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Most everyone has heard the term “comps” as applied to valuation. It means data from comparable sales of businesses.
But the data itself is problematic. The most common databases are Pratt’s Stats and BizComps. These sources of data are organized by Standard Industry Codes (SIC).
This may seem reasonable, but in fact there is as much variation within a particular SIC as between different SICs. The reason is there are an infinite number of business models, and each produces a different return on investment. This, and other facts outlined below indicate that the so-called “data” is not really data at all as used in the statistical analysis sense. It is really a series of loosely correlated data points, which much be interpreted virtually as anecdotal evidence. It also is usually unverifiable, coming from third party volunteer sources.
The shortcomings generally are that they represent different:
3. accounting methods
4. business models
5. scale (i.e. gross sales)
6. geographic areas
7. economic climates
In analytical terms, it is a multivariate array which makes attempts to analyze it with conventional statistical analysis tools, using normal distributions and standard deviations. It's meaningless.
So how do you deal with this information? American ValueMetrics uses a methodology known as “heuristics.”
The Source of the word “Heuristics” (pronounced hyu-RIS-tiks) is the Greek "heuriskein" meaning "to discover." This is the same source as the word “Eureka” meaning “I have found it.”
In the past twenty years, it has been refined into a modern problem solving and analysis methodology, usually associated with systems science. It deals with decision making where specific reliable data is not available, and/or the time and cost of obtaining such data necessary for a conventional statistical analysis is simply not feasible. Heuristics is the art of drawing inferences when faced with limited, incomplete or fuzzy data.
For valuations in the area of business assets, or unregistered, exempt, closely held securities or where transaction data is sparse, it is an appropriate methodology for determination of central tendencies, such as price/earnings ratios. It is especially useful for evaluating minority and control discounts in a defensible way.
In practice, the information is ranked and weighted, based upon the judgment of the appraiser. This requires experience. The heuristics process is dependent upon an innate quality of the human brain called “pattern recognition.” We all operate this way. A good example is our understanding of words in a language context. Words are comprised of letters, which approximate sounds, which are strung together to convey ideas. When you hear a spoken sentence, your mind completes pattern recognition and you can intelligently understand (hopefully) what the speaker is saying without trying to analyze each letter or each sound.
The same process holds true in an appraiser’s analysis of comparable business sales data. An experienced appraiser sees patterns in the data and can readily rank or weight the data as to relevancy to his subject business. The problem is broken into many small decisions, and reintegrated using a mathematical approach, usually a weighted average. When the result is a capitalization rate or a capitalized value, it is amazing how accurate it is.
So, when data is not limited to one variable, and is not widely available, and is not verifiable, the heuristics approach is the most efficacious approach available for making sense of the comparable sales data.
*Disclaimer*: Harvard Business Services, Inc. is neither a law firm nor an accounting firm and, even in cases where the author is an attorney, or a tax professional, nothing in this article constitutes legal or tax advice. This article provides general commentary on, and analysis of, the subject addressed. We strongly advise that you consult an attorney or tax professional to receive legal or tax guidance tailored to your specific circumstances. Any action taken or not taken based on this article is at your own risk. If an article cites or provides a link to third-party sources or websites, Harvard Business Services, Inc. is not responsible for and makes no representations regarding such source’s content or accuracy. Opinions expressed in this article do not necessarily reflect those of Harvard Business Services, Inc.