Savvi begins by selecting a group of properties on which the regression analysis will be done. The steps in the process are:
1) Selecting a large and varied set of properties in the same market as the subject property for the analysis.
2)Screening the available MLS data to not include properties that are too dissimilar from the subject properties in terms of year of construction, location and other property characteristics although we do need some variation, otherwise there is nothing to model. The need for some but not too much variation in the sample of properties to be analyzed (the “Goldilocks principal”) is the balancing trick required for selecting properties to model.
3) Dropping properties with extreme values or property characteristics that are inapplicable (e.g., basement square footage and finished basement square footage for properties in coastal areas) from the regression model as neededare inapplicable (e.g., basement square footage and finished basement square footage for properties in coastal areas) from the regression model as needed.