Data Collection for Infrastructure Investment Benchmarking

A roadmap

Blanc-Brude (2014) put forward a five-step roadmap for the creation of infrastructure investment benchmarks, including:

  1. Achieving clear instrument definitions;
  2. Developing adequate asset pricing methods;
  3. Arriving at simple yet comprehensive data collection guidelines;
  4. Populating a global database of infrastructure investment data;
  5. Aggregating individual investments into reference portfolios of private infrastructure debt and equity.

This roadmap integrates the question of data collection upfront, including the requirement to collect only the information that is necessary to implement robust asset pricing and risk models.

Next, we review recent progress made with this agenda.

Data collection: objectives, reality check and reporting guidelines

Definitions of infrastructure investment

A decade ago, investors, regulators and policy-makers were thinking about infrastructure in terms of industrial sectors and a coherent definition was nowhere in sight. Indeed, most papers on the subject started with the caveat that “there is not widely-agreed definition of infrastructure”. Energy or telecoms were equally likely to be included or excluded in definitions that went from the very narrow (“infrastructure equals roads”) to the very broad (from the rails to the rolling stock).

Still, defining infrastructure investments from a financial perspective the only relevant perspective to build investment benchmarks was a necessary first step.

For the purpose of building investment benchmarks, the point of defining infrastructure investment is not to declare once and for all what tangible infrastructure is, but to clearly define what we are interested to observe and what it is representative of as an empirical phenomenon.

For this purpose, a clear distinction must be made between infrastructure as a matter of public policy, in which case the focus is rightly on industrial functions (water supply, transportation, etc.) and that of financial investors who may be exposed to completely different risks through investments providing exactly the same industrial functions (e.g a real toll road and an availability payment road).

Moreover, firms delivering infrastructure services may branch out in new business areas that are altogether different: For instance, a number of utilities have had a tendency to look into the media business. Likewise, from a business model point of view, some airports are more akin to shopping malls than infrastructure.

When observing infrastructure investments, we aim to collect data that is not too “noisy”: corresponding to an investments as close as possible to the intuition that we called the “infrastructure investment narrative”, the existence of which we are trying to assess.

In the most recent round of industry consultations led by EIOPA (EIOPA 2015a), substantial progress has been made towards identifying those characteristics that on the basis of financial economics  can be expected to systematically explain the financial performance of infrastructure investments.

In particular, the growing consensus around the limited role of industrial sector categories in explaining and predicting performance, and the much more significant role played by contracts and by different infrastructure “business models” such as “merchant”, “contracted” or “regulated” infrastructure, or different forms of utility regulation, is encouraging.

Two recent papers (Blanc-Brude, Hasan, and Whittaker 2016a; Blanc-Brude, Hasan, and Whittaker 2016b) using newly collected, large datasets of infrastructure firms cash flows find that the business model and lifecycle attributes of infrastructure firms explain the dynamics of their cash flows well, when sector categorisations do not.

These papers show the existence of well-defined stochastic processes for cash flow data belonging to similar business model “families”, while there exist significant differences between the cash flows corresponding to different groups. For example, Blanc-Brude, Hasan, and Whittaker (2016a) show that the volatility of revenues is different between different types of infrastructure business models (and also that it is different from non-infrastructure firms). Likewise, Blanc-Brude, Hasan, and Whittaker (2016b) show that mean and variance of the debt service cover ratios of infrastructure projects follow a different path in each family of business models.

In the debate about defining infrastructure investment, non-recourse infrastructure project finance has become a first and useful point of reference in terms of capturing the expected behaviour of infrastructure investments (see Blanc-Brude 2014 for a detailed discussion). While project finance equity or debt cannot be said to represent all investable infrastructure, it offer the opportunity observe the cash flows of long-term instruments created solely for the purpose of financing individual infrastructure project companies that are dedicated dedicated to delivering a single infrastructure projects. Project finance also presents the advantage of having a clear and widely accepted definition since the Basel-2 Accords.

Of course, a number of businesses can be project-financed that are not strictly speaking infrastructure investments delivering public services, such as casinos or heavy industry. Conversely, infrastructure services can also be delivered by more common forms of corporations such as utilities or private airport or port companies. These firms have their own business models and characteristics, but they may also create a lot of noise around the “infrastructure investment phenomenon” that we are trying to observe. For instance, and contrary to project finance SPVs, they may change their financial structure as they see fit, embark on overseas investment adventures, receive income from technology licences or consulting services. They may also branch out in new business areas that have nothing to do with infrastructure altogether (e.g. a number of utilities have had a tendency to look into the media business.)

