific. There

are separate sections for different baseline inputs (e.g. past datasets) and a range of delta

values corresponding to driver variable sensitivities;

Input processing tabs – compute the final dataset to be run through the model based on

the selections made in the user control interface and the set of assumptions.

Internal computation sheet – performing calculations to convert switch selections and

raw data inputs into final bill impact; and

Output visualiser sheet – recording the detailed modelling inputs and outputs for the

current baseline and one chosen alternative scenario, with a set of graphical tools to

compare chosen aspects of the results from the baseline with those of the chosen scenario.

Each of the categories is described in more detail in the following sections.

3.1.1. Control Interface

The scenario manager worksheet allows a user to define scenarios using two sets of

switches: one set of switches for choosing from an exhaustive list of the levels offered for

each driver variable, and another set of switches for choosing from an exhaustive list of the

available choices for each policy. The user is also able to assign one of the defined scenarios

as the comparison group (typically the model baseline) against which the user is able to

compare the results from one other scenario, including a high or low sensitivity.

The scenario manager automatically assigns a unique ID for each defined scenario to ensure

consistent referencing across the model. The unique ID contains the model version number,

which will be set out on a version control worksheet, in order to facilitate replication of the

results when the model is updated. The user will have the ability to assign a customised

name for each scenario. The unique ID appears on any output sheets for easy reference.

We expect there to be updates to input data, for example as Ofwat makes PR14 final

determinations and companies’ water resources management plans are updated. The control

interface therefore also contains a dataset manager tab to allow the user to specify which

data to run through the model. There is a switch to allow the user to alternate between data

inputs from different dates and sources.

3.1.2. Raw Data Inputs

The model block contains several worksheets which correspond to assumption parameters,

company data inputs, generic macro scenario data, and the scenario sensitivities around the

model’s driver variables. This section corresponds to the green tabs displayed in Figure 3.1.



The assumptions tab contains populated input cells corresponding to items that NERA has

input, such as elasticity parameters, metering (cost of installation, volume impact, etc.), and

industry financial parameters. It also contains inputs on the policy effect range for the high

and low policy sensitivities. These are usually calibrated based on the effects in the high and

low case of the associated IA compared to the IA’s central estimate.

The assumptions sheet contains a set of cost allocation assumptions on a company-specific or

industry-wide basis. For the company-specific cost allocations we draw on the regulatory

accounts for 2013/14 to obtain the opex and capital maintenance costs by value chain element.

We allocate industry-wide splits of RCV by value chain element according to the WASC

industry-average regulatory account MEAVs for 2012/13. Some of the remaining cost items

are assumed to be well-represented by the value chain splits so we assign the same split as

that used to split the RCV into value chains. Other cost items are assigned based on our

judgement and input from the TSG. Table 3.1 and Table 3.2 display the industry-wide cost

allocations for water and sewerage respectively.25

Table 3.1

Water Cost and RCV Value Chain Allocations

Water Cost Item

Water Resources

Raw Water Distribution

Water Treatment

Treated Water

Distribution

Capex for growth and unallocated enhancement

10.86% 4.97% 6.69% 77.48%

Addressing low pressure 0.00% 0.00% 0.00% 100.00%

Meeting Lead Standards 0.00% 0.00% 20.00% 80.00%

Improving Taste / Odour / Colour

0.00% 0.00% 50.00% 50.00%

Ecological Improvements at Abstractions

100.00% 0.00% 0.00% 0.00%

Additional Environmental Capex

10.86% 4.97% 6.69% 77.48%

Additional Quality Driver 10.86% 4.97% 6.69% 77.48%

Leakage 0.00% 5.00% 0.00% 95.00%

Allocated growth opex 50.00% 0.00% 50.00% 0.00%

Allocated leakage opex 0.00% 5.00% 0.00% 95.00%

RCV allocation unfocussed 10.86% 4.97% 6.69% 77.48%

RCV allocation focussed 48.03% 0.96% 32.11% 18.90%

Source: NERA analysis of net MEAVs in 2012/13 Regulatory Accounts (all non-round numbers); and NERA

assumptions (round numbers)

25 The allocation assumptions have a small effect on the relative magnitudes of the value chain elements, but they have

very little impact on the overall bill levels. The company-specific opex and CM cost allocations are too numerous to

display here, but interested readers can view them on the model’s Assumptions sheet.



Table 3.2

Sewer Cost and RCV Value Chain Allocations

Sewerage Cost Item Sewerage Network

Sewage Treatment

Sludge Treatment

Sludge Disposal

Capex for growth and unallocated enhancement

91.72% 6.99% 1.25% 0.03%

Sewer Flooding 100.00% 0.00% 0.00% 0.00%

Private Sewers 100.00% 0.00% 0.00% 0.00%

Odour26 0.00% 84.80% 15.20% 0.00%

WFD Compliance 10.00% 90.00% 0.00% 0.00%

Additional Environmental Capex 91.72% 6.99% 1.25% 0.03%

Sludge treatment and disposal 0.00% 0.00% 80.00% 20.00%

RCV allocation (unfocussed) 91.72% 6.99% 1.25% 0.03%

RCV allocation (focussed) 32.97% 56.42% 10.32% 0.29% Source: NERA analysis of net MEAVs in 2012/13 Regulatory Accounts (all non-round numbers); and NERA

assumptions (round numbers)

The raw data input worksheets also contain the company-specific data worksheets. As we

expect company data to evolve with current regulatory processes, we have created separate

company data input worksheets for each data update to ensure source- and time-consistency

of company inputs. The categories of inputs in a company data input worksheet include:

Quality variables – e.g. additional environmental capex, adoption of private sewers and

WFD compliance measures;

Customer variables – e.g. metering and number of properties;

WRMP-related variables – e.g. baseline water supply and demand balance, baseline

capex, economic level of leakage, RSA initiatives; and

Financials – e.g. RCV, PAYG, gearing, initial costs by value chain element.

The dataset manager (discussed in section 3.1.1) allows the user to select new company data

after it is added to the model. If the dataset manager is set to use the most recent data

available, then any changes to the input data automatically feed through the data processing

block provided it has been correctly dated on the input sheet.

The scenarios worksheet contains categories of inputs that are common across all companies.

Within the worksheet, there are separate sections for each different baseline and a range of

delta values for scenarios. The categories of inputs in this worksheet include:

Macroeconomic variables – e.g. interest rates, RPI/CPI inflation rates, GDP growth

rates, and population growth rates;

26 Odour was split across the sewerage and sludge treatment value chains only, according to their relative shares of the

unfocussed RCV split.



Efficiency Assumptions – assumed cost-savings efficiency rates;

Enhancement expenditure – sensitivity ranges for the high and low cases of each

enhancement cost item (e.g. low pressure, sewer flooding, etc.) on a line-by-line basis;

and

Financial parameters – e.g. the time profile of the risk free rate in the baseline, high and

low cases and the level of notional gearing.

The model only processes the data for one scenario at a time. If the data change affects the

results for both the comparison and live scenario, then we advise the user to rerun the model

to simultaneously generate outputs for both the baseline and the selected input scenario for

the scenario comparison tool.

3.1.3. Input Processing

The input processing sheets are designed to gather the raw input data and convert it into the

forms required by the model. This section corresponds to the purple tabs displayed in Figure

3.1. There are three main strands of input processing: policy effects, driver shocks, and

company data forecasting. The policy effects sheets compute the policy effects that are

implicitly baked into the input figures, as well as separately computing the policy effects

selected in the scenario manager. Each policy can be chosen to have benefits-only, costs-

only, and net effects. The current policy tab then computes the net effect of the policies that

is above and beyond any effect that has already been implicitly included in the input data.

The driver shock sheets convert the driver variable and enhancement expenditure sensitivities

into shocks. These shocks are then fed into a compound shock sheet where the

interdependencies between them experience compounding effects. After the interdependency

effects, the shocks feed into the volume and cost calculation sheets described in section 3.1.4.

The final strand of input processing relates to compiling and creating a complete data set for

each company. This is done by aggregating the company data contained in the model and

forecasting any missing items or figures in later years of the horizon. Once the company

dataset is complete, the data feeds into a final sheet where it can be overwritten by the user

prior to feeding into the model’s internal computation sheets.

3.1.4. Internal Computation

The next model block further processes the input data in three different stages of computation.

This section corresponds to the pale blue tabs displayed in Figure 3.1. The model processes

one scenario at a time to reduce the model size and computation time.

3.1.4.1. Stage 1

The first stage involves making intermediary calculations to convert switch selections and

processed inputs into a current scenario dataset. This stage of internal computation is mainly

focussed within the Supply-Demand sheet. This stage is different for water than for sewerage

due to some service-specific cost drivers.

The first stage for water includes:



Water demand projection: The model contains each company’s baseline water demand

projection over the modelling horizon. Each company’s baseline demand is based on the

dry year annual average demand as reported in the company’s WRMP. Where a selection

of a policy option or driver variable in the Scenario Manager influences water demand,

the model applies the corresponding delta values from the scenarios worksheet to the

baseline demand.

Water supply projection: The model contains each company’s baseline water supply

projection over the modelling horizon. Each company’s baseline supply is based on the

dry year annual average demand plus headroom as reported in the company’s WRMP.

Where a selection of a policy option or driver variable in the Scenario Manager influences

water supply, the model applies the corresponding delta values from the scenarios

worksheet to the baseline supply.

Water supply-demand balance: The model calculates each company’s water supply-

demand balance as the difference between the company’s supply and demand projections

from above. Where there is a supply-demand deficit, the model calculates the additional

capacity and cost required to restore the supply-demand balance to zero for the company

in the year of the deficit (as below).

Water totex projection: The model holds each company’s annual water totex

expenditure programme over the modelling horizon from two data sources. The first

source is Ofwat’s PR14 DDs which contain annual base totex expenditure (botex) and

annual enhancement expenditure for the period from 2015 to 2020. The model calculates

botex in subsequent years based on AMP6 Opex levels from the DDs and forward-

looking CM long-term trend data (derived from company submissions) subject to the

industry-wide assumed level of efficiency savings. Supply-demand enhancement totex in

subsequent years are based on long-run average incremental cost (LRAIC) from WRMP

multiplied by projected new volume of supplies required to balance water supply and

demand. An advantage of using LRAIC from WRMP is that it already includes capital

maintenance costs so no assumptions on capital maintenance on new assets are required.

The first stage for sewerage includes:

Sewage volume projection: The model contains each company’s baseline sewage

volume projection over the modelling horizon. The August Return submitted by

companies to Ofwat for PR14 contains a sewage volume projection for each company,

which forms the baseline in the model. Where the selection of a scenario in the Scenario

Manager influences sewage volume, the model applies the corresponding delta values

from the scenarios worksheet to the baseline volume.

Sewerage totex projection: The model contains each company’s annual sewerage totex

expenditure programme over the modelling horizon from two data sources. The first

source is Ofwat’s PR14 which contains annual base totex expenditure (botex) and annual

enhancement expenditure for the period from 2015 to 2020. The model calculates botex

in subsequent years by based on AMP6 Opex levels from the DDs and forward-looking

CM long term trend data subject to an industry-wide annual efficiency factor. Totex for

growth in sewage volume is based on average unit cost multiplied by projected volume

growth.



3.1.4.2. Stage 2

The second stage involves collating all outputs from different processes in the first stage to

construct a full dataset for each company that the model uses to generate revenue

requirements in the third stage. This stage corresponds to the water and sewer totex and

returns tabs.

The worksheet contains forecasts of:

Water supply and demand balance for each company after exports and imports;

Macroeconomic assumptions including inflation and RPEs;

Sewage volume projection for each company;

Water totex projection and sewerage totex projection for each value chain element for

each company;

Number of billed metered and unmetered household customers for each company;

Number of billed non-household customers for each company; and

Financing assumptions including PAYG Ratio, runoff/depreciation rate, and WACC.

Key outputs from stage 1 and 2 are then fed into the scenario inputs tab where they will then

be drawn on during stage 3.

3.1.4.3. Stage 3

The third stage converts the full dataset in Stage 2 into revenue requirements, real and

nominal, disaggregated by company and element of value chain. There is one worksheet for

each type of calculation to reduce potential for errors and allow flexibility to amend the

model at later stage. Calculations in this stage include:

The RCV calculation: The RCV for wholesale water and wholesale sewerage in 2015 is

based on Ofwat’s PR14 final determination for each company. Annual additions to the

RCV over the modelled period are based on the projected water totex and sewerage totex

in the model multiplied by a PAYG ratio. Depreciation allowances are deducted from the

RCV to compute closing RCV for the year for the value chain element according to a

runoff rate (for the pre-2015 RCV) and a depreciation rate (for the post 2015 RCV).

The regulatory P&L: The calculation of each company’s revenue requirement is based

on the assumption of continued application of Ofwat’s building block approach, allowing

for a margin-approach to retail. We model a regulatory view of companies’ profits and

losses through eight separate P&Ls per company – one for each of the wholesale water

and sewerage value chain elements.

For each P&L, the worksheet first calculates pre-tax revenue requirements by drawing

from the RCV worksheet for PAYG, return on RCV, and depreciation. Then, it back-

solves for taxes using the imputed pre-tax revenues to avoid introducing tax-revenue

circularity. Adding the taxes to the pre-tax revenues yields total revenue requirement. The

model calculates revenue requirements in real terms (it later inflates them into nominal

terms in the average bills sheet). Revenue requirements are then aggregated across water

and sewerage value chains to arrive at company allowed revenue figures.



Average Bills: The bills for household and non-household customers are based on initial

and projected company-specific ratios from households and non-households. The model

disaggregates average household bills by according to the following customer groups: HH

measured, HH unmeasured, HH weighted average of measured and unmeasured, NHH.

After the stage 3 computation is complete, the output sheets repopulate and generate

summary charts. The outputs are described in more detail and displayed in section 4.

3.1.5. Outputs Snapshot

The model runs twice for each model run; once to calculate the baseline results and another

time to calculate the input scenario results. After each run the model will capture the results

and underlying input details in a separate output sheet.

An Output sheet for each run contains a section for the regions of each of the ten water and

sewerage companies (WaSCs) and each of the nine independent water-only companies

(WOCs). Each output section contains projected water totex and sewerage totex;

disaggregated building blocks for wholesale water, wholesale sewerage, retail water and

retail sewerage; disaggregated RCV for wholesale water and wholesale sewerage; build-up of

water bills and sewerage bills across contributions from different elements in the value chains.

The output visualiser worksheets draw from the output sheets to aggregate and present the

results in easily navigable summary tables and charts. The model contains functionality to

allow the user to aggregate the results by industry, company region or, potentially, river basin

areas. Several model results are displayed in section 4.



3.2. Baseline Model Variables and Scenario

This section describes the variables used to form the baseline projections.

3.2.1. Baseline Variables

3.2.1.1. Baseline Supply and Demand

We use the supply and demand data from companies’ WRMP “Water Resource Planning

Guideline supply-demand workbook” tables (hereafter referred to as WRPG tables). The

supply figures provided in these tables are based on each company’s best projection of the

most likely resource availability conditions over the modelled period and its investment

programme. It therefore incorporates the companies’ view on climate and on new legal or

regulatory requirements. However we would expect the Final Determination figures for

demand and for supply/demand totex to be used when available for the period up to 2020.

The WRMP demand projections embody the companies’ best view on longer-run population,

properties, industry demand, leakage, water imports and exports, reductions to restore

sustainable abstractions, and metering - all of which have annual figures going out to 2040.27

3.2.1.2. Baseline Costs and Financing

To model company costs we rely on the draft determination (DD) figures for the period up to

2020, expecting these to be updated when the final determinations become available. We

then bring these figures forward using long term trends based on a set of industry data

provided for this purpose.

The baseline financing cost projections required by the model are the cost of equity (COE),

cost of debt (COD), PAYG ratio, runoff/depreciation ratio, notional gearing, and a simplified

tax rate. This set of assumptions enables us to provide a sufficient but minimum financial

representation of companies for the purpose of estimating water bills.

The PAYG ratio and financial parameters in the business planning process and in the DDs are

used in the model. The PAYG ratio from 2015-2025 is taken directly from the DD data

published by Ofwat. After this point, we model the ratio based on the approximate company-

specific total opex + capex costs incurred (which will be adjustable to reflect changes in the

proportion of capex/opex spending changes across each five year period). As requested at the

first technical steering group (TSG) meeting, the model is based on notional rather than

company specific financial figures.

3.2.2. The Baseline Scenario

The baseline scenario represents a central view on each of the variables that influence supply,

demand, and costs. Some of these central figures are contained in the WRPG tables (such as

population, properties, supply, etc.) whereas others are available from the DDs, or were

requested from the companies or the EA.

27 See for example South West Water’s data tables, available at:

https://www.southwestwater.co.uk/media/pdf/7/s/Water_Resources_Management_Plan_Tables_June_2014.pdf

https://www.southwestwater.co.uk/media/pdf/7/s/Water_Resources_Management_Plan_Tables_June_2014.pdf



The long-term baseline scenario for the model will be the one set out as the baseline in the

latest WRMPs. In this a prime measure of demand is the dry year annual average demand,

which is a period of low rainfall and unconstrained demand following WRMP practice.28

Table 3.3 summarises the demand measures that the EA requires companies to consider

and/or publish. We represent the dry year annual average demand as it is the basis for a

company’s WRMP investment needs. However, the weighted annual average demand is also

in the plan and is closer to the basis for the company’s revenue forecast when Ofwat sets

price limits. 29

We draw on the dry year demand and the weighted annual average demand

scenarios for the supply/demand capacity investment and the annual cost elements of the

model respectively.

Table 3.3

Demand Measures Published in WRMPs

Scenario Calculate Publish in the plan

Dry year annual average Yes Yes

Dry year critical period Optional – company dependant

Optional – only if there is a deficit

Normal year Yes No

Weighted annual average demand Yes Yes

Utilisation Yes- only if an option is required

Yes – only if an option is required

Source: EA 2012 Water Resource Planning Guideline

The latest WRMPs contain data sources going out to 2040. For the period beyond that year

we forecast the variables based on either the GDP growth rate or the population growth rate.

3.2.2.1. Baseline (Central) Values for All Drivers

Any changes to the values of the baseline variables, to reflect external variable sensitivities or

policy change scenarios, are reflected within the model as ‘delta changes’ to volumes of

supply or demand, or shifts in the cost of service provision. As a result, all variables have a

“central” value as part of the baseline. We try to ensure all baseline variables are internally

consistent in all years. Wherever possible, the central values are taken from the assumptions

stated in the WRMPs or BPs/DDs.

The EA’s guidance documents set out planning principles that companies should use as the

underlying conditions for their WRMP baselines. These principles were not prescriptive to

the point of setting out firm assumptions on GDP growth and inflation that companies should

use. As a result, companies have taken a range of approaches to their baseline scenarios.

For our baseline model inputs we use national estimates from reputable sources. We provide

the functionality for the model to allow the user to specify alternative baseline inputs so that

28 EA June 2012 Water Resource Planning Guideline, page 21

29 EA June 2012 Water Resource Planning Guideline, page 22



different assumptions can be used - including baselines that are based on regional

conditions.30

We discuss this issue further in the following section.

3.2.3. Policy Assumptions in the Baseline

Some of the policy effects described in this document are also contained in the baseline. We

define the baseline as containing all of the existing regulatory and statutory elements that are

currently in place as well as the policies that Defra views as likely to occur as these are

implicitly factored into industry views and long term figures.

Reforms changing the arrangements governing retail competition for all NHH customers in

England from 2017 are contained in the Water Act and the baseline.31

We understand that

upstream competition reforms are not embodied in the current legal framework. However,

we include Defra’s preferred option for upstream competition in the baseline. The baseline

policies also include assumed effects of PR14 incentive mechanisms and regulatory

mechanisms which apply to 2020 and beyond if not specifically changed by policy switches.

3.3. Sources for Driver Variables

This section describes the data that we propose to use for each driver variable.32

In Table 3.4 and Table 3.5 we list the drivers by category and list our data source for the

baseline, high and low cases. The table shows that we typically obtained this information

from Ofwat or EA documents or company DDs. or accounts. In some cases stakeholders

were asked to provide information or make consistency checks. In others cases simple and

transparent assumptions were made, especially for long term projections. We discussed these

proposals with Defra, the EA, company representatives, and Ofwat during a technical

steering group meeting on June 19th

2014.

We define each variable’s sensitivities in terms of its effect on average bills. Where the user

is uncertain of the direction of a variable’s sensitivity, we suggest that they consult the

“scenarios” sheet and compare the corresponding variable to its base values.

30 For example, Severn Trent’s draft WRMP is based on regional forecasts from Experian’s standard UK Regional

Planning Service (RPS). This source provides detailed regional data and forecasts for the period 1982-2026.

31 Retail competition in Wales continues to apply only those NHH customers purchasing over 50 megaliters per day, as

this was not changed by the Act

32 A driver variable is defined as either an exogenous variable (e.g. GDP growth) that affects items in the model or an

endogenous variable that is influenced by a process within the model but also has an effect on some other aspects of it

(e.g. leakage).



