If you use Excel to model businesses, business processes, or business transactions, this course will change your life. You’ll learn how to create tools for yourself that will amaze even you. Unrestricted use of this material is available in two ways.
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In this final session of the series on capital leases, we explain how to model the effects of a stream of lease events that occur under the terms of a single lease agreement. Since each event triggers its own set of cash transactions, interest payments, principal payments, and depreciation, tracking the effects of such a stream on the three financial statements can be nightmarishly complex.
We’ll show you how to use convolution and the Lease Characteristic Array to make a simple model that predicts these effects as well as can be known. Using this technique, the fidelity of your model will be limited not by your ability to deal with complexity, but by your ability to forecast the business terms of the lease and the schedule of purchases. This is our goal — to remove complexity as a barrier to model fidelity.
Below is a summary of pages for Session 10.
Links to other materials for Session 10.
Last Modified: Wednesday, 22-Apr-2015 00:52:55 UTC
The Lease Characteristic Array is an example of a construct that makes more effective the modeling of a wide class of problems involving leasing. Certainly, it can be generalized, and certainly there are other possible constructs that could also be helpful with these kinds of problems. But let’s not stop there.
Certainly there are other problems that are susceptible to similar approaches. That is, we can create spreadsheet “idioms” that have applicability to problems more general than the problem immediately at hand. In a field as rich and complex as spreadsheet modeling, it is extremely unlikely that you will find all such techniques described in books, course, or Web sites. From time to time, you’ll have to invent them yourself.
You’ll do best if you can figure out how to produce useful idioms that can help solve wide classes of problems.
Many models are linear with respect to some of their input parameters and input streams. This means, among other things, that when we multiply the input by a constant K, the output also increases by a factor of K. If you know that your model is supposed to be linear with respect to some input parameter or input stream, you can use this fact to check your model: doubling the input should double the output.
You can build these checks into your model from the beginning by defining parameter multiplication factors. For instance, suppose you know that the model’s personal computer expenses should be proportional to the number of employees. By introducing a scaling parameter (normally 1.0), which multiplies the initial number of employees, and multiplies the hiring streams, before they’re used in the rest of the model, you can easily investigate the effects of changing the number of employees, to verify proportionality.