Data collection for infrastructure benchmarking


Frederic Blanc-Brude, Raffaelle Della Croce
Cledan Mandri-Perrot, Jordan Schwartz
Tim Whittaker

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In this paper, we propose guidelines for collecting and reporting infrastructure investment data for the purpose of building investment benchmarks of private infrastructure debt or equity. The approach proposed is both comprehensive and parsimonious and aims to collect the data that is both necessary and sufficient to run advanced cash flow and asset pricing models adapted to private assets.

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Executive Summary

The growing interest of investors for infrastructure investment has been motivated by what calls the “infrastructure investment narrative”: the notion that infrastructure projects uniquely combine the following characteristics:

  • Low price-elasticity of demand for service, hence low correlation with the business cycle
  • Monopoly power, hence pricing power, hence an inflation hedge
  • Predictable and substantial free cash flow
  • Attractive risk-adjusted cash flows, available over long periods
  • Access to unlisted, illiquid financial assets

That is, investing in infrastructure implies:

  • Improved diversification
  • Better liability-hedging, including inflation protection
  • Less volatility than capital market instruments

Unfortunately, adequate benchmarks that could assess the validity of this intuition do not exist today, as 94 percent of the respondents of a new EDHEC/Global Infrastructure Hub Survey of asset owners involved in infrastructure investing attests .

In recent years, frequent calls have been made in policy fora for data collection efforts to be stepped-up with respect to infrastructure investment, but it is often unclear which data should be collected to achieve what end and how.

In this paper, we propose guidelines for collecting and reporting infrastructure investment data for the purpose of building investment benchmarks of private infrastructure debt or equity. To establish what data needs to be collected, we start from the reasons why infrastructure investment benchmarks are in demand and list the key questions that such benchmarks should be expected to answer.

What are the relevant questions?

  • Asset allocation: investors need risk-adjusted measures of performance;
  • Prudential regulation: regulators want measures of extreme risks;
  • Liability-driven investment: some investors also want to understand the “liability-friendliness” of infrastructure investing.

Why these questions cannot be answered today

These questions are important to the future of infrastructure investment by long-term investors, such as investors with a liability profile and subjected to prudential rules. However, the current state of investment knowledge does not allow answering them for the following reasons:

  1. Market proxies are ineffective;
  2. Existing research using private investment data is too limited;
  3. Reported financial metrics are inadequate.

Recent progress: from definitions to data collection

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 information known to exist in a reasonably standardised format and limited to what is necessary to implement robust asset pricing and cash flow models.

Since them the first two steps recommended in this roadmap have been taken, and with this paper a framework required to define and launch the data collection process (step 3) now exists.

Step 1: Definition

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 respect, substantial progress has now been made towards identifying those characteristics that 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” or “contracted” infrastructure, or different forms of utility regulation, is encouraging.

A number of corporate forms can thus be included in the definition of infrastructure investing as long as, from the perspective of observing the phenomenon of interest, we can ensure that a “pure” infrastructure business is being observed and not a combination of, say, seaport operations and a real estate income.

Step 2: Valuation

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 compute robust estimates of the metrics needed to understand infrastructure investment in an asset allocation and prudential context.
A two-step approach to measuring performance is necessary:

  1. Documenting cash flow distributions (debt service and dividends) in order to address the fundamental problem of unreliable or insufficiently reported NAVs or losses given default (LGDs);
  2. Estimating the relevant (term structure of) discount rates, or required rates of returns, and their evolution in time.

Here too, progress has been made and recent research reviewed in this paper provides a framework addressing both aspects, taking into account the availability of data, while applying best-in-class models of financial performance measurement.

These advances allow us to define a list of required data items to implement these improved methodologies.

Data types and attributes

 

Guidelines for data collection

We propose a data collection framework respecting the following first principles:

  1. The financial instruments used to invest in infrastructure must be well-defined;
  2. Benchmarking results must be based on best-in-class models of financial performance and economic impact measurement;
  3. The required data must already exist and be sufficiently standard to be observable on a large scale; and
  4. It must be limited to a parsimonious list to keep the collection process efficient and realistic.

We argue that realised and forecast cash flow and event data, adequately categorised by “physical” and “business model” attributes, and corresponding to a clear set of financial instruments and their attributes, is sufficient to measure the performance of portfolios of private infrastructure investments.

A first step consists in the identification of all investable infrastructure in a given country, and the attribution of a unique identifier to each firm corresponding to a potential investment in either equity or different kinds of debt.

For each identified firm, two types of observable data points are of interest:

  1. Cash flows (and cash flow ratios, which may or may not be derived from balance sheet items)
  2. Events (or milestones) in the development of the firms and, possibly, the evolution of its risk profile

Next, cash flow and event data need to be categorised according to economically meaningful attributes. These fall into three categories:

  1. Physical attributes of the firm: what and where the firm is as an infrastructure investment
  2. Business model attributes of the firm: sources of revenues and costs of the firm and whether or not the risk inherent in these exposures is insured against via contracts with third parties.
  3. Attributes of available financial instruments: type of payout structure, control rights and terms applicable to the claimants to the firms liabilities and equity

The Figure above provides an illustration. All firm and instrument attributes should also be reported and recorded dynamically. For instance, a loan may change interest rate over time (and this may be known in advance), or a firm may see it’s take-or-pay off-take contract expire before the end of the investment’s life. Capturing realised and forecast changes in time of the attributes of either firms or instruments is of particular importance in the case of infrastructure investments because of the path-dependency and sequential resolution of uncertainty, which characterises these type of investments.

For example project debt may change its maturity date post-restructuring, which is instrumental in the context of asset pricing and computing duration.
Applying the framework detailed in this paper, we propose the following data collection guidelines:

  1. Building investment benchmarks of highly illiquid private assets like private infrastructure debt and equity requires collecting data reported at the underlying firm level;
  2. These firms should be categorised according to a limited set of ’attributes’ which can be expected to systematically explain the risk profile of individual investments: not only the variance but, most importantly, the co-variance of cash flows and of returns; these include:
    • Physical attributes: investment size, technology, sector, location, lifecycle stage
    • Business model attributes: nature of income and cost streams, role of contracts and regulation
  3. Individual financial instruments used to invest in such firms should also be recorded and documented to be in a position to predict the payoffs to different investors
    • Instruments should be categorised by type of payoff profile (fixed, variable)
    • Any conditions (covenants, embedded options, prepayment) should be documented to properly model the expected payoff to investors
  4. The two main types of data to collect relating to the relevant firms and instruments are standardised events and cash flow items
    • Firm and instrument attributes are control variables that explain the dynamics of different stream of cash flows to different claimants (investors)
  5. Each data point should be reported using a dual timeframe, capturing both the time of observation/reporting and that of occurrence (past, present or future)

Applying these guidelines to collect the relevant data allows implementing the type of asset pricing and risk models that, in turn, can be used to compute the metrics needed to better benchmark infrastructure investments in private debt and equity. This framework for collection data about private investment in infrastructure is illustrated in further details in the companion spreadsheet to this paper, which can be downloaded here.

Populating the database

Having progressed towards clear definitions of underlying assets, and built robust, state-of-the-art pricing and risk models that avoid the pitfalls of existing practices, it is now time to collect the relevant information.

The data collection framework and template proposed in this paper have been designed to correspond to the requirements of the relevant asset pricing and risk models. Hence, a rationale exists to collect data effectively and efficiently to build infrastructure investment benchmarks.

Collecting this information now requires large-scale cooperation between investors, creditors, academic researchers and the regulators that can help make such reporting part of a new standard approach to long-term investment in infrastructure by institutional players.