Best Practice Demand and Revenue Modelling: Five Things you Need to do to Understand Your Revenue - Transurban Group Westlink M7

Best Practice Demand and Revenue Modelling: A Five Part Case Study on Understanding Revenue - TRANSURBAN GROUP WESTLINK M7
© Airline Intelligence & Research 2017 | www.airintelligence.com.au | ABN 56 614 208 810 2
ABOUT AIRLINE INTELLIGENCE & RESEARCH
Airline Intelligence & Research generates business intelligence from statistical analysis. We serve businesses and
governments worldwide solving complex problems with our deep expertise in the aviation, tourism, transport and
investment sectors.
We use leading-edge statistical methods and demand modelling to generate business insight. From forecasting
passenger or traffic volumes and revenue, to estimating the correlation between the Australian dollar and the oil price,
or presenting on the state of play in the macro economy and it’s impact on business, Airline Intelligence & Research has
delivered value to clients that changes the way they see their own business.
Dr Tony Webber is the CEO and founder of Airline Intelligence & Research and has over 30 years’ of complex
macroeconomic modelling experience for significant business decision making including serving as a Chief Economist
of one of Australia’s flagship airline carriers. See more of our analytics work by visiting www.airintelligence.com.au or
emailing tony.webber@airintelligence.com.au

INTRODUCTION
Are you in the transport, tourism or travel sectors and have attempted to model and forecast demand and revenue with
limited success? In this case study Airline Intelligence & Research (AIR) illustrate five things you need to do to truly
understand your revenue through implementing best practice demand and revenue modelling and forecasting.
Our Principal, Dr Tony Webber has consistently found that by applying best practice analytical methods clients have
been able to improve shareholder value. Why? Because the intelligence obtained from best practice demand and
revenue modelling has the capacity to drive economic profit - because the business is in a better position to pull short,
medium and long run operational levers in a way that maximises profit.
Further, demand modelling drives more pro table decisions by allowing you to better estimate the optimal price for given
quantities. When combined with entity-wide sensitivity analysis, you can then get a better handle on the likelihood of
a particular revenue outcome. Such information can drive sales forecasts, budgets and ultimately, strategy. A more
scientific approach to risk management can also be supported by sophisticated demand and revenue modelling.
In this case study we have chosen Transurban Group’s Westlink M7 as a tool for learning; our strategy is to get you
engaged with best practice analytical techniques that can support better business decisions – once you understand
that, there should be nothing stopping you from implementing these techniques across your enterprise.
Given this, we regard implementation of best practice capability in data analysis as critical to gaining an advantage over
competitors. And to that end, having staff engaged with the process of forecasting and managers who value this critical
skill is a first step to realising profit potential and strategic advantages. We would welcome discussion of the techniques
in this paper and you can get in touch with us through tony.webber@airintelligence.com.au or on LinkedIn.
The case study is in five parts :
1. Identification and Effects of Key Macroeconomic Exposures;
2. Building a Statistical Model to Understand and Forecast Revenue;
3. Estimating the Sensitivity of Revenue to Key Macroeconomic Variables;
4. Estimating the Optimal Toll Rate that Maximises Revenue; and
5. Forecasting Revenue over the next 12-months and Conducting Sensitivity Analysis.
The Westlink M7 motorway opened on 16 December 2005. It is a tolled motorway that connects the M5 Southwestern,
the M4 Western and the M2 Hills motorways. It is located in New South Wales, Australia and is 50 per cent owned by
the Transurban Group (ASX:TCL).

