This article was originally posted on Georgedell.com
Traditional appraisal says we should describe and analyze the subject neighborhood.
In residential form reports, there is a section devoted to the neighborhood.
In general narrative reports, there is a section devoted to the neighborhood. In fact, often there is great discourse about the town, the city, and greater things. We have often heard criticism about these long descriptions of areas that seem to have little to do with the goal of ‘supporting’ a personal professional opinion of market price (called ‘value’).
In traditional valuation dogma, the next step is picking comps. Comps for overall, comps for income, and comps for land and costs. Pick comps.
A neighborhood is defined as a group of complementary land uses. So obviously, if you are appraising a house, or a corner office, you need to delete to pick comps. Delete the gas stations, the apartments, the stores, and the parks, any hotels, the paid parking lots, and many other uses and non-similar properties. Most of all, you need to get rid of those not-similar houses. (Or the non-corner short office buildings.)
Everyone knows that picking comps is less than the neighborhood. (Reduction of the data set.)
Except when it is not! Sometimes you need to go outside the neighborhood to get a good comp. (Expansion to “enough” comps.)
Traditional valuation: “Always pick comps either inside, or outside the neighborhood.”
Evidence-Based Valuation: The ideal data set is the Competitive Market Segment (CMS)©. The similarity is established by use of the (data science) five dimensions of similarity. This idea leads to one or more of several similarity algorithms used in analyses.
In the traditional “valuation process” the appraiser first picks comps based on their experience, training, and “knowledge of the market.” Although a subjective process, the appraiser is admonished to be diligent in data selection to pick sales which are similar, competitive, and “able to be compared.” The ultimate test in current standards (USPAP), is to be “credible” defined as “worthy of belief.” Reliability is not mentioned or defined.
In EBV (Evidence Based Valuation)©. The competitive data set is defined from market behavior evidence, not opinion. Data science is rife with algorithms and logic designed specifically to this purpose. The key benefit of market-derived data sets is that the process can be replicated, and therefore is reproducible. Reproducibility is a key element of valid scientific-analytic research and decision-making.
Admittedly, portrayal of a neighborhood can help in an understanding of the nature of the subject’s neighborhood as compared to other neighborhoods. And perhaps an understanding of the subject land use to other nearby supportive land uses.
On the other hand, depiction of a neighborhood is the basis of much current debate. “Neighborhood” boundary lines have been equated to bank red-lining. Neighborhood restrictions have (in the past) formed the basis of restrictions on certain buyers, including the exclusion of certain races, religions, national origins, and even sexual preferences.
There is strong argument to replace the “Neighborhood” section of appraisal reports with definitive, objective analysis of the actual CMS, the Competitive Market Segment©, in its place.
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