Table 3.4 - Driver Variables and Data Sources (1 of 2)

Source: NERA

Variable Case 2015-2019 2020-2039 2040-2049 Notes

Low PWC low case PWC low case; then base - 0.3% Baseline - 0.3%

BasePWC Scenario Assumptions -

Appendix 1

PWC Appendix 1 2020-21; then OBR

long run average 2022-39OBR long run average

High PWC high case PWC low case; then base + 0.4% Baseline + 0.4%

Low PWC low case PWC low case; then base - 0.5% Baseline - 0.5%

BasePWC Scenario Assumptions -

Appendix 1

PWC Appendix 1 (2020 to 2021);

then OBR long run average 2022-39OBR long run average

High PWC high case PWC low case; then base + 1.5% Baseline + 1.5%

LowDECC E&E projections- low

prices

DECC E&E - low prices 2020-30; then

DECC E&E - low price average rate

DECC E&E projection - low price average

rate

Base DECC E&E - ref pricesDECC E&E - ref prices 2020-30; then

DECC E&E - ref price average rateDECC E&E - ref price average rate

HighDECC E&E projections- high

prices

DECC E&E - high prices 2020-30; then

DECC E&E - high price average rate

DECC E&E projection - high price average

rate

Low Same as Base Baseline -0.5% Baseline -0.5%

Base0% Assumption (assumed to be

included in DD figures)0.5% Assumption 0.5% Assumption

High Same as Base Baseline +0.5% Baseline +0.5%

Low Same as Base Baseline -0.5% Baseline -0.5%


included in DD figures)0.5% Assumption 0.5% Assumption


Low Baseline -0.5% Baseline - 1% Baseline - 1%

Base Ofwat R&R figure (1.25%) DMS long run average (2.3%) DMS long run average (2.3%)

High Ofwat R&R (Comp Max 2.1%) Baseline + 1% Baseline + 1%

Low Same as Base Same as Base Same as Base

Base Ofwat DDs Rolled the 2019 DD Figure Rolled the 2019 DD Figure

High Same as Base Same as Base Same as Base

Low Baseline -1% Baseline -1% Baseline -1%

Base WRMP tables row 51FP WRMP tables row 51FP Extrapolation based on WRMP row 51FP

High Baseline + 1% Baseline + 1% Baseline + 1%


Base WRMP tables row 48FP WRMP tables row 48FP Extrapolation based on WRMP row 48FP


We apply a compounding growth rate from

2031-2050 based on the average growth rate

(2.82% in base case). Relative to RPI

RPE: Capex

Assumption was cross checked against COPI

yearly (1955-2012) index for all new

constructions, repair and maintanence.

Relative to RPI

The distance from baseline in the low/high

cases from 2020-49 is based on the range

provided by Experian for UK GDP as reported

by SVT's dWRMP

The distance from baseline in the low/high

cases from 2022-49 is an assumption based on

recent observed high/low RPI rates over 10

year historical periods. RPI (CHAW) 1987-2013

The ranges are 1% above and below the

baseline but the user will be able to input any

other desired range

The ranges are 1% above and below the

baseline but the user will be able to input any

other desired range

Based on Ofwat Draft Determinations.

Company Financial Models - Executive

Summary Tab, Income Statement

From 2020 we use Dimson, Marsh and

Staunton long run average. DMS (February

2014), “Credit Suisse Global Investment

Assumption was cross-checked against a long

term observed Opex RPE trend based on three

elements: energy, labour, and materials cost.

Materials - ONS PPI (1996-2013) for industry

"Collection, purification and distribution of

water" (MC3U); Labour - ASHE annual survey

of hours and earning E&W, full time workers,

annual gross pay from 1998-2013; Energy -

DECC (as above). Relative to RPI.

GDP growth

rates

RPI Inflation

Input Prices:

Energy

Tax

Risk Free

Rates

RPE: Opex

Properties

Population



Table 3.5 - Driver Variables and Data Sources (2 of 2)

Source: NERA

Driver Variable Case 2015-2019 2020-2039 2040-2049 Notes


Base WRMP tables row 40FP WRMP tables row 40FP Extrapolation of WRMP row 40FP

High Baseline +5% Baseline +5% Baseline +5%


Base WRMP table row 8.2BL WRMP table row 8.2BL Extrapolation of WRMP row 8.2BL



Base WRMP table row 13FP WRMP table row 13FP Extrapolation of WRMP row 13FP


Low Same as Base N/A N/A

BaseOfwat 2014/15 year end value;

then internal model calculationInternal model calculation Internal model calculation

High Same as Base N/A N/A

Low Same as Base Same as Base Same as Base

Base Ofwat DDsOfwat DDs from 2020-25; then

modelled as (IRE + Opex)/TotexModelled as (IRE + Opex)/Totex

High Same as Base Same as Base Same as Base


Base Ofwat DDsOfwat DDs from 2020-25; then

based on AMP7 asset livesBased on AMP7 asset lives


Low Baseline -0.5% Baseline -0.5% Baseline -0.5%

Base Ofwat R&R (nominal) Ofwat R&R (nominal) Ofwat R&R (nominal)

High Baseline +0.5% Baseline +0.5% Baseline +0.5%


Base Ofwat R&R (real) Ofwat R&R (real) Ofwat R&R (real)


Low Same as Base Baseline -1% Baseline -0.5%


included in DD figures)-1% Assumption 2020-25 -0.5% Assumption 2025-49



Base WRMP tables row 45FP WRMP tables row 45FPExtrapolation based on WRMP row

45FP


NHH Retail MarginsThe user is able to input any other series of values

including a series that changes over time

The user is able to input any other series of values


The user is able to input any other series of values


We model the high and low sensitivities as a level step

up or down.

RSA Reduction of

Water Availability

HH Retail MarginsThe user is able to input any other series of values


Cost efficiency

improvements

The assumptions used for cost efficiency savings follow

from discussions with the TSG - See cost efficiency

sensitivity section for more details.

Metering The ranges are 1% above and below the baseline but

the user will be able to input any other desired range.

Value of RCV

We obtained the 2014/15 year end RCV value by

company from the Ofwat RCV update spreadsheets

available on the Ofwat website. We split the RCV

according to the relative share of company value chain

net MEAVs using the 2012/13 regulatory accounts.

Depreciation

We modelled value-chain specific depreciation rates

that arrive at totals that are consistent with Ofwat DD

depreciation rates by service. The user can

alternatively use the DD service-specific depreciation

PAYG The user is able to input any other series of values


Climate Change

Reduction of Water

Availability

Leakage



For ease of reference, Figure 3.2 shows the key input assumptions underlying the baseline

case. The model produces an overview sheet summarising the key inputs (macroeconomic

and long term cost efficiency savings assumptions) as well as additional displaying the WFD

inputs that feed into the modelled scenario so that the user can check them for plausibility and

internal consistency.

Figure 3.2

Baseline Inputs

Source: NERA

The remainder of this section sets out the data sources that we have identified for use as the

central variable values or as the basis for informing the magnitude of the driver sensitivities.

We set out the drivers according to the following categories:

Macroeconomic drivers;

Demographic data;

Regulation and statute;

Quality levels;

Financing; and

Climate Change.

Water quality levels are not modelled and are assumed to be maintained at acceptable levels

through Ofwat’s regulatory mechanisms and incentives. The model also assumes that

sewerage capacity is always maintained.

-2.5%

-1.5%

-0.5%

0.5%

1.5%

2.5%

3.5%

4.5%

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

2031

2032

2033

2034

2035

2036

2037

2038

2039

2040

2041

2042

2043

2044

2045

2046

2047

2048

2049

Gro

wth

Rat

e %

GDP Growth RPIReal Price Effect: Opex Real Price Effect: CapexCost Efficiency Incentive Effect CPI (if applicable)Risk-free Rate



3.3.1. Macroeconomic Variables

3.3.1.1. GDP

Company WRMPs appear to use GDP growth as a factor in determining non-household

demand. Several companies’ assumptions on GDP growth were not available from public

sources. Of those that were, Severn Trent’s draft WRMP update to Appendix G provided a

transparent presentation of GDP assumptions based on regional forecasts from Experian.

These regional forecasts were consistent with Experian’s UK forecasts, which are displayed

in Table 3.6.

Table 3.6

Experian - UK GDP Growth Forecasts33

Source: Severn Trent dWRMP - Appendix G

Based on our review of the available data, we propose to use the central GDP assumptions

from the Ofwat Business Plan GDP figures published in PWC’s Economic Assumptions for

PR14 Risk Analysis for the baseline from 2015 to 2022, and on the ONS long-run average for

the period from 2023 to 2050. We inform the magnitude of the range for the long-run high

and low cases of GDP from the 2020-40 Experian UK forecasts. Our GDP growth forecasts

are displayed in Table 3.7. Users can optionally input their own GDP assumptions which

they can design following cyclical or any other desired patterns.

Table 3.7

Proposed Model GDP Growth Forecasts (%)

2015 – 2021 2022 - 2050

Low PWC low GDP

estimates Ranging from -0.1% to 2.2%

OBR LR Av minus 0.3% 2.0%

Central PWC central GDP

estimates Ranging from 1.7% to 2.2%

OBR LR Average 2.3%

High PWC high GDP

estimates Ranging from 2.0% to 3.1%

OBR LR Av plus 0.4% 2.7%

Sources: (1) BP estimates from PWC Economic Assumptions for PR14 risk analysis (2) OBR historical long-run

average 1982-2013 for: Central Case for 2022-2050; (3) Experian for: High/Low Ranges for 2021-2050

3.3.1.2. Inflation

As was the case with GDP growth, several companies’ draft WRMPs assumptions on the

magnitude of RPI growth were not available from public sources. Of those that were

33 These estimates were published in Severn Trent’s updated draft WRMP Appendix G, page 62. Available at:

http://www.severntrent.com/draft-wrmp-documents

2013-2020 2020-2040

Central High Low Central High Low

GDP Growth Rate (%) 2.0 2.5 1.6 2.4 2.8 2.1




available, Severn Trent’s draft WRMP update to Appendix G provided a transparent

presentation of RPI assumptions based on Experian’s UK estimates, which can be seen in

Table 3.8.

Table 3.8

Experian - UK RPI Forecasts34

2013-2020 2020-2040

Central High Low Central High Low

RPI Inflation Rate (%) 3.5 5.3 1.2 2.9 5.0 0.1

Source: Severn Trent dWRMP - Appendix G

We use the Ofwat Business Plan RPI figures published in PWC’s Economic Assumptions for

PR14 Risk Analysis for the baseline to 2021. We then use the OBR long-run average (3%)

for the remaining duration of the modelling window.35

These estimates are reasonably close

to the central Experian estimates set out above.

Rather than relying on the high and low Experian sensitivity values for RPI, we instead

inform the range for the long-term high and low estimates of inflation using an assumption

calibrated to approximate the highest/lowest 10 year periods of inflation during the period

from 1987-2013. Our proposed low, central, and high forecasts are displayed in Table 3.9.

We also enable the model to allow the user to specify any other RPI growth forecast.

Table 3.9

Proposed Model RPI Forecasts (%)

2015 -2021 2022 -2050

Low BP low RPI estimate OBR LR Average –0.5%

Central BP central RPI estimate OBR LR Average

High BP high RPI estimate OBR LR Average +1.5%

Sources: (1) BP estimates from PWC Economic Assumptions for PR14 risk analysis (2) OBR: Central Case for

2021-2050

3.3.1.3. Input Prices

Real price effects (RPE) represent the change in particular water input prices relative to the

level of inflation in the economy as measured by the retail price index (RPI). A review of

companies’ WRMPs, draft PR14 BPs, and Ofwat’s BP data table templates suggests that

input price forecasts are not provided by the companies in any documents in the public

domain. Instead, we draw upon reputable sources in order to construct indices of real prices

effects (RPEs), for opex and for capex. These RPE figures are net of RPI, so that they are in

34 These estimates were published in Severn Trent’s updated draft WRMP Appendix G, page 62. Available at:


35 We calculate the OBR RPI long-run average as the compound annual growth rate from 2000 to 2013.




addition to (and therefore consistent with) any modelled RPI scenarios. We determine these

indices based on the following sources:

Energy price inflation (for opex): The Department for Energy and Climate Change

(DECC) annually publishes long-term projections of growth in energy prices. The most

recent edition (September 2013) contains energy price projections up to the year 2030.

See Figure 3.3 below. We draw on this publication for the energy price forecasts used to

determine the energy price elasticity effects, as well as using it to cross-check the OPEX

RPE assumptions used in the model;

Figure 3.3

DECC Energy Price Scenarios

Source: DECC Updated Energy & Emissions Projections - September 2013

Materials inflation (for opex): The ONS publishes the producer price index (PPI) for

“Collection, purification & distribution of water” industry group. To cross-check the

OPEX RPE assumptions used in the model’s baseline we use a long-run average of

historical PPI inflation for the industry group as a proxy for future inflation in prices of

materials;

Labour price inflation (for opex): The ONS publishes the results of its Annual Survey

of Hours and Earnings (ASHE). The Survey contains estimates of the rate of growth in

the earnings of employees in different regions of the UK which we use to cross-check the

OPEX RPE assumptions used in the model’s baseline we in order to proxy for regional

labour price inflation; and

Construction price inflation (for capex): The Department for Business, Innovation and

Skills (BIS) quarterly publishes construction output price indices (COPI). To cross-check

the CAPEX RPE assumptions used in the model’s baseline we use a long-run historical

compound average rate of change in the COPI as a proxy for future inflation in prices of

construction.

0

1

2

3

4

5

6

7

8

9

10

Wh

ole

sale

Ele

ctr

icit

y P

rice (

£ / k

Wh

)

Reference prices Low Prices High Prices



We apply the growth rates to the relevant costs items with the opex cost indices applying to

the approximate share of opex corresponding to energy, materials, and labour.36

To determine the high and low sensititivities, we cross-checked our assumption of +/- 0.5%

relative to the baseline for the Opex and Capex RPEs respectively by examining the 10, 15

and 20 year periods coinciding with the highest and lowest COPI price inflation.

3.3.1.4. Interest Rates

Interest rates influence the allowed rate of return set by Ofwat based on the weighted average

cost of capital approach employed at each price review. We propose using:

Figures specified by Ofwat that underpin the company BPs and DDs for the period from

2015 to 2020; and

Long-run historical average for the period from 2020 to 2050: This forecast is based

on taking a very long term historical average of the real rate of return on UK risk-free

assets to avoid introducing market volatility into the forecast. We use the long-run

historical average of 2.3% from the Dimson, Marsh and Staunton (DMS) database, which

provides estimates for the period from 1900 to 2013. We add the PR14 debt premium.

3.3.1.5. Effective Taxation

The TSG agreed that detailed elements of taxation are not required within the scope of the

model. Instead, we use PR14 effective rates to 2020, then an approximation of the actual tax

rates faced by companies. To do this we take an average of the proportion of actual taxes

paid during AMP6.

3.3.2. Demographic Variables

3.3.2.1. Population and Property Data

The WRPG tables include projections for population to 2040 which we use as the basis for

the model. In order to determine the baseline population inputs from 2040 to 2050, we use

the compound annual growth rate from the preceding ten years. Population shocks affect

household size which has a knock on effect on overall consumption, even in the absence of

property growth.

Similarly, we draw on the WRMP figures for our properties figures from 2015 to 2040. We

then grow properties in line with GDP growth for the period from 2040 to 2050, given that

household formation has been shown to be positively correlated with economic growth.

Additional properties are assumed to consume the same volume of water as the average

consumption for that type of property.

36 Weightings for Opex RPE are: 10% for energy, 12% for labour, 6% for materials, and the remaining 72% is assumed to

grow with RPI (i.e. with no RPE effect). These are based on the expenditure incurred by a sample of the larger

companies (we used Anglian, Yorkshire, Thames, Severn Trent, Southern data) during 2010/11 (JR data)



3.3.2.2. Metering Penetration Rates and Leakage

The WRPG tables provide annual forecast data on meter optants, compulsory metering, and

selective metering as well as the number of metered properties which we propose to use in

the baseline. Users can build sensitivities based on simple and transparent assumptions (e.g.

increase in expected metering by 1%, saturated at 95%) or based around established views on

rates of metering uptake. We base the costs and effects of metering on demand on Ofwat’s

“Exploring the costs and benefits of faster, more systematic water metering in England and

Wales”.37

We allow the user to test sensitivities around reducing leakage beyond the sustainable

economic level of leakage (SELL).38

The effect of this reduction beyond the SELL, by

definition of the SELL, is to reduce the level of distribution input while raising the cost of

service provision. We estimate the cost of leakage reductions using each company’s capacity

addition costs from their WRMP.

3.3.3. Capex Expenditure Categories

We set out below the list of capex cost drivers or categories used by Ofwat in its PR14

methodology and in the August submissions. To estimate cost by enhancement category, we

draw on more granular longer term non-public data received by Defra directly from

companies on a confidential basis. We received this data from ten different companies

covering varying durations from 2015 to 2050. We drew on these submissions to determine

reasonable long-term average industry forecasts which we then applied to all companies

(including those which submitted long-term data). The cost categories used in the model for

water and sewerage are displayed in Table 3.10 and Table 3.11.

37 See http://www.ofwat.gov.uk/future/customers/metering/pap_tec201110metering.pdf

38 The SELL is the lowest service-cost level by construction and may change as innovation and input prices change over

time. As a result, maintaining the SELL level of leakage may entail some leakage reductions or increases that have a

cost impact.

http://www.ofwat.gov.uk/future/customers/metering/pap_tec201110metering.pdf



Table 3.10

Water Service Capex Cost Drivers

Defra Water Bills Model Ofwat PR14 BP Submissions – Table W3 Lines

1. Capital maintenance 4. Maintaining the long term capability of the infrastructure assets

5. Maintaining the long term capability of the non-infrastructure assets

2. New development and growth

9. New developments

3. Addressing low pressure 2. Addressing low pressure

4. Improving taste/colour/odour

3. Improving taste/colour/odour

5. Meeting lead standards 6. Meeting lead standards

6. Enhancement to SDB 7. Enhancements to the supply / demand balance (dry year critical / peak conditions)

8. Enhancements to the supply / demand balance (dry year annual average conditions)

7. W WFD Related 1. Making ecological improvements at abstractions (habitats directive, SSSI, BAPs)

10. Investment to address raw water deterioration (THM, nitrates, Crypto, pesticides, others)

8. Additional environmental capex

13. NEP - Flow monitoring at water treatment works

14. NEP - Drinking water protected areas

9. SEMD 12. SEMD

10. Resilience 11. Resilience

Source: NERA based on Ofwat PR14 BP Data Tables



Table 3.11

Sewerage Service Capex Cost Drivers

DefraWater Bills Model Ofwat PR14 BP Submissions – Table S3 Lines

1. Capital maintenance 4. Maintaining the long term capability of infrastructure assets 5. Maintaining the long term capability of sewage treatment works 6. Maintaining the long term capability of sewage pumping stations 7. Maintaining the long term capability of management and general

assets 8. Maintaining the long term capability of all other non-infrastructure

assets

2. New development and growth 23. New development and growth

3. Sewer flooding 27. Reduce flooding risk for properties

4. Private sewers 28. Private sewers

5. First time sewerage 1. First time sewerage

6. Sludge treatment and disposal

2. Sludge treatment & disposal - enhancement 3. Sludge treatment & disposal - base

7. Odour 22. Odour

8. WFD compliance 10. NEP - Event Duration Monitoring at intermittent discharges 13. NEP - Storage schemes to reduce spill frequency at CSOs, storm

tanks, etc. 14. NEP - Chemicals removal pilot/full-scale demonstration plants 15. NEP - Groundwater schemes 16. NEP - Investigations 17. NEP - Nutrients (N removal) 18. NEP - Nutrients (P removal at activated sludge STWs) 19. NEP - Nutrients (P removal at filter bed STWs) 20. NEP - Reduction of sanitary parameters 21. NEP - UV disinfection (or similar)

9. Additional environmental capex

9. NEP - Conservation drivers 11. NEP - Flow monitoring at sewage treatment works 12. NEP - Monitoring of pass forward flows at CSOs

10. SEMD 27. SEMD

11. Resilience 26. Resilience

12. Company specific -

Source: NERA based on Ofwat PR14 BP Data Tables

For the AMP6 period, we distributed the totex figures into these cost categories according to

the average proportionate share of those companies that submitted confidential data, while

ensuring that the corresponding totex figures were consistent with those from the DDs. For

expenditure in the period from 2020 to 2050, our approach to using the longer term data was

to calculate an industry level trend based on the companies who have provided data, and

apply that an industry-wide trend to all companies in the model. For each enhancement

capex item, in each AMP, we calculate the submitted confidential data from that item as a

proportion of AMP6 opex, and use that proportion for each subsequent AMP period. Capital

maintenance is calculated as a percentage change on expenditure over the previous AMP

3.3.4. Cost Efficiency Effects

Over the history of the industry since privatisation, companies have increasingly become

more cost efficient. We model this cost efficiency effect based on assumptions calibrated to

reflect diminishing levels of efficiency improvement over time. We assume modest levels of

improvement during AMP7, with lower levels of improvement thereafter to reflect the

increasing maturity of the industry since privatisation. For AMP6 we assume that the Ofwat



DDs reflect the perceived efficiency improvements that are likely to occur, so we do not

assume any above and beyond those implicitly contained in the input data for this period.