1. IDENTIFICATION OF KEY
MACROECONOMIC VARIABLES
Getting acquainted with the data
One of the first steps in understanding what drives demand is looking at the data; this means plotting out a time series
of volume or quantities over time. Our purpose in doing this is to look for patterns in the data over time – for example,
trends, seasonality or significant structural changes. Once, you’ve done this begin to think about the macro and micro
factors that could be driving significant changes over time.
When we did this for the Westlink M7 here’s what we found.
Figure 1 highlights the seasonality in the quarterly profile of trips on the M7 motorway, with troughs in the March quarter
and very mild peaks in the September and December quarters. The seasonality is expected to track the school holiday
periods. See Red Arrow (B).
We can also see in Figure 1 how heavily M7 trips were affected by the Global Financial Crisis. As indicated by Red Arrow
(A), M7 trips fell from 12.0 million in the March 2009 quarter to 11.4 million during the March 2010 quarter, which is
a reduction of 5.0% YoY. The GFC caused M7 trips to fall 9.4 percentage points below the trend rate of growth, which
represents a fall of two standard deviations below the mean or a 1 in 20 event. The lesson here is that M7 trips, and
thus Transurban’s demand and revenue stream, are extremely sensitive to the NSW jobs market and economy – we will
provide more detail around this point later.
The blue dotted line in Figure 1 represents the trend increase in the number of trips on the motorway. By using the slope
of this trend equation, we estimate the compound annual growth rate of M7 motorway trips is 4.4%. This is interpreted
as the trend rate of M7 trips growth, representing around 1.5 times the average growth rate in NSW Gross State Product.


Best Practice Demand and Revenue Modelling: Five Things you Need to do to Understand Your Revenue Transurban Group
Westlink M7
4

Airline Intelligence & Research
Transport | Tourism | Travel
Figure 1: Total Trips on the Westlink M7 Motorway

Source: Transurban | Airline Intelligence & Research © 2017
Understanding Macro and Micro Drivers of Trips
Once you have a handle on what the data ‘looks like’ Airline Intelligence & Research recommends that the next step is to use this
information to determine the macroeconomic and microeconomic forces that potentially explain the changes in the total number
trips.
In thinking about the M7, there are a number of both macroeconomic and microeconomic forces that are expected to explain
changes in the total number of M7 trips over time. These forces will depend on the purpose of travel, of which there are three:
— Monday to Friday passenger commuter traffic;
— Truck and other large vehicle traffic; and
— Leisure traffic.
If you can understand the customer funnel then you are in a better position to appreciate the purpose of travel; it is only a short
leap from there to identifying the macro and micro forces driving these traffic types (in the case of the M7).
B. March
Quarters
Figure 1: Total Trips on the Westlink M7 Motorway
Source: © Transurban | Airline Intelligence & Research 2017
Understanding Macro and Micro Drivers of Trips
Once you have a visual on the data Airline Intelligence & Research recommends that the next step is to use this
information to determine the macroeconomic and microeconomic forces that potentially explain the changes in the total
number trips.
In thinking about the M7, there are a number of both macroeconomic and microeconomic forces that are expected to
explain changes in the total number of M7 trips over time. These forces will depend on the purpose of travel, of which
there are three:
• Monday to Friday passenger commuter traffic;
• Truck and other large vehicle traffic; and
• Leisure traffic.
If you can understand the customer funnel then you are in a better position to appreciate the purpose of travel; it is
only a short leap from there to identifying the macro and micro forces driving these traffic types (in the case of the M7).

In the case of commuter traffic, this is primarily driven by people who need
to use the road to travel to work, with passengers travelling from the West to
work in the CBD, and travellers living in the city and surrounds commuting
to western Sydney for work or to do business.
The truck and other large vehicle traffic is generated by freight transport
between the East and the West of the city, as well as the North and South
and points in between. It is also generated by the need for B2B and B2C
service traffic (such as plumbers, builders, electricians, construction
workers and landscapers).
Finally, the remaining traffic will include travellers who wish to use the road
to enjoy leisure at their destination, including local leisure traffic (travel
within the Sydney basin by locals), journeys involving travel by residents who live in the greater Sydney basin who wish
to travel outside of Sydney and the State (outbound leisure travel), and journeys involving travel by non-residents who
wish to travel to or through Sydney (inbound travel). All of these leisure trips would include for example, travel to attend
sporting events.
Given these purposes, the dominant macroeconomic and microeconomic forces that will drive these three traffic types
are set out in Figure 3.

