If you are personally acquainted with me or merely follow my blog, you probably know that I am an appraiser who is a fan of using
to support adjustments and to analyze trends in the appraisal process. In fact, I recently made a
video on the topic.
One comment often heard from other appraisers is that one needs large samples of data for statistical regression to work. This statement is only partially true. Large samples
are required when there is great variation among the comparable sales or many outliers, as appraisers are familiar with when filling the
1004MC Market Conditions Addendum.
However, if the sales are similar in most ways, the sample size may not need to be large. For example, I performed a regression analysis of land sales within a single development to determine the contributory value of each additional square foot of site size.
Since all of the sales were very similar in terms of most factors (except for the size), only six sales were necessary to produce a strong estimate with near perfect linear correlation.
When working in rural areas or areas that have less comparable sales data, I actually use regression analysis more often than in urban areas. This is because when comparable
sales are less than ideal, one needs to spend more time carefully supporting the adjustments to come to a credible opinion of value. On the other hand, if comparable sales are almost exactly like the subject, ranging little in sales price before adjustments,
it is easy for appraisers to come to the most reasonable value opinion thru proper weighting in reconciliation, regardless of how large or small the individual adjustments are for each factor on each comparable sale. When comparable properties differ a great
deal in terms of location, date of sale, site size, living space, or other factors, statistics can be used to better support these important quantifiable adjustments and to yield a more credible final opinion of value.
I recently appraised a rural manufactured home on 40 acres. There are few sales of similar properties, but I was able to take a sample of similar size and quality manufactured
homes between one and two acres (there are lots of sales of these in the competitive market area) to support adjustments for the living area. In the absence of other data, I made a strong case that each additional square foot of living space on similar manufactured
improvements with smaller sites is consistent with the adjustment for a property with 40 acres. Even if one could argue that the buyers of the 40-acre property would be willing to pay more or less per square foot of living space than the buyers of one and
two-acre manufactured homes, analysis of the statistical data helps me have a starting point to make a more reasoned adjustment estimate.
For this same rural property, I also used statistical regression to support time adjustments (using market data from the entire competitive market area trended over time), site
size adjustments (controlling my data by looking only at vacant land), and location adjustments (comparing samples of similar properties from different areas). The moral to the story is that appraisers should embrace statistics for help when little data exists,
not pull away.
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