Infrastructure development has the power to enrich communities, providing critical public services such as electricity, transport and water. However, it also possesses the potential to be very disruptive to the communities that it serves. Such disruptions include loss of amenity, increased pollution (both noise and air) as well as impacts on the local wildlife. These effects might be considered be minor for society at large but, for the local community, they can be very significant, creating negative sentiment and diminishing public support for the infrastructure development. As a result, a failure to identify and react to deteriorating public endorsement towards infrastructure projects has the potential to lead to delays or even project cancellation.
In this paper, we develop a measure of social sentiment for wind farms. This is a timely approach for analysis amid the current push towards cutting emissions from electricity production, especially given that wind power is a relatively mature renewable technology.
Transition risk assessment using traffic and geospatial data.
In this paper, we develop a methodology to estimate the carbon footprint of thousands of airport infrastructures around the world and test for the existence of a relationship between carbon emissions and realised or expected returns in the private airport investment sector.
We propose a consistent methodology to assess the scopes 1, 2 and 3 of infrastructure companies (in this case Airports) and implement it for several thousands entities around the world. We use detailed geospatial and traffic data to predict scope 1 and 2 emissions several thousand airports
across the globe. We also derive scope 3 emissions from highly granular cruise and landing and take-off (LTO) data.
We then analyse the link between carbon emissions and financial performance: we build a so-called factor replicating portfolio of high minus low carbon intensity using monthly price return data for private airports provided by infraMetrics® and attempt to determine whether this potential ‘factor’ has predictive power in terms of the returns of airports equity returns.
Flood damage factor estimation and bond yields in U.S. airports.
In this paper, we develop a methodology to calculate the potential damage associated with different types of physical risks at the asset level, and conduct a practical implementation for flood damages in the airport sector in the United States. We then use these results to analyse the relationship between airports’ capital costs and exposure to physical climate risk.
Using a new dataset of 470 airports, including over 1,000 runways and more than 800 terminal buildings, we use a 30-meter resolution flood model for a 50- year return period (2% probability) and an airport-specific damage function to calculate damage factors at the airport level.
Next, we look at the impact of physical risks on the cost of debt of infrastructure companies and whether investors in revenue bonds issued by airports price physical climate risks. Using a hand-collected dataset of 2,000+ revenue bonds issued by US airports, our analysis concludes that physical climate risk is not currently priced in the cost of debt of US airports.
Insights from 20 years of energy transition in the UK.
This EDHECinfra Research Note examines the impact on the risk profile of wind and solar power investments of the increasing dominance of renewables in the energy mix of a given country.
As green power sources that are intermittent become central to a power system, without commercially viable medium- and long-term storage options, what is the likely impact on the electricity market and system as a whole and do wind and solar investments, which have historically benefited from a safer, privileged position in the power sector, become riskier?
We use the case of the UK as an example of an economy that has made a rapid transition to renewables and away from coal, while relying on very limited hydro and nuclear capacity i.e., the typical transition required of most advanced economies.
The realised and expected financial performance of green power infrastructure investment, 2010-2021
In this paper, we reviewed the empirical evidence of historical outperformance of green infrastructure investments and consider whether this finding implies continued future outperformance.
• Historical performance of the infraGreen® index in comparison with the Core and Core+ segments of the unlisted infrastructure universe
• Comparative analysis of a green and a brown power infrastructure portfolio
• The presence of a systematic green effect in the valuation of green power infrastructure investments
• The role of excess demand in explaining the historical performance of green power investments
• The evolution of the cost of capital of green vs. brown power infrastructure
• How the current green price premium implies that future investors in greener infrastructure should expect lower returns
In this paper, we compare the behaviour of unlisted infrastructure equity investments with that of traditional assets, with a focus on the effects of shocks such as recessions, financial market crises and policy shocks. We compare the return correlations and drawdown characteristics of geographically comparable indices of unlisted infrastructure equity, listed equity, treasuries and corporate bonds. We then examine their return drawdown and co-variance, as well as higher co-moments of returns (co-skewness and co- kurtosis), to determine the presence or absence of joint extreme risks.
The infraMetrics fund strategy analyser allows benchmarking the gross and net performance of unlisted infrastructure funds using robust IRR and multiple quartiles that are not biased or skewed by the limitation of manager contributed data. With this tool, thousands of observations of the typical performance of infrastructure funds in hundreds of segments, dozens of geographies and 20 years of vintages are available and updated quarterly with no lag. Simulated results are both congruent with contributed market data at the aggregate level over a long period, and more robust and precise at the vintage year or sub-segment level. With this tool, infrastructure manager selection and fund monitoring are not hindered by unreliable and biased reported fund performance data anymore.