But as suggested above, the objective of benchmarking infrastructure investment can only start with the possibility to observe a corporate phenomenon which as close as possible to the intuition justifying infrastructure investment in the first place (which we called the “infrastructure investment narrative”).

Project financing, as an observable phenomenon, provides us with this opportunity. Benchmarking project finance debt and equity by broad categories of concession contracts, financial structures and life-cycle stage is thus a first concrete step towards creating reference portfolios that can be used as infrastructure benchmarks benchmarks.

Other types of underlying infrastructure business models (e.g. “RPI-X” vs. “rate of return” utility regulation) can be integrated in a broader benchmarking exercise of privately-held infrastructure investments, as long as, from the perspective of observing the investment phenomenon of interest, we can ensure that a “pure” infrastructure business is being observed and not a combination of, say, airport operations and a shopping mall.

The conclusion of the industry consultations led by EIOPA in 2015-16 is – for the most part – congruent with our argument. In a first step, EIOPA proposed to define infrastructure investment for the purpose of re-calibrating Solvency-II as a combination of characteristics that equated infrastructure with project finance (EIOPA 2015b). In a second step, in an attempt to widen the scope and number of qualifying investments, EIOPA considered recognising as qualifying infrastructure assets a number of “corporates” as long as they can be clearly identified as corresponding to the infrastructure business model (EIOPA 2016).

Asset pricing principles

Once the financial instruments that correspond to infrastructure investment are usefully defined, the second necessary step is to design a performance and risk measurement framework, that can provide robust answers the questions identified above.

Of course, measuring the performance of privately-held infrastructure debt and equity requires deriving the appropriate discount rates for a given estimate of future cash flows, as for any other financial asset.

But these instruments are not traded frequently and cannot be expected to be fully “spanned” by a combination of publicly traded securities. It follows that they are unlikely to have unique prices that all investors concur with at one point in time. Instead, individual investors can arrive at different valuations of the same infrastructure debt or equity depending on their attitudes towards risk, liquidity, inflation, duration, etc, and large bid/ask spreads may persist.

Asset pricing models applied to such investments should be able to measure a range of applicable valuations for certain types of infrastructure investments. Indeed, the average realised performance or required returns corresponds to a “representative” investor that many actual investors may not recognise themselves in. Capturing this range of valuations and how it evolves in time is an integral part of benchmarking privately-held investments like infrastructure equity or debt.

This point highlights the fact that in private markets, cash flow volatility and discount rate volatility must be treated as separate (albeit related) phenomena.

In other words, while the pricing of publicly-traded securities implicitly combines a cash flow forecast with a required rate of return,1 valuing privately-held investments requires explicitly forecasting cash flows and then deriving the required discount factors.

Hence, a two-step approach is necessary:

  1. first documenting the cash flow distributions (debt service and dividends) found in underlying infrastructure investments, taking into account their characteristics (e.g. covenants), and
  2. estimating the relevant (term structure of) discount rates or required rates of returns and their evolution in time, given the risk of teh payoff and the initial value paid by private investors.

Understanding cash flow dynamics

In order to address the fundamental problem of unreliable reported NAVs in private investment discussed above, it is essential to develop an independent view of the statistical distribution of cash flows to creditors and asset owners that can serve as the basis for a valuation of privately-held infrastructure investments.

Forecasts of future cash flows spanning the entire life of the investment in infrastructure projects are in fact available for both debt and equity investors. Such “base case” scenarii of debt service, dividends and free cash flow are the result of significant due diligence at the time of investment and duly documented at the time. Moreover, investors and creditors regularly revise these forecasts and these new forecasts are documented as well.

Base case and revised dividend and debt service forecasts may however vary between investors for comparable projects and substantially deviate from the true statistical expectation of dividends. Still, they are observable.

In two recent papers, Blanc-Brude, Hasan, and Ismail (2014) and Blanc-Brude and Hasan (2015) show that the combination of base case scenarios with the well-documented statistical distribution of two types of financial ratios (the debt service cover ratio or DSCR, and the equity service cover ratio or ESCR)2 is sufficient to derive robust estimated of expected cash flows (in the statistical sense) and their volatility.

