Data Collection for Infrastructure Investment Benchmarking
The growing interest of investors for infrastructure investment has been motivated by what Blanc-Brude (2013) 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
Data collection: objectives, reality check and reporting guidelines
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 (Blanc-Brude, Chen, and Whittaker 2016).
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.
The current demand for infrastructure investment benchmarks springs from three sources:
- Long-term investors who need to formulate investment beliefs before they can make asset allocation decisions, require benchmarks to evaluate their infrastructure investment managers or strategies, and also want to evaluate the social and environmental impact of their investments;
- Prudential regulators who are required to adequately calibrate long-term infrastructure equity and debt investment within their respective risk-based frameworks such as Solvency-II;
- Policy makers who have been calling for a greater use of long-term savings to invest in capital projects that can have a positive impact on economic growth.
These actors have in common the goal to properly frame infrastructure investment so that long-term capital can be adequately deployed in the infrastructure sector.
To establish what data needs to be collected, we take the following approach: 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 in chapter 2.
The answers to these questions (about the risk-adjusted performance, extreme risks and liability friendliness of infrastructure investments) represent different aspects of the project to create infrastructure investment benchmarks.
Unfortunately, as we discuss in chapter 3, it remains very difficult to answer such questions today, for lack of the relevant information.
We thus propose data collection guidelines respecting the following principles:
- the financial instruments used to invest in infrastructure must be well-defined;
- benchmarking results must be based on best-in-class models of financial performance and economic impact measurement;
- the required data must already exist and be sufficiently standard to be observable on a large scale;
- it must be limited to a parsimonious list to keep the collection process efficient and realistic.
Chapter 4 discusses the first two principles and reviews recent progress made with clarifying the definition and valuation of infrastructure investments. In particular, the definition of important principles when approaching the asset pricing and performance measurement of privately-held infrastructure has a direct impact on the requirement for data collection.
Chapter 5 describes a framework capturing the financial data that is both necessary and sufficient to answer investors’, regulators’ and policy-makers’ questions, using robust and transparent techniques, while keeping the data collection process realistic and efficient.
We argue that all relevant data should be collected at the firm level, focusing on two types of data points: events and cash flows, each of which can be given a number of standardised attributes corresponding to different “physical” or “business model” characteristics of the firm.
These data also correspond to a set of financial instruments found on the liability side of each firm’s balance sheet, which have their own attributes explaining the payoffs received by investors.
These data then constitute the necessary and sufficient inputs to implement a class of generic cash flow forecasting and asset pricing models to measure the performance of portfolios of private infrastructure debt and equity investments.