Comparable selection is fundamental.
In the traditional “valuation process,” first the “relevant characteristics of the property” are identified. Then in the “data collection” step, “Comparable Property Data” is collected.
The subject is depicted by relevant characteristics (predictor variables), and comparables are chosen to fit that depiction. In the traditional procedure, these are called “elements of comparison.” In the current edition of The Appraisal of Real Estate it states: “This data includes legal, physical, locational, cost, [financial arrangements], and income and expense information . . .” It goes on to relate to supply data, demand data, and estimated future demand.
The above provide good outlines, particularly in historical circumstances where data was sparse and difficult to obtain. Data collection was subjective and subject to availability. Yet it was the most important part of the appraisal process. Appraisers ‘owned’ the data, or had the connections. There was no MLS, no online commercial data, and no electronic public records. A trip to the county courthouse was the norm.
Times have changed.
Picking comparables is still important. But in most areas, complete data sets are available. We have moved from scratching together information to discarding all but the five or six ‘best’ comps. Is this a good data selection model?
What we can do now is work toward an ideal data set. Whether for one of the three traditional approaches to value, for regression, or for the new, Evidence Based Valuation (EBV) – we need to know what gives us the most reliable results. This means, to get the ideal data set, we must figure out two things:
- What is the nature of the right data set? and,
- Exactly how much data is too much, or not enough?
Or as baby bear said: “just right!”