In the baseline, we assume that companies experience a cost savings reduction of 1% per

annum (with a compounding effect) on all of their expenditure from 2020 to 2025, and 0.5%

thereafter. In the high/low case we assume a 0.5%/2% improvement per annum for 2020 to

2025 and 0%/1% for the remainder of the horizon. These assumptions are presented in Table

3.12.39

Table 3.12

Cost Efficiency – Strength of Effects

2015-20 2020-25 2025-49

Low 0% 2% 1%

Baseline 0% 1% 0.5%

High 0% 0.5% 0%

Source: NERA Assumption based on TSG Discussions

These compounding levels of cost efficiency assumptions can have large effects on company

expenditure, particularly during the later stages of the horizon. The model also enables users

to experiment with any custom levels of assumed efficiency effects if desired.

3.3.5. Financial Parameters

Finance parameters feed into the calculation of companies’ projected allowed revenues which

drive final bills. These finance parameters include:

The allowed rate of return (ARoR): ARoR is estimated as the PR14 figure to 2020 then

the projected cost of capital. For wholesale elements ARoR is multiplied by companies’

RCVs to derive the returns building block. The approach requires projections of the cost

of debt, cost of equity, and gearing. We use the parameters in Ofwat’s Risk and Reward

publication as a baseline to 2020,40

and update the parameters to reflect the projected

market cost of debt (based on changes to the risk free rate, while holding the debt

premium constant) where relevant for later years;

Value of RCV: Ofwat publishes companies’ opening RCVs as part of the draft

determinations. We draw on these opening values, and update them using the PR14 RCV

updating method after that (i.e. add 1-PAYG, subtract runoff and depreciation);

Retail margins: Ofwat provided guidance on retail net margins for PR14 in its Risk and

Reward publication. Ofwat’s views of the appropriate net margins, which we will use as

39 The model considers these effects as negative values to represent their effect of reducing costs. We show them as

positive here to avoid confusion for the reader.

40 Ofwat ( January 2014), “Setting Price Controls for 2015-20 – Risk and Reward Guidance”



the baseline, are 1% for household retail and 2.5% for non-household retail.41

We hold

these constant over the modelling horizon; and

PAYG ratio, and runoff and depreciation rates: These parameters affect the profiling

of companies’ allowed revenues, and therefore final bills to customers. We draw from

companies’ proposed PR14 PAYG ratios, RCV run-off rates, and depreciation rates to

2025 based on the figures available in the DDs. From 2025 onwards we model the

PAYG ratio based on the share of opex+IRE over totex. We roll forward the runoff rates

at their 2025 values throughout the horizon, and we use the calibrated AMP7 value chain-

specific depreciation rates for the 2020 to 2050 period.

3.3.6. Climate Change and Restoring Sustainable Abstraction (RSA) Effect on Water Available for Use

We draw on the WRPG tables to inform the baseline availability of water ready for use

(WAFU). Additionally we make simple assumptions for the driver variable sensitivities that

affect water availability (e.g. for the high sensitivity we increase company estimates of the

impact of climate change/RSA on water availability by 20%). The model allows the user to

input any percentage increase or reduction in these variables as a custom scenario.42

We were able to identify data relating to restoring sustainable abstractions in the Water

Resource Planning Guideline supply-demand workbook table WRP1. We use this data in the

baseline and allow the user to test sensitivities using a high, low, or custom sensitivity.

3.4. Policy Scenarios

This section describes the main policy scenarios that the user can model using policy

switches. The baseline scenario set is the foundation of the model. The policy switches have

a ‘delta effect’ interpretation –as changes from the baseline set. This ‘delta effect’ treatment

works in much the same way as that caused by changes to individual “external” driver

variables. We are attentive to whether variables within the baseline already reflect views on

some of the policy reforms in order to ensure that these are not double-counted. Table 3.13

lists the policy scenarios built into the model.

41 We use adjusted NHH retail margin figures provided by Ofwat for Welsh Water and Dee Valley Water since they are

not subject to the same changes as the English companies in the Water Act 2014. For Welsh Water’s NHH retail

margins we use 1.23% and for Dee Valley Water we use 1.36%.

42 Falling water availability may result in shortfalls in the supply-demand balance which will result in bringing forward

the investment profile and therefore increase totex.



Table 3.13

Policy Matrix

Topic User Choice Additional Options

Contained in model

Retail Competition

Policy Switch for non-domestic retail competition in England (all) & Wales (>50Ml/D) with voluntary separation for NHH retail

WSL reforms (no exit) Yes, central option (with NHH exit) in

the baseline

Regulatory Mechanisms

The PR14 mechanisms are in the baseline. The switch shows the effect of stronger incentives , from increased retention or ODIs or duration, or weaker (e.g. PR09)

Adjustments to strength of incentives (efficiency) and

cost of capital

Yes, PR14 mechanisms are in

the baseline

Upstream Competition

Policy Switch for upstream competition reforms

Upstream reforms option only. Timing of

implementation. Cost of capital levels

Yes, central upstream reforms is

in the baseline

Private Supply Pipes

Adoption of private supply pipes Strength options only Yes, not in baseline

PCC targeting Switch for per capita consumption targeting Strength options only Yes, not in baseline

Abstraction Reform

Policy Switch for abstraction reform (midway option is average of water shares and system plus)

Water shares, system plus, and midway option. Timing

of implementation Yes, not in baseline

Greater-Resilience

Switch for significant increase of industry resilience

Strength options with adjustments to (Ml/d)

headroom targets and capex costs

Yes, not in baseline

Source: NERA

Note: All policies have low/base/high strength options, and all but the regulatory mechanisms are able to be

switched off by the user

The following sections describe the policies that will be included in the model.

3.4.1. Baseline Scenario Set

We use our current understanding of the PR14 measures that will be introduced from 2015 as

the model baseline regulatory mechanisms “policy”. This includes Totex benchmarking,

reward/penalty bulk water trading, the new SIM measures, and expected known changes

resulting from the Water Act. These are described in more detail in section 3.4.7.

The baseline scenario set provides a standardised set of outputs corresponding to the

company investment plans described by the WRMPs and BPs/DDs. Any changes to these are

‘delta effects’ - changes that move away from the baseline scenarios.

3.4.2. Retail Competition

Retail competition for non-households from 2017 is in the baseline and is likely to have an

impact on the sector through many channels, many of which are already implicit in the PR14

BP/DD cost and demand and margin levels. Based on the Water Bill and studies including

Defra’s 2011 Impact Assessment (IA) and the Cave Review, the following are the most

important impacts to model:



Reductions in retail costs for water and sewerage arising from cost efficiencies driven

by competitive pressure. These will be most pronounced in the contestable section of the

market (non-households), though there are likely to be spillovers to the non-contestable

market due to the spread of best practice and information gathered by Ofwat. In theory,

retail exit could either increase efficiency spillovers (because it gives incumbents stronger

incentives to invest in their retail activities) or reduce them (in the event of non-household

exit, a household retailer is no longer subject to the same competitive pressure). In the

model we rely on spillover assumptions in the voluntary exit option in the IA. We note

that this option has recently been revised to reflect NHH-only exit. Although the

underpinning assumptions related to abatement (proportion of companies that exit at

market opening and over time) and the size of the household spillover have been updated,

the headline impact in terms of net benefits is broadly identical to the original option.

Hence our analysis in terms of bill impacts is broadly representative of the revised option.

Reductions in wholesale costs for water and sewerage, resulting from effective

consumer advocacy from retailers and the revelation of information on wholesale costs

that follows from wholesale/retail splits (and Ofwat’s subsequent ability to better regulate

these costs).

Margin reductions as competitive pressure deprives retailers of excess returns.

Reductions in water demand as retailers compete by offering their customers assistance

with water efficiency measures. This will primarily affect bills by reducing the amount of

capital investment needed in water and (to a lesser extent) sewerage network and

treatment.

Regulatory costs associated with Ofwat setting up and then enforcing market codes. We

expect these to raise customer bills via licence fee contributions.

Settlement and switching costs related to setting up and then operating a central market

authority, which we assume are paid for by retailers, with associated retail activity costs.

Customer acquisition expenditure by retailers on promotional activity aimed at

attracting new customers and retaining existing ones.

Incumbent restructuring costs incurred in order to ensure compliance with competition

law and regulatory requirements.

Table 3.14 below outlines how these effects are quantified, primarily based on the IA’s

analysis.



Table 3.14

Effects of Retail Competition Policy

Impact Description Category Approx. value Comments

Contestable retail efficiency

Driven by retail mergers, reduction in bad debts, reduction in metering, IT,

telecoms cost.

Water / sewerage retail

2.5% initial saving +0.38% per annum.

From IA: “No separation” scenario where 50%* “abatement factor” applied to savings under legal

separation.

Non-cont retail efficiency

Spillovers from spread of best practice, info gathered by Ofwat,

mergers of retailers.

Water / sewerage retail.

0.63% initial then .094% per annum.

From IA: Assumes 19%* of proportional effect on contestable retail spills over. Merger impact

dubious?

Wholesale efficiency

Separation reveals costs, and retailers champion consumer interests.

Water / sewerage opex

0.13% applied to “in-house” opex.

From IA: 25% of the 0.5% efficiency savings of legal separation.

Bundling savings

Combine water, w/w, and other utilities.


£15 per customer p.a.; 7-year transition to full

bundling. From IA: 25% of £15 per customer assumed.

Demand reductions

Incentive for companies to offer water efficiency advice.

Water / sewerage capex

5-year transition to 0.5% usage reduction.

From IA: 25% of full 2% reduction.

Margin effects Competitive pressure reduces

margins. Retail margins. PR14 level.

Possible sources: analysis of comparable sectors.

Regulatory Costs

Developing market codes and then ongoing monitoring.

Licence fees £5.7m upfront; £4.87m

p.a. ongoing.

From IA: set-up costs equivalent to those incurred by WICS; ongoing costs 400% of WICS

given competition enforcement.

Incumbent costs

Costs associated with managing switches and customer contacts.


5 FTEs per WaSC, 2 FTE per large (>1m

customers) WOC, 1FTE per small WOC.

From IA: do not include costs of renegotiating bonds etc. on assumption these aren’t voluntarily

incurred.

Settlement & switching

Setting up and operation of central authority.


£6.4m one-off, then £5m p.a.

From IA: based on double cost observed in Scotland.

Acquisition & retention

Expenditure on obtaining new customers


5% of contestable cost base p.a.

From IA.

Source: NERA Analysis of Defra 2011 Retail Competition Impact Assessment

*The model was calibrated using the 2011 IA option with voluntary separation excluding the finance costs of separation. However, we understand that the results are very

similar to the updated IA. As a result, we present the updated parameters used in Defra’s Oct 2014 IA updated exit option.



In the IA, most impacts comprise an initial cost effect followed by an ongoing trend, and

sensitivities are conducted as follows:

Abatement factors have a low/medium/high range of 0.15/0.25/0.5;43

“Baseline” efficiency gains (i.e. pre-application of abatement factors) have

low/medium/high ranges of:

− Contestable retail one-off: 5%/10%/15% of cost base.

− Contestable ongoing: 1%/1.5%/2% of cost base per annum.

− Non-contestable spillover: 0.25/0.5/0.75 of contestable savings.44

− Wholesale spillover: 0.25%/0.5%/0.75% of cost base.

We draw on these ranges to construct our high and low ranges for retail competition effects.

In addition, we base retail exit on an adjusted version of the voluntary exit option in the IA.45

3.4.3. Regulatory Mechanisms and Nature of Control

Ofwat’s Future Price Limits statement of principles includes an impact assessment of changes

to the regulatory framework, in NPV terms over a 30 year period.46

This assessment has also

been evaluated for Ofwat by PwC.47

We use the assumptions set out in these documents to

gauge the cost effects of changes to regulatory incentives and mechanisms over the modelling

horizon away from the “continued PR14” baseline. We describe the main elements of the

regulatory mechanisms in the following subsections, but we do not model these individually.

Instead, we model the regulatory mechanisms as a package based on the estimated aggregated

effects of the elements on cost efficiency at the totex level.

3.4.3.1. Totex cost assessment and menu regulation

Ofwat has introduced a totex cost assessment approach, removing the “capex bias” under the

previous regime. Under PwC’s scenario based analysis, this could lead to net sector benefits

of £60m (which could range between £10m and £310m) in NPV over 30 years.48

This gain is

implicit in the PR14 mechanisms baseline.

43 In the updated Defra IA the central abatement figure used is 0.5 and the central non-contestible spillover used is 0.19.

We understand that these changes roughly cancel out leading to a very similar size of impact. We also note that the

updated IA has not yet been published and is still being reviewed by the Regulatory Policy Committee.

44 See previous footnote.

45 We did not include financing cost which accounted for roughly one quarter of the retail costs (NPV£199m in the Defra

IA) on the basis that much of this was motivated by the possibility of debt renegotiation or default triggered by the need

for separation, whereas the voluntary approach to exit (eventually adopted in the Water Act) should allow much of that

cost to be avoided

46 Ofwat (2011), “Future price limits – statement of principles Appendix1: Impact Assessment”.

47 PwC (2013) “Updated Price Limits Assessment: Water Services Regulatory Authority (Ofwat)”.

48 This calculation is based on between 2% and 10% of capex being replaced by opex, and whole-life costs being 1%-5%

higher under the current regime with capex bias.



Menu regulation is also being introduced at the Totex level to incentivise companies to

truthfully reveal costs, and incentivise outperformance on these costs. The incremental

savings of this reform above the existing efficiency profiles and savings accruing due to

reforms such as totex cost assessment are highly uncertain, but are estimated to be of the

order of £0.5bn to £2bn.49

We assume that the baseline benefit is £1.1bn.50

.

3.4.3.2. Water trading incentives

Given Ofwat’s desire to increase efficient bulk water trading between companies, include a

policy switch affecting the relationship between regulation and water trading, away from the

baseline level assuming the AIM.

The baseline contains the current view of bulk imports and exports, from the latest WRMP

tables. Table 3.15 displays current estimates of water supply accounted for by imports or

export from one company to another, based on draft WRMPs.

Table 3.15

Bulk Water Trading (Ml/d)

Source: Water company draft WRMPs for 2015-2040 and/or supporting tables (WP1)

To explore variations away from the baseline we rely on Ofwat’s 2011 study of upstream

markets in the E&W water sector. This document suggests that trades currently not in place

could yield net economic benefits of at least £959m in NPV terms.51

The enhanced incentive

49 PwC (2013) “Updated Price Limits Assessment: Water Services Regulatory Authority (Ofwat)”, page 40. We assume

this impact is calculated as an NPV over 30 years.

50 We assume that this mid-point approximately represents the regulatory mechanisms that underlie the PR14 price review.

PwC (2013) “Updated Price Limits Assessment: Water Services Regulatory Authority (Ofwat)” page We would

advise a comparison of FD expectations with the PWC estimates as a check when the FDs become available.

51 Ofwat (2011) “A study on potential benefits of upstream markets in the water sector in England and Wales”

Ml/D ANH AFW BRL BWH DVW NES NWT PRT SES SEW SRN SST SVT SWT TMS WSH WSX YKY Total

ANH 91.0 1.2 8.0 100.2

AFW 8.1 36.0 0.1 2.9 47.1

BRL 11.7 11.7

BWH 0.2 0.2

DVW 0.1 0.1

NES 3.1 0.0 0.7 3.7

NWT 0.0 80.0 80.0

PRT 4.5 4.5

SES 0.1 0.1

SEW 0.0

SRN 1.3 31.1 0.3 32.7

SST 1.4 1.4

SVT 0.0 0.1 48.6 48.7

SWT 0.0

TMS 14.5 91.0 5.0 0.0 110.5

WSH 0.0 338.3 338.3

WSX 1.1 1.1

YKY 0.3 0.3

Total 11.5 106.8 1.1 0.0 0.0 92.2 0.7 0.0 41.0 31.1 4.6 0.0 347.7 0.0 0.1 80.0 15.2 48.6 780.5

Importer

Exp

ort

er



scheme in place for PR14 is estimated to realise savings in the NPV range £160m to £690m.

We use the midpoint of this range as the baseline net benefit estimate..

3.4.3.3. Abstraction incentive mechanism (AIM)

Ofwat will put in place the AIM over Amp6, as a reputational incentive on companies to

reduce abstraction from water resources with high environmental costs at certain

locations/points in time. These sites will be identified in agreement with the EA, but the

scale of the impact will depend on the number of sites included in the AIM, and the strength

of the incentives. As the AIM is not currently proposed to be incentivised financially, the

strength of these incentives is highly uncertain, but may be substantial at a regional level. We

do not directly incorporate any financial effects of the AIM into the model’s policy impacts

but they may be implicitly considered to be part of the family of regulatory mechanisms.

3.4.3.4. Separation of wholesale and HH and NHH retail controls

HH retailers face strengthened financial related to the provision of low-cost service (as far

below the average cost to serve as possible). The separation of the retail and wholesale

controls will make company performance more transparent and help drive efficiencies in this

segment of the value chain. The benefits from this part of the PR14 impact assessment are

estimated at £1190m.52

Similarly, the separation of the NHH retail controls are expected to generate significant

additional benefits. The reforms based on the default tariffs and levels of service framework

will enable companies to drive down their retail costs where possible while protecting

customers in high-cost areas from bill increases. Many of the efficiencies achieved due to

competitive pressure are expected to spillover into the HH and wholesale segments. The

benefits assumed from the separation of the NHH retail control are estimated at £360m. 53

3.4.4. Upstream Competition

We expect that the main upstream competition reform will not be implemented by legislative

change in advance of the PR19 price review. We correspondingly use 2020 as the baseline

year of implementation, but the user can select any other implementation year.

The upstream competition reforms are perhaps the most significant of those being presently

considered.

For our purposes upstream competition is defined as:

Upstream Water: Enabling water competitors and new entrants to abstract and treat

water and input it into the water network; and

Upstream Sewerage: Enabling sewage competitors and new entrants to remove and treat

sewage from the sewerage network and treat and dispose of sludge.

52 PwC (2013) “Updated Price Limits Assessment: Water Services Regulatory Authority (Ofwat)” page 41.

53 PwC (2013) “Updated Price Limits Assessment: Water Services Regulatory Authority (Ofwat)”, page 46.



We base policy impacts for introducing water upstream competition on the most recently

updated Impact Assessment from 2013. There is currently only one policy option being

considered. It is termed “Upstream Reforms”, and is a package of reforms to the current

water supply licencing (WSL) regime that will encourage competition in the provision of new

water and sewerage treatment capacity and water resources.54

This reform will be achieved in

part by unbundling licences so that an entrant can input water into the network without

needing to have a corresponding final customer to sell it to. We include this option in the

baseline.

The effects of the upstream competition reforms can be described as follows:

Synergy and interaction effects:

− Links may exist between abstraction reform and upstream competition in

water:55

it is unclear whether these reforms influence each other so we do not model

any explicit synergy effects.

− The degree of competition expected would depend on the access pricing levels

and perhaps in turn on the treatment of RCV by value chain element. See

section 2.5.1 for details on the focussed and unfocussed approaches to attribution of

the RCV;

Transition costs and benefits:

− No transition costs incurred by the government: The Cave review suggested that

there would be very few changes to the regulatory framework under these reforms and

so no exceptional costs are expected during the transition year;

− A large transition benefit arises from company efficiency quickly catching up to

the frontier: The IA assumes that the presence of competition forces companies to

quickly improve their efficiency to the point of catching up to the efficiency frontier

for contestable elements. This effect is calculated as a reduction in the upstream cost

base subject to effective competition56

of 12% for annual capex costs and 10% for

annual opex. (We note that the efficiencies generated by the reform are expected to

contribute to a decline in RCV due to reductions in capex spending.) We implement

these as sector-wide improvements but the model allows the user to input company-

specific efficiency improvements;

Ongoing costs and benefits:

− Ongoing regulatory costs will increase: Ofwat will incur about 10% more opex

costs, corresponding to £2 million, which will be funded by the water companies and

therefore feed into water bills. Similarly, the DWI and the EA will each require an

additional £0.33 million;

54 We note that this option does not entail mandatory separation of upstream activities.

55 By upstream we mean treatment capacity (and the water for it).

56 The upstream cost base subject to effective competition is calculated by taking 20% of the opex and capex spent on

resources and treatment.



− Company finance costs will increase significantly: the IA estimates a higher cost of

capital for capex enhancement expenditure subject to effective competition as a result

of the additional risks that investors will bear. The estimated increase in the cost of

capital is equal to 2.5% in the low costs case, 1% in the central costs case, and 0.5%

in the high costs case which is applied to the capex enhancement expenditure

proportion of the cost base that is subject to effective competition. The proportion of

costs subject to effective competition is 10% in the low benefits case, 20% in the

central benefits case, and 30% in the high benefits case. Post-implementation

additions to RCV (i.e. post-2020 additions) in each of the value chains57

that are

subjected to competition will be subject to the adjusted WACC. The share of higher-

WACC RCV items are pooled with the unaffected-WACC RCV items to give a

weighted average WACC that applies to the entire value chain element in

question;5859

− Company ongoing costs will increase as a result of the extra scrutiny required to

run their businesses: the IA followed the Cave review assumption that companies

will incur additional annual opex costs of £1.11 million;60

− Efficiency improvements in delivering upstream services: The existence of

competition is expected to deliver ongoing efficiency gains through the development

ofdelivering upstream services at lower cost, substituting demand management

measures, or adopting measures to control pollution at source. These effects are

quantified as the following reductions to capex and opex costs for new and

replacement capacity:

− New capacity Capex: 0.48%;

− Replacement capacity Capex: 0.33%

− New capacity Opex: 0.36%;

− Replacement capacity Opex: 0.24%.