Westlink M7

to use the road to travel to work, with passengers travelling from the West
to work in the BD, and travellers living in the city and surrounds
commuting to western Sydney for work or to do business.
between the ast and the West of the city, as well as the North and South
and points in between. It is also generated by the need for B2B and B2
Finally, the remaining traffic will include travellers who wish to use the
road to en oy leisure at their destination, including local leisure traffic
(travel within the Sydney basin by locals), ourneys involving travel by
residents who live in the greater Sydney basin who wish to travel outside
of Sydney and the State (outbound leisure travel), and ourneys involving travel by non residents who wish to travel to or through
Sydney (inbound travel). All of these leisure trips would include for example, travel to attend sporting events.
Given these purposes, the dominant macroeconomic and microeconomic forces that will drive these three traffic types are set out in
Figure .
Figure : Dominant Macroeconomic and Microeconomic Forces that Drive Traffic Types Westlink M7

Source: Airline Intelligence & Research © 2017

ur ses M ra e

ure ur ses M ra e

Figure 1: Purposes of M7 Travel 1
Figure 3: Dominant Macroeconomic and Microeconomic Forces that Drive Traffic Types – Westlink M7
MACROECONOMIC

to use the road to travel to work, with passengers travelling from the West
to work in the BD, and travellers living in the city and surrounds
commuting to western Sydney for work or to do business.
between the ast and the West of the city, as well as the North and South
and points in between. It is also generated by the need for B2B and B2
Finally, the remaining traffic will include travellers who wish to use the
road to en oy leisure at their destination, including local leisure traffic
(travel within the Sydney basin by locals), ourneys involving travel by
residents who live in the greater Sydney basin who wish to travel outside
of Sydney and the State (outbound leisure travel), and ourneys involving travel by n
Sydney (inbound travel). All of these leisure trips would include for example, travel to
Given these purposes, the dominant macroeconomic and microeconomic forces that w
Figure .
Figure : Dominant Macroeconomic and Microeconomic Forces tha
ure

• Sydney and NSW
economic growth and
wealth
• Sydney and NSW
employment and labour
hours
• Sydney and NSW
population growth
• Weather events
MICROECONOMIC

he case of commuter traffic, this is primarily driven by people who need
use the road to travel to work, with passengers travelling from the West
work in the BD, and travellers living in the city and surrounds
mmuting to western Sydney for work or to do business.
e truck and other large vehicle traffic is generated by freight transport
ween the ast and the West of the city, as well as the North and South
d points in between. It is also generated by the need for B2B and B2
vice traffic (such as plumbers, builders, electricians, construction
rkers and landscapers).
ally, the remaining traffic will include travellers who wish to use the
d to en oy leisure at their destination, including local leisure traffic
avel within the Sydney basin by locals), ourneys involving travel by
idents who live in the greater Sydney basin who wish to travel outside
Sydney and the State (outbound leisure travel), and ourneys involving travel by non residents who
ney (inbound travel). All of these leisure trips would include for example, travel to attend sporting ev
en these purposes, the dominant macroeconomic and microeconomic forces that will drive these thr
ure .
Figure : Dominant Macroeconomic and Microeconomic Forces that Drive Traffic T
ur
ure ur ses M r

• Fuel cost
• Toll price
• Opportunity costs of travel time and
congestion
• Total travel time using the M7
• Availability of alternative paths of
road travel
• Total travel time and explicit costs
of road travel alternatives; Cost
of alternative, non-road modes of
transport that don’t use the road
• Cost of public transport or passenger aggregators (such as buses)
• Cost of excess speed fines when using the motorway (which is
a function of the probability that the car’s speed is above the
100km/hr limit on the M7, or the limit indicated on variable speed
signposts, and the size of the fine)
• Cost of injury or death caused by motor vehicle accidents on the
M7 (which is a function of the likelihood of an accident and the
expected cost if an accident were to occur)
• Car pooling
• Vehicle depreciation costs
Source: © Airline Intelligence & Research 2017

