A fairly new method of dealing with imprecision (called Fuzzy Logic) has been developed. So far its principal applications have been in the physical sciences, but some researchers are beginning to recognize its potential applicability to the social sciencesspecifically real estate decision making.
Does an estimated rate of return on a real estate investment of 10.5 percent mean a return of 10.5 percent? Perhaps in a sense yes, but in reality, no. We all know that even a return calculated after disposition of a property is an estimate, and that an internal rate of return (IRR) of 10.5 percent means a return that could vary from perhaps 9.5 percent to 11.5 percent. The number, 10.5, is really a "best approximation" based on similarly imprecise estimates of the numbers used to calculate the IRR.
Does this lack of precision render the estimate useless? Certainly not. It can be compared with other, similarly imprecise estimates for decision-making purposes. For example, if we compare a forecast IRR of 10.5 with another forecast IRR of 9.5, and we believe that both forecasts are subject to the same degree of imprecision, we would (other things being equal) choose the investment yielding 10.5 percent. It does mean, however, that decisions based on the estimate may be incorrect because the degree of imprecision may produce incorrect numbers for decision-making purposes (for example, the 10.5 percent may in reality be 9.8 percent, while the 9.5 percent may in reality be 10.2 percent).
Even historic estimates of some numbers are imprecise. For example, can we say with precision that a building depreciated by 10.0 percent over the preceding five years? Obviously, the 10.0 percent is also an imprecise estimate. Similarly, the estimated future net operating income (NOI), terminal capitalization rate, and tax liability are imprecise estimates. So, even in retrospect, an IRR is an imprecise number.
Other types of estimates are even more obviously imprecise. For example, access to a shopping center may be rated as convenient, inconvenient, or somewhere in-between. Or, we may rate the attractiveness of a shopping center as high, medium, or low. Similar imprecise ratings may be required for a shopping center's layout, convenience of parking, adequacy of parking, ease of maintenance, energy efficiency, and signage. Such ratings will probably be required when either 1). estimating the value of the shopping center or 2). evaluating the performance of the shopping center. Analogous ratings would be required for other property types.
We live with imprecision in most aspects of life, and real estate decision making is no exception; the examples cited above are but a few illustrations of the imprecision that pervades all types of decisions. The question then becomes, how do we deal with imprecision? We could:
1. Ignore it. We base our decisions on numbers and ratings that we pretend are precise. The problem with this approach is that in effect we are gamblers, and we either win or lose. There is no protection against the possibility that our estimates are incorrect on the unfavorable side.
2. Recognize it implicitly. We hedge our bets and keep our options open, even when some hedges and some options cost more than their value. In other words, we know that our estimates are imprecise, but we have no estimates of how imprecise or in which direction.
Therefore, the ways in which we often deal with the imprecision of today are not satisfactory for making truly informed decisions. However, a fairly new method of dealing with imprecision (called Fuzzy Logic) has been developed. So far its principal applications have been in the physical sciences, but some researchers are beginning to recognize its potential applicability to the social sciences-specifically real estate decision making. The purpose of this article is to describe generally what fuzzy logic is, to show a simple example of its application to real estate, and to illustrate how a more complex fuzzy system might be constructed to deal with imprecision in real estate decision making. …