Latest Developments: Bert Craytor, August 2021
I have just completed a new workflow for URAR appraisals based on the R Earth version of MARS (Multivariate Adaptive Regression Splines). This workflow will replace the use of Minitab’s SPM 8.3 that has had its license fee increased 44 fold this past year from $360/year to $16,000/year.
The conversion has added some improvements, such as full automation of the processing of MLS input through submission to earth(), parsing of the R::earth output to Excel spreadsheets with adjustments, and aggregation of the adjustments to URAR fields per configuration files the user can modify to alter:
- the fields to be analyzed,
- the activation of two-way vs. one-way interactions,
- the variables allowed for two-way interactions when the user chooses that option, and
- the URAR fields to aggregate the model-specified adjustments.
The automated workflow does significantly speed up the appraisal process and improve the accuracy of the appraisal.
The accuracy improvements come from a more accurate CQA to Residual mapping (or function). This function, written in C++, replaces the function generation previously done through Minitab/Salford-Systems MARS.
I used R Studio to develop the new workflow system in R script and C++. R Studio is also used for execution, although I plan to create a front end in C# to manage workflow. The system contains about 1500 lines for R script code and 500 lines of C++ code.
The system will generate URAR adjustments for an unlimited number of sales comparables, adjusting all to within 0.00001% of their average.
I used the variation of the Sales Comparison Approach variously called the
- Subjective Value Containment Approach (SVCA),
- Intangible Value Containment Approach (ICVA), or
- Contribution Value Approach (CVA)
for this workflow. Of these names, probably the Contribution Value Approach is the most accurate, because it emphasizes that the calculation of adjustments occurs as a result of first calculating the contribution values of property features, plus a typical basis value.
On the other hand, “Intangible Value Containment Approach” emphasizes the real advantage of the approach, that is, a far more precise method for estimating Market Value. However, a downside of this description is that the value captured through the regression residual more accurately contains the value contributions of all variables that did not enter the Stage I regression analysis, not just intangible variables. More specifically, some of the variables that account for the residual value may be partially tangible, but wind up as residual components because they are not measurable.