Economic Considerations in the Sales Comparison Approach

In the "old days" of valuation, appraisers compared properties in the Sales Comparison Approach on the basis of Physical Units of Comparison. This included comparisons (after adjustments for cash equivalency, time or market conditions and conditions of sale), for location, land to building ratios, size, quality, land area or land to building ratios, property condition, income characteristics, other items (parking, deferred maintenance etc...) and numerous other physical characteristics.

Though the definitions of items to be adjusted included "Income Characteristics", seldom did these items become a part of the adjustment grid as they tended to be "all inclusive." The physical condition adjustments were made to the sale as compared to the subject. The adjustments themselves were preferably ascertained from "Matched Pairing" or extraction.

In a matched pair adjustment, the appraiser would attempt to find two sales that differed from each other in only one way such as age, clear height or average unit size. The difference developed by comparing the two sales could therefore be construed as the measurement of that particular item.

In extracting an adjustment from two sales or more, one or more differences must be isolated from one of the sales so that the only difference in the sales is the characteristic to be measured. This process is still utilized today, but obviously the data has to be very comparable and needs to be analyzed with a careful eye to aberrations or other differences in the sales themselves.

Today, we often see appraisers comparing properties on the basis of economic considerations. This obviously assumes that for the most part the income generated by a particular property is a function of all of its physical characteristics, and that by comparing the income generated by a property with another ALMOST ALL differences could be accounted for.

Though not entirely correct, the theory is that by correlating or comparing NOIs of a given set of properties with their sale price per SF, a reasonable array of anticipated values per SF would be indicated for the subject property. For example, if a $3.00 NOI produced a sale of a property at $30.00 PSF and a $4.00 NOI produced a sale of a similar property at $40.00 PSF then it would be the theory that a $3.50 NOI would indicate a value of $35.00 PSF. Though simplified, the theory is that a given buyer will only pay so much to capture a given NOI.

With a sufficient amount of data this theory lends itself well to regression analysis. The dependent variable is normally the sale price per SF, as it would be assumed to depend on the NOI. In total, as the buyers look more and more at the income characteristics of properties, it is likely that more of the comparisons for properties in stabilized markets will depend on the income characteristics.

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