TRAFFIC & REVENUE PROJECTIONS FOR TRANSPORT CONCESSIONS. Luis Willumsen

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1 TRAFFIC & REVENUE PROJECTIONS FOR TRANSPORT CONCESSIONS Luis Willumsen

2 Motivation Real money is at stake Clear focus on specific outcomes Modelling and forecasting are immediately valued Some very bright people are involved Short timescales, quick results Useful lessons for other modelling and forecasting applications WHY? 2

3 Contents Context Revenue Risk Modelling approaches Toll Roads and Rail Concessions Risk Analysis What makes a good forecaster CONTENTS 3

4 Context: the concession process CONTEXT 4

5 Agents Government, Regulatory Offices Promoters, construction companies Rolling stock manufacturers Integrators Operators Financial Institutions Rating Agencies Monoline insurers and other insurance companies Lawyers, Other Consultants AGENTS 5

6 Project Finance PROJECT FINANCE Risk Cost of finance Price of bid Financial close Risk Cost of Finance How to reduce risk Better concession contracts Insurance (several) Guarantees (especially minimum revenue) Good Revenue Projections (international level) Financial close is paramount 6

7 Risks General risks Country Currency Political risks RISK PROFILES Project specific risks Construction Operating costs Force Majeure Revenue Traffic volume, composition and abstraction Growth Future competition Toll and fare adjustments (indexing and how) Ramp-up 7

8 Risk (nominal) Typical risk profile of toll road over time Construction costs 0.60 Opening RISK PROFILE OF TOLL ROAD Traffic & Revenue 0.10 Operating costs Pre completion Year Post completion Handover 8

9 Past performance of revenue projections is poor J P Morgan found in 14 Case Studies (USA) 2 underestimates (10 to 30% below actual) 4 moderate overestimates (12 to 25% over actual) 8 blue sky overestimates (45 to 75% over actual) Why? Ignored price and alternatives Ignored willingness to pay tolls Overestimated growth prospects TRAFFIC PROJECTIONS MAY BE UNRELIABLE 9

10 Improve reliability and reduce risk How? We need to provide Confidence in the inputs, extensive and up to date Confidence in the methods Confidence in the results Risk Analysis and treatment Good communication skills to deliver trust in the Traffic and Revenue Projections IMPROVE RELIABILITY AND REDUCE RISK 10

11 Some important aspect in forecasting Early Risk Identification and evaluation Focus on what really matters! In-scope traffic Willingness to pay tolls/fares Growth Competition Contractual obligations Understanding the economic and social context Competitive advantage METHODOLOGIES 11

12 Trends Competition Generic model MODELLING WILLINGNESS TO PAY TRANSPORT MODEL IN SCOPE TRIPS CAPTURE TRAFFIC AND REVENUE BENEFITS OF NEW FACILITY GROWTH 12

13 Willingness to pay Drivers pay to save time and costs (comfort, security) Subjective Value of Travel Time Depends on Income, Purpose (group), quality of the driving task WILLINGNESS TO PAY Public transport travellers are also willing to pay money for a better journey Travel time, reliability, seat, how crowded, opportunity to read/work, comfort Stated and Revealed Preferences Income distribution analysis Segmentation of demand is critical 13

14 Austria Belgium Denmark Finland France Germany Ireland Italy Netherlands Norway Portugal Spain Sweden UK USA Canada Argentina Chile GDP per Capita $ Value of Time $/hr VA LU ES OF TI M E 60,000 50,000 Value of Time by Country ,000 30,000 20,000 10, Country "GDP per capita US$" VOT Business VOT General 14

15 Trucks and freight Who chooses route/mode? Clients shipper haulier - drivers Value of Time for Freight Depends on the value of the commodity and type of contract (JIT) Vehicle Operating Costs Savings (gradients, stops) Stated and Revealed Preferences Company maturity Large and small operators Large and small trucks WILLINGNESS TO PAY, TRUCKS 15

16 Sydney Orbital System: an example 39 km long 15 junctions Toll 25 cents/km, all vehicles Toll capped at $5 (20 km) Two lanes each way Tolled using freeflow ETC 30 years concession Completes orbital route around Sydney Serves main area of expansion of Sydney 16

17 Frequency Willingness-to-Pay, heavy commercial vehicles WSO Heavy Veh VTTS 2001 VALUE OF TIME FOR FREIGHT VTTS ($/hour) Heavy vehicle Free Flow Heavy vehicle Slowed Down Total time 18

