10/01/2017

Scenario forecasting in the aviation industry

Scenario forecasting in the aviation industry

In this Insight, Ishka asks Ed Hansom, the chief risk officer at Seraph Aviation Management, how successfully investors can predict aviation downturns by examining macroeconomic factors such as changing fuel prices, world GDP or rising interest rates.

 

Most research on the aviation industry cycle examines what the impact any given crisis may have on key outputs that measure success or failure in the aviation industry.

These output metrics include: traffic growth, yield change in constant US$ terms, the airline industry’s profitability the aircraft survival cycle and finally the aircraft value cycle.

However, there is little public discussion of whether there is an inherent relationship between macroeconomic factors and these specific aviation output variables.

In short, scenario forecasting in aviation tends to ask how will the aviation industry perform if any of these aviation outputs vary, but not what will happen to the industry if World GDP declines, or after a terrorist attack.  This Insight asks whether there are identifiable correlations between such events and key aviation outputs and speculates on the best strategies to help deal with them.

The Ishka View is that while a macroeconomic disruption will typically exaggerate an aviation industry downturn there is actually very little direct relationship between most macroeconomic variables and the fortunes of the aviation industry. This is due to the geographical diversity of aviation and the fact there is weak correlation between traffic growth across regions.

 

What scenarios should we focus on?

 

Downturns are a regular occurrence and there is a degree of similarity in the way the industry reacts to such events e.g. reduced aircraft utilisation and load factors, ultimately affecting deliveries (aircraft supply).

The aim of this exercise was to better understand the relationship between traffic, aircraft productivity, deliveries, aircraft retirements, airline profitability and aircraft valuations that could help to better manage industry cycles.

Another objective was to keep the methodology simple and avoid complexity.

The following key macroeconomic factors (inputs) and industry variables (outputs) were identified.

Macroeconomic inputs included

  • World GDP growth
  • Fuel price in constant US$ terms
  • 5-year treasury note yield
  • US$ trade weighted FX rate
  • OECD Terrorist fatalities

Industry output variables covered

  • Traffic growth
  • Yield change in constant US$ terms
  • Airline industry profit margin
  • Aircraft survival cycle
  • Aircraft value cycle

Aircraft supply and demand metrics have been developed internally at Seraph Aviation Management. The research covers data starting from 1980 until 2015. The Seraph analysis does not take into account early technology aircraft such as the B707 or 50-seat regional jets since both aircraft categories suffered early economic obsolescence and are not really comparable with mainstream single-aisle and twin-aisle jets.

 

‘Auditioning’ the macro inputs

 

 

Among the macro input variables chosen for the study, only World GDP growth and fuel price seem to matter and have some impact on the key industry variables, although not all of them. While GDP impacts traffic, yields and airline profitability it has limited effect on the aircraft supply and demand metrics and valuation cycles. On the other hand, fuel price tends to lack correlation with traffic, yield and profitability but does matter for aircraft survival and valuation cycles.

While the OECD terrorist fatalities seems to have some degree of correlation with airline profitability and in the right direction, the results could be skewed significantly by 9/11 and condition of the US airline industry at the time. The US airline industry at the time was highly vulnerable with weak financial and cost structures and just waiting for a trigger (in this case 9/11) to cause a financial catastrophe.

 

GDP is the key to traffic growth

 

 

As seen in the correlation matrix table, among all the macroeconomic input variables, world GDP growth has the strongest relation with majority of the key industry variables. Furthermore, the linkage between the two variables is stronger over longer periods so as to eliminate short-term fluctuations. The traffic/GDP multiple captures the gradual maturing of the industry. A linear trendline of traffic/GDP multiple has an r-squared of 0.68 using 10-year rolling CAGR.

 

Geopolitical risk and traffic growth

 

 

Surprisingly, geopolitical factors tend to have a weak direct linkage with traffic. Correlation between fuel price and terrorism incidents and traffic growth is the wrong way around or minimal at 0.27 and -.16 respectively. Part of the reason is the increasing regional diversity of airline industry – there is weak correlation between traffic growth across regions. This suggests that global impact of geopolitical risk is mitigated by regional differences.