The upstream competition option that is incorporated into the model contains high/low

variants as well as a user-specified parameters on the strength of the WACC effect in those

sensitivities.

57 The value chains that are expected to be subjected to competition are the non-network elements: water resources and

treatment, sewer treatment, and sludge treatment and disposal.

58 The upstream cost base for effective competition is calculated by taking 20% of the opex and capex spent on resources

and treatment. Of this share, only the capex enhancement and MNI will go into the RCV and constitute an additional

cost effect through the effect on WACC. As a result, the proportion of this cost base relative to the RCV is likely to be

relatively small, thus bringing down the percentage effect on the cost of capital to much lower levels. For example, if

the RCV is nine times the size of the capex enhancement +MNI cost subject to effective competition then the central

case WACC would only be 0.1% higher.

59 We note that an error in the treatment of depreciation underlying the IA figure leads to higher IA costs than those that

feed through the model. This is caused by underestimating the amount of depreciation, hence overestimating the

amount of RCV subject to an increase in WACC. This leads to higher cost estimates in the IA. The resulting magnitude

of the model’s upstream NPV net benefit is therefore larger than that from the IA.

60 £0.95 million in 2009 prices adjusted up by RPI CHAW factor 1.170332.



Table 3.16

Effects of Upstream Competition Policy (IA)


Synergy effect on competition

The links between upstream and abstraction reform are not

clear. N/A N/A

We do not model any synergies between abstraction reform and upstream

competition. This is due to uncertainty about whether linkages exist.

Efficiency catch-up

Companies efficiency levels are expected to quickly rise to the frontier for the upstream

segments

Water and sewerage capex

12% of capex cost base subject to effective competition, which is 20% of

resources and treatment capex. Applies to transition year only

Applies to transition year only. Efficiency assessments may provide a basis for which companies will see greater cost

reductions

Efficiency catch-up

Companies efficiency levels are expected to quickly rise to the frontier for the upstream

segments

Water and sewerage opex

10% of opex cost base subject to effective competition, which is 20% of

resources and treatment opex. Applies to transition year only

Applies to transition year only. Efficiency assessments may provide a basis for which companies will see greater cost

reductions

Government administration cost savings

Ofwat will incur an additional £2m p.a. and EA and DWI will

each incur an extra £0.33m p.a.


£2.66 million p.a. Results from regulation costs being

passed onto companies.

Increased company finance costs

Companies will face higher finance costs due to bearing

additional risks WACC

0.5%, 1.0%, or 2.5% increase in the proportion of WACC related to

enhancement capex

Results from additional risks of competition

Increased costs related to company scrutiny

The effect of competition will force companies to be more

attentive to market conditions


£1.11 million p.a. Additional costs related to scrutiny of the

market

Ongoing efficiency gains

Efficiencies related to ongoing development of cost savings and demand management


and capex

Reductions of 0.48% in enhancement capex; 0.33% for capital maintenance; 0.36 for new capacity opex; and 0.24%

for replacement capacity opex

Development of new ways of delivering upstream services at lower cost,

substituting demand management measures, or adopting measures to

control pollution at source

Source: NERA Analysis of Defra 2013 Upstream Competition Impact Assessment. We note that an error in the treatment of depreciation underlying the IA figure leads to

higher IA costs than those that feed through the model. The resulting magnitude of the model’s upstream NPV net benefit is therefore larger than that from the IA.



In the baseline we assume the implementation of upstream reforms in the year 2020, though

this is still uncertain. We understand that policy decisions on the implementation date for

upstream reform have not yet been taken, so we provide the flexibility for the user to input

any other start date - which will shift the entire impact profile backwards or forwards in time.

At this stage it remains unclear whether the entire upstream market will be made competitive

or whether competition is phased in for new incremental capacity only. We follow the

analysis underlying the IA by assuming that only the incremental capacity will be subjected

to the new WACC.

3.4.5. Adoption of Private Supply Pipes

The model includes a policy switch to activate additional costs associated with the transfer of

private water supply pipes (PWSP). Defra recently carried out a public consultation on

supply pipes. The evidence and views showed that there are benefits to be gained from

transferring ownership of private supply pipes to water supply companies. However, there is

less certain evidence about the range of potential impacts on water bills for various customers

and geographical regions. Defra decided not to carry out further work on transferring

ownership of supply pipes at the current time due to the Coalition Government’s commitment

to maintain pressure to keep household bills down. Hence we propose to leave this policy out

of the baseline.

The transfer of PWSP would raise the cost of providing water services, as the responsibility

for repairs and maintenance would be passed on to water companies. The benefits would be

better management of supply pipes, potentially resulting in reduced leakage and savings to

residents who would have incurred repair costs. These benefits are not captured by the model.

This policy switch would therefore raise costs for the provision of a given level of supply.

Impacts that this reform is likely to have on the model are:

Increases in capital maintenance costs as water companies become responsible for

maintaining supply pipes for households and non-households;

Increased administration opex costs required to allow reporting of faults and subsequent

repair coordination;

Likely increases in some quality of service measures as, for example, more faults are

tackled rather than customers ignoring problems, possible associated reductions in

leakage levels and avoidance of more serious (and therefore more costly) issues;

All opportunities to exploit economies of scale in repair and maintenance of the supply

pipes are taken.

We inform the magnitude of the effects from this policy switch based on the recent

assumptions calculated by Defra as part of the Water Bill analysis that considered this topic.61

61 Defra Impact Assessment, “Transfer of private water supply pipes to Water and Sewerage Company ownership

(WaSCs)”, 2013.



3.4.6. Per Capita Consumption Targeting

Company TSG members suggested the addition of a policy switch to test sensitivities around

having per capita consumption targets implemented in the future. These were thought to be

important due to the relative importance of PCC changes over time in order for companies to

meet their demand targets without bringing too much supply

We defined the PCC targeting “policy” as a cost-free reduction in the volume of water

consumed. The cost-free assumption that underlies this policy effect is unlikely to be

realistic and should be refined in subsequent upgrades of the model. The baseline reduction

PCC targeting reduction is 10%, with the high and low sensitivities set at 5% and 15%

respectively.

3.4.7. Abstraction Management System Reform

Defra are aiming to legislate for abstraction reform early in the next Parliament (from 2015).

Our scenarios use 2025 as the year of implementation of the reform, but the user is able to

select any other implementation year. The abstraction reform focus is intra catchment trading

- unlike Ofwat’s enhanced water trading incentives which are also in the baseline and can be

inter catchment.

Two options were considered for the consultation IA under the heading of abstraction licence

reform. The first option is “Current System plus”, which takes many of the characteristics of

the existing system such as flow-based restrictions, but arguably makes the system more

flexible, more responsive to water availability, more supportive of trading, and fairer for

abstractors. The second option is “Water Shares” which entails similar changes as those in

the current system plus but introduces a share based licencing system which aligns

abstractors’ interest in a jointly managed variable resource. These options are very similar in

terms of how they may impact bills and we feed them through the model in the same way.

We understand that a third option which may constitute a hybrid of the water shares and

system plus options is being considered. To reflect this we add a ‘midway’ policy option to

the model by taking the average of the two other options. This midway option can then be

overwritten when the final IA is published in early 2015. Note that for this reason, the current

cost - benefit profile for the abstraction license reform options are subject to change.

Abstraction reform is by itself not expected to yield large effects on water bills. The main

goal of the reform is to achieve environmental objectives more efficiently and to enable a

more functional upstream market that is more conducive to upstream competition.62

We

therefore consider two types of abstraction management system reform effects: transition

costs and ongoing costs/benefits.

We use the most recent IA (produced in 2013) as the basis for informing the model inputs

which are affected by either policy option switch. The only relevant difference between the

two policy options relates to the magnitudes of the effects. For each of the policy options we

62 We consider abstraction reform to pertain only to licence trading and allocation trading. We consider investment into

new treatment plants at the abstraction site to fall under upstream competition.



consider high and low cases based on the figures provided in the IA. Note that this IA does

not contain the cost breakdown in terms of percentages of different cost categories, so we rely

on the NPV cost estimates to derive proportional shares of the respective opex and capex cost

categories for the average annual year costs.63

The effects of abstraction reform can be described as follows:

Transition costs:

− Transition costs incurred by the government: the EA and Natural Resources Wales

will incur costs related to moving the existing abstraction licences to a new system.

In order to be conservative, we assume that water companies will ultimately bear and

pass on these costs in the form of water bills. While we realise that these costs will

likely be shared across several groups of abstractors, we assume that they are paid for

entirely by the water industry as they are relatively insignificant and only occur

during the year of implementation. We reflect these costs during the transition year as

an increase in company opex equivalent to £21.0 million and £23.5 million in 2013

NPV terms for system plus and water shares respectively;

Ongoing benefits and costs:

− Reductions in administration costs to government: the EA and Natural Resources

Wales will benefit from lower ongoing administration costs as a result of the change

to the new system which we expect will be passed through to companies. We pass an

opex cost savings equivalent to £74.2 million and £62.8 million in 2013 NPV terms

for system plus and water shares respectively;

− Reductions in administration costs to businesses: according to the IA, the

administration costs of moving to the new system will be reduced relative to their

current levels, representing an ongoing cost reduction to businesses’ opex equal to

£37.6 million and £36.9 million in 2013 NPV terms for system plus and water shares

respectively;

− Reductions in the NPV capital investment profile (and associated operating

costs): water companies will be able to take advantage of the market for licences to

manage their supply and demand balance as the climate changes. This will allow

them to postpone the profile of their capital investment requirements , according to

the IA creating a capex benefit of £214 million and £219.4 million in 2013 NPV terms

for system plus and water shares respectively;

− Improved gross margins for business related to increased access to high river

flows and abstraction trading (but not selling abstraction rights): these additional

benefits relate to opex savings which are equivalent to £0.7 million and £1.3 million

in 2013 NPV terms for system plus and water shares respectively. We understand

that these benefits may not go to the water industry but we continue to include them in

the current modelling work as they are negligible;

Additional ongoing costs not contained in the IA:

63 We attribute all the transitional costs to the single transitional cost category.



− Increased abstraction charges to businesses: water companies and other abstractors

will face a cost that is more reflective of water scarcity and will therefore be different

than the current (near-zero) abstraction costs they face. We do not consider these

changes to charges within the current scope of this project;

− Windfall gains from the sale of abstraction licences: We assume that the transition

will be managed such that no windfall gains are realised by abstractors. We therefore

view this item as outside the current scope.

Table 3.17

Abstraction Reform Policy Effect (IA)


Synergy effect on competition

It is unclear whether there are synergies between abstraction reform and upstream

competition.

N/A N/A

Due to the uncertainty about whether synergies

exist we do not model them.

Transition costs incurred by government

Costs of moving to the licence trading system

Water opex

All transition costs

Applies to transition year only.

Government administration cost savings

Lower ongoing administration costs

resulting from simplified system

Water opex

22.7% or 19.6% of total p.a. net

benefits

System plus or water shares

percentages, or midpoint

Business administration cost savings

Lower ongoing business administration costs

resulting from simplified system

Water opex


benefits



Capital investment deferral savings

Lower annual average capex due to deferring capital enhancement

spending

Water capex


benefits



Increased gross margins due to additional water availability

Cost savings related to increased access to high

river flows and abstraction trading

Water opex


benefits



Source: NERA Analysis of Defra 2013 Abstraction Reform Consultation Stage Impact Assessment

To obtain our estimates we use the transition cost and average annual costs from the IA for

the system plus and water shares options respectively, and also use the midpoint between the

two options as the baseline ‘midway’ option. We attribute a 46.9% share of the total costs

and benefits to the water industry since that is its share of the total volume of water

abstracted.64

65

64 We obtained the proportion of the water industry’s share of total water abstracted from the IA, page 11. This figure

appears to be consistent with Figure A1 of Defra’s “Abstraction Reform Consultation Technical Detail Annex C:

Additional detail on specific elements of reform”, page 6.



In the model we take the net annual benefit for the industry and divide it out among each

water company in proportion to the volume of water that they abstract. (This simplification

may not properly represent the distribution of benefits as they are only realised in ‘enhanced’

catchments, i.e. generally those facing water stress in the South/South East, as categorised in

the IA. The number of ‘enhanced’ catchments increases over time).

Future versions of the model may include a model upgrade to allow the model to contain

information related based on catchment benefits – see section 5for potential upgrade

descriptions. The high/low figures in the IA are used to inform the strength options in the

model. These figures are not statistical representations; they are based on taking estimates

from equally likely climate change and socio economic scenarios.

The abstraction reform implementation date is set at 2025. We provide the flexibility for the

user to input any start date for the reforms to take place. Changes to the start date will shift

the entire net benefits profile backward or forward in time.

3.4.8. Greater-Resilience

The model contains a switch to represent a possible shift from current levels of resilience in

case these are perceived to be insufficient in the future (for example due to greater pressures

arising from climate change). We implement the effects of companies responding to a new

increased resilience scenario through changes to levels of target headroom as well as to the

costs that companies incur to maintain and improve their services. This greater resilience

(GR) scenario is not included in the model baseline.

In the model we assume that the central greater resilience scenario entails a tripling of all

resilience costs in water and sewerage (e.g. expenditure to reduce flood risk, SEMD, and any

enhancement expenditure falling under the Ofwat definition of resilience) as well as a 20%

increase in the level of target headroom in the base case GR option.66

The additional

resilience costs are a reflection of the doubling of water supply pipes, installation of larger

sewers to prevent overflows at bottleneck locations, and other such additions that affect

reliability through the network and/or resources but do not necessarily influence the level of

output. The increase to the level of target headroom reflects spending on resilience of the

network and/or resources (for example by adding capacity from multiple sources) and for

increasing capacity at existing sites as a form of protection against external risks such as

natural disasters or harsh climatic conditions.

65 We note that the use of the water industry share of total abstraction ignores issues of consumptiveness (the public water

supply sector is considerably more consumptive than many of the other abstracting agents) and also displaces water

considerably further from the point of abstraction.

66 More specifically, we defined the WASC average proportional expenditure on “resilience” as (Resilience + SEMD) /

(Net Capex + Opex) for water and (Resilience + SEMD + Expenditure to Reduce Flood Risk) / (Net Capex + Opex) for

sewerage. We then uplifted all companies’ actual costs by double this proportion in the base case; making the industry

average resilience spend three times larger in the base case option. For the low case, we double average resilience and

increase target headroom by 10%, and for the high case we quadruple resilience costs and increase target headroom by

40%.



3.5. Output Specification

The model incorporates all drivers to generate forecasts of final bills for customers under a

range of macroeconomic, demographic, climate and policy scenarios from 2015 to 2050.

This section sets out how we structure the outputs of the model.

3.5.1. Intermediate and Final Outputs

The model allocates each company’s modelled annual allowed revenues into three revenue

baskets for each service: household metered customers, household non-metered customers,

and non-household customers. From these allocated revenues, the model calculates average

bills for each company, customer category and service, reported in real and nominal terms

annually from 2015 to 2050.67

We also plot the average historical bills for the years 1989-

2015 for a comparison of the headline weighted average household bill results.

In addition, the model presents an approximate aggregation of bills by river basin using river

basin mapping data provided by the EA.

The model also reports the build-up of average bills, across contributions from different

elements of the water and sewerage value chains. It shows cost breakdowns such as splits of

wholesale totex into enhancement, capital maintenance, and opex.

These outputs are displayed in easily navigable summary tables and charts, each comparing

the (baseline) projection results with the results for the user’s chosen scenario. So that users

can further analyse output results, it is possible to view the output results, including tables

and charts, for both the baseline and the user’s chosen scenario.

3.5.2. Distributional Impacts

The distributional impacts of bills form an important consideration in policy decision making.

The model is able to assess some of the distributional impacts of projected total water and

sewerage bills in each company region by determining the proportion of households where

the average bills are above a user-defined level of average disposable income. We enable the

user to specify the threshold level of disposable income that projected bills are reported

against. We do not specify any particular threshold level as part of the baseline.

Our approach to calibrating this assessment is to start with the initial distribution of

household income in each UK statistical region from the Family Resources Survey as

published by the Department for Work and Pensions. This data is mapped to company

regions from the UK statistical regions in the Survey.

To project the income distribution forward, as a first approximation we adopt the simple

assumption that all households experience income growth at the regional average rate of

economic growth. We project regional economic growth based on a projection of gross value

added (GVA) growth rates by region from the UK Employment and Skills Almanac 2011.

67 The model also calculates national and river basin average bills according to the weighted average of all household

customers, based on the revenue baskets and property figures corresponding to metered and unmetered households.



The model then computes nominal wage estimates based on the user’s choice of either RPI or

CPI inflation. The nominal bill levels are then compared with nominal income and the

proportion of people paying above each threshold of income are displayed in a chart. We

provide the functionality to consider these distributional effects at the company or industry

level.

3.5.3. Sensitivity Checks

Partly to help provide assurance on robustness, we offer the ability to easily report sensitivity

results as “deltas” from the baseline levels. When variants from the baseline drivers are set

as inputs, through policy switches or sensitivities, the model routinely displays the baseline

scenario outputs alongside those for the desired variant. This helps ensure that the model

responds to changes in the baseline as one would generally expect.

To the extent possible, we perform scalable or replicable calculations within the same

worksheet to ensure model flow consistency and accessibility. All calculation sheets have a

system of embedded internal checks to help ensure accuracy of calculations performed.

Integrated Final Report Key Model Results


4. Key Model Results

4.1. Introduction

The Water Bills Projection Model has been designed to assist decision making in the water

sector by showing the impact of policy, regulatory, and company investment choices on final

customer bills in England & Wales over the period from 2015-50. The model allows users to

project bills under a range of policy scenarios and assumptions about relevant

macroeconomic and environmental factors, reporting typical bills for various classes of

customer by company or by region. Along with this report and the excel model itself, there is

also an accompanying model user guide.

4.2. Limitations

This section briefly sets out some of the main caveats to the results which are presented in

this chapter. When considering the results, it must be borne in mind that the model implicitly

assumes the projected situations are institutionally feasible, as the model makes no test of

sector compliance with sector laws such as compliance with WFD requirements transposed

into UK law, makes no test of compliance with competition laws, and makes no definitive

test of the financeability of the projections (though it is easy to construct projections which

would fairly obviously not be financeable).

In terms of WFD costs, the input data is fairly consistent with EA projections for scenario 4

cost. 68

In this projection, approximately 90% of capex costs were realised prior to the “full

compliance” date of 2027. While this forms the basis of our projections, we recognise that

the target full compliance date may shift. There may also be another phase of environmental

regulation post 2027, that is currently unknown - particularly if challenging climate

conditions arise. There is no available evidence to inform our modelling assumptions, but this

type of additional environmental expenditure could have a significant effect on the later

years of the modelling horizon. In contrast, Opex costs are expected to be incurred over a

longer time horizon. When applying EA WFD scenario estimates we model Opex costs at a

constant level throughout the modelled horizon.

Climate change impacts are represented via the WRMP inputs, but the higher climate risk

scenarios covered by other sources (e.g. EA Case for Change) were not incorporated into the

model due to a lack of suitable sources on cost implications for the industry. These climate

impacts constitute a significant area of uncertainty for the effects and we recommend that

further work be undertaken to specify plausible ranges of effects and corresponding levels of

investments. We have attempted to cover off the bill effects arising from the need for greater

levels of resilience through an additional scenario. However, we note that, unlike for the

water service, we have not directly considered capacity additions for sewerage due to climate

change or the potential need for sizable sewerage replacement expenditure beyond our input

data. The input data contains a declining amount of enhancement expenditure - this situation

68 EA, “Water for Life and Livelihoods: A consultation on the draft update to the river basin management plan - Part 3:

Economic analysis”, 2014



may be perceived to be a reflection of failure to allow for ‘‘known unknown’’ cost and

quality drivers that may materialise in the future.

4.3. Inputs and Assumptions

This section sets out the historical and forecast inputs used in the model as well as listing key

assumptions that significantly impact the results.

We have quality assured the model to ensure that results are robust and incorporated

stakeholder feedback through several iterations. Stakeholder feedback was gathered through

workshop sessions with the TSG. Any issues where agreement was not reached during these

workshops were resolved through follow-up emails or memos listing potential approach

options.

The model baseline was constructed to be consistent with draft determinations for PR14.

Most of the expenditure categories follow directly from AMP6 estimates used in Ofwat’s

DDs, which we believe provides an extremely robust platform from which to build the

dataset. There has been an internal audit of the model formulae and functionality, spot

checks on the expenditure predictions compared to historic spend, and detailed comparisons

to forecast expenditure from other sources. The inputs and assumptions were also double-

checked by Vivid Economics as part of the model’s external quality assurance checks and

any Vivid comments related to the inputs and assumptions were revised.69

Some of the major cross-check we performed were based on comparisons with previous

water models. Figure 4.1 shows a high level comparison of the main expenditure categories

between the water bills projection model and the “Changing Course” study carried out by

Severn Trent.70

In each of the three five year blocks, the dark grey (high) “business as usual”

SVT case is presented on the left, followed by the pale gray (low) SVT “Alternative course”

on its right and the high/baseline/low model scenarios shown in increasingly lighter shades of

blue in the third, fourth and fifth columns.

