Editor’s Note: This is Part 6 of the series on Neighborhood.
We’re told we should analyze a neighborhood. This is mostly a residential concept. For non-residential appraisal the focus is often a “district” – a group of similar property uses. (Rather than a group of complementary uses.)
In previous Analogue Blogs, we have looked at why the analysis of neighborhoods may be not useful today. We conceded that in historical practices, the appraiser should “drive the area” to get a ‘feel’ for the subject influences. We also noted that ‘comps’ will come from both inside and outside the neighborhood.
Evidence Based Valuation (EBV)©, using modern data analytics – does depend on neighborhood description and characterization. However, the dependency is quite different from vintage appraisal thinking and appraisal form-filling.
How?
Modernized appraisal practice separates data selection from data ‘adjustment.’
Traditional thinking merges these two analytics when picking comps. The appraiser “drove the neighborhood” to get a sense of what is ‘able to be compared’, similar, and competitive. Based on experience and excellent judgment, the appraiser picks some unbiased comps. This results in an opinion which is “independent, impartial, and objective.”
So how may neighborhood characterization help in modern asset valuation? You may recall from our teachings that there are only three basic predictive algorithms: association, contrasting, and sequencing. Association applies simple regression. Contrasting compares two controlled groups. Sequencing uses ordinal (order) statistics.
Bounding descriptives deprive the use of any similar data outside the neighborhood. No association use there.
Similarly, sequencing leaves out data from outside the area which may be of better comparability to sales from inside the boundaries. This leaves contrasting as the only possible usefulness for quantifying features.
Of the five dimensions of similarity©, it is not likely to be property rights, contract elements, time elements, nor even personal preference features. So it must be location! Aha!
We need some basis to compare similar sales outside the subject area to calculate a location ‘adjustment’ as between the subject and the comparable neighborhoods.
Knowing the nature of property uses and the characteristics of each area can help us compare and contrast between. We contrast each compared neighborhood with the subject neighborhood. Now this becomes a tool for exploring and quantifying location differences and similarities.
We conclude that the description helps very little in data selection. Comps come from both inside and outside a neighborhood.
On the other hand, the description can be instrumental in identifying and quantifying location adjustments.
In some future blog here, or in the Valuemetrics.info curriculum or webinars, we will further explore the concise, quantitative use of neighborhood characterization in valuation and risk/reliability scoring.