ATRF2011_Di Bona Toll Roads Business Cycles 110927
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Transcript of ATRF2011_Di Bona Toll Roads Business Cycles 110927
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How Will Upswings in Price Inflation and Interest Rates Change Toll Road Risk
Profiles? A Study of Developing East Asian Economies with
Broader Implications
Richard Di Bona, Hong Kong
Australasian Transport Research Forum 2011,
Adelaide, 29 September 2011
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Contents 1. Background
2. (Business) Environmental Analysis
3. Business Cycles
4. Accuracy and Demand Risk Issues
5. Questionnaire Survey – what do Practitioners Think?
6. Risk Simulation Modelling
7. Conclusions
8. (And a quick Post Script on the Global Economy)
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Background
1980s and 1990s: • Resurgence in private sector involvement in
infrastructure provision, e.g. toll roads • Generally declining price inflation and interest
rates Now: • Price inflation and interest rates likely to rise
rather than fall • What impacts (if any) on project risk?
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Geographic Scope
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Specific Hypothesis
“There is a significant change in the nature and extent of
project finance risks for private stakeholders in East Asian toll
roads during a period of increasing price inflation and
interest rates”
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PESTLE Analysis
• Political: Stability concerns – though not necessarily derailing investment
• Economic: Generally growing, but with income disparities
• Social: Increasing desire for travel. In some locations demand substantially suppressed. Varying nationalism, with impacts on contracts
• Technological: lots of manual tolling • Legal: wide range of systems, but much corruption • Environmental: Economic development objectives
predominate
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Scope for Toll Roads
• Correlation between roadspace and car ownership: one can proxy for the other – Khan, A. and Willumsen, L.G. (1986) “Modelling Car
Ownership and Use in Developing Countries”, Traffic Engineering and Control, 27(11), pp554-60
• Tested on study countries, plus Mexico, Poland, South Korea, UK and USA – Population per km of paved road vs GDP per capita – Land area (km2) per km of paved road vs GDP per capita
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Relationship: Wealth versus Roads per Capita
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Relationship: Wealth versus Road Density
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Three Groups of Countries
• Relatively developed networks, in countries with significant prior experience of transport infrastructure privatisation: China, Indonesia, Malaysia and Thailand
• Relatively undeveloped networks, correlating to a relative lack of infrastructure privatisation: Cambodia, Laos and Myanmar
• Intermediate countries: with some problematic experience of privatisation (Philippines) or nascent interest in privatisation (Vietnam)
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Weighted Average Cost of Capital (WACC)
• Fisher-Hirshleifer Theorem: Undertake projects if return is greater than investors’ required return
• Capital Asset Pricing Model (CAPM): return is: rj = ri + βj (rm – ri) • Weighted Average Cost of Capital (WACC): WACC = [(EMV x Ke) + (DMV x Kd)]/(EMV + DMV)
– EMV = total market value of equity employed – DMV = total market value of debt employed – Ke = cost of equity: (Dividend/Share Price) + Expected
Dividend Growth – Kd = cost of debt: (Interest Rate/ Debenture Price (% of
face value)) x (1 - TaxRate)
ri + βj (rm – ri) ≥ [(EMV x Ke) + (DMV x Kd)]/(EMV + DMV)
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Business Cycles
Schumpeter (Business Cycles, 1939) brought together others’ work: • Kitchin (1923): fluctuations in business inventories
(39 +/- months) • Juglar (1863): business investment in plant and
equipment (7-11 years) • Kondratieff (1926): development of new
technologies/ sectors/ markets and impacts of these changes on socio-economic conditions (48-60 years) (a.k.a. The “K-Wave”)
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Nikolai Dmitriyevich Kondratieff (1892-1938)
• Specialist in statistics and agricultural economics • Involved with early Soviet Five Year Plans • Investigated nature of capitalist crises • Discovered periodicity, correlated with long waves
in agricultural price inflation and interest rates • First gulaged, and subsequently murdered on
Stalin’s orders • Rehabilitated in the USSR in 1987
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The K-Wave: Key Characteristics
• Downswings: price inflation and interest rates decline (e.g. 1980s, 1990s) – Corresponds with growth in private investment in
infrastructure – Refinancing projects cheaper than anticipated during ex
ante evaluation – Cost of delayed/ incomplete toll adjustment less than
anticipated during ex ante evaluation
• Upswings: economic growth, price inflation and interest rates increase – Growth drives demand for goods (price inflation) – Growth drives demand for capital (interest rates) – Costs of debt and equity likely to increase – Refinancing and toll escalation likely to be more critical
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The K-Wave: Postulated Position
How Will Upswings in Price Inflation and Interest Rates Change Toll Road Risk Profiles?