69 In particular, Vivid drew attention to the RPI, RPE and cost efficiency variables, as well as some series that had

inconsistent trends. We have reviewed our approach to RPI, RPEs, and cost efficiency assumptions as a result. We

have also changed some of the forecasting methods in the cases where Vivid pointed out trend issues.

70 Severn Trent Water, “Changing Course – Delivering a sustainable future for the water industry in England and Wales”,

2010.



Figure 4.1

Comparison of Expenditure in Water Bills Projection Model and SVT Model

Source: NERA output from water bills projection model and SVT “Changing Course”

We understand that the Severn projections were based on a fuller definition of WFD

compliance which made it likely to produce high estimates of expenditure.71

As a result, it

may be most appropriate to consider it against the “upper” model scenario. The model’s

expenditure levels are generally lower than those from Severn. This appears to be largely due

to the magnitude of Severn’s CM and Opex forecasts, which appear to be turning out to be

excessive (at least for the period to 2020 for which the model’s baseline estimates are much

lower).72

Figure 4.2 shows a similar comparison with the Ofwat’s Water Industry Forward Look

(WIFL).73

The WIFL report contained figures at the level of capex rather than CM and

enhancement expenditure, so we combine those cost categories from the model to make the

comparison. Figure 4.2 shows the high/average/low charts from the WIFL report in shades of

brown on the left side of each block, and compares these to the blue high/baseline/low

estimates from the model on the right.

71 We understand that the Severn projections included non-cost beneficial items and was therefore closer to the EA’s

scenario 3, described in more detail in section 4.6.5

72 Note that the Model “Baseline” figures for 2015-20 are relatively accurate as they are consistent with the DDs. The

2015-20 period can therefore be used to examine the validity/bias in the severn figures if we assume that any bias

persists into the later periods.

73 Ofwat, “Water Industry Forward Look 2010-30 – Some Possible Views on the Future”, 2006.

0

10

20

30

2015-20 2020-25 2025-30

£ B

illio

n (2

012/

13 p

rice

s)

SVT BAU (High) SVT Alternative (Low) Model "Upper" Model "Baseline" Model "Lower"

CM Enh Opex CM Enh Opex CM Enh Opex



Figure 4.2

Comparison of Expenditure in Water Bills Projection Model and WIFL

Source: NERA output from water bills projection model and Ofwat’s Water Industry Forward Look 2010-30

Both models appear to have broadly similar levels of capital expenditure. The model capex

projections start out at a very similar level for 2015-20 and, with the exception of the high

scenario, they stay roughly in line with them to 2030. The high scenario capex is

considerably higher – in part due to the elevated EA Scenario 3 WFD costs which are largely

incurred prior to 2027. For opex however, the model estimates are roughly 20% lower than

the WIFL figures in their respective central cases and they remain lower than the respective

WIFL equivalents throughout the horizon.

Overall the Defra projection model has lower levels of total expenditure, and the difference

between the respective central scenarios is 8%, 4%, and 1% for the AMP6, AMP7, and

AMP8 windows respectively. Given that we can have significantly more confidence in the

AMP6 figures from the model compared to those that were available at the time of the WIFL

publication in 2006, it appears that the WIFL model overstated near-term expenditure levels,

particularly for opex. As a result, the similar differences for AMP7 and AMP8 with the best

available comparator suggests a degree of plausibility in the results from the Defra model.

4.3.1. Description of Input Data

The model is populated with the most current data available from public sources or company

projections. We used companies’ final WRMP public data whenever available as well as a

complete set of figures from all companies Draft Determinations as published by Ofwat in the

companies’ respective populated financial models.74

The only companies for whom final

74 Note that where we identified inconsistencies across the DD and WRMP data inputs we favoured the DDs. We also

favoured using projections of DD values over actual WRMP figures where we had noted these inconsistencies during

the AMP6 period. Our assumpitons tended to result in higher HH bill levels over time and therefore can be seen as

conservative. We recommend a model upgrade that reviews and develops a complete and fully internally consistent

data set in section 5.1.

0

10

20

30

40

2015-20 2020-25 2025-30

£ B

illio

n (2

012/

13 p

rice

s)

WIFL Scenario A (High) WIFL Average of Scenarios WIFL Scenario D (Low)Model "Upper" Model "Baseline" Model "Lower"

Capex Opex Capex Opex Capex Opex



WRMP were unavailable are Southern Water, United Utilities, and the Essex and Suffolk

region of Northumbrian water. For these companies or areas we have used the draft WRMPs.

In addition to the public data mentioned above, we also requested some granular longer term

non-public data directly from companies on a confidential basis. We received data responses

from ten different companies covering varying durations from 2015 to 2050. We drew on

these submissions to determine reasonable long-term average industry forecasts which we

then applied to all companies (including those which submitted long-term data). This

approach makes use of the confidential data while not preventing the model from being

circulated due to confidentiality agreements. It also means that the model’s projections for

some companies will be different from their own best views.75

In addition to the above-mentioned data sources, we also received company mappings onto

River Basins and WFD compliance cost estimates broken down into opex and capex costs

from the Environment Agency. We used this data to test the long term trend data that we

forecasted based on the approach described in the preceding paragraph. These tests are

described in more detail in section 4.6.5.

4.3.2. WFD Inputs

This section briefly sets out the WFD costs which underlie the baseline. The WFD costs are

implicitly included in the DD figures and the longer term input cost forecasts and this section

aims to make them more transparent. In order to arrive at the WFD costs presented, we asked

companies and the EA to tell us what proportion of each of their enhancement cost items

corresponded to WFD compliance measures. We note that some company forecasts may

only apply to the WFD requirements that are currently known. As a result, the longer term

expenditure forecasts (which are based on the weighted average company submissions) for

the period beyond 2027 (when the current WFD cycle is expected to conclude) may fail to

account for future expenditure requirements. To determine the share of opex costs relating to

WFD compliance we assumed that the ratio of WFD opex to capex costs was approximately

equal to that arising from the opex-capex breakdown of scenario 4 WFD costs provided by

the EA.

Figure 4.3 displays our WFD input assumptions at the industry level for water and sewerage.

We assumed that all water quality expenditure were allocated to sewerage treatment and all

water resource expenditure was allocated to the water resource value chain element. The

figure shows that the costs are much greater on the sewerage side and that these are also more

front-loaded.

75 Our approach to using the longer term data is to calculate an industry level trend based on the companies who have

provided data, and fit this an industry-wide trend to all company regions in the model. For enhancement capex, we

calculate the submitted enhancement cost of all companies that submitted confidential data, and calculated it as a

proportion of AMP6 opex from those same companies. We then repeated the process for the companies that submitted

data for each subsequent AMP period. We then hold this industry level proportion constant across all companies to get

an estimate of long term enhancement expenditure. Capital maintenance is calculated as a percentage change on

expenditure over the previous AMP.



Figure 4.3

Baseline WFD Input Cost Figures

Source: NERA inputs based on DDs and long term input forecasts over a set of cost categories relating to WFD

We discuss WFD costs in more detail and perform a cross check with the EA’s WFD cost

estimates for Scenarios 3 and 4 in section 4.6.5. We also suggest that a further exercise be

undertaken in the coming months in order to more accurately reflect WFD costs over the long

term.

0

50

100

150

200

250

300

20

15

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

20

26

2027

20

28

2029

20

30

2031

20

32

2033

20

34

2035

20

36

2037

20

38

2039

20

40

2041

2042

2043

2044

2045

2046

2047

2048

2049

£m (

2012

/13

pri

ces)

WFD input costs - Sewer WFD input costs - Water



4.3.3. Table of key assumptions

Table 4.1 sets out some of the key assumptions used in the model. These primarily pertain to

policy effects or the functionality of other modelling shocks.

Table 4.1

Key Assumptions

Topic Description

Intra-Policy Effects Benefits and costs within all policies have additive effects; the net policy effect is derived from adding the cost (negative) and the benefit

Inter-Policy Effects All policies are treated as independent and therefore multiplicative

Upstream Finance Costs Cost calibrated based on Projected Baseline 2020 RCVs

Leakage and Growth Costs Leakage and growth costs are based on the same supply curve – i.e. the cost of reducing leakage and the cost of adding capacity are the same

DYAA and DYCP data Model uses DYAA and weighted average annual figures - no direct use of DYCP data

Growth expenditure costs Companies with no supply data in their WRMP are modelled using closest reasonable comparator76

Baseline policy effects Baseline effects are implicitly incorporated in input figures

Non-baseline policy effects Non-baseline effects are additional to the input figures

Variable forecasts and shocks

The ordering assumption used is that variables are forecast first, then shocked

Efficiency savings are additive to company data inputs

We assume that companies achieve a varying level of cost efficiency saving that is above and beyond any cost input to the model, including the DD figures and longer-term estimates – we examine a sensitivity to this in Section 4.6.4

Long Term trend forecasts Long term data is forecast using additional data submissions from a subset of companies77

Policy effects at cost category level

Effects must be calibrated to cost category (e.g. botex or enhancement) by value chain

Source: NERA

4.3.4. Outputs Conditional on Current Inputs

The modelling results produced for this document are based on our best endeavours to

compile a robust set of input data from public and private sources. We believe that this data

set represents the best currently available set of public data. We understand that the inputs

76 SWW/WSH get UU curve; NES gets WSX; YKY gets SEW; CW/SB/DVW get PRT

77 See description of input data in Section 4.3.1.



will be further updated by Defra as the final few WRMPs are published and after Ofwat

produce company Final Determinations..

In interpreting these results it should be borne in mind that:

There is little information available about the more distant future. For many of

our external input variables, such as cost inflation, there are no long-run forecasts

directly suitable for use as data inputs. There is also considerable uncertainty about

the likely regulatory and policy environment, for example about the long-run statutory

requirements to improve wastewater discharges. Assumptions must be made by the

model user - we try to be transparent about those used in this report;

In making assumptions to form long-term data inputs, we face the difficulty that the

future may be different from the past. For some important input data series such as

capital maintenance expenditure requirements we use current average levels and

short/medium-term forecasts made by water companies as a basis for long-run

forecasts, though this relationship might change, for example capital maintenance

needs might increase more than expected as assets become older and change with the

service-quality and climate context. An unforeseen change in the underlying

relationships could make our results misleading, when hindsight can be applied;

Though the model is designed to reflect available data, it is also only a model, one

also designed to be within the computational capacity of Excel avoiding use of

macros. Consequently the most granular level of data, relationship, and result

treated in the model is the company value chain level and an annual time step; no

effects at finer levels are modelled; few feedbacks are covered within the model.

4.4. Baseline Results and Exogenous Variable Sensitivities

This section displays the baseline average bill results as well as those from sensitivities

applying to all of the exogenous drivers at once. In all cases presented in this section the

baseline policies of retail competition, abstraction reform, and upstream competition are all

held at their central “baseline” impact levels. This section also includes some intermediate

output results that show the constituents of water bills in more detail.

4.4.1. Headline Results from a Range of Scenarios

In this section we present the average household bill levels under a range of scenarios for the

factors that are thought to matter most to costs in the sector, hence to bills.

Figure 4.4 shows that by 2050, the range of national average annual household bills goes

from £240 in the lower scenario setting, to £553 in the upper scenario, in real terms. The

black line shows the estimate of real household bills produced under the “baseline” driver

assumptions and scenarios in the model, falling from £355 in 2015 to £343 by 2050. The

variation displayed during the AMP6 period is largely illustrative since the average bills for

this period will be determined by Ofwat’s FDs (unless overruled by Ofwat Interim

Determinations of K or Competition and Markets Authority (CMA) Determinations).



Figure 4.4

Baseline and Sensitivity Range for Average Household Bills – Real

Source: NERA

The dark blue area around this baseline projection shows the national household average bill

range – in real terms - from setting all of the driver variables (e.g. GDP, population growth,

construction cost inflation, etc.) and baseline policy options (retail competition in 2017,

upstream reform in 2020, and regulatory mechanisms) to their low or high sensitivities. The

high sensitivity includes the WFD Scenario 3 cost estimates which includes non-cost

beneficial solutions (this scenario also accounts for the cost spike in 2015).

The light blue areas at the top and bottom of the fan correspond to the “upper” and “lower”

modelled scenarios, which result from setting all of the drivers and all of the policies to their

high and low settings respectively.78

The impact of setting all the policy drivers to their “low”

settings is more muted, as for some policies the low setting is the same as having them turned

“off” in the baseline, and there is therefore no change from them in the “lower” scenario. In

contrast, the “upper” scenario measures include substantial extra resilience expenditure from

2020 as well as the EA WFD Scenario 3 cost estimates. For these “upper” and “lower”

scenarios we emphasise that currently unforeseen policies and/or extreme conditions could

have impacts that are currently not captured by the model.

78 The upper/lower scenarios includes the non-baseline policies (abstraction reform, greater resilience, PCC targeting, and

private supply pipe adoption) in addition to the baseline policies (retail competition, upstream reform, and regulatory

mechanisms).

200

250

300

350

400

450

500

550

600

1989

1991

1993

1995

1997

1999

2001

2003

2005

2007

2009

2011

2013

2015

2017

2019

2021

2023

2025

2027

2029

2031

2033

2035

2037

2039

2041

2043

2045

2047

2049

£/P

rop

ert

y (2

01

2/1

3 p

rice

s)

High/Low Bill Scenarios Range Upper/Lower Scenarios Range Model Baseline

Historic Forecast

AMP6



In addition, the input data contained a declining amount of enhancement expenditure - a

situation that may be perceived to be a reflection of failure to allow for ‘‘unknown unknown’’

cost and quality drivers that are likely to materialise in the future. Figure 4.31 in section

4.7.4 displays the declining share of enhancement that results from this situation and leads to

the resulting baseline reduction in average HH bills.

Table 4.2 displays the average household bill levels of the five scenarios at three intervals in

time. The “upper” scenario brings bills up to £553 by 2049, 61% higher than the baseline

estimate for that year. The “lower” scenario bill for 2049 is £237, or 31% below the baseline

by 2049, reflecting the lower levels of bill level reduction uncertainty.

Table 4.2

Average HH Bill Projections by Scenario (Real)

Scenario 2015 2030 2049

Upper £446 £480 £553

High £446 £439 £487

Baseline £355 £364 £343

Low £340 £316 £252

Lower £340 £299 £237

Source: NERA

The individual effects of each policy are discussed in more detail in the following sections.

4.4.2. Results from baseline exogenous variables and sensitivities, holding baseline policies constant

The following subsections describe the model average bill projections for household and non-

household consumers respectively. The sensitivities labelled “high” and “low” indicate that

the high or low sensitivities are applied to all driver variables and baseline policies.

4.4.2.1. Weighted Average Household Bills

Figure 4.5 displays the weighted average HH bills in the baseline compared to the high and

low scenarios over the course of the modelling horizon. In the high case, average HH bills

start higher than the baseline bills with an initial spike in 2015 corresponding to the EA

Scenario 3 WFD costs. After coming down from the initial spike they then grow slowly for

the rest of the horizon. In the baseline scenario the bill levels gradually decline over the

horizon, but the period of gradual decline does not set in until after AMP7 – the point where

additional cost and quality drivers are likely to materialise in the future. The gradual

divergence of bills across these scenarios results from the driver and policy differences used

to form the high case - all drivers (including GDP growth, efficiency effects, enhancement

capex, opex, etc.) and all baseline policies (retail competition, upstream competition, and

regulatory mechanisms) are specified at their higher sensitivity levels in the high case. The

peak bill for the baseline occurs in 2020 at £370, before declining to £342 by the end of the

period. The high case average HH bill peaks at £491 in 2049.



Figure 4.5

HH Bills - Weighted Average – Baseline, Low and High Scenarios – Real

Source: NERA

The low case HH bills follow an opposite pattern; starting slightly lower than the baseline,

remaining fairly constant over the early years before setting off in a stronger decline from

2020 until the end of the horizon. The difference in bill effects results from the combination

of driver and policy differences. The peak bill for the low case occurs in 2020 at £343. The

end of period bills for the baseline and low drivers cases are £343 and £252 respectively.

The high, low, and baseline cases all remain relatively similar over the AMP6 period

reflecting a degree of consistency of the inputs with the Ofwat DDs. The efficiency rates and

RPEs – two of the most powerful real macroeconomic cost drivers – are held at the same

levels for this period. Any differences to these series can mostly be attributed to assumed

changes in the risk free rates, higher/lower enhancement expenditure levels relative to those

set out in the DDs, or from relatively benign sensitivities over population and property

growth. In concert these divergences result in a relatively narrow and constant bill corridor

over the AMP6 period, which then fans out to reflect the additional uncertainty present

beyond 2020, as can be seen in above (or in context with the historical bills in Figure 4.4).

Given that bills remain roughly constant over the AMP6 period (albeit at slightly different

levels) we present snapshots of the evolution of bills at specific intervals with percentage

change relative to their respective levels in 2020. We display these changes in Table 4.3.

200

250

300

350

400

450

500

5502

01

52

01

62

01

72

01

82

01

92

02

02

02

12

02

22

02

32

02

42

02

52

02

62

02

72

02

82

02

92

03

02

03

12

03

22

03

32

03

42

03

52

03

62

03

72

03

82

03

92

04

02

04

12

04

22

04

32

04

42

04

52

04

62

04

72

04

82

04

9

£ /

HH

pro

pe

rty

Low Baseline High



Table 4.3

Cumulative Effect of the High and Low

Sensitivities on Average HH Bills (Real)

Base Case Low Case High Case

Year Average Bill % Change from 2020

Average Bill % Change from 2020


2020 £370 £344 £414

2030 £364 -1.8% £316 -8.0% £439 5.9%

2049 £343 -7.5% £252 -26.7% £487 17.6%

Source: NERA

Figure 4.6 shows the weighted average nominal household bill levels in the baseline case

compared to the high and low sensitivities respectively. The effect of RPI, and to a lesser

extent CPI, generates a much wider range of bills than that seen in the real bill charts above.79

In the baseline, RPI is assumed to range between 3.1% - 3.5% during the period to 2021, after

which we hold it constant at 3%. In the low and high cases, it ranges from 1.8% - 3.1% and

3.1% - 4.0% in the early years, before remaining constant at 2.5% and 4.5% respectively. In

contrast, in the period to 2021 CPI is assumed to range between 1.8% - 2% in the baseline,

1.6% - 2% in the low case, and 1.8% - 2.3% in the high case. From 2022 onwards CPI is

assumed to remain constant at 2%, 1.7%, and 3% in the baseline, low, and high cases

respectively.

Figure 4.6

HH Bills – Weighted Average – Baseline, Low, and High Cases Based on RPI and CPI

Inflation – Nominal

Source: NERA

79 We note that the actual nominal bills are determined according to RPI inflation, as stated in company licences. The

CPI-based nominal bills are therefore illustrative only.

0

500

1000

1500

2000

2500

20

15

20

16

20

17

20

18

20

19

20

20

20

21

20

22

20

23

20

24

20

25

20

26

20

27

20

28

20

29

20

30

20

31

20

32

20

33

20

34

20

35

20

36

20

37

20

38

20

39

20

40

20

41

20

42

20

43

20

44

20

45

20

46

20

47

20

48

20

49

£ /

HH

pro

pe

rty

Low RPI Baseline RPI High RPILow CPI Baseline CPI High CPI



4.4.2.2. Non-Household Bills

Figure 4.7 displays a comparison of the baseline with the high and low non-household real

bill levels respectively over the course of the modelling horizon. In the high scenario there is

an initial spike coinciding to the EA Scenario 3 WFD costs, following which bills drop

sharply and then modestly increase in bills over time. In the baseline they gradually decline

except for a slight increase coinciding with the start of AMP7. This jump in 2020 is caused

in part by an increase in the risk-free rate to bring it back to its long-term average level,

which feeds into a higher return on capital (see section 4.7).

Figure 4.7

Average NHH Bills – Baseline, Low and High Scenarios – Real

Source: NERA

The cumulative change in non-household bills over the period to 2030, from 2030-2050, and

over the entire horizon are shown in Table 4.4.

Table 4.4

Cumulative Effect of the High Drivers and Low Drivers

Sensitivities on Average NHH Bills

Base Case Low Case High Case

Average Bill

% Change from 2020



2020 £1,817 £1,593 £1,965

2030 £1,657 -8.8% £1,438 -9.7% £2,066 5.1%

2049 £1,523 -16.2% £1,120 -29.7% £2,246 14.3%

Source: NERA

Figure 4.8 shows the nominal non-household bill levels in the baseline case compared to the

high and low sensitivities respectively. As was the case for HH bills, the case-varying effect

1000

1200

1400

1600

1800

2000

2200

2400

20

15

20

16

20

17

20

18

20

19

20

20

20

21

20

22

20

23

20

24

20

25

20

26

20

27

20

28

20

29

20

30

20

31

20

32

20

33

20

34

20

35

20

36

20

37

20

38

20

39

20

40

20

41

20

42

20

43

20

44

20

45

20

46

20

47

20

48

20

49

£ /

NH

H p

rop

ert

y

Low Baseline High



of CPI and RPI on nominal bills creates quite a large range in average NHH bills despite the

much smaller underlying variation in real bills seen in the previous figures. 80

Figure 4.8

Average NHH Bills – Baseline, Low and High Scenarios Based on RPI and CPI

Inflation - Nominal

Source: NERA

4.4.3. Annual Company-Specific Bills and Other Outputs

This section briefly describes the model capabilities in terms of modelling companies at an

individual level. The model is capable of generating company-specific average bills broken

down by wholesale value chain element and retail component for measured HH, unmeasured

HH, weighted average HH, and NHH customer groups. It can produce these outputs for

water-only, sewer-only, or combined services.