Understanding the macro and micro drivers of trips allows you to begin to estimate the model using multivariate
regression. Essentially regression is just another way of building a model using the data you have available in a way that
allows you to explain changes in the data.
When Airline Intelligence & Research took the Transurban data on quarterly trips, the revenue generated by those trips,
and key macroeconomic and microeconomic data, we estimated the following multivariate regression model between
September 2005 and December 2016.
2. BUILDING A STATISTICAL MODEL TO
UNDERSTAND AND FORECAST REVENUE
The estimated regression tells us that 94.8% of the variation in M7 trips between September 2005 and December 2016
can be explained by the regression on contemporaneous and 3 quarter lags of NSW labour hours, contemporaneous
Sydney fuel prices, the average toll price and seasonality in March. The ability of the multivariate regression equation to
explain the number of trips on Westlink M7 can be seen in Figure 5 by the close proximity of the blue lines.
Figure 4: Estimated Regression for Variation in M7 Trips
Tripst = 39.9109 – 0.5929 × March + 0.0000257 × NSW Labour Hourst +
0.0000136 × NSW Labour Hourst-3 – 0.02108 × Fuelt – 0.57428 × Tollt
R2
= 94.8%
Where:
• Tripst
= the number of trips in quarter t on the M7 Westlink motorway
• March is a dummy variable that takes on the value 1 when the quarter in question is March and 0 when
the quarter in question is June, September or December
• NSW Labour Hourst
is the labour hours worked for the NSW economy in quarter t measured in
thousands of hours worked
• NSW Labour Hourst-3
is the labour hours worked for the NSW economy 3 quarters prior to t measured in
thousands of hours worked
• Fuelt
is Sydney metropolitan area average unleaded petrol prices in quarter t measured in cents per litre
• Tollt
is Transurban M7 revenue divided by the number of toll road trips in quarter t

1. IDENTIFICATION OF KEY
MACROECONOMIC VARIABLES
Figure 5: Ability of the Regression Equation to Predict Trips on the Westlink M7

st Practice Demand and Revenue Modelling: Five Things you Need to do to Understand Your Revenue Transurban Group
Figure : Ability of the Regression uation to Predict Trips on the Westlink M7


nce we have an estimated regression, we can use it to:
Identify the controllable and uncontrollable exposures of Transurban as they relate to the M7 and estimate the sensitivity of
revenue on the route to key uncontrollable or macroeconomic variables;
Determine the average toll levels that maximise revenue and the amount of revenue left on the table by not setting tolls at
optimal levels; and
arry out M7 revenue forecasts for 2017.
Once we have an estimated regression, we can use it to:
• Identify the controllable and uncontrollable exposures of Transurban as they relate to the M7 and estimate the
sensitivity of revenue on the route to key uncontrollable or macroeconomic variables;
• Determine the average toll levels that maximise revenue and the amount of revenue left on the table by not setting
tolls at optimal levels; and
• Carry out M7 revenue forecasts for 2017.