Regarding future debt service, Blanc-Brude, Hasan, and Ismail (2014) show analytically and empirically that knowledge of the distribution of DSCRs in time is sufficient to compute the credit metrics required by a structural credit risk model e.g. distance to default and to predict technical3 and hard defaults in infrastructure debt.

They also show that adequate debt service forecast should integrate the “embedded options” available to senior lenders in the event of default, because they have a significant impact on the different debt service scenarii.

Indeed, infrastructure projects demand large amounts of sunk capital and most of these funds are typically provided by senior creditors that require significant control-rights in the event of covenant breach. Such contingent control rights (or embedded options), can lead to the restructuring of senior debt, can have a large impact on expected losses and thus on expected and realised performance.

In practice, infrastructure project loans have a “tail” (often described as the number of years beyond the original maturity of the debt during which the firm is still generating an operating income) and failing to value the option to restructure senior debt into the tail is likely to lead to overestimating LGD and VaR and underestimating recovery rates.4

The authors show that a standard model of debt restructuring applying simple, rational rules can determine the potential outcome of predictable credit events and provide an complete estimation of future cash flows to creditors in all states of the world.

Likewise, a full distribution of future dividends can be derived from the combination of the expected value and volatility of the ESCR (the tendency to meet the base case) throughout the life of the investment.

Blanc-Brude and Hasan (2015) show that documenting ESCRs requires observing realised and base case dividends, as well as expected and realised project status (e.g. dividend lock-up) and milestones (e.g. construction completion).

Hence, the statistical distribution (mean and variance) of cash flows to creditors and equity investors at each point in the life of the investment can be modelled by relying in a limited number of data points, as long as basic information about payment priority, covenants and control-rights are also known.

Key data points required to properly document these distributions include:

  • base case and revised cash flow forecasts for equity and debt investors;
  • actual realised debt service and dividends;
  • Key financial ratios, in particular the DSCR, and the determinants of their distributions: this requires documenting the factors driving the levels and volatility of these ratios in infrastructure projects, including revenue risk models and other risk-sharing or revenue support mechanisms, financial structure, etc but also jurisdictions, sectors and any other factor which may be included in a model of DSCR and ESCR ratios;
  • loan covenants and tail, to estimate the value of embedded options to senior creditors;
  • Expected and realised milestones and status of the firm.

The technical implementation of such cash flow models may vary and depends largely on the quantity and quality of data available. Blanc-Brude, Hasan, and Ismail (2014) and Blanc-Brude and Hasan (2015) provide illustrations of how a limited amount of existing and reasonably standardised data may be used to estimate the expected value and volatility of cash flows to creditors and equity investors in privately-held infrastructure investments. Once this data has been collected, future research can also lead to new cash flows model designs.

Understanding pricing dynamics

Once, the expected value and volatility of cash flows to creditors and investors is known as best as current information allows, the relevant term structure of discount rates needs to estimated to derive past and forward-looking measures of performance, risk and liability-hedging.

Indeed, in light of the perils of using constant discount rates for infrastructure investments discussed above, a term-structure of expected returns (discount factors) must be derived.

This is instrumental to:

  • measure current asset values and realised performance and build forward-looking measures of performance for asset allocation;
  • derive the full (conditional) distribution of expected losses and be in a position to predict VaR or LGD levels for prudential regulation;
  • compute duration properly using the correct future discount rates for liability-hedging purposes.

To derive this term structure, two (equivalent) approaches can be used:

  1. Factor extraction: for a given future distribution of cash flows (including conditional volatility), a term structure of implied discount rates (required returns) can be derived by observing initial investment values (prices). Ang et al. (2013) use this approach in the case of private equity funds and Blanc-Brude and Hasan (2015) provide an application to infrastructure project equity using a Kalman filter (other techniques are available depending on the quantity and quality of the data).
  2. Risk-neutral valuation: given expected cash flows to investors, a new “shifted” distribution of cash flows can be obtained that integrates the (range of) required reward/risk ratio of investors at the time of investment (e.g. basis points per standard deviation of cash flow distribution), that can then be discounted at the relevant risk-free rate. This approach is a standard application of the structural model of credit risk developed by Merton (1974), is described in (Kealhofer 2003). Blanc-Brude, Hasan, and Ismail (2014) provide an application to private infrastructure debt that integrates the Black and Cox (1976) framework of structural models allowing for debt restructuring.