Additionally, the model also produces company-specific totex, allowed revenue, and average-

year RCV outputs, many of which are discussed at the industry level in section 4.7.

Figure 4.9 shows the decomposition of industry-wide allowed household revenue over the

modelling horizon by company. The water only companies make up a small share – just

seven per cent – of industry level allowed revenues, while the larger WASCs account for the

lion’s share of revenues; e.g. Thames Water81

, Severn Trent Water, Anglian Water and

United Utilities comprise 54-55 per cent of allowed revenues in each of the intervals between

2015 and 2050.

80 We note that the actual nominal bills are determined according to RPI inflation, as stated in company licences. The

CPI-based nominal bills are therefore illustrative only.

81 We include the Thames Tideway Tunnel whenever we make reference to Thames Water in the model and the report.

0

2000

4000

6000

8000

10000

12000

20

15

20

16

20

17

20

18

20

19

20

20

20

21

20

22

20

23

20

24

20

25

20

26

20

27

20

28

20

29

20

30

20

31

20

32

20

33

20

34

20

35

20

36

20

37

20

38

20

39

20

40

20

41

20

42

20

43

20

44

20

45

20

46

20

47

20

48

20

49

£ /

NH

H p

rop

erty

Low RPI Baseline RPI High RPILow CPI Baseline CPI High CPI



Figure 4.9

Allowed HH Revenue by Region

Source: NERA

Figure 4.10 and Figure 4.11 show the evolution of average household bills in real terms in

2015, 2030 and 2050 by service.

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

2015 2030 2049

Tota

l Allo

we

d R

eve

nu

e (£

m 2

01

2-1

3) All WOCs

Wessex Water

Northumbrian Water

South West Water

Southern Water

Yorkshire Water

Welsh Water

United Utilities

Anglian Water

Severn Trent Water

Thames Water



Figure 4.10

Average Water Bills by Region in 2015, 2030 and 2050 - Real

Source: NERA

0

50

100

150

200

250

2015 2030 2049

£ 2

01

2-1

3 /

Pro

pe

rty

South West WaterWelsh WaterNorthumbrian WaterAnglian WaterThames WaterSouthern WaterSevern Trent WaterYorkshire WaterUnited UtilitiesWessex WaterAffinity WaterBristol WaterCambridgePortsmouth WaterSouth East WaterSutton and East SurreySembcorp Bournemouth WaterDee Valley WaterIndustry Weighted Average



Figure 4.11

Average HH Sewer Bills by Region in 2015, 2030 and 2050 - Real

Source: NERA

Figure 4.12 shows the modelled evolution of baseline average household combined water and

sewerage bills in real terms in 2015, 2030 and 2050 for each of the WASC regions. South

West Water’s average bills are the highest, while Severn Trent Water’s are the lowest. There

is some convergence towards the industry average due to a greater decrease in the bills in

regions which initially have higher-than-average bills, with the exception of Welsh Water

which moves away from the average over time. South West Water has the highest average

HH bills while Severn Trent Water has the lowest. Most company bills are relatively stable,

although there are some modest decreases for Anglian Water and United Utilities and an

increase for Welsh, Thames and Yorkshire Water. Thames Tideway costs are included in the

figures. The decline in bill levels toward the end of the horizon matches the declining

baseline average bill level presented in Figure 4.4. This is the result of all the baseline

assumptions in concert, principally through their effect on sector cost levels.

0

50

100

150

200

250

300

350

2015 2030 2049

£ 2

01

2-1

3 /

Pro

pe

rty

South West Water Welsh Water Northumbrian Water Anglian Water

Thames Water Southern Water Severn Trent Water Yorkshire Water

United Utilities Wessex Water Industry Weighted Average



Figure 4.12

Combined (Water and Sewer) Average HH Bills

Source: NERA

4.5. Policy-Specific Results - using baseline exogenous variables

The following sections show the magnitude of each of the baseline policies. The effects that

these policies have were taken from their respective IA’s. The types of effects described in

the IA were of three types:

One-off: a single effect (typically in the year of implementation);

Productive: an effect that has a constant impact in every year; and

Dynamic: an effect that becomes more and more important over time (in a compounding

fashion).

The policies typically have several effects of different types taking place at the same time.

This can cause the magnitude of the bill effects to reverse (typically after the first year),

diminish, or grow over time.

4.5.1. Retail Competition

We demonstrate the reduction in bills from retail competition policy by displaying the

percentage difference between the model baseline excluding retail competition and the

unadjusted model baseline.82

We adopt this approach to obtain bill effect figures that are

reflective of bill levels that are as accurate as possible, given any synergies between policies,

in contrast to comparing the effects with the other baseline policies switched off. The figures

displayed assume implementation of retail competition in 2017 as specified by the 2014

Water Act.

82 Note that the model baseline includes the effects of retail competition, PR14 regulatory mechanisms, and upstream

competition policies. The driver variables (e.g. GDP, RPI, etc.) are held at their central values.

0.00

100.00

200.00

300.00

400.00

500.00

600.00

2015 2030 2049

£ 2

01

2-1

3 /

Pro

pe

rty

South West Water Welsh Water Northumbrian Water Anglian WaterThames Water Southern Water Severn Trent Water Yorkshire WaterUnited Utilities Wessex Water Industry Weighted Average



The green and blue bars in Figure 4.13 display the real bill reduction of retail competition

over time for all HH and NHH bills respectively. Positive (negative) changes can be

interpreted as percentage increases (decreases) in bills caused by the retail competition policy.

Most of the effect of this policy is the result of efficiency spillovers from NHH competition

onto HH bills. The effects begin in 2017 when retail competition is implemented.

Figure 4.13

Reduction in Average HH and NHH Bills from NHH Retail Competition

Source: NERA

As can be seen in Figure 4.13, the policy reduces average HH and NHH bills by an

increasingly large amount over the course of the modelling horizon. This follows from the

assumptions underlying the IA, which include compounding “dynamic effects”. The

reduction in the final year of the horizon for HHs is 1.1%, which is equivalent £4 per

household. For NHHs, the savings reach 1.4% in 2049, which is equivalent to £21 per NHH

property.

The impact of the policy relative to the baseline bill levels over time and the cumulative

savings for HH and NHH properties are displayed in Table 4.5 and Table 4.6 respectively.

-0.50%

-0.25%

0.00%

0.25%

0.50%

0.75%

1.00%

1.25%

1.50%

20

15

20

16

20

17

20

18

20

19

20

20

20

21

20

22

20

23

20

24

20

25

20

26

20

27

20

28

20

29

20

30

20

31

20

32

20

33

20

34

20

35

20

36

20

37

20

38

20

39

20

40

20

41

20

42

20

43

20

44

20

45

20

46

20

47

20

48

20

49

% B

ill R

edu

ctio

n

HH Bill Reduction (% change) NHH Bill Reduction (% change)



Table 4.5

Effect of Retail Competition on Average HH Bills

Baseline

Change in Bills w/ Retail Competition

Change in Bills w/o Retail Competition

Cumulative Bill Impact from retail

competition

Average Bills % Change from 2015 % Change from 2015 Savings from 2015

2015 £355

2030 £364 2.4% 2.7% £3

2049 £343 -3.5% -2.4% £47

Source: NERA.

Table 4.6

Effect of Retail Competition on Average NHH Bills

Baseline

Change in Bills w/ Retail Competition

Change in Bills w/o Retail Competition

Cumulative Bill Impact from retail

competition


2015 £1,758

2030 £1,657 -5.7% -5.4% £14

2050 £1,523 -13.4% -12.2% £269

Source: NERA

4.5.2. Upstream Reforms

We demonstrate the effects of the upstream reform policy by displaying the percentage

difference between the model baseline excluding upstream reform and the unadjusted model

baseline. 83

Figure 4.14 displays the HH and NHH bill reduction percentages resulting from upstream

reform relative to the baseline scenario. Positive changes can be interpreted as percentage

reductions in bills caused by the upstream reform policy. The savings are principally driven

by ongoing totex efficiencies and an assumed efficiency catch-up from less efficient firms as

a result of the competitive pressures arising in the year of the reforms. The figures displayed

assume implementation of upstream reforms in 2020.





Figure 4.14

Reduction in HH and NHH Bills from Upstream Reforms

Source: NERA

As can be seen in the figure, the policy reduces average HH bills by an increasingly large

amount over the course of the modelling horizon. The reduction in the final year of the

horizon is 3.7%, or £13, per HH and 4%, or £62, per NHH property relative to the baseline.84



Table 4.7

Effect of Upstream Reforms on Average HH Bills

Baseline

Change in Bills w/ Upstream Reform

Change in Bills w/o Upstream Reform

Cumulative Bill Impact from

upstream reform


2015 £355

2030 £364 2.4% 4.0% £33

2049 £343 -3.5% 0.1% £216

Source: NERA

84 Underlying this average bill reduction, in the final year the cost base is reduced by anywhere between zero and 17%

depending on the value chain element and cost component (botex or enhancement). The enhancement effects are

roughly 45% larger than the botex effects.

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

4.5%2

01

52

01

62

01

72

01

82

01

92

02

02

02

12

02

22

02

32

02

42

02

52

02

62

02

72

02

82

02

92

03

02

03

12

03

22

03

32

03

42

03

52

03

62

03

72

03

82

03

92

04

02

04

12

04

22

04

32

04

42

04

52

04

62

04

72

04

82

04

9

% B

ill R

edu

ctio

n




Table 4.8

Effect of Upstream Reforms on Average NHH Bills

Baseline

Change in Bills w/ Upstream Reform

Change in Bills w/o Upstream Reform

Cumulative Bill Impact

fromupstream refrom


2015 £1,758

2030 £1,657 -5.7% -4.2% £166

2049 £1,523 -13.4% -9.9% £1,050

Source: NERA

4.5.3. Regulatory mechanisms

We demonstrate the effects of the regulatory mechanisms corresponding to PR14 incentives

by displaying the percentage difference between the model baseline with PR09 incentives and

the unadjusted model baseline.85

The regulatory mechanism reforms for PR14 affect bills through five main channels which

we grouped together into one aggregate effect. These channels are: the move to Totex,

menu-regulation, water trading incentives, and the separation of the HH and the NHH retail

controls from the wholesale controls. The abstraction incentive mechanism is also

considered by the IA but it does not quantify financial incentives and therefore has no direct

effect on the bill impacts presented here. The IA bill impacts assume the implementation of

retail competition and upstream reform as their counterfactual - so the effects shown are in

addition to those resulting from those policies.

Figure 4.15 displays the real bill effects of the PR14 incentives and regulatory mechanisms

on HH and NHH bills. Positive percentage changes can be interpreted as decreases in bills

caused by the PR14 incentives. The figures displayed compare the effect of moving from

PR09 strength incentives to those from PR14 over the entire modelling horizon.





Figure 4.15

Reduction in HH and NHH Bills due to PR14 Incentives and Regulatory Mechanisms

Source: NERA

The effect of the incentives is to reduce bills at a gradually increasing rate with a modest

jump in 2025. The changes in bill reduction percentages in 2025 is not a result of changes to

the incentives themselves (as these are held constant through the horizon); they result from

the knock on effects of changes to expenditure due to other causes (such as changes in

expenditure across AMPs, policy effects, changes in driver variable levels to reflect long term

averages, etc.).



Table 4.9

Effect of PR14 Incentives and Regulatory Mechanisms on Average HH Bills

Baseline

Change in Bills w/ PR14 Incentives

Change in Bills w/o PR14 Incentives

Cumulative Bill Impact of PR14

Incentives


2015 £355

2030 £364 2.4% 4.0% £68

2049 £343 -3.5% -1.8% £183

Source: NERA

0.0%

0.5%

1.0%

1.5%

2.0%

2015

2016

2017

20

18

2019

20

20

2021

2022

2023

2024

20

25

2026

2027

2028

2029

20

30

2031

20

32

2033

2034

2035

2036

20

37

2038

20

39

2040

2041

2042

2043

20

44

2045

2046

2047

2048

20

49

% B

ill R

edu

ctio

n




Table 4.10

Effect of PR14 Incentives and Regulatory Mechanisms on Average NHH Bills

Baseline

Change in Bills w/ PR14 Incentives

Change in Bills w/o PR14 Incentives

Cumulative Bill Impact of PR14

Incentives


2015 £1,758

2030 £1,657 -5.7% -5.1% £316

2049 £1,523 -13.4% -12.6% £837

Source: NERA

4.6. Non-Baseline Policies and Policy-Based Sensitivities

This section begins by describing the non-baseline policies which we assume to not currently

be factored in to company expectations. These policies include abstraction reform, private

water supply pipe adoption, and additional per capita consumption targeting measures.

In addition, this section also describes some key sensitivities that have been incorporated into

the model through the channel of policy effects. These sensitivities can be interpreted as

exogenous scenarios, as plausible policies, as checks of the input data that were simplest to

perform through the model’s policy channel, or as checks over combinations of policy effects

in unison. These include a greater resilience scenario, checks on the baseline WFD costs, and

a counterfactual scenario sense check that aims to identify the joint effect of implementing

the three baseline policies.

4.6.1. Abstraction Reform

We demonstrate the effects of the abstraction reform policy by displaying the percentage

difference between the unadjusted model baseline and the model baseline with abstraction

reform. 86

Figure 4.16 displays the real bill effects of abstraction reform on HH and NHH bills. Positive

(negative) percentage changes can be interpreted as decreases (increases) in bills caused by

the abstraction reform policy. The figures displayed assume implementation of abstraction

reforms in 2025. The effect of the policy is to increase bills in the first year of the reform but

then reduce them thereafter. The directional effect on the respective HH and NHH customers

is very similar. The economic significance of this reform on bill levels is very minor.





Figure 4.16

Impact on HH and NHH Bills from Abstraction Reform – Relative to the Baseline

Scenario

Source: NERA

As can be seen in the axis, the policy increases average HH and NHH bills by a very small

amount (0.1%) in the year of the reform to cover the implementation costs, followed by an

even smaller (0.04%) and roughly constant cost savings over the remainder of the modelling

horizon. The impact of the policy relative to the baseline bill levels over time and the

cumulative savings for HH and NHH properties are displayed in Table 4.11 and Table 4.12

respectively.

Table 4.11

Effect of Abstraction Reform on Average HH Bills

Baseline

Change in Bills w/o Abstraction Reform

Change in Bills w/ Abstraction Reform

Cumulative Potential Impact


2015 £355

2030 £364 2.4% 2.4% £0

2049 £343 -3.5% -3.5% £3

Source: NERA

-0.12%

-0.10%

-0.08%

-0.06%

-0.04%

-0.02%

0.00%

0.02%

0.04%

0.06%2

01

52

01

62

01

72

01

82

01

92

02

02

02

12

02

22

02

32

02

42

02

52

02

62

02

72

02

82

02

92

03

02

03

12

03

22

03

32

03

42

03

52

03

62

03

72

03

82

03

92

04

02

04

12

04

22

04

32

04

42

04

52

04

62

04

72

04

82

04

9

% B

ill R

edu

ctio

n




Table 4.12

Effect of Abstraction Reform on Average NHH Bills

Baseline

Change in Bills w/o Abstraction Reform

Change in Bills w/ Abstraction Reform



2015 £1,758

2030 £1,657 -5.7% -5.7% £1

2049 £1,523 -13.4% -13.4% £12

Source: NERA

4.6.2. Private Supply Pipe Adoption

We demonstrate the effects of the private supply pipe adoption by displaying the percentage

difference between the unadjusted model baseline and the model baseline with the supply

pipe adoption policy switched on.87

Figure 4.17 displays the real bill effects of supply pipe adoption over time for household bills.

The negative “reduction” in bills can be interpreted as bill percentage increases caused by the

private supply pipe adoption policy. The figures displayed assume that the adoption of

private supply pipes is undertaken in 2020.

Figure 4.17

Private Supply Pipe Adoption Increases Average HH and NHH Bills – Relative to the

Baseline Scenario

Source: NERA

The effect of the policy is to increase bills slightly in the first year with an additional gradual

increase over time. The additional costs are driven by increasing repair and replacement costs

87 Note that the unadjusted model baseline includes the effects of retail competition, PR14 regulatory mechanisms, and

upstream competition policies. The driver variables (e.g. GDP, RPI, etc) are held at their central values.

-0.50%

-0.40%

-0.30%

-0.20%

-0.10%

0.00%

20

15

20

16

20

17

20

18

20

19

20

20

20

21

20

22

20

23

20

24

20

25

20

26

20

27

20

28

20

29

20

30

20

31

20

32

20

33

20

34

20

35

20

36

20

37

20

38

20

39

20

40

20

41

20

42

20

43

20

44

20

45

20

46

20

47

20

48

20

49

% B

ill R

edu

ctio

n




and increased scrutiny costs. The increase in the final year of the horizon is 0.4% per HH and

0.4% per NHH property. The impact of the policy relative to the baseline bill levels over

time and the cumulative bill increases for HH and NHH properties are displayed in Table

4.13 and Table 4.14 respectively.

Table 4.13

Effect of Private Supply Pipe Adoption on Average HH Bills

Baseline

Change in Bills w/o Supply Pipe Adoption

Change in Bills w/ Supply Pipe Adoption


Average Bills % Change from 2015 % Change from 2015

Increases from 2015

2015 £355

2030 £364 2.4% 2.7% £6

2049 £343 -3.5% -3.1% £29

Source: NERA

Table 4.14

Effect of Private Supply Pipe Adoption on Average NHH Bills

Baseline

Change in Bills w/o Supply Pipe Adoption

Change in Bills w/ Supply Pipe Adoption



Increases from 2015

2015 £1,758

2030 £1,657 -5.7% -5.5% £28

2049 £1,523 -13.4% -13.0% £126

Source: NERA

4.6.3. Greater Resilience

We demonstrate the effects of a potential need for greater resilience by displaying the

percentage difference between the model baseline with the greater resilience scenario and the

unadjusted model baseline.88

Greater resilience can validly be considered to be an exogenous

scenario where resilience expenditure is increased without a specific intervention.

Greater resilience is defined as a tripling of the resilience costs for the water and sewerage

services and a 20% increase in the level of target headroom in the base case option.89

The

additional resilience costs are a reflection of the doubling of water supply pipes, installation

of larger sewers to prevent overflows at bottleneck locations, and other such additions that

affect reliability through the network and/or resources but do not necessarily influence the

88 Note that the unadjusted model baseline includes the effects of retail competition, PR14 regulatory mechanisms, and

upstream competition policies. The driver variables (e.g. GDP, RPI, etc) are held at their central values.

89 More specifically, we defined the WASC average proportional expenditure on “resilience” as (Resilience + SEMD) /

(Net Capex + Opex) for water and (Resilience + SEMD + Expenditure to Reduce Flood Risk) / (Net Capex + Opex) for

sewerage. We then uplifted all companies’ actual costs by double this proportion in the base case; making the industry

average resilience spend three times larger in the base case option. For the low case, we double average resilience and

increase target headroom by 10%, and for the high case we quadruple resilience costs and increase target headroom by

40%.



level of output. The increase to the level of target headroom reflects spending on resilience

of the network and/or resources (for example by adding capacity from multiple sources) and

for increasing capacity at existing sites as a form of protection against external risks such as

natural disasters or harsh climatic conditions.

Figure 4.18 displays the bill percentage effects of greater resilience over time for HH and

NHH average bills. Negative changes can be interpreted as percentage increases to bills

caused by the greater resilience policy. The figures displayed assume implementation of the

change in 2020.

Figure 4.18

Greater Resilience Increases Average HH Bills – Real

Source: NERA

The effect of the policy is to increase HH bills significantly in the first year with an additional

gradual increase over time. The economic significance of this reform is considerable, even in

the base case option (which is the case presented in the figures). As a result, this policy has a

large impact on the overall results when all policies are switched on. In particular, the high

greater resilience sensitivity plays a large role in the magnitude of the upper range of the fan

chart of average HH bills displayed Figure 4.4.

As can be seen in Figure 4.18, the policy increases average HH and NHH bills by

approximately 4% initially, and this cost gradually increases to around 8% in the final year of

the horizon. It is also interesting to note that the model projects falling average HH and NHH

bills in real terms for this scenario, even with this costly additional resilience requirement.


savings for HH and NHH properties are displayed in Table 4.15 and Table 4.16.

