At Risk

Alec J. Pacella, CCIM

One of the most popular things in our office is a large white board. It is primarily used to post announcements and track progress on critical projects. But one day, someone wrote a question, specifically “what is your dream vacation?” From that simple question, a star was born.

Each morning, everyone looks forward to the question of the day. Recently, the question “what is your fav board game?” generated the usual scores of answers but one caught my eye. The game of Risk. As a kid, I would spend hours playing this game with friends – drinking way too much RC Cola and eating way too many Doritos in the process. I thought back on the various strategies (I loved loading up in Ural and Siberia) and the friends with whom I could (and could not) strike an alliance. But ultimately, it all came down to a roll of the dice – red if attacking and white if defending.

This month, we are going to discuss another way to roll the dice, not in the world of board games but in the world of commercial real estate investment. So, if you’re feeling lucky, read on.

When considering the acquisition of a real estate investment, investors

will usually develop a scenario that they think will unfold during their ownership period. This scenario, often called a proforma or projection, is based on various assumptions, include items such as rental rate projections, vacancy assumptions, anticipated expense growth and a future disposition price, amongst a myriad of other items. These assumptions are used in building a performance measure, which can be as simple as a gross rent multiplier or as complicated as a leveraged internal rate of return (IRR). But regardless of the measure, the projection is based on forward-looking variables that may or may not reflect what actually will happen.

There are a few ways to acknowledge and compensate for this underlying risk. One is to develop multiple scenarios. Rather than just base the performance measure on one scenario, an investor can use multiple scenarios. The most common approach is to develop three. The first is considered the most likely scenario and all of the variables reflect what the investor believes will occur.

When considering the acquisition of a real estate investment, investors will usually develop a scenario that they think will unfold during their ownership period. This scenario, often called a proforma or projection, is based on various assumptions… which are used in build a performance measure.

For example, an investor is evaluating a multi-tenant industrial property, and, over the next five years, they believe that net operating income (NOI) will be flat. The projection they build, and resulting IRR calculated, uses this assumption to establish NOIs in the future. The second scenario is considered the worst case and all of the variables reflect the most pessimistic reasonable outcome. This scenario forms the floor. Using the same example above, the investor believes that, if things go worse than anticipated, NOI will actually fall at an annual rate of 2%. The model is adjusted to reflect this and a worst-case IRR is developed. The third scenario is considered the best case and all of the variables reflect the most optimistic reasonable outcome. This scenario forms the ceiling. Again, going back to our example, the investor believes that, if things go better than expected, NOI will increase at a 4% annual rate, The model is rerun a third time, using an NOI growth rate of 4%, to calculate the best-case IRR. This approach is very effective in compensating for risk, as it not only illustrates the anticipated performance associated with the most likely scenario but also forms a floor if things go worse than expected and a ceiling if things go better than expected.

Using multiple scenarios is good, but if you really want to dig into risk-adjusted returns, Monte Carlo simulations up the ante. Before we get into how it can be used, let’s talk about what it actually does. And the fact that it shares the same name as a famous casino should be your first clue. Just like a famed roulette wheel, a Monte Carlo simulation will run an analysis literally thousands of times. When spinning a roulette wheel, where the ball lands on any individual spin will be a unique event. But the greater the number of spins, the greater the probability of a uniform outcome occurring. The more times the wheel is spun, the closer we get to a 46.37% probability that the ball will land on red. With a roulette wheel, the odds are known in advance but with a real estate investment, there is much less certainty when it comes to future events.

There are a variety of ways that an investor can utilize a Monte Carlo simulation to address risk and one way incorporates multiple scenarios to form the basis of the simulation. In the example above, each of the scenarios for NOI growth had a 33.3% probability of occurring. While we could weight each of these scenarios by applying a more specific probability, introducing Monte Carlo is the equivalent of spinning the wheel a thousand times. The three scenarios form what is known as a tri-angular distribution – worst case is the low end, most likely case is the middle and best case is the high end. Each of the three scenarios result in a specific associated IRR but, as the expected NOI growth rate is adjusted between the established limits, the IRR will change. A Monte Carlo simulation will run this calculation a thousand times by randomly choosing a NOI growth rate and then calculating the associated IRR. The more the proverbial wheel is spun, the more well-developed the range of returns becomes.

Figure 1 illustrates an example of this process while Figure 2 illustrates a summary of the resulting range of returns. There are many other ways to use a Monte Carlo simulation, as the analysis can incorporate multiple variables. And if you think you’ll need a degree in computer programming,

think again. Software such as Crystal Ball and @Risk make the simulation calculations a snap. Most of the marathon games of Risk from my childhood never had a definitive end. Rather, it became obvious that one player controlled too much territory and had too large of an army and, one by one, the other players would drift away. A Monte Carlo simulation is similar; rather than provide a definitive answer, it relies on statistics to illustrate a likely winner. And no pop and chips are needed!

Alec Pacella, CCIM, president at NAI Pleasant Valley, can be reached by phone at 216-455- 0925 or by email at apacella@naipvc.com. You can connect with him at www.linkedin.com/in/ alecpacellaccim or subscribe to his youtube chan- nel; What I C at PVC.

For October Properties Magazine