This is an extract of the Sales Comparison Approach, using Intangible Value Containment, for a small 800 square foot home in Pacifica. Most of these size homes are updated (in the sense of remodeled) on resale because it is fairly inexpensive to update/upgrade such a small home. This home, however, has had only a few minor upgrades in the past 70 years. The exterior has been satisfactorily maintained and the heating unit has been upgraded. Otherwise, few if any upgrades are apparent. For this neighborhood, the lack of upgrades puts it very near the bottom in Condition, Quality, Aesthetics – and Features.
The Stage I regression was initially based on 1,706 sales going back to 2001. However, the number of sales was eventually trimmed down to 363 sales most comparable to the subject. Because of this, the comparable data set had a good size dataset of many different variations in features; thus I was able to extract adjustments for such things as the type of flooring.
Of particular interest should be Stage III, where the subject is ranked. Note that for this appraisal, I used a CQA score range of 0.0 to 10.0. – Understand, the CQA score assigned to the 10 comparables (in fact a CQA is automatically assigned to all 1706 comparables that went into the regression), was done systematically and objectively simply using an Excel macro. The ONLY subjective input from the appraiser here is really the scoring, i.e. setting the value of CQA, for the subject. And that turned out to be a rather low 0.5.
Normally determining the value of a home this far down in the condition/quality scale would involve diving into the cost approach. Here, it is done strictly with the sales comparison approach – which is made possible with the Subjective Value Containment Approach. You need to study the 10 sales comparables carefully. Read the MLS listings attached to the report and study the pictures, with respect to the assigned CQA score. Ask yourself how you would rank the subject. You should see, there really is not much leeway around a CQA value 0.5. Also, consider the steep value curve in Figure 38. This figure shows the typical characteristic CQA curve for residuals. Look how drastically the contribution of CQA changes near 0.5. Typically ranking or scoring the subject when it is near the far left or right of the curve is easier than in the middle of the curve where it is hard to place the subject. However, at the same time, due to the steeper curve you typically find near the ends, it becomes more important to be accurate. Yet, of course, you may not have that many comps to work with. It often is simply a question of finding which to sales comparables the subject best fits between.
NOTE: The subject pictures are not those of the subject valuation, but similar in condition, quality, and other features. Also, taken directly from an MLS, the quality of the photos is lacking, but should suffice. Some of the data has been modified or redacted, based on guidelines from the MLS.SampleSubjectiveValueContainment_Redacted