-9.00%

-8.00%

-7.00%

-6.00%

-5.00%

-4.00%

-3.00%

-2.00%

-1.00%

0.00%

20

15

20

16

20

17

20

18

20

19

20

20

20

21

20

22

20

23

20

24

20

25

20

26

20

27

20

28

20

29

20

30

20

31

20

32

20

33

20

34

20

35

20

36

20

37

20

38

20

39

20

40

20

41

20

42

20

43

20

44

20

45

20

46

20

47

20

48

20

49

% B

ill R

edu

ctio

n




Table 4.15

Cumulative Effect of Greater Resilience on Average HH Bills

Baseline

Change in Bills w/o Greater Resilience

Change in Bills w/ Greater Resilience



Increases from 2015

2015 £355

2030 £364 2.4% 8.3% £170

2049 £343 -3.5% 4.1% £657

Source: NERA

Table 4.16

Cumulative Effect of Greater Resilience on Average NHH Bills

Baseline

Change in Bills w/o Greater Resilience

Change in Bills w/ Greater Resilience



Increases from 2015

2015 £1,758

2030 £1,657 -5.7% 0.0% £844

2049 £1,523 -13.4% -6.0% £3,180 Source: NERA

4.6.4. Cost Efficiency Sensitivity

This section briefly considers a sensitivity on the cost efficiency effect assumptions used in

the model to account for innovation and technological change. This parameter compounds

annually and can therefore have a significant effect on bills, particularly at the later stages of

the horizon. The cost efficiency parameter is applied to opex and capex spending. We have

observed that there is an established precedent for using a 1% efficiency rate reduction – for

example, 1% was the rate assumed in the 2011 Defra retail competition IA.90

91

However,

following discussions with the TSG we note that there is a degree of consensus that a 1%

compounding efficiency rate may not be appropriate over a longer term horizon. In addition,

because Ofwat has already factored in efficiency improvement when arriving at the DDs,

they are also implicitly contained in the DD figures used as inputs in the model.

90 Defra, “Introducing Competition in the Water Sector”, 2011, page 36.

91 We understand that the same rate was assumed for upstream competition and (implicitly) for the PR14 regualtory

mechanisms IA which used retail competition and upstream reform as its counterfactual. The Water Industry

Commission for Scotland also makes this assumption on controllable costs in its SR10 price review, available at:

http://www.scottishwater.co.uk/assets/about%20us/files/strategic%20projections/appendix14costsandefficiency.pdf

http://www.scottishwater.co.uk/assets/about%20us/files/strategic%20projections/appendix14costsandefficiency.pdf



Table 4.17 displays the efficiency rate assumptions that we have assumed for the model. We

make no adjustment during the AMP6 period to be consistent with the DDs by avoiding

double-counting the efficiency savings. In the baseline, we use the 1% efficiency savings

during AMP7 before moving to 0.5% from 2025 onwards. We assume that the high

sensitivity has twice the level of efficiency savings as the baseline. The low case has half as

much during AMP7 and no further savings from 2025. 92

Table 4.17

Assumed Cost Efficiency Rate

Period 2015-2020 2020-2025 2025-2049

Base 0% 1.0% 0.5%

High 0% 2.0% 1.0%

Low 0% 0.5% 0.0%

Source: NERA Assumption based on TSG discussions

Figure 4.19 displays the baseline case with both of the cost efficiency rate sensitivities as well

as a zero efficiency scenario. The fan is approximately symmetric for the high and low

efficiency sensitivities. The low efficiency case has slightly lower bills than the no efficiency

savings scenario, since the effects of the low efficiency sensitivity’s AMP7 savings carry on

throughout the horizon.

Figure 4.19

The Removal of the Cost Efficiency Assumption Increases Average HH Bills – Real

Source: NERA

92 In the model the high and low cases of the cost efficiency incentives are inverted - the high case represents high bills

(and therefore lower efficiency savings). The model’s efficiency rates are also presented as negative values. We do not

present them in this way in the report to avoid confusion.

200

250

300

350

400

450

20

15

20

16

20

17

20

18

20

19

20

20

20

21

20

22

20

23

20

24

20

25

20

26

20

27

20

28

20

29

20

30

20

31

20

32

20

33

20

34

20

35

20

36

20

37

20

38

20

39

20

40

20

41

20

42

20

43

20

44

20

45

20

46

20

47

20

48

20

49

£ /

HH

pro

pe

rty

Baseline w/ high efficiency BaselineBaseline w/ low efficiency Baseline w/ zero efficiency



Given the importance of the cost efficiency assumptions that go into the model we would

advise that more detailed further work be carried out to reach a long term profile for expected

efficiency savings. This could also be particularly valuable as a basis for the (typically 30+

year horizons) that underlie the NPV estimates in most IAs.

4.6.5. EA’s WFD Scenario Cost Sensitivities

This section examines the WFD costs in the baseline as well as the consistency of the input

WFD data from the DDs and company long-term projections compared to the EA’s scenario

3 and scenario 4 estimates of the cost of WFD compliance by river basin. 93

94

We apply the

EA WFD data as a sensitivity (rather than the baseline) as these figures are EA’s preliminary

estimates.95

The baseline WFD figures are those implicitly contained in the DDs and the

long-term company data submissions that were drawn upon to obtain industry cost trends.

The EA’s scenario 3 pertains to all technically feasible WFD measures, while scenario 4

relates to all cost-beneficial measures only.

Figure 4.20 presents the WFD costs incurred in the baseline scenario.96

As can be seen from

the figure, the sewerage costs are much greater than those for water. The costs are front-

loaded, for sewerage in particular, as WFD planning cycles extend to 2027.

93 EA, “Water for Life and Livelihoods: A consultation on the draft update to the river basin management plan - Part 3:

Economic analysis”, 2014

94 We do not examine the other EA WFD scenarios due to a lack of data.

95 The EA WFD figures used in the sensitivities described in this section are based on water industry figures extracted by

the EA from the National Appraisal Summary Spreadsheet prepared for the Draft Impact Assessment of the 2nd round

of River Basin Management Plans. These figures are preliminary estimates that should be viewed as within +/- 30% of

the true costs.

96 These costs incurred are “outputs” – they are slightly different from the baseline WFD inputs.



Figure 4.20

Baseline WFD Costs

Source: Model outputs based on DDs and long term data submission

Figure 4.21 shows the impact of the baseline WFD costs on HH bills. It can be seen that the

costs peak at about £10 per property in the final years of AMP7 before dropping to £4-£7

level for the remainder of the horizon. As visible in Figure 4.20, the cost of WFD

compliance is front-loaded in the first two AMPs which results in larger bill impacts from

2015-24.

Figure 4.21

WFD Cost Impact on HH Bills

Source: NERA – difference in projected bill levels in the baseline with and without the NERA baseline WFD

costs

0

50

100

150

200

250

300

350

400

450

500

2015

2016

2017

2018

2019

2020

20

21

20

22

20

23

20

24

20

25

20

26

20

27

20

28

20

29

20

30

20

31

20

32

20

33

20

34

20

35

20

36

20

37

20

38

20

39

20

40

20

41

20

42

2043

2044

2045

2046

2047

2048

2049

£m (

2012

/13

pri

ces)

WFD modelled costs - Sewerage WFD modelled costs - Water

0.0

2.0

4.0

6.0

8.0

10.0

12.0

20

15

20

16

20

17

20

18

20

19

20

20

20

21

20

22

20

23

20

24

20

25

20

26

20

27

20

28

20

29

20

30

20

31

20

32

20

33

20

34

20

35

20

36

20

37

20

38

20

39

20

40

20

41

20

42

20

43

20

44

20

45

20

46

20

47

20

48

20

49

Ch

an

ge

in B

ills

(£/P

rop

ert

y)

Difference Between "Baseline" and "Baseline"Off (left axis)



The costs were forecast using company long term data submissions, so the estimates

presented may not be particularly accurate. For example, some companies provided constant

AMP6 costs for the entire horizon, therefore implying constant WFD costs.Other companies

may have assumed that no further environmental regulations will be required after the current

WFD cycle concludes.97

We also assume that WFD opex costs are held constant over the

entire horizon.

This sensitivity using the EA figures therefore provides a useful cross-check of whether the

baseline WFD inputs are of the right magnitude. In order to perform the sensitivity, NERA

allocated the river basin cost estimates (provided by the EA) amongst the companies

according to a river basin mapping, which was also provided by the EA. This mapping was

consistent with the EA’s published estimates at the national level (but nothing more granular),

so we examined these sensitivities at the national level only. At the service/value chain levels

used in the Defra model, we allocated all water resource and water quality WFD expenditure

to the water resources and the sewage treatment value chain elements respectively.

The EA figures for scenarios 3 and 4 were estimated at the opex/capex level of granularity.

We applied the capex figures to the model according to the annual profile provided by the

EA.98

For the opex figures, we calculated the annuity value of the total NPV cost and applied

it equally across every year of the horizon. When applying the EA WFD capex figures we

avoid double-counting by:

Netting off from the DD baseline the implied WFD capex expenditure using the

breakdown of costs contained in the company long-term data submissions;

Netting off from the DD baseline a share of opex corresponding to WFD costs, which we

estimate as a proportion from the DD baseline of the implied WFD capex contained in the

company long-term data submissions.99

Figure 4.22 displays the totex projections comparing the baseline to the EA’s scenario 3. The

scenario 3 costs are substantially higher than those in the model baseline. This illustrates the

potential implications of pursing WFD objectives without any provision for less stringent

objectives on the basis of disproportionate expense. The scenario 3 costs peak in 2015 and

2025, when a considerable amount of the capex spending is assumed to be incurred (and was

not spread out in the preliminary EA projections we received). We present the figures as

provided in the EA cost estimates, as interpreted by NERA, although we are aware that this

time profile of the expenditure is not intended to be realistic, particularly for 2015. In terms

of the overall cost profile the scenario 3 costs are higher than those in the baseline (even

without the spikes).

97 Although companies may also have anticipated other equivalent costs in place of those corresponding to the WFD. We

suggest further work on this issue to arrive at more precise estiamtes of the WFD’s impact.

98 The WFD capex includes renewal costs corresponding to the level of WFD costs in the baseline scenario. However, the

difference in capex from the EA WFD cost sensitivities is fed through the model as an input (rather than a driver or

policy shock), such that it does not trigger additional renewal capital maintenance expenditure.

99 For consistency with the opex implied in the EA scenarios, we calculate opex on an annuity basis both when applying

EA cost estimates and when netting off opex costs from company long-term data submissions.



Figure 4.22

Projected Totex by Cost Component – Baseline including NERA WFD Baseline Costs or

Baseline with EA WFD Scenario 3 Costs

Source: NERA analysis of preliminary EA WFD cost estimate profile

Figure 4.23 displays the Totex projections comparing the baseline to the EA’s scenario 4.

This scenario relates to all cost-beneficial WFD measures. The EA scenario 4 totex costs, as

interpreted by NERA, are similar to those implied by the DD/company submissions overall.

The most notable exception occurs in 2015, when they are considerably higher as the result of

significant capex spending in the EA projections for that year. These figures are preliminary

at this stage, and we understand that they are subject to a range of error of +/-30%. For the

remainder of the horizon they are similar to the baseline - reflecting a reasonable degree of

consistency between the DDs/company figures and the EA estimates.

Figure 4.23

Projected Totex by Cost Component – Baseline including NERA WFD Baseline Costs or

Baseline with EA WFD Scenario 4 Costs

Source: NERA analysis of preliminary EA WFD cost estimate profile

4.7. Intermediate Baseline Outputs

This section describes some of the intermediate outputs for the baseline scenarios. These

include allowed revenue by building blocks, RCVs, aggregate supply, aggregate demand and

different constituents of Totex.

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

2031

2032

2033

2034

2035

2036

2037

2038

2039

2040

2041

2042

2043

2044

2045

2046

2047

2048

2049

£m (

2012

/13)

"Baseline" "Baseline w/ EA WFD Sc 3"

0

2,000

4,000

6,000

8,000

10,000

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

2031

2032

2033

2034

2035

2036

2037

2038

2039

2040

2041

2042

2043

2044

2045

2046

2047

2048

2049

£m (

2012

/13)

"Baseline" "Baseline w/ EA WFD Sc 4"



4.7.1. Total Allowed Revenue Building Blocks

This section compares the bill elements in terms of the respective shares of the allowed

revenue building blocks. These include the PAYG element of Totex, the capital depreciation

element, the portion corresponding to the return on capital (WACC multiplied by RCV), the

tax allocation, and the retail element. Figure 4.24 displays each of these elements for the

baseline case.

Figure 4.24

Total Allowed Revenue by Building Blocks – Baseline

Source: NERA

The figure shows that the industry’s total allowed revenue level grows slightly in the first ten

years of the horizon and then remains roughly constant to the end of the horizon. The return

on capital element increases in 2020 as a result of the assumed risk-free rate returning to its

long-term average rate.100

Each of the revenue building blocks remains fairly constant for the

second half of the period.

4.7.2. Average Year RCV

This section considers the changes to average year RCV over the course of the modelling

horizon. Figure 4.25 and Figure 4.26 display the level of depreciation against enhancement

spending and capital maintenance respectively. This gives an indication of the factors

underlying the changes to the RCV over time. The enhancement capex adds to the RCV

while depreciation reduces it. The CM adds to it, but only the share proportional to one

minus the PAYG ratio (which we make clearer in the subsequent figure).

100 During AMP6, we set the risk-free rate according to the figures reported by PWC in a technical appendix to Ofwat’s

business planning expectations document.

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

2031

2032

2033

2034

2035

2036

2037

2038

2039

2040

2041

2042

2043

2044

2045

2046

2047

2048

2049

Ind

ust

ry T

ota

l (£

m)

Tax Retail Depreciation Return on Capital PAYG Element of Totex



Figure 4.25

Depreciation and Enhancement Capex – Baseline Case

Source: NERA

Figure 4.26

Depreciation and Capital Maintenance – Baseline Case

Source: NERA

Figure 4.27 shows the net changes to the RCV over the modelling horizon. The figure

displays the industry’s enhancement expenditure (as shown in Figure 4.25) added to the share

of CM that is added to the RCV. The RCV is growing when this sum is greater than the

depreciation series, which is the case in the first ten years of the horizon. Following that

point, the RCV stabilises in the base case as the increasing levels of CM additions are offset

by a gradual decline in enhancement expenditure.

0

500

1,000

1,500

2,000

2,500

3,000

3,500

2015

2016

20

17

2018

20

19

2020

2021

20

22

2023

20

24

2025

2026

20

27

2028

20

29

2030

20

31

2032

2033

20

34

2035

20

36

2037

2038

20

39

2040

20

41

2042

2043

20

44

2045

20

46

2047

2048

20

49

£m (

2012

/13)

Depreciation Enhancement Capex

0

500

1,000

1,500

2,000

2,500

3,000

3,500

4,000

2015

2016

20

17

20

18

2019

2020

2021

20

22

20

23

2024

2025

2026

20

27

2028

2029

2030

20

31

20

32

2033

2034

2035

20

36

20

37

2038

2039

2040

20

41

20

42

2043

2044

2045

20

46

2047

2048

2049

£m (

201

2/13

)

Depreciation Capital Maintenance



Figure 4.27

Net Changes to the RCV

Source: NERA

Figure 4.28 displays the aggregate year-average RCV for the industry over time. The

baseline assumption is that the initial RCV allocation is based on the unfocussed approach,

where the RCV is allocated proportionally to each value chain’s MEAV. As a result, it can

be seen from the figure that the year-beginning 2015 RCV is heavily weighted toward the

network elements (sewage network and treated water distribution). Over time, the RCVs are

continually depreciated and any costs incurred are allocated to the relevant value chains,

thereby producing a slightly larger share of sewage treatment, sludge treatment, and water

resources within RCV.

Figure 4.28

RCV by Value Chain Element - Baseline Case – Unfocussed Case

Source: NERA

4.7.3. Supply and Demand Volumes

Figure 4.29 displays the aggregate supply and demand picture for the industry. The supply

measure displayed is the volume of water available for use (WAFU). The demand volume is

based on the modelled volume of distribution input (DI). The gap between the two measures

0

500

1000

1500

2000

2500

3000

3500

4000

4500

0

500

1000

1500

2000

2500

3000

3500

4000

4500

20

15

20

16

20

17

20

18

20

19

20

20

20

21

20

22

20

23

20

24

20

25

20

26

20

27

20

28

20

29

20

30

20

31

20

32

20

33

20

34

20

35

20

36

20

37

20

38

20

39

20

40

20

41

20

42

20

43

20

44

20

45

20

46

20

47

20

48

20

49

£m

(2

01

2/1

3)

£m

(2

01

2/1

3)

Depreciation Capital Maintenance Additions to RCV Enhancement

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

2031

2032

2033

2034

2035

2036

2037

2038

2039

2040

2041

2042

2043

2044

2045

2046

2047

2048

2049

£m (

2012

/13)

Water Resources Raw Water Distribution Water Treatment Treated Water DistributionSewerage Network Sewage Treatment Sludge Treatment Sludge Disposal



constitutes the level of headroom. When an individual company cannot meet its target level

of headroom it builds additional capacity. In the aggregate, capacity is roughly constant. The

volume of DI however initially slumps reflecting the large scale metering roll-outs planned

by most companies. After an initial decline that coincides with aggressive metering

programmes, DI gradually climbs to reflect the underlying population and property growth

(eroding any excess headroom for those companies that have it) and it appears that additional

capacity will be required at the end of the modelled horizon.

Figure 4.29

Aggregate Supply and Aggregate Demand - Baseline Case

Source: NERA

4.7.4. Outputs by River Basin

The model is also capable of presenting outputs by river basin area. Table 4.18 displays the

AMP6 bills for each of the eleven river basin areas. Bills for the Humber and Severn river

basins are the lowest while those for South West and Western Wales are the highest. The

mapping of bills from company regions to river basin areas was performed using river basin

data provided by the EA.

12,500

13,000

13,500

14,000

14,500

15,000

15,500

16,000

16,500

17,000

17,500

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

2031

2032

2033

2034

2035

2036

2037

2038

2039

2040

2041

2042

2043

2044

2045

2046

2047

2048

2049

Ml /

Day

Water Available for Use Distribution Input



Table 4.18

AMP6 Average HH Bills by River Basin Area (Real)

River Basin Area 2015 2016 2017 2018 2019 AMP6

average

Anglian RB 377 373 369 368 363 370 South East RB 378 376 377 374 374 376 Thames RB 336 344 344 341 344 342 Humber RB 320 314 315 320 329 319 Northumbria RB 342 352 356 357 357 353 Severn RB 338 335 332 328 330 332 South West RB 443 444 443 435 430 439 Dee RB 368 368 367 365 366 367 North West RB 361 359 360 363 368 362 Western Wales RB 421 420 414 409 408 414 Solway Tweed RB 360 361 363 364 369 363

Source: NERA

4.7.5. Constituents of Totex

4.7.5.1. Baseline Totex by Value Chain and Cost Components

Figure 4.30 sets out the Totex projection by value chain element. The largest component for

water is treated water distribution. For sewerage, the largest value chains elements are

sewage treatment and the sewerage network. The categories that are declining faster than

average have lower ongoing costs allocated to them relative to the level of depreciation

(which is based on the assumed value chain assets lives). Table 4.19 presents the figures

underlying the figure in 2015, 2030, and 2049.

Figure 4.30

Projected Totex by Value Chain Element - Baseline Case

Source: NERA

0

2,000

4,000

6,000

8,000

10,000

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

2031

2032

2033

2034

2035

2036

2037

2038

2039

2040

2041

2042

2043

2044

2045

2046

2047

2048

2049

£m (

2012

/13)

Water Resources Raw Water Distribution Water Treatment Treated Water DistributionSewerage Network Sewage Treatment Sludge Treatment Sludge Disposal



Table 4.19

Baseline Projected Totex Expenditure Figures by Value Chain – Real (£m)

Value Chain 2015 2030 2030 % change

from 2015 2049

2049 % change

from 2015

Water Resources £562 £519 -8% £448 -20%

Raw Water Distribution

£147 £130 -11% £129 -12%

Water Treatment £989 £979 -1% £1,001 1%

Treated Water Distribution

£2,176 £1,947 -11% £1,987 -9%

Sewerage Network

£1,630 £1,390 -15% £1,392 -15%

Sewage Treatment

£1,690 £1,533 -9% £1,671 -1%

Sludge Treatment £507 £462 -9% £504 -1%

Sludge Disposal £153 £104 -32% £97 -37%

Industry Total £7,854 £7,064 -10% £7,228 -8%

Source: NERA

Figure 4.31 sets out the totex projection by cost component. The opex component is roughly

the same size as the combined enhancement and maintenance capex spend. Table 4.20

presents the figures underlying the figure in 2015, 2030, and 2049. The figure shows that the

gradual decline in totex is largely due to a reduction in enhancement capex spending. This

reduction in capex roughly coincides with the 2027 conclusion of the river basin management

plan cycles in the current WFD. At present, no known large scale capex programme is

projected after the WFD, but it is possible that further quality or environmental improvements

will need to be implemented. As a result, the decline in totex should be taken to represent a

starting point from which additional options will be considered.

Integrated Final Report Possible Model Developments


Figure 4.31

Projected Totex by Cost Component - Baseline Case

Source: NERA

Table 4.20

Baseline Projected Totex Expenditure Figures– Real (£m)

Value Chain 2015 2030 2030 %

change from 2015

2049 2049 %

change from 2015

Operating Expenditure

£3,314 £3,213 -3.1% £3,024 -8.8%

Capital Maintenance

£2,662 £2,791 4.8% £3,416 28.3%

Enhancement £1,877 £1,060 -43.5% £789 -58.0%

Industry Total £7,854 £7,064 -10.1% £7,228 -8.0%

Source: NERA

5. Possible Model Developments

This chapter lists some of the upgrade options that may be desirable in the future. The model

contains a wealth of easily accessible information in its current form, but the results it

provides often raise additional questions. These further questions will sometimes require

additional model development which raises the question of answering which developments

constitute a sufficiently productive use of Defra resources.