 

 

How to model the airline profit cycle?

 

Data also suggests that airline profitability depends not only on economy (macroeconomic factors like GDP growth) but also capacity utilization. Aircraft survival cycles are a measure of industry capacity (aircraft supply and demand).

 

 

As can be seen from the charts, there appears to be a strong linkage between both airline profits, world GDP growth and the aircraft survival cycle. A multiple regression between the variables yielded an adjusted r-squared of .28. While this may not seem high compared to the traffic growth regression above, the relationships are all statistically significant. There is a fairly strong correlation of 0.47 between airline profitability and aircraft survival cycle. This makes intuitive sense since airlines should have stronger margins when capacity is tight.  This topic would benefit from further study, particularly to see if the global relationships hold up at the regional and country level and whether additional factors could be added to strengthen the model.

 

Measuring supply and demand for aircraft

 

 

The left-hand chart above shows survival curves for single-aisle and twin-aisle passenger aircraft. The curves measure the percentage of aircraft remaining in passenger service by age. Survival curves can shift to the left or the right over time –  aircraft longevity peaked in the late 1990’s and early noughties, and has shifted left since 2008 though not substantially. There are several reasons for this, after 2008 financial crisis, unlike all previous economic crises, deliveries actually went-up after a year because of cheap capital and high fuel prices. This was significant incentive for airlines to acquire new aircraft and the sheer volume of deliveries acted as a push factor for accelerating fleet retirements. In addition, there was a pull factor created by the early part outs for some aircraft. In these cases, it made more economic sense for airlines or lessors to break the aircraft than to spend money on the maintenance or modifications required to prolong the aircraft’s service life.

Survival curves can be used to calculate the expected number of aircraft in service at a given point in time by applying the in-service percentage for new aircraft to all aircraft produced in the relevant year, the in-service percentage for one-year old to all aircraft produced the previous year etc. This in turn can be compared with the actual fleet and if the actuals are higher than expected, then it implies that capacity is somewhat tight by historic standards and vice versa.

On the right-hand side is a scatter chart of historic current market values. But the scatter is based on current market values as a percentage of replacement cost. Replacement costs are Stellwagen’s internal estimates based on third-party appraised values and estimated average OEM unit revenues by aircraft type. This analysis can be used to generate expected used aircraft values based on drawing a trendline through the data. Once again comparing actual with expected values can provide a measure of the aircraft supply and demand cycle. Both the aircraft survival cycle and value cycle are strongly correlated at 0.75.

 

Aircraft survival and value cycles

 

 

Even at an aircraft class level the correlation is robust - single aisle correlation is 0.71, twin aisle correlation is 0.68. However, both cycles lack any correlation with GDP growth which possibly can be explained by the aircraft deliveries which has a negative 0.38 correlation with GDP. At expected, aircraft survival is negatively correlated (-0.62) with fuel price as cheap fuel can act as an incentive for keeping old aircraft in service longer.

 

The Ishka View


The findings of this Insight show that macroeconomic factors/disruptions tend to trigger industry downturns, however there is very little direct relationship between most macroeconomic variables and key industry variables such as passenger traffic growth, airline yield or airline profits.

Part of the reason is the increasing regional diversity of airline industry – there is weak correlation between traffic growth across regions. Another reason is the inherent inflexibility in terms of both lead times for individual aircraft and lead times for changes in production levels. As a result, although the world GDP will have an impact on the airline industry, it does not have a substantial impact on supply and demand for aircraft because deliveries do not adjust in real time.

The findings also confirm that there is more than one cycle – airline industry cycle and the aircraft supply and demand cycle to name two. The aircraft leasing industry is very good at managing cyclical risks through geographical lessee diversification and spreading lease expiry dates. The diversity or the lack of correlation across different regions makes diversification a good risk-mitigation strategy.

It is nevertheless still exposed to equipment selection and pricing risk. Managing the equipment related risk requires discipline in terms of growth plans. Also, for a lessor an active trading strategy is a good risk management tool and lessors, particularly lessors of young aircraft have great scope to use aircraft trading to manage their risks.
 
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