Before we can estimate the sensitivity of revenue to key macroeconomic variables, we recommend spending further
time understanding the macroeconomic variables that are controllable or uncontrollable. It allows a deeper dive into
the exposures of the revenue to movements in underlying drivers of the entity. When we did this for Transurban Group’s
Westlink M7, this is what we found.
Controllable and Uncontrollable Revenue Exposures
The statistical modelling indicates that Transurban’s revenue is exposed to the following macroeconomic variables over
which it has little control:
• The NSW economy, proxied by NSW labour hours worked; and
• The price of unleaded petrol in Sydney.
We note that the price of unleaded petrol in Sydney is a function of three further uncontrollable macroeconomic variables:
• The price of crude oil;
• Gasoline refining margins; and
• The AUD/USD exchange rate.
In total, Transurban has five macroeconomic exposures to its revenue over which it has little control. These are not,
however, standalone exposures and cannot be treated as such by risk management at Transurban. The price of crude oil
shares a strong positive correlation with the AUD/USD exchange rate, and even the performance of the NSW economy
may be linked to the price of crude oil and gasoline refining margins. These macroeconomic interrelationships will need
to be carefully considered, quantified and implemented as part of risk management.
The only controllable variable that influences M7 revenue in the case of the statistical modeling, is the Average Toll. We
call this a ‘short horizon’ controllable, on which we will elaborate in the following section.
Over a longer horizon there are ‘infrastructure’ controllables that will also affect trips and revenue. These include: road
maintenance, signage improvement, development of more automated toll payment systems, extending the road, and
building more exit and entry points. These infrastructure controllables are not considered in the following analysis,
although we recognise their importance.
Once you have a handle on the controllable and uncontrollable revenue exposures you’re then in a better position to
conduct sensitivity analysis.
3. ESTIMATING THE SENSITIVITY OF REVENUE
TO KEY MACROECONOMIC VARIABLES

Revenue Sensitivities – Macro Variables
In this section, we analyse the impact of 1% increase in each of the macroeconomic exposures (labour hours, and fuel)
on the number of M7 trips and revenue at current average toll levels.
Revenue Sensitivity to Labour Hours
Figure 6 below, presents the elasticity of M7 trips with respect to a change in NSW labour hours worked.
Figure 6: Elasticity of M7 Trips to a Change in NSW Labour Hours

est Practice Demand and Revenue Modelling: Five Things you Need to do to Understand Your Revenue Transurban Group
estlink M7
evenue Sensitivities Macro ariables
this section, we analyse the impact of 1 increase in each of the macroeconomic exposures (labour hours, and fuel) on the
umber of M7 trips and revenue at current average toll levels.
evenue Sensitivity to Labour ours
gure below, presents the elasticity of M7 trips with respect to a change in NSW labour hours worked.
Figure : lasticity of M7 Trips to a hange in NSW Labour ours

gure indicates that a 1 increase in NSW labour hours worked in uarter t leads to a 1. increase in M7 trips in uarter t and a
4 increase three uarters later, with a total increase in trips of .7 . Westlink M7 trips demand and revenue on the M7 are
herefore highly sensitive and exposed to the strength of the NSW labour market.
rmed with this information, the business intelligence that you are able to apply for Transurban (in this case) is that at current levels
f trips and labour hours, a 1 increase in labour hours will lead to an additional 2 trips on the M7 per uarter, which at an
verage toll per trip of . implies extra revenue of .7m per uarter.

Figure 6 indicates that a 1% increase in NSW labour hours worked in quarter t leads to a 1.3% increase in M7 trips in
quarter t and a 2.4% increase three quarters later, with a total increase in trips of 3.7%. Westlink M7 trips demand and
revenue on the M7 are therefore highly sensitive and exposed to the strength of the NSW labour market.
Armed with this information, the business intelligence that you are able to apply for Transurban (in this case) is that at
current levels of trips and labour hours, a 1% increase in labour hours will lead to an additional 626K trips on the M7
per quarter, which at an average toll per trip of $5.9 implies extra revenue of $3.7m per quarter.
At current levels of trips and
labour hours
At 1% increase in labour
hours will lead to an extra
626K trips
At an average toll of $5.90, this
implies extra revenue of $3.7m
per qtr

The high levels of sensitivity of Transurban to economic conditions would suggest that the NSW economy, and in
particular the state of the NSW labour market is a significant exposure to Transurban.
Revenue Sensitivity to Fuel Price
Figure 7 presents a time series of the elasticity of Transurban M7 Westlink trips to a change in the price of unleaded fuel
in metropolitan Sydney (blue line). Since the fuel price comprises the crude oil price and the AUD/USD exchange rate,
this elasticity effectively measures the exposure of Transurban Westlink M7 to these macroeconomic variables.
Figure 7: Elasticity of M7 Trips to a Change in Fuel and Average Toll Prices