The volatility of infrastructure equity investments is the risk which investors take to receive a reward for holding such assets. Therefore, a robust measure of risk and its drivers is an essential part of the inclusion of infrastructure investments in the portfolio, from strategic asset allocation to risk management and reporting, to manager compensation. However, measuring this risk is difficult because the only available data is often limited and typically report unrealistic total return volatility. In this paper, sponsored by the Long-Term Infrastructure Investor Association (LTIIA), we examine the drivers of the volatility of unlisted infrastructure equity investments, that is also, the reasons why the market prices of such investment can and do vary over time.
The market value of these investments is determined by the combination of expected cash flows (dividends), and a discount rate that combines a term structure of interest rates (the value of time) and a risk premia to compensate investors for the uncertainty of the future payouts. On average, the applicable market discount rate is also a reflection of investors’ expected return.
Using our approach to mark unlisted infrastructure to market, we find that the combination of changes in expected dividends (e.g. following a change in demand for transport services or energy) and of changes in expected returns lead to a level of total return volatility in the 7-12% range. The resulting risk-adjusted returns are realistic while still attractive.
Our analysis uses the EDHECinfra database of unlisted infrastructure equity investment data, which covers hundreds of companies over 20 years and a new robust approach to measure the market value of these investments over time. Thanks to this technology, which predicts actual market prices very precisely, it is possible to measure the variability of unlisted infrastructure equity prices and to describe its fundamental components.
In the paper, we conclude that with adequate and reliable measures of volatility, infrastructure can be addressed from a total portfolio perspective (strategic allocation), from a prudential perspective (e.g. Solvency-II) using methods that apply across asset classes.
The robustness of better data & advanced methods
As more investors consider allocations to unlisted infrastructure, the need to bring the asset class into the mainstream of risk management, asset allocation and prudential regulation is increasing rapidly. New prudential rules, the Covid-19 pandemic and the increasing visibility of infrastructure in individual retirement products have made the frequent reporting of fair infrastructure valuations all the more urgent.
Measuring the fair market value and therefore the risks of unlisted infrastructure is made more difficult by the paucity of data, Appraisal values are typically stale and do not reflect the market conditions including the latest price of risk applicable to private infrastructure. In the absence of comparable transactions, most unlisted infrastructure investments have effectively been booked at or near their historical cost.
Thanks to recent advances in data collection and asset pricing techniques, it is now possible to estimate the evolution of fair market prices for unlisted infrastructure equity investments. In this note, we report that:
1. Common risk factors explain observable market valuations of unlisted infrastructure companies.
2. The risk premia of these factors can be measured on an ongoing basis, as new transactions table place. Thanks to these risk premia, individual assets that do not trade but are exposed to the same factors can also be priced.
3. This approach predicts transactions prices accurately within 5% of observed transaction prices and produces robust series of returns with no smoothing.
This technology allows measuring the true yield of infrastructure investments, their optimal contribution to multi-asset portfolios, duration and much more.
In this paper, we show how the traditional indexes used as proxies for unlisted infrastructure fail to represent the qualities of the asset class. Listed infrastructure indices are highly correlated with the wider equity universe – if the asset class behaved in this way, there would be little point in investors buying it as it would not add much in terms of diversification or improving the risk-return profile of the portfolio. Appraisal-based indices are correlated with nothing at all, making them singularly useless for the task in hand – their construction gives results that are so “smooth” that volatility is very low and correlations close to zero, which would signal unrealistically high risk-return rewards that are simply unfeasible in the real world. EDHECinfra’s indices of unlisted infrastructure, on the other hand, such as the infra300®, represent the characteristics of this asset class well, making them the best available proxy for investors to use.
We also show how investors can carry out a simple asset allocation exercise to calculate the optimal allocation they should be making to unlisted infrastructure based on their individual portfolio needs. Using different optimisation techniques and parameters, and considering different investor profiles, our research signals consistent allocations to infrastructure in the region of 10%, many times current levels. Our indices also offer a granularity that can help portfolio design in a way that broader and less well-defined proxies are unlikely to achieve for those seeking to optimise risk-adjusted returns.
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