18 Values of time in model Category Level VTTS AUS$/hr % of users in category Commuters Low % Medium % High % Employer pays % Average % Non-Commuters Low % Medium % High % Employer pays % MARKET SEGMENTATION Light Commercial Average % Low % Medium-High % Average % Heavy Commercial Low % Medium-high % Average % 19

19 Ramp up forecasting RAMP-UP 20 The transitional period from opening to stable (modelled) flows, or ramp up, is very important as early revenues are critical to the financial success of a concession (and banks look very closely to early revenues) Sadly, the ramp up period is almost impossible to model using conventional tools It depends on how quickly can people become familiar with the advantages (and costs) of the new facility Therefore commuter traffic has a shorter ramp up than inter-city Significant advantages result in shorter ramp-ups Complex pricing leads to longer ramp up periods Information and marketing can shorten the ramp up The best guidance is other similar cases and judgement

20 Revenue model Traffic is never directly converted into revenue; there will be: Ramp up Seasonal variations and the need for annualisation Exempted vehicles Non-payers, evasion Leakage Revenue sharing In the case of public transport concessions Sharing of revenue with integrated fare systems is complex There are more discounted fares, season tickets Usually a spreadsheet models take care of these issues REVENUES 21

21 Public transport concessions Increasing participation of private sector Infrastructure usually needs public sector contribution But revenue risk is often allocated to operations (fraud, fare collection and evasion, marketing, integration) LRT, BRT AND METRO Mode transfer from cars is an issue (policy and revenue) Assumptions about what the competition will do are key Benchmarking is useful 23

22 Risk analysis GDP, Population, Competitors, Prices Policies Segmentation Behavioural responses SVT Trips/tours Zones & Networks KEY ISSUES Uncertainty about future inputs Errors and imperfections in model Uncertainty about forecasts Traffic Revenue streams Confidence that the model represents the present and future well Build different scenarios to cope with a changing future De-construction of model outputs to show dependencies Stochastic risk analysis to provide envelope of uncertainty 24

23 Model and future uncertainties Update the model and forecasts to recent data and changes Check model produces sensible and defensible results Agree basis for scenarios with bidder and financial advisors Produce at least three scenarios: Stressed (downside), Expected (base) and Optimistic (upside) forecasts More scenarios may be needed if uncertainty about competitors responses or other factors are critical Undertake sensitivity tests Willingness to pay Revenue mix joint tickets Evasion Growth in population and GDP Reorganisation of public transport, parking policy KEY ISSUES 25

24 Deconstruct the model in its components Modelling Demand captured from other roads or PT Demand growth What is capturable from other modes Re-distribution Pure generated traffic Demand segmentation By type of vehicles Urban and long distance travel Week day and week end ETC and conventional tolls Season ticket and single fare users Discounts OUTPUT MAKE UP 26

25 Conceptual LRT forecasts PUBLIC TRANSPORT DECONSTRUCTION 27

26 Stochastic risk analysis Beloved by Financial Institutions Generally based around Base Case Take 2-3 key variables : GDP, SVT, etc..and look into their historical variability (standard deviation ) The model is used to track variability in revenue resulting from variability in key inputs, usually via a simplification in Excel FutureRevenue = RevenueFactor * Base Case Revenue A value for RevenueFactor of 1 indicates Base Case Also presented as the level of revenue that is likely to be exceeded 90 or 95% of the time (P90 and P95) STOCHASTIC RISK ANALYSIS 28

27 Revenue Factor Example of output 1.15 GDP variation σ: 0.5% SVT variation σ : 2.5%*mean VST TYPICAL OUTPUT Year 29

28 Some recommendations Bear in mind the need to reach financial close and still have a project that generates a profit to investors Transparency and traceability are paramount You will be audited by financial institutions sooner or later If problem is very simple, use Excel; otherwise, use a well established software package PRACTICAL ASPECTS Beware of perverse incentives 32

29 What makes a good modeller forecasting professional? Curiosity: broad range of interests to enable you to understand the underlying reasons for demand beyond any model or theory Good listening skills Communication skills Analytical ability Questioning mind, in particular of your own results Realism: all forecasting projects take longer than you anticipate; plan accordingly The final test: would you put your retirement fund on this project? CLOSURE: THE TRANSPORT MODELLER PROFESSIONAL 33

30 THANK YOU Luis Willumsen Consultancy

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