How Bad Is Bad? Estimating Risk Probabilities in Commercial Real Estate

By Zoeller, Mark | The RMA Journal, May 2007 | Go to article overview

How Bad Is Bad? Estimating Risk Probabilities in Commercial Real Estate


Zoeller, Mark, The RMA Journal


[ILLUSTRATION OMITTED]

Mark Zoeller takes a common, often overlooked, problem and offers a path toward a workable solution. In this first of two articles, Zoeller feels that risk analysts are missing the boat by neglecting an important aspect of steering it. It's the old question of having the data to show risk probabilities but not having the tools to interpret it in a way that dictates an action. In this article, he encourages bankers to adapt an Excel[R] spreadsheet model to fit the needs of their institutions and to help them correct course when needed. Next month, Zoeller offers technical rationale and some of the alternatives used within the financial community to estimate risk in commercial real estate.

Do you stress test your commercial real estate loans? Do you test cash flows and debt service coverage? What about collateral values and loan-to-value ratios? Will the adequate coverage you show today withstand the test of time? With what probability?

Cash flows, debt service coverage (DSC), collateral values, and loan-to-values (LTVs) are dynamic. Although many banks stress test by showing how interest rate changes affect the DSC, few discuss the results in terms of probability. The testing seems to be informational only--it does not convey any sense that it affects loan decisions or structure.

Such phrases as "most likely," "worst case," and "best case" often accompany stress tests. These descriptions, though, seem arbitrary. How probable is the worst case? Would it make a difference in your loan structure, price, or risk rating if, at the end of five years, your initial 80% LTV has a 15% probability of increasing to 95%? What if your initial 1.10x DSC has a 20% probability of being less than 1.0x? (1)

The model presented in this article calculates those probabilities and can help you understand the dynamics of the LTV and DSC probabilities. It uses common techniques within the most common financial modeling tool, Excel[R]. It incorporates some methods already used in the banking community to measure value-at-risk (VaR). The investment community also uses some of the methods to value stocks. I have adapted them for use in CRE. I present the model not as a turnkey spreadsheet fully fleshed out for you to use as is. Rather, it is an example of how you can use the methods to construct your own models to meet your circumstances. (To download a live copy of the model in Excel, see http://richardgarrettassociates.com.) As you work with this model, I hope you can continue to improve it.

Statistics

Many of us took a statistics course in college and may remember, vaguely, some of the concepts. In our day-to-day work as lenders, we may see no benefit to the concepts we did learn. The relevance is just not there. Most college-level statistics courses have little relevance to what we do in credit.

We may even disdain a statistical approach to lending because it is too mechanical. With years of experience, successful lenders may be more comfortable with the touch and feel of lending. Statistics is not touch and feel. More important, statistics is not destiny. It is not intended to be destiny. If we use statistical methods judiciously, however, they can give us information and insight that allow us to be better informed about risk.

A model, such as the one presented in this article, can help us understand the risk (probability) that the DSC might decrease or that the LTV might increase beyond critical levels.

Monte Carlo

Monte Carlo simulation is a statistical/mathematical technique using random numbers. Developed during World War II for other purposes, banks have come to use it for value-at-risk calculations. CreditMetrics[R], (2) for example, uses Monte Carlo simulation. Stock analysts use it to study potential movements in stock price. By using random numbers, it shows how changing the subparts of a system affects the end product or amount over time. …

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