Of the world’s top four owners of airliners, two are lessors: GECAS, with 1,732 planes, and ILFC, with 1,031, soar miles above Delta (800) and American Airlines (775).

Airlines lack cash to finance their big plans for fleet renewal, and they cannot borrow cheaply to buy new planes. Deals in which airlines sell part of their existing fleet to a lessor and rent it back are becoming more common

Aviation is becoming more like the hotel business: one type of firm specialises in owning the assets, while another operates them. But there is an important difference: a hotel owner cannot easily seize his premises back from a hotelier who skips the rent

Renting makes sense for small, young airlines that lack capital, for larger airlines trying out a new line of business for which they need different planes, or when manufacturers’ order books are full and the only way to get a plane is to rent it.

But big airlines are better off buying planes and keeping them for their full lifespan of 30 years or so. Like houses, buying is cheaper than renting in the long term.


Recently founded companies in high growth markets who cannot afford to buy their aircraft because of a lack of assets


Investors are also placing a premium on "lean" companies. The one with the lower assets will have actually a higher stock market value

Comments

Post a Comment

Popular posts from this blog

Ensemble

Boosting: In lieu of training all the models separately as in bagging, boosting trains models sequentially. Each new model is trained to correct the errors made by the previous ones. The first tree is examined and the weights of those observations that are hard to classify are increased and the weights for those that are easy to classify are reduced. This modified data is used to build the next tree. This process is repeated for a defined number of iterations. Predictions of the final ensemble model is therefore the weighted sum of the predictions made by the previous tree models. GBM uses loss functions Since each tree is fit to residuals as against the original output parameter, each tree is small and improves prediction in the parts where prediction is bad. All the models might make the same mistake in the standard ensemble method. Compute error by deducting forecasted value from target value (e1= y - y1_forecasted) Build a new model on errors (e1_forecasted) as target variabl...
Read more

Bias-Variance tradeoff

Bias refers to the deviation of the predicted values from the correct value. The error occurs when you make wrong assumptions about data. In other words, it is the error that is created when you represent a real-life complex problem using a simpler model while it might be making them easier to understand. For instance, when you build a linear model to solve for a non-linear problem. It results in under fitting and makes them less flexible. Parametric algos like Linear Regression can produce high bias while non-parametric algos like Decision Trees make good assumptions about the training data and target function and hence do not have high bias. Variance refers to the change that occurs when the model is applied on a different training data. It occurs when the model captures not just the underlying pattern but noise as well. It results in overfitting. In other words, it is memorising the data. It is often observed in Decision Trees. When the observations are limited but the...
Read more

AI

we are now at a critical juncture where many of the systems we need to master are fiendishly complex, from climate change to macroeconomic issues to Alzheimer’s disease. The problem is that these challenges are so complex that even the world’s top scientists, clinicians and engineers can struggle to master all the intricacies necessary to make the breakthroughs required. It has been said that Leonardo da Vinci was perhaps the last person to have lived who understood the entire breadth of knowledge of their age. Since then we’ve had to specialise, and today it takes a lifetime to completely master even a single field such as astrophysics or quantum mechanics. The systems we now seek to understand are underpinned by a vast amount of data, usually highly dynamic, non-linear and with emergent properties that make it incredibly hard to find the structure and connections to reveal the insights hidden therein.  Kepler and Newton could write equations to describe the motion of p...
Read more