atrf2011_di bona toll roads business cycles 110927

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

[email protected]

Australasian Transport Research Forum 2011,

Adelaide, 29 September 2011

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)

How Will Upswings in Price Inflation and Interest Rates Change Toll Road Risk Profiles?

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?


Geographic Scope


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”


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


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


Relationship: Wealth versus Roads per Capita


Relationship: Wealth versus Road Density


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)


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)


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”)


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


http://en.wikipedia.org/wiki/File:%D0%9D%D0%B8%D0%BA%D0%BE%D0%BB%D0%B0%D0%B9_%D0%9A%D0%BE%D0%BD%D0%B4%D1%80%D0%B0%D1%82%D1%8C%D0%B5%D0%B2.JPG

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


The K-Wave: Postulated Position


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

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


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


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?”


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?


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


Respondent Experience


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)

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)


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


Risk Simulation Modelling


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


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


Cumulative Probability Distribution: FIRR (excluding FIRR < 0%)


0%

20%

40%

60%

80%

100%

5% 10% 15% 20% 25%

Cu

mu

lati

ve %

Conventional Respondents' Kondratieff

Cumulative Probability Distribution: Payback Period (years)


0%

20%

40%

60%

80%

100%

8 10 12 14 16 18 20 22

Cu

mu

lati

ve %


Cumulative Probability Distribution: NPV (16% discount rate)


0%

20%

40%

60%

80%

100%

-500 -400 -300 -200 -100 0 100 200 300 400

Cu

mu

lati

ve %


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


Change in Relative Risk


• There is autocorrelation • Inflation affects all cost elements • Interest rates escalate in importance as they

increase, especially when refinancing required

Key Findings


• 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

Post Script


• 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

In Conclusion


“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!

atrf2011_di bona toll roads business cycles 110927

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