What is the aircraft you’re considering buying or selling actually worth?
By Anthony Kioussis
President, Asset Insight
By any standard, an aircraft acquisition represents a major investment. But it is also a depreciating asset and, as such, its Residual Value (RV) is an important factor for anyone with a fi nancial interest in the aircraft, such as the buyer, lessor, or the company financing the aircraft.
Lessors sometimes rely on Residual Value Insurance (RVI) to guaranty their downside RV risk, and their lessee is expected to return the aircraft under a predetermined set of “lease return provision.”
On the other hand, loan fi nancing entities rely on the borrower’s credit, and often on personal guarantees, to secure their risk should the borrower default. Owners have the option to acquire RVI, but usually just make an assumption of what their aircraft will be worth at some future point and “hope” – never a good strategy – they achieve the figure.
Residual Value forecasting
Residual Value forecasting has been viewed as a black art by some, a best guess by others, and even as a figure that has only “dartboard” precision. Historically speaking, there may have been merit to some of those views.
To begin with, the number of data points reviewed by appraisers in the past was limited to the facts they had available. Additionally, the difficulty of manually analyzing the complex relationships between multiple data sets covering a lengthy time period was so daunting that the RV’s precision was usually reduced to an average annual depreciation figure based on the make/ model’s historical trend.
Another problem has been that RVs have historically focused on a single, defi ned set of Comparable Aircraft (Comparables) that are subsequently adjusted to mimic the Subject Aircraft. In reality, the “for sale” market is continually changing and each Comparable moves through its life independent of the Subject.
That needs to be taken into account by swapping Comparables throughout the RV term with aircraft that are truly “comparable” to the Subject. This is not possible in a timely manner absent digital support.
Computers aid in Residual Value analysis
Today, computing speed allows for scientific analysis of the entire market when valuing a single aircraft, assuming the appraiser possesses the modeling skills and the necessary data. RV forecasting is now able to take into account data points for the specific aircraft under review, every serial number for the make/model under review, other synergistic models, and the overall market.
See Table A. Perhaps the greatest difference between the changing parameters in RV calculations is that “Traditional Residual Valuation Parameters” simply “age” the Subject and Comparables based on an understanding of where the market is today.
“Current Technology-Leveraged Valuation Parameters” actually allow for the analysis of an ever-changing subset within the overall market. RV precision has also been improved through maintenance analytics able to objectively grade the maintenance condition of a specific asset as well as all comparable aircraft. In addition, these analytics can derive a value for any Hourly Cost Maintenance Program enrollment, and every aircraft’s current and projected Maintenance Equity (the embedded value of maintenance an aircraft has available).
For a detailed explanation of Maintenance Equity, see “Understanding how Asset Quality affects value of an aircraft offered for sale” (Pro Pilot, Jan 2018, page 12).
Today, the traditional RV trend line (blue line on Table B) is still sought by many due to its simplicity and, to be fair, its accuracy has improved.
However, more and more entities holding a fi nancial interest in an aircraft are interested in the actual RV line (red line on Table B). In addition to more accurately reflecting the anticipated RV at any point in time, technology allows for the projected RV line to be modifi ed and tracked based on the aircraft’s changing maintenance status.
Additionally, computing speed and advanced modeling techniques allow for the ongoing, simultaneous analysis of all comparable assets, thereby placing any aircraft’s forecasted RV in context relative to the anticipated performance of comparable assets.
Table C details how much more information is now available through online, automated RV calculation and tracking systems. By computing each Comparable Aircraft’s maintenance condition on a monthly basis, it is now possible to place the Subject Aircraft in perspective, meaning within its Comparable Range, thereby providing additional confidence data point.
Improved accuracy in evaluating RV Scientific analytics, leveraged by automation, have greatly improved RV precision. Accuracy will further improve in cases where software programs reside on true Artificial Intelligence (AI) platforms.
There are many systems today capable of running complex modeling. However, the accuracy level achieved by AI platforms such as the one employed by Asset Insight, will continue to improve by virtue of embedded algorithms. Algorithms are programs that learn from themselves and thereby become smarter as they process data.
As computing power increases, that learning capability moves exponentially with the growth of computing speed. One additional value to software programs is their ability to continually and exactly replicate analytical processes.
This is important, as it eliminates subjectivity and the opportunity for human error, while ensuring the integrity of the analytical process is maintained. The one item RV figures do not presently take into account is changes to the economy.
This should not be surprising considering the challenges economists have in making such predictions. However, by utilizing science-driven, economy-neutral RV figures, anyone with a financial interest in the aircraft has a good starting point from which to make additional projections.
Anthony Kioussis is President of Asset Insight, which offers aircraft valuation and aviation consulting services. His 40+ years of experience in aviation includes GE Capital Corporate Aircraft Finance, Jet Aviation, and JSSI.