Westlink M7
10

The high levels of sensitivity of Transurban to economic conditions would suggest that the NSW economy, and in particular the state
of the NSW labour market is a significant exposure to Transurban.
Revenue Sensitivity to Fuel Price
Figure 7 presents a time series of the elasticity of Transurban M7 Westlink trips to a change in the price of unleaded fuel in
metropolitan Sydney (blue line). Since the fuel price comprises the crude oil price and the AUD USD exchange rate, this elasticity
effectively measures the exposure of Transurban Westlink M7 to these macroeconomic variables.
Figure 7: lasticity of M7 Trips to a hange in Fuel and Average Toll Prices


Figure 7 indicates that a 10 increase in Sydney unleaded petrol prices will lead to a 1. reduction in M7 trips at current trip levels
and fuel prices. Alternatively, this means that a 1.2 cents per litre increase in unleaded fuel prices from current levels in Sydney wil
reduce the number of trips on the M7 by 2 4 per uarter, which at current average toll levels translates into a loss of revenue of
A 1. m per uarter.
The own price elasticity of demand is also reported in Figure 7, that being the sensitivity of M7 trips to a change in the average tol
(blue line). This is currently estimated to be 0.2, which implies a 10 increase in the average toll, which is around cents at
current average toll levels, will lead to a 2 or reduction in the number of M7 trips.
Figure 7 indicates that a 10% increase in Sydney unleaded petrol prices will lead to a 1.5% reduction in M7 trips at
current trip levels and fuel prices. Alternatively, this means that a 1.2 cents per litre increase in unleaded fuel prices
from current levels in Sydney will reduce the number of trips on the M7 by 254K per quarter, which at current average
toll levels translates into a loss of revenue of A$1.5m per quarter.
The own price elasticity of demand is also reported in Figure 7, that being the sensitivity of M7 trips to a change in the
average toll (blue line). This is currently estimated to be -0.2, which implies a 10% increase in the average toll, which is
around 59 cents at current average toll levels, will lead to a 2% or 339K reduction in the number of M7 trips.

The revenue impact of a change in the average toll must balance two forces:
• The beneficial impact of the price increase; with
• The negative impact of the reduction in the number of trips made.
Analytically, the impact on revenue of a change in the average toll can be described by the following equation:
Change M7 Revenue = Revenue Before Toll Change × (1 + Own Elasticity) × % Change Avr Toll
The net impact of a 10% increase in the average toll is that revenue increases by around 8%, which represents an
increase of around A$8m per quarter – more on this in the next section.
Given you now have an in-depth understanding of how the revenue is likely to be affected by movements in underlying
drivers of demand we can begin to work on more advanced aspects of demand and revenue modeling.

By estimating the number of trips as a function of the average toll, we are able to compute M7 revenue as a function
of the average toll at current levels of NSW labour hours and Sydney unleaded fuel prices. At current average toll levels
on the M7 of around $5.90,
the statistical model estimates toll revenue per quarter of around A$100m. Refer to Figure 8 below, which presents the
Transurban M7 toll revenue functions for the March quarter and the non-March quarters.
Figure 8: Average Toll Levels and M7 Revenue – Revenue Functions
4. ESTIMATING THE REVENUE
MAXIMISING TOLL
Westlink M7
4. stimating the Revenue Maximising Toll
By estimating the number of trips as a function of the average toll, we are able to compute M7 revenue as a function of the aver
toll at current levels of NSW labour hours and Sydney unleaded fuel prices. At current average toll levels on the M7 of around
the statistical model estimates toll revenue per uarter of around A 100m. Refer to Figure below, which presents the Transur
M7 toll revenue functions for the March uarter and the non March uarters.
Figure : Average Toll Levels and M7 Revenue Revenue Functions


n analytical terms, we can write these uarterly revenue functions as follows:
RevenueMar 1 1 . 7 × Average TollMar 1 0. 742 × Average 2
16MarToll −
Revenue un Dec 1 20.1 0 × Average TollMar 1 0. 742 × Average 2
16DecJun-Toll −
In analytical terms, we can write these quarterly revenue functions as follows:
• RevenueMar-16
= 19.597 × Average TollMar-16
– 0.57428 × Average Toll2
Mar-16
• RevenueJun-Dec-16
= 20.190 × Average TollMar-16
– 0.57428 × Average Toll2
Jun-Dec 16