Both approaches allow deriving the average required returns of a representative investor but also capturing a range of such values, which is the result of the range of prices (investment values) observed in each period and corresponding to similar cash flow processes (same distributions of DSCR or ESCR)

Thus, initial investment values, which are observable, are required to be collected to implement the first approach, while risk-neutral pricing requires collecting credit spreads to compute the required reward per unit of risk of infrastructure creditors.

As project cash flows are realised and observed, the relevant DSCR/ESCR distributions or buckets can be determined for each investment and realised/expected performance re-assessed, as is the case with public stocks announcing dividends and earnings forecasts.

Of course, once individual debt and equity investments can be priced, they can be combined in series of portfolios representing “infrastructure” and their performance, extreme risk measures and liability-hedging properties can be derived as well.



  1. This is the essence of the Gordon growth model of stock pricing.

  2. DSCR: ratio of current debt service to free cash flow or cash flow available for debt service; ESCR: ratio of realised to base case dividends, as presented in Blanc-Brude and Hasan (2015)

  3. Default under the Basel-2 definition

  4. Conversely, loans with very short tails can see a sharp rise in expected losses towards the end of the loan life, even with very low default probabilities.


Ang, Andrew, Bingxu Chen, William N Goetzmann, and Ludovic Phalippou. 2013. “Estimating Private Equity Returns from Limited Partner Cash Flows.”

Black, Fischer, and John C Cox. 1976. “Valuing Corporate Securities: Some Effects of Bond Indenture Provisions.” The Journal of Finance 31 (2): 351–67.

Blanc-Brude, Frédéric. 2014. “Benchmarking Long-Term Investment in Infrastructure.” EDHEC-Risk Institute Position Paper, June.

Blanc-Brude, Frédéric, and Majid Hasan. 2015. “The Valuation of Privately-Held Infrastructure Equity Investments.” EDHEC-Risk Institute Publications, EDHEC, Meridiam and Campbell-Lutyens Research Chair on Infrastructure Equity Investment Management and Benchmarking January (January). Singapore: EDHEC-Risk Institute.

Blanc-Brude, Frédéric, Majid Hasan, and Omneia R H Ismail. 2014. “Unlisted Infrastructure Debt Valuation & Performance.” EDHEC-Risk Institute Publications, EDHEC and NATIXIS Research Chair on Infrastructure Debt Investment Solutions July. Singapore: EDHEC-Risk Institute.

Blanc-Brude, Frédéric, Majid Hasan, and Tim Whittaker. 2016a. “Revenue and Dividend Payout in Privately Held Infrastructure Investments.” EDHEC Infrastructure Institute Publications, EDHEC, Meridiam and Campbell-Lutyens Research Chair on Infrastructure Equity Investment Management and Benchmarking March (March). Singapore: EDHEC Infrastructure Institute-Singapore.

Blanc-Brude, Frédéric, Majid Hasan, and Timothy Whittaker. 2016b. “Cash Flow Dynamics of Private Infrastructure Project Debt, Empirical Evidence and Dynamic Modelling.” EDHEC Infrastructure Institute Publications, EDHEC and NATIXIS Research Chair on Infrastructure Debt Investment Solutions March (March). Singapore: EDHEC Infrastructure Institute.

EIOPA. 2015a. “Consultation Paper No. CP15004 on the Call for Advice from the European Commission on the Identification and Calibration of Infrastructure Investment Risk Categories.” EIOPA.

EIOPA. 2015b. “Final Report on Consultation Paper No., 15/004 on the Call for Advice from the European Commission on the Identification and Calibration of Infrastructure Investment Risk Categories.” Frankfurt, Germany: European Insurance; Occupational Pension Authority.

———. 2016. “Final Advice on Infrastructure Corporates to the European Commission.” Frankfurt, Germany: European Insurance; Occupational Pension Authority.

Kealhofer, Stephen. 2003. “Quantifying Credit Risk I: default Prediction.” Financial Analysts Journal. JSTOR, 30–44.

Merton, Robert C. 1974. “On the Pricing of Corporate Debt: The Risk Structure of Interest Rates.” The Journal of Finance 29: 449–70.