0
2
4
6
8
10
12
14
16
18
1950
1955
1960
1965
1970
1975
1980
1985
1990
1995
2000
2005
Tre
asu
ry B
ill
Inte
rest
Rate
s (
%)
0
100
200
300
400
500
600
700
800
Go
ld P
rice (
US
D p
er
oz)
10 Year T-Bill 3 Month T-Bill Gold(USD/oz) K-Wave
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O&M
Traffic &
Revenue
Ramp Up
Construction
Costs
Construction
Delay
Change Orders
-2 -1 0 1 2 3 4 5 10
Han
dove
r
Year
Ris
k (
no
min
al)
Primacy of Traffic and Revenue Risk
Willumsen, L. and Russell, C. (1998) “Reducing Revenue Risk”, European Transport Conference, Loughborough, 14-18 September 1998
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Accuracy (or Lack Thereof)
• Average initial year traffic: 70% of forecast • For lender-commissioned studies: 82% • For other studies: 66%
– Bain, R. and Wilkins, M. “Credit Implications of Traffic Risk in Start-Up Toll Facilities”, Standard & Poor’s, September 2002
• Due to Ramp Up? • Can last 3-5 years
– Streeter, W. and McManus, K. “Challenges of Start-Up Toll Roads”, Project Finance Special Report, Fitch ICBA, 1999
• Or optimism bias constant in years 2 to 5? – Bain, R. and Polakovic, L, “Traffic Forecasting Risk Study Update 2005:
Through Ramp-Up and Beyond”, Standard & Poor’s, August 2005
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Resources, Psychology, Ethics
• Models’ opaqueness • Lack of resources (e.g. time, data) • Ethical factors
– Discrepancy between lenders’ and others’ forecasts
• Modellers delude themselves – Supposed “neutrality” or “infallibility” Brinkman, P.A. (2003) The Ethical Challenges and Professional Responses of Travel Demand Forecasters, PhD Dissertation, University of California at Berkeley Kilsby, D. (2004) “Ethical Challenges and Professional Responses of Travel Demand Forecasters – Review”
• Or cycles? – More studies during boom times – Projects open after the boom has ended?”
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Price Inflation & Interest Rate Impacts
• Value of Time – Income and substitution effects – what is the elasticity – (Negative) savings effects during credit expansion?
• Competing Routes – Faster network expansion in boom times?
• Toll Increases and Revenue Guarantees – As price inflation grows, greater potential for loss if
increases not granted/ delayed (or political risk of toll rise approvals during slumps)
• Construction, Operating & Maintenance Costs – Price inflation for raw materials & wages – (Negative) savings effects during credit expansion?
• Toll Leakage – Greater incentive for malfeasance?
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Practitioner Questionnaire Survey
Test views versus literature environmental analysis: • Scope of project experience • Relative weightings of macro and micro-level risks • Data availability and quality • Accuracy of forecasts and key metrics used • Market outlook for the 9 Study Area countries • Expectations for economic parameters
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Respondent Experience
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30+
26%
20 to 29
30%
10 to 19
31%
5 to 9
6%
1 to 4
7%
0
20
40
60
80
100
Financial,
Legal,
Operator
Transport
Planner,
Economist
Engineer,
Architect
Government,
Aid Agency
Academic Others
Respondents by Type
Respondent Experience (years)
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Selected Questionnaire Findings
• Primacy of legal & political factors on viability • Once modelling commences, economic factors
predominate • Business cycles deemed unimportant, but:
– A degree of acceptance of increasing price inflation/ fuel prices
– Some expectation of interest rates increasing (but not having an impact)
– Stronger economic growth expected (but this perhaps due to adaptive expectations – survey in 2006)
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Risk Simulation Modelling • Economic, traffic and financial model
– Notional network, using Excel/ Visual Basic
• 3 Cases: – Conventional – recent interest rates, inflation continue – Respondents’ – based on questionnaire, with small
increases in fuel prices, general prices, interest rates – Kondratieff – more substantial price inflation, interest
rate and economic growth
• 33 variables: 27 common for all cases; 6 had values specified base on case. 10,000 iterations: – Define random parameters – Quarterly simulation for 30 years, from construction – Traffic assignment for each quarter (once road opens) – Financial analysis (FIRR, NPV, payback) – Any run with FIRR<0 or payback>120 quarters = failure