Note that there is one revenue function for the March quarter (the top function) and another function for the remaining
quarters (June, September and March) by virtue of the fact that March is a weaker seasonal quarter for travel on the
M7 compared to other quarters.
On the basis of these revenue functions, and the fact that current average toll levels sit well to the left of the revenue
peaks, we estimate that Transurban could make an additional A$70m to $80m per quarter if they raised their average
tolls to $17.1 in the March quarter and $A17.6 in other quarters. These are the revenue maximising average toll levels.
This is not to say that Transurban would or could or should raise prices to these levels; there are other concerns that limit
the power of Transurban to act in this circumstance such as competition and market power issues, price restrictions that
may be imposed by the NSW Government, or simply ethical or moral concerns of management at the time.
The value of this kind of business analytics capability is found in the numbers – the value is the amount of money that
could be made if the optimal toll were to be imposed given current market circumstances. If you know this, you’re in a
better position as an organisation to make strategic choices about when and how you might change prices, for example.

In this final section we wish to generate revenue forecasts of Westlink M7 for calendar 2017. To do this we use the
multivariate regression model together with forecasts of NSW labour hours, the average toll and Sydney unleaded petrol
prices. We employ five sets of scenarios to generate a distribution of revenue forecast outcomes and conduct best
practice sensitivity analysis.
To provide insight into the assumptions that are behind the five scenarios, we present the current and average growth
rates of the two key macroeconomic variables that are used in the revenue forecasts - NSW labour hours and Sydney
unleaded petrol prices – refer to Table 1 below.
Table 1: Average and Current Growth Rates of Key Driver Variables and their Standard Deviations
5. REVENUE FORECASTS FOR 12-MONTHS
AND SENSITIVITY ANALYSIS
NSW Labour Hours Sydney Unleaded Prices
Current Growth Rate 0.4% -4.0%
Trend Growth Rate YoY 1.4% 1.8%
Standard Deviation 1.8% 11.9%
Table 1 indicates that on average, NSW labour hours grow at around 1.4% per annum. Currently the NSW economy
is growing at well below this pace, at 0.4% YoY. In fact, the NSW economy has grown at this below trend pace for the
past two quarters (September and December). Although the current growth rate is much slower than trend, it is still not
at recession levels which would involve labour hours falling by up to 2% as was seen during the Global Financial Crisis
(which was a two standard deviation event in an unfavourable direction).
Sydney unleaded petrol prices are significantly more volatile than NSW economic growth, which is fortunate for
Transurban as its M7 product is significantly more exposed to the NSW economy than Sydney petrol prices. The average
growth rate over the past decade in Sydney unleaded petrol prices is just 1.8%, however the price can grow or fall at
double-digit levels over the space of a year with reasonable probability. Over the past year the unleaded petrol price
has fallen considerably by virtue of falling world oil prices, however the last two quarters has seen the spot price of oil
increase which is likely to result in higher unleaded petrol prices in calendar 2017.
This information is used as input into developing five scenarios for Transurban forecasts of M7 revenue. These are
presented in Table 2 below.