This section is set out as follows:

Section 5.1: Lists some dataset updates that are likely to become desirable;

Section 5.2: Describes some potential structural upgrades to the model;

Section 5.3: Sets out a range of possible policy option additions or refinements; and

Section 5.4: Displays a non-exhaustive set of possibly useful output additions and further

output analysis tools.

0

2,000

4,000

6,000

8,000

10,000

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

2031

2032

2033

2034

2035

2036

2037

2038

2039

2040

2041

2042

2043

2044

2045

2046

2047

2048

2049

£m

(2

01

2/1

3)

Opex Capital Maintenance Enhancement Capex



5.1. Dataset Upgrades

This section describes some relatively straightforward dataset. These typically involve small

changes to the input sheets and potential refinements of the data processing or pre-processing

(the later relating to processing of data prior to inputting it into the model). Figure 5.1

displays the relevant model areas for these upgrades superimposed onto the model’s technical

structure map.

Figure 5.1

Dataset Upgrades Mapping

Source: NERA

Table 5.1 displays some of the more relevant potential dataset updates or upgrades. These

include updating the dataset following the publication of Ofwat’s Final Determinations,

updating the WFD cost figures that are fed into the model, and developing a more robust

approach to building a long term dataset.



Table 5.1

Potential Model Upgrades: Data Updates

Item Description Resource

Implication

Dataset update following release of the

FDs

Data update and quality assurance following the December 2014 release of company Final Determinations and any subsequent CMA

Determinations

Low

WFD or other specific cost updates

Refinement of WFD or other specific cost data and testing over a more comprehensive range of options

and sensitivities Medium

Constructing a Future WFD scenario

Working with stakeholders to identify key assumptions for constructing a future WFD scenario that could begin after the 2027 ‘full implementation’

date of the current WFD programme

Low/ Medium

Development of a new long term dataset

Incorporate long-range inputs derived from any new sector long-range scenario studies and conduct full

review / update of consistency across all data inputs Medium

Review of long term RfR, RPEs, and Cost Efficiency forecast

assumptions

Perform a thorough review of the compounding parameters that have a large bearing on company expenditure in the long term - including the rate of efficiency assumptions, risk free rate, and real price

effects

Medium

Update retail policy effects

Formally update the retail policy effects using the updated (Oct 2014) retail competition IA101

Low/ Medium

Dataset update matching retail reform

developments

Update system costs, wholesale cost allocations, and retail margin projections, as they become known

Model: Low Data: Medium

Source: NERA

101 We understand that, due to the offsetting nature of the changes in the 2014 update, the bill impacts are likely to be

almost unchanged.



5.2. Structural Upgrades

This section describes some of the structure upgrades that would require changes to the core

of the model. These would likely involve major changes to the model calculation tabs and

potentially some refinements to the user interface and macro scenarios sheets. Figure 5.2

displays the relevant model areas for these upgrades.

Figure 5.2

Structural Upgrades Can be Mapped to Various Areas of the Model

Source: NERA

Table 5.2 displays a set of potential structural model upgrades. These include adding a value

chain element, allowing for companies to split or merge into smaller or larger entities, and

developing confidence intervals using Monte Carlo simulations.



Table 5.2

Potential Model Upgrades: Structural


Implication

Addition of Flood Assets Value Chain

Adding flood asset value chain modelling by converting one of the spare value chain elements

Model: Low Data: Unknown

Allowing De-Integration and/or Merger

Adding the functionality to have company regions break apart into smaller units or be merged

Medium/ High

Company-Specific Policy Effects

Add controls allowing the user to select company-specific policy-impact factors that transform the industry-wide effects into a more tailored effect

across each of the companies

Medium

Options to Impose Constraints into the

Model

Develop model interface options that allow the user to set variables (such as the PAYG ratio or the level

of the RCV) as fixed Low

Align borrowing conditions with Totex

and RCV level

Develop industry-wide or company-specific WACC rates based on existing totex and RCV levels

Medium/ High

Research on focussed vs unfocussed RCV

Determine potential consequences of focussed and unfocussed approaches to splitting the RCV, in

particular regarding impact of upstream competition Medium

Develop links between climate/population and

resilience/capacity

Build in interactions between climate and population growth conditions with resilience costs and network

capacity Medium

Tidying formulas for transparency

Changes to model formula to reduce the existence of #N/A or #Value errors in places where there is no data available (i.e. which are not modelling errors)

and addition of more checks

Low

Economies of Scale: Fixed and Variable

costs switch

Build in an assumption switch which allows users to set the level of sewerage treatment and other costs which are based entirely on fixed unit costs versus those which have decreasing unit costs for higher

levels of treatment due to economies of scale

Medium

Monte Carlo Simulation

Allowing a loop to form and simulate a series of scenarios relating to climate and macroeconomic

conditions (GDP, RPI, WACC, etc.) and report results as distributions

Medium/ High

Source: NERA



5.3. Policy Upgrades

This section describes some of the policy upgrades that would require changes within the

model’s policy impact sheets. In addition, these upgrades would involve a significant amount

of work on pre-processing the new policy effects into inputs that can be fed into the model.

Figure 5.3 displays the relevant model areas for these upgrades.

Figure 5.3

Policy Upgrade Mapping

Source: NERA

Table 5.3 lists various additional policy options with widely ranging resource implications.

These include refinements to the current baseline policies and model upgrades to account for

different types of innovation and potential bulk water trading solutions.



Table 5.3

Potential Model Upgrades: Further Policy Options

Policy Item Description Resource

Implication

HH Retail Competition Add household retail competition, as an option Medium

NHH Retail Competition Add non-household retail competition as an option for all E&W

Low

More Granular Representation of Upstream Competition

Add options representing the impacts of upstream competition on water, sewerage, and sludge individually

Medium

Abstraction Reform Add options for different degrees of trading: “enhanced”, all zones

Medium

Abstraction Reform Review policy impacts using updated estimates as they become available and revisit assumptions underlying all impact categories

Low/ Medium

Levels of Innovation Options for various forms of innovation in detail Low

Representation of Regulatory Mechanisms Individually

Analysing the effects of each regulatory mechanism independently (Totex, AIM, ODIs, SIM,…), including potential RCV penalties

High

Integrate Bulk Water Trading

Make exports and imports decided within the model

Very High

Source: NERA



5.4. Output Upgrades and Output Analysis Tools

This section describes some of the policy upgrades that would require changes within the

model’s output tabs. These upgrades could also potentially consist of standalone excel

documents that model results can automatically be fed into for analysis. Figure 5.4 displays

the relevant model areas for these upgrades.

Figure 5.4

Ouput Upgrade Mapping

Source: NERA

The model’s approach for storing outputs allows the user to produce custom graphical results

using standard Excel tools. Table 5.4 outlines some possible upgrades to the model for a

richer visualisation of results beyond the set currently provided.



Table 5.4

Potential Model Upgrades: Further Outputs


Implication

Detailed Model Architecture

Addition of a Logic Tree schematic showing the interaction of the model’s key elements.

Low

Formula Description Description of key formulas in words for ease of model comprehension and auditing

Medium

Influence Diagrams Schematic that shows the relative impacts of each of the different macroeconomic and driver assumptions

Medium

Measured vs Unmeasured HH bill differential analysis

Analysis companring the size of HH vs NHH bills by service for each company region or river basin.

Low

Effect of bill changes at the value chain level

Expand the bill change outputs by providing charts and tables showing the changes and their effects at the value chain level (at the industry or company level)

Medium

Capital enhancement analysis by VC

Breakdown of capital enhancement expenditure by value chain

Low

Aggregation and comparison of bills

Comparisons of bills by region (i.e. South and east vs north and west averages), all bills comparisons over time (e.g. UM-HH, M-HH, NHH, 2020 vs 2030 average)

Low

Tool for identifying and analysing policy effects

Detailed comparison of differences between bill effects for policy option A and policy option B

Low

Collecting and comparing multiple scenarios

Tool for analysing multiple scenarios simultaneously Medium

Source: NERA

Integrated Final Report Elasticity Effects


Appendix A. Elasticity Effects

Economic theory suggests that customers respond to changes in product prices by adjusting

their final demand for that product. The measure of customer responsiveness to price

changes is termed price elasticity of demand.

Because the price elasticity of demand for water is not zero, an ideal water bill model should

contain a feedback loop between projected price and projected demand. The feedback loop

would take the model projected price, given a set of projected totex, and apply the price

elasticity figure to calculate the change in demand given the change in price. Then, it would

calculate the change in totex given the change in demand, and use the revised totex to

calculate a new price. The process is iterated until the change in the level of demand as a

result of a change in price is within a tolerance limit.

To capture the effects of prices on demand, while keeping the model as simple and flexible as

possible, we will include a forward-only feedback effect (where demand levels at t+1 depend

on prices at t). The forward-only feedback effect will allow the user to test for the effects of

prices changes as a result of metering, for example. This effect will apply to increases and

reductions in prices. This simplification compensates for the omission of an in-period

feedback loop while making the model considerably simpler and more flexible to update.

Each of the elasticities is off in the baseline. This section describes the effects of these price

elasticities that can optionally be activated in the model. There are three elasticity options:

measured HH elasticity with respect to price, NHH elasticity with respect to price, and

elasticity with respect to energy prices. We examine each of these individually in the

following subsections.

5.4.1. Measured HH Elasticity

This section displays the impact of the price elasticity on HHs. This elasticity is a volume

consumption response to the change in price observed the previous year. For example, if

average measured bills increase by 1% from the year 2015 to 2016, then measured HH

consumers reduce their volumetric consumption in 2017 by the magnitude of the elasticity

(defined at 0.14%) from what they otherwise would have been. The lag in the volume

adjustment was specified in order to make the model faster to run and simpler, as no

equilibrium dynamics are required.

Figure A.1 displays the effect of Measured HH price elasticity compared to the baseline on

average HH bills. It can be seen from the axis that the elasticity effects have very little

overall impact. There are a few small jumps coinciding with years where sizable falls or

increases in bills occur. Most notably, there is a bill reduction in 2021 following the increase

in average bills that occurs in 2020 (the 2020 increase occurs for reasons unrelated to the

price elasticity). The measured HH price elasticity has a similarly minor effect on average

NHH bills.



Figure A.1

Reduction of Average HH Bills from HH Price Elasticity - Real

Source: NERA

5.4.2. NHH Elasticity

This section displays the impact of the price elasticity on NHH customers. This elasticity is a

volume consumption response to the change in price observed the previous year. For

example, if average measured bills increase by 1% from the year 2015 to 2016, then NHH

consumers reduce their volumetric consumption in 2017 by the magnitude of the elasticity

(defined at 0.14%) from what they otherwise would have been. As was the case for the

measured HH elasticity, the lag in the volume adjustment was specified in order to make the

model faster to run and simpler.

Figure A.2 displays the effect of the NHH price elasticity on NHHs. It can be seen from the

axis figures that the elasticity effects have very little impact. This is because the effects tend

to cancel out across companies and over time. The NHH elasticity also has no visible impact

on HH bills.

325

330

335

340

345

350

355

360

365

370

375

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

2031

2032

2033

2034

2035

2036

2037

2038

2039

2040

2041

2042

2043

2044

2045

2046

2047

2048

2049

Ave

rage

Bil

ls (£

/Pro

pe

rty)

Chan

ge in

Bil

ls (

£/P

rop

ert

y)

Difference Between "Baseline" and "Baseline w/ HH Elasticity" (left axis) Baseline Average Bills (right axis)



Figure A.2

Reduction of Average NHH Bills from NHH Price Elasticity – Real

Source: NERA

5.4.3. Elasticity with Respect to Energy Prices

The model’s third elasticity relates to HHs reducing their volumetric water consumption as a

result of wanting to cut back on energy costs. NHHs are not subject to this elasticity in the

model. For HHs, the elasticity is a volume consumption response to the change in energy

prices observed the previous year, as measured by a DECC energy index forecast. For

example, if the energy cost index increases by 1% from the year 2015 to 2016, then measured

HH consumers reduce their volumetric consumption in 2017 by the magnitude of the

elasticity (defined at 0.17%) from what they otherwise would have been. As before, the lag

in the volume adjustment was specified in order to make the model faster to run and simpler.

Figure A.3 and Figure A.4 display the effects of HH energy price elasticity on HHs and

NHHs respectively. The volumetric response for households leads to a decrease in water use

and an overall decrease in average HH bills. Contrastingly, NHHs experience slightly higher

average bills because their proportional share of water consumed increases as a result of the

HH volume reduction.

325

330

335

340

345

350

355

360

365

370

375

-0.3

-0.2

-0.1

0.0

0.1

0.2

0.3

0.4

0.5

2015

2016

20

17

2018

2019

20

20

2021

20

22

20

23

2024

20

25

2026

2027

20

28

2029

2030

20

31

2032

20

33

2034

2035

20

36

2037

2038

20

39

2040

20

41

20

42

2043

20

44

2045

2046

20

47

2048

20

49

Ave

rage

Bill

s (£

/Pro

pe

rty)

Re

du

ctio

n in

Bil

ls (£

/Pro

pe

rty)

Difference Between "Baseline w/ NHH Elasticity" and "Baseline" (left axis) "Baseline" Average Bills (right axis)



Figure A.3

Reduction of Average HH Bills from HH Energy Price Elasticity – Real

Source: NERA

Figure A.4

Reduction of Average NHH Bills from HH Energy Price Elasticity – Real

Source: NERA

325

330

335

340

345

350

355

360

365

370

375

0.0

2.0

4.0

6.0

8.0

10.0

12.0

2015

2016

2017

2018

2019

2020

2021

2022

2023

2024

2025

2026

2027

2028

2029

2030

2031

2032

2033

2034

2035

2036

2037

2038

2039

2040

2041

2042

2043

2044

2045

2046

2047

2048

2049

Ave

rage

Bil

ls (£

/Pro

pe

rty)

Chan

ge in

Bil

ls (

£/P

rop

ert

y)

Difference Between "Baseline" and "Baseline w/ Energy Elasticity" (left…Baseline Average Bills (right axis)

1,350

1,400

1,450

1,500

1,550

1,600

1,650

1,700

1,750

1,800

1,850

-200

-180

-160

-140

-120

-100

-80

-60

-40

-20

0

20

20

15

20

16

20

17

20

18

20

19

20

20

20

21

20

22

20

23

20

24

20

25

20

26

20

27

20

28

20

29

20

30

20

31

20

32

20

33

20

34

20

35

20

36

20

37

20

38

20

39

20

40

20

41

20

42

20

43

20

44

20

45

20

46

20

47

20

48

20

49

Ave

rage

Bil

ls (

£/P

rop

ert

y)

Chan

ge in

Bill

s (£

/Pro

per

ty)

Difference Between "Baseline" and "Baseline w/ Energy Elasticity" (left axis) "Baseline" Average Bills (right axis)

Integrated Final Report Specification Issues Agreed with TSG


Appendix B. Specification Issues Agreed with TSG

NERA invited comment on certain unresolved aspects of the proposed modelling structure

during the TSG meeting scheduled for June 19th

. These issues and a brief summary of the

conclusions reached by the TSG are listed below:

Representation of the AMP6 period (2015-2020): during the period to 2020 the

model’s projected bill levels in the baseline case will be those in the BPs/DDs/FDs as

available. When alternative scenarios are modelled we propose that the bills flex

annually, to reflect changes in the drivers. We propose that this be done in the alternative

scenario for the AMP6 years’ bills as well, for modelling simplicity, even though those

bills will be a poor approximation. The alternative modelling approach is that in the

alternative scenario the AMP6 bills be overwritten by the PR14-set bills, perhaps with a

recourse adjustment in 2020 for the outturn differences. We propose not to include these

recourse effects in the model to keep it simple.

− The TSG agreed with our view that the building block approach should be used

throughout the modelled period, including during PR14.

Representation of regulatory mechanisms: in the baseline the model will implicitly

assume that the PR14 regulatory mechanisms continue to apply, annually, throughout the

modelled period unless a policy change having a different strength of regulatory

incentives is switched in by the user. As a result, bills will immediately adjust to reflect

changes in costs caused by (for example) a change in energy prices. This is clearly

approximate but is simple to model and seems fit for the purpose of the model.

− The TSG confirmed that this was a sensible approach.

No explicit representation of entrants: we propose to model a sole notional supplier of

each value chain element in each company-region in each year – the incumbent – with

final bills reflecting the associated costs. The alternative is to explicitly represent the

entrants as well, but this substantially increases the size and complexity of the model.

− The TSG confirmed that modelling entrants was outside the scope of the project.

Representation of price elasticity: we propose not to find an equilibrium supply-

demand position in each scenario in each year. This is to keep the modelling simple. We

intend to investigate whether a forward-only price elasticity effect can be introduced

(where demand levels at t+1 depend on prices at t), without slowing the model too much.

− The TSG agreed with our view but request that we prioritise integrating a forward-

only price elasticity effect.

Representation of upstream competition options: we propose to model the options

from the 2013 IA, with the preferred option in the baseline. However the eventual

implementation here seems very uncertain. Confirmation or alternative suggestions are

sought.

− The TSG confirmed that this was a sensible approach.

Representation of retail competition including the exit possibility: we propose to

model NHH retail competition from 2017 in the baseline, with the ability to switch in

greater or lesser strength effects. However exit from retail is now being discussed.

Confirmation, or suggestion of a varied option to model, is sought.

Integrated Final Report Specification Issues Agreed with TSG


− The TSG requested that we have a policy option for voluntary exit based on the

corresponding ‘optional separation’ option in the latest IA, adjusted to remove the

effects of HH competition.

Providing and confirming long run data, and value-chain data: for some drivers we

believe that water and sewerage companies will have a longer term view of the likely

expenditure requirements than has been published and perhaps than is held by Ofwat or

the EA. Examples of such areas might be capital maintenance, and resilience, and LoS

areas. We also believe that in some cases the companies will have a better idea on how

expenditures should be attributed to different value chain elements. We propose to ask

companies to check our proposed data inputs for such items, and to suggest their own

figures where these are better. We invite discussion on company preparedness to

undertake this exercise, and on any confidentiality conditions that might be necessary for

the exercise.

− Company representative of the TSG thought that the companies would likely be

willing to supply data for the project provided the exercise is not too onerous and

conditional on their other obligations regarding the price review.

Extent of Representation of Interdependencies: many elements of the model are in

reality interdependent to an extent. We propose to model only the clear and major causal

effects. This is to keep the modelling simple and to avoid having so many “automatic

adjustments” that it becomes hard to trace the effect of an input change on an output

variable. We invite comment on this approach; in particular, we invite suggestions as to

the interdependencies that TSG members have found to be important in previous

modelling work of this sort.

− The TSG suggested two additional interdependencies which we will consider

incorporating into the model: (1) Greenness which could be made to reduce demand;

and (2) GDP which could be linked to non-PWS water demand hence the cost at the

abstraction point.

Integrated Final Report Glossary


Appendix C. Glossary

AMP – Asset Management Plan (AMP6 corresponds to 2015-2020)

BP – Business Plan

CoC – Cost of Capital

DD – Draft Determinations

Defra – Department for Environment, Food & Rural Affairs

EA – Environment Agency

FD – Final Determinations

HH – Household

NHH – Non-Household

IA – Impact Assessment

IRE – Infrastructure Renewals Expenditure

LoS – Levels of Service

MNI – Maintenance Non-Infrastructure

Ofwat – The Water Services Regulation Authority

RB – River Basin

RCV – Regulatory Capital Value

RfR – Risk Free Rate

RPE – Real Price Effect

WASC – Water and Sewerage Company

WFD – Water Framework Directive

WOC – Water-only Company

WACC – Weighted Average Cost of Capital

Integrated Final Report


Report qualifications/assumptions and limiting conditions

This report is for the exclusive use of the NERA Economic Consulting client named herein.

This report is not intended for general circulation or publication, nor is it to be reproduced,

quoted or distributed for any purpose without the prior written permission of NERA

Economic Consulting. There are no third party beneficiaries with respect to this report, and

NERA Economic Consulting does not accept any liability to any third party.

Information furnished by others, upon which all or portions of this report are based, is

believed to be reliable but has not been independently verified, unless otherwise expressly

indicated. Public information and industry and statistical data are from sources we deem to be

reliable; however, we make no representation as to the accuracy or completeness of such

information. The findings contained in this report may contain predictions based on current

data and historical trends. Any such predictions are subject to inherent risks and uncertainties.

NERA Economic Consulting accepts no responsibility for actual results or future events.

The opinions expressed in this report are valid only for the purpose stated herein and as of the

date of this report. No obligation is assumed to revise this report to reflect changes, events or

conditions, which occur subsequent to the date hereof.

All decisions in connection with the implementation or use of advice or recommendations

contained in this report are the sole responsibility of the client. This report does not represent

investment advice nor does it provide an opinion regarding the fairness of any transaction to

any and all parties.

NERA UK Limited, registered in England and Wales, No 3974527 Registered Office: 15 Stratford Place, London W1C 1BE


15 Stratford Place

London W1C 1BE

United Kingdom

Tel: 44 20 7659 8500 Fax: 44 20 7659 8501

www.nera.com

developing a water bills projection model: integrated...

Documents