Although a “perfect” adjustment may not exist, the search for perfect adjustments in appraisal reports is becoming a hot topic with many interesting articles, continuing education courses, and webinars addressing the subject. The message is clear. If an appraiser can’t support the adjustments, the results of the appraisal are subject to question, leading to several possible outcomes; none of them good. So, we will define the “perfect” adjustment factor as the best and most appropriate adjustment that should be made to the comparables.
I think we would all agree that the accuracy of an appraisal is primarily dependent upon the selection of the best comparables. The Sales Comparison Approach uses each of the selected comparables as a starting point and then adjusts the actual sales price for differences in date of sale, sales concessions, site size, location, view, condition, quality, condition, gross living area (GLA), bath count, parking, porch/patio/deck configuration, fireplaces, swimming pools and other features impacting value.
The more similar the comparables are to the subject, the fewer adjustments will be required. And the adjustments will be smaller as well. Adjustments are calculated by multiplying an adjustment factor times the quantity difference between the subject and comparable. For example, if the GLA for the subject is 2200 sq ft and for a comparable, 2000 sq ft, the difference, 200 sq ft would be multiplied by the adjustment factor. If the GLA adjustment factor was determined to be $60/sq ft, the adjustment would be $60/sq ft x 200 sq ft = $12,000 added to the comparables sales price. Obviously, the smaller the difference between GLAs, the smaller the adjustment, regardless of the magnitude of the adjustment factor. So, the greater the similarity between the subject and comparables, the smaller the impact of a too high or too low adjustment factor.
In selecting the comparables to be used in the sales comparison grid, it is also important to “bracket” the high impact variables like GLA, condition and site area. By selecting comparables with variables that are higher and lower than the subject, placing the subject towards the middle, the positive and negative adjustments will tend to cancel each other out. This also reduces the impact of using adjustment factors that are too high or too low.
My point is, that while it is important to use reasonable adjustment factors, If I were to follow the current push toward comprehensive detailed support for every adjustment factor used for every appraisal, it would increase the amount of time spent to the point that my clients simply wouldn’t pay for it.
In addition to good comparable selection, what’s the solution? Put time and effort into high impact parameters and use low impact adjustment factors that are judged to be reasonable, resulting from previous analysis; matched pair or regression.
But what happens if you use a “wrong” adjustment factor? The impact will probably be minimal as long as the comparables are reasonably similar to the Subject and bracket the Subject’s high impact parameters.
The following “Sales Comparison Sensitivity Illustration” spreadsheet illustrates the point. The hypothetical sales comparison grid contains five comparables along with the subject, adjustment factors and adjustments. The comparable sales prices have been adjusted so the comparable adjusted values are all equal at $320,000. That makes the adjustment factors “perfect”.
The grid titled “Results w/ Various Adjustment Factors Applied” shows what happens if too high or too low “imperfect” adjustment factors (in red) are used. Case #1 uses the “perfect” adjustment factors for every parameter but GLA. In this case, the GLA factor used is 30% of the average sales price per square foot or $44.24/sq ft compared to the perfect adjustment factor of $73.74/sq ft (50%). When this is done, the weighted adjusted value of the five comparables is $319,000 which is 99.7% of the correct $320,000. Not so bad for such a big “mistake” with a high impact parameter!
Case #2 substitutes nine adjustment factors and leaves the GLA perfect adjustment factor. The result is a weighted adjusted value of $324,000 or 101.3% of $320,000. Cases #3, #4 and #5 similarly show the impacts of various mixes of perfect and imperfect adjustment factors on the weighted adjusted value. The range of the five cases is $319,000 to $324,000 or approximately 99.7% to 101.3% of $320,000.
The conclusion to be drawn from this example is that the use of imperfect adjustment factors does not necessarily compromise the validity of the results. This is not to minimize the importance of using reasonable adjustment factors. It is to point out the fact that good comparable selection – including bracketing – can go a long way to mitigate the adverse impact of the use of imperfect adjustment factors. This should be kept in mind when prioritizing limited resources.