Table 2: Five Scenarios, Revenue Forecasts and Likelihoods of Revenue Outcome
Assumptions
Revenue
Forecast CY17
Likelihood
Scenario 1
Stagnant Economy and Fuel
0% Growth in NSW Labour
0% Growth in Fuel Prices
A$389m 10%
Scenario 2
Weak Economy, Stagnant
Fuel
0.4% Growth in NSW Labour
A$394m 30%
Scenario 3
Supply-Side Fuel Price Shock
0.4% Growth in NSW Labour
A$390m 25%
Scenario 4
Demand-Side Fuel Price
Shock
A$408m 15%
Scenario 5
Strong Growth Stagnant Fuel
A$411m 10%
Scenario 6
Recession
-0.4% A$378M 10%
Expected Revenue $395m
We assume across all five scenarios that the average toll price is fixed at current levels of $5.90. This may not be a
realistic assumption, however without further information (we are undertaking this work independently of Transurban)
this is the best assumption to apply.
Figure 9 presents the range of revenue forecast outcomes for the Westlink M7 over calendar 2017, noting that actual
revenue for calendar 2016 was A$371m. Revenue is forecast to lie between A$378m and A$411m with various
probabilities. At the probabilities presented in Table 2, the most likely scenario is Scenario 2, with a revenue outcome of
A$394m for the year, which represents an increase of A$23m on calendar 2016. Taking into account the probabilities
in the right hand column of Table 2, revenue is expected to be around A$395m for the year.

Figure 9: Westlink M7 CY2017 Revenue Forecasts by Scenario

rline Intelligence & Research
nsport | Tourism | Travel
Figure : Westlink M7 Y2017 Revenue Forecasts by Scenario

ary
we started writing this case study we had learning in mind; our goal was to inspire and encourage readers to engage w
al techni ues as a way of understanding and bringing real value to their businesses. There is a lot to process in this cas
and some of the concepts are not easy to grasp initially so we welcome any comments, uestions or feedback on an
of the case study. Most of all we hope that you will have found this useful and that you will use the information to mak
rofitable business decisions.
Us
Webber is the O and founder of Airline Intelligence and Research and has over 0 years’ of complex macroeconom
ng experience for significant business decision making including serving as a hief conomist of one of Australia’s flags
. See more of our Aviation Analytics work by visiting the website, oining the conversation at the newly formed Aviatio
mics Group on LinkedIn set up by AIR or following air economist on Twitter.
Summary
When we started writing this case study we had learning in mind; our goal was to inspire and encourage readers to
engage with analytical techniques as a way of understanding and bringing real value to their businesses. There is a lot
to process in this case study – and some of the concepts are not easy to grasp initially – so we welcome any comments,
questions or feedback on any aspect of the case study. Most of all we hope that you will have found this useful and that
you will use the information to make more profitable business decisions.
Contact Us or Follow Us
Dr. Tony Webber
0423 028 720 | tony.webber@airintelligence.com.au
www.airintelligence.com.au
LinkedIn: www.linkedin.com/in/drtonywebber/
Twitter: @air_economist

Disclaimer
The information in this Case Study is provided for general information only and should not be taken as constituting
professional advice from the copyright owner – Airline Intelligence and Research.
Airline Intelligence and Research is not a financial adviser. You should consider seeking independent financial,
investment or other advice to check how the information relates to your unique circumstances.
Airline Intelligence and Research is not liable for any loss caused, whether due to negligence or otherwise arising from
the use of, or reliance on, the information provided directly or indirectly, by use of this Case Study.
Copyright Notice
No part of this Case Study may be reproduced without the express permission of the copyright owner – Airline Intelligence
and Research – except as permitted by the law of the Commonwealth of Australia.

Best Practice Demand and Revenue Modelling: Five Things you Need to do to Understand Your Revenue - Transurban Group Westlink M7

Recommended

Recommended

More Related Content

Similar to Best Practice Demand and Revenue Modelling: Five Things you Need to do to Understand Your Revenue - Transurban Group Westlink M7

Similar to Best Practice Demand and Revenue Modelling: Five Things you Need to do to Understand Your Revenue - Transurban Group Westlink M7 (20)

Recently uploaded

Recently uploaded (20)

Best Practice Demand and Revenue Modelling: Five Things you Need to do to Understand Your Revenue - Transurban Group Westlink M7