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Risk Simulation Modelling
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Modelling Results (No Risk Simulation)
Results without simulation: • Conventional Case:
FIRR = 16.8% Payback = 10.73 years NPV (@16%) = $17.9m
• Respondents’ Case: FIRR = 17.9% Payback = 10.68 years NPV (@16%) = $45.9m
• Kondratieff Case: FIRR = 17.0% Payback = 12.09 years NPV (@16%) = $27.5m
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Risk Simulation Modelling Results • Conventional Case:
Mean FIRR = 17.2% (s.d. = 3.74%) Mean Payback = 10.83 years (s.d. = 2.32 years) Mean NPV (@16%) = $28.1m (s.d. = $80.3m) Financial Failure = 0.6% of runs
• Respondents’ Case: Mean FIRR = 18.0% (s.d. = 3.77%) Mean Payback = 10.85 years (s.d. = 2.41 years) Mean NPV (@16%) = $46.4m (s.d. = $96.3m) Financial Failure = 1.1% of runs
• Kondratieff Case: Mean FIRR = 17.6% (s.d. = 4.11%) Mean Payback = 11.60 years (s.d. = 2.73 years) Mean NPV (@16%) = -$37.7m (s.d. = $308.9m) Financial Failure = 12.5% of runs
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Cumulative Probability Distribution: FIRR (excluding FIRR < 0%)
How Will Upswings in Price Inflation and Interest Rates Change Toll Road Risk Profiles?
0%
20%
40%
60%
80%
100%
5% 10% 15% 20% 25%
Cu
mu
lati
ve %
Conventional Respondents' Kondratieff
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Cumulative Probability Distribution: Payback Period (years)
How Will Upswings in Price Inflation and Interest Rates Change Toll Road Risk Profiles?
0%
20%
40%
60%
80%
100%
8 10 12 14 16 18 20 22
Cu
mu
lati
ve %
Conventional Respondents' Kondratieff
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Cumulative Probability Distribution: NPV (16% discount rate)
How Will Upswings in Price Inflation and Interest Rates Change Toll Road Risk Profiles?
0%
20%
40%
60%
80%
100%
-500 -400 -300 -200 -100 0 100 200 300 400
Cu
mu
lati
ve %
Conventional Respondents' Kondratieff
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Analysis of Risk Groups
• Some variables had large but poorly correlated affect on FIRR. Others had smaller but better correlated effect. So to assess importance: – Range (maximum – minimum) calculated – Range multiplied by coefficient from linear regression – This impact then multiplied by R2
• Then group simulation variables by category • Same five groups ranked top in each case, but the
rankings changed
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Change in Relative Risk
How Will Upswings in Price Inflation and Interest Rates Change Toll Road Risk Profiles?
• There is autocorrelation • Inflation affects all cost elements • Interest rates escalate in importance as they
increase, especially when refinancing required
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Key Findings
How Will Upswings in Price Inflation and Interest Rates Change Toll Road Risk Profiles?
• Impacts of delayed toll increases or refinancing become greater than anticipated (ex ante) when interest rates & price inflation increasing
• Mitigating this, increased economic growth (driving demand for capital and commodities) can increase demand
• But outcomes likely to be more volatile • Specific hypothesis is thus supported • However, the implications of this appear to be
under-estimated
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Post Script
How Will Upswings in Price Inflation and Interest Rates Change Toll Road Risk Profiles?
• There have been recent issues with economic growth in many parts of the world, especially the developed economies – though with countries such as Australia, New Zealand and Canada relatively protected by their commodity bases
• These brought about, in my opinion, by excessive credit expansion which creates distortions to “natural” economic rhythms
• There is a “debt overhang” suppressing growth in many countries
• And it could be argued that propping up old firms/ industries has starved the next wave of innovations of capital to drive the next K-Wave upswing, so it has not been as pronounced as it would otherwise have been
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In Conclusion
How Will Upswings in Price Inflation and Interest Rates Change Toll Road Risk Profiles?
“There is a significant change in the nature and extent of project finance risks for private stakeholders in East Asian toll roads during a period of
increasing price inflation and interest rates”
– and elsewhere too! Please feel free to contact me ([email protected]) for the full MBA Dissertation on which this is based
Thank You!