- Joined
- Jun 27, 2017
- Professional Status
- Certified General Appraiser
- State
- California
Thought I would post this from my recent post in the Facebook AI Group.
I would just comment,
(1) Sure, theory, and protocols are one thing, practice is another.
(2) You would expect the theory to be correct, but it is trash. SCA depends on the Principle of Substitution - which the AI has apparently spent years and hundreds of thousands of dollars lobbying for. The Principle of Substitution, the foundation of the SCA, is trash and so is the technique of matched pairs analysis which builds on it. Why is the Principle of Substitution trash? - Because it only works if all of your significant variables have good measures. The issue of unmeasured variables such as condition, quality, aesthetics/design, functional utility, view, etc.,, is treated as if it were not important - yet in many areas such as the SF Bay Area, it is indeed very important. Secondly, the matched pairs technique is trash because it pulls adjustments out of mid-air. An adjustment is a difference. So, if you have adjustment A, there should be some S (subject attribute value) and C (comparable attribute value) so that A=S-C. Why do you need S and C? - These are the value contributions that go into the final sale price in the case of the comparable or the estimated sale price in the case of the subject. You need to go through the determination of value contributions to get adjustments - that is "having your feet on the ground" - your support. (One can also mention that matched pairs invariably disregards the set of unmeasured attributes as if they were not important - or assumes they are the same between comparables when they are not - and finding a good match just based on measured variables is difficult enough ...) - Now a good regression technique like MARS, capable of providing high R2 values with other useful features, can get you the value contributions of significant measured variables (it determines which ones are significant) - but it can only give you the total value of all unmeasured variables (from the residual difference between the estimate based on measured variables and the actual price of each comparable) - which, as it fortunately, turns out - is good enough. So, the theory and protocols could be corrected. The caveat being, a much higher level skill set is needed to put that theory into practice