The bottom line about any real estate investment analysis is that it is a risk analysis. If risk was not an issue with investing, and all the results of any given investment were known with certainty, than creating an analysis for any type of real estate investment would simply be a matter of arithmetic. But the truth about real estate investing is that many factors come into play (i.e., the economy, tenant trends, etc.) that make it impossible to ever know with absolute certainty enough about a typical property to remove every element of the unknown.
Since the ability to accept varying levels of risk will differ from investor to investor, many simply avoid real estate altogether and opt to put their money only in relatively risk-free investments such as government Treasury bills. But the price for this lower level of insecurity, of course, is a lower rate of return. Why, because a relationship always exists between risk and rate of return. Therefore, when investors are attracted to the certainty, they in effect force down the rate of return they are willing to accept as a tradeoff for their unwillingness to accept uncertainty.
Okay, so what about the risk takers? What can investors who prefer to collect the higher rates of return associated with real estate investment do to deal with (and perhaps minimize) the ambiguity? Investors must exploit tools that can potentially measure this risk. One method is by applying what is known as a “probability distribution” to prospective real estate investment opportunities.
For example, rather than using just one set of rents to ascertain potential cash flows and returns for a rental property, the investor should consider several rent scenarios that reflect an estimated probability of their occurrence.
In my real estate investment software, for instance, a form is provided that allows users to apply three different rent scenarios to a rental property. This way, rather than just having to accept whatever rents are presented by the seller, the investor can analyze the cash flows and returns based upon a range of rent probabilities (i.e., most likely, somewhat likely, and not likely but “wow, wouldn’t it be great”).
The logic is straightforward. Say, for example, that you’re doing an analysis on a ten-unit apartment complex made up of ten two-bedroom, one-bath units each reportedly with the potential of renting for $700 per month. My own experience warns me that “potential” rents may (or may not) be likely, so I always prefer to run my own rent scenarios. In this case, then, you would use our Rent Scenarios form and assign three rent probabilities based upon your own measurement of risk, and instantly you are the results so you can analyze what impact each rent might have on cash flows, rates of return, and profitability. The outcome if monthly rents are more likely at $650, for instance, could affect your willingness to chance buying the property.
This is only one of a variety of mathematical and statistical approaches to risk analysis that will help you address the uncertainties of real estate investment. But you get the idea. The best way to deal with uncertainty is to measure it. And the probability distribution we illustrated for rents is a good first step.
You can see a screenshot of our Rent Scenarios form at http://www.proapod.com/Tour/basic/screenshot_4.htm