Project finance is a key financial vehicle, especially in times when there is a need for investment in infrastructure coupled with the ever tighter government budgets. The demand is growing but many market participants face the challenge to evaluate reliably, and with confidence, the level of risk related to a project. The challenge comes from the inherent complexity of the asset class and its predominantly qualitative analysis, which is unique to each transaction, as well as available collective sufficient data capturing project defaults.
S&P Global Market Intelligence, based on its experience in the sector, has developed tools that capture the assessment of Probability of Default (PD) as well as Loss Given Default (LGD) for project finance transactions. Our PD assessment tool (PD Scorecard) is designed to broadly align with S&P Global Ratings Project Finance criteria, published in 2014, and captures relevant risks for a project finance transaction. In line with S&P Global Ratings criteria, it explicitly separates construction risk from operation risk assessment. We believe the risk assessment of a project should reflect the credit quality of the project during its weakest period until the obligation is repaid through project cash flows. Separation of construction and operation risk assessment allows identifying whether the weakest period is during the construction or the operation phase of the project.Our LGD scorecard is based on the S&P Global Market Intelligence’s expertise and it is complemented by unique insight sourced from the Annual Global Project Finance Default and Recovery Study published by S&P Global Market Intelligence as well as relevant research and criteria published by S&P Global Ratings. In June 2016, the latest publication of the study was issued, capturing the aggregate information of close to eight thousand unrated project finance deals originating globally from 1980 to 2014. The dataset represents more than 75% of the syndicated loan market and about 80% of the unrated project finance space. Each deal is categorised into one of ten sectors, the top 3 being, “Power”, “Infrastructure” and “Oil & Gas” which represent about 80% of the database.
The size and breadth of the data set allows us to calculate metrics, such as annual and marginal default rates, as well as identify performance trends across regions and industries. For example, from 2000 the observed annual default rate for Infrastructure had averaged 0.6% until a significant increase to 1.7% in 2009; defaults peaked to 2.2% in 2010 and have improved to 0.5% since then. The analysis of annual default rates across regions suggests historical downturn periods and default volatility patterns. However based on the Annual Global Project Finance Default and Recovery Study, there is little evidence, overall, to support a hypothesis that projects in emerging markets are materially more risky compared to similar deals in developed markets. Nonetheless, the operating environment of a project can be a critical driver of risk. For example, infrastructure projects are typically either directly or indirectly funded or supported by a government, thus they face significant exposure to regulatory and political risk. Political risk exposes the ways in which a government can define policy and the likelihood of it changing. Such aspects are captured through a country risk assessment, provided for 112 countries, a key component of our Scorecard.
The database captures over 600 defaulted projects. Looking at these we try to establish the common reasons for a projects’ default. Regional and industry analysis suggests that almost 75% of defaults occur within the first 5 years after a closing, indicating that this is the critical “high risk” period when a deal is most vulnerable. Closer analysis indicates the risk is most acute at the point when a project starts its transition to operations and is confronted by a confluence of risk events such as market or industry downturns, change in regulation, cost overruns, etc. essentially altering the industry operating environment for which the deal was structured. While projects expected cash flows are modeled to withstand market and operational downside scenarios by including dedicated liquidity or other credit-protective features, once these buffers are depleted, due to restriction on asset sales or change in strategy, a stressed project has limited recourse.
The Annual Project Finance Study suggests that the 10-year cumulative default rate for project finance is in line with the 10-year cumulative default rate of BBB-rated corporates. This is an important finding, since in general project finance is perceived as more risky compared to corporates from a credit risk point of view. In addition, project finance exhibits a strong average recovery rate of 77%, which is stronger compared to average recoveries observed in the corporate world (for more details please refer to our “ Annual Global Project Finance Default and Recovery Study, 1980-2014”, published June 2016).
As pointed out earlier, project finance is one of or maybe even the most complicated sector to manage given that project structures and operational fundamentals are inherently unique. This poses a challenge from modelling perspective. Based on our experience in project finance model validations, we’ve seen that models often can misinterpret the specifics of the asset class or they can lack key drivers of risk.
For more details on the topic please refer to our mini-webinar “Lessons Learned from 3 Decades of Global Project Finance Defaults”.
Alternatively, you can request more information on the data and analytical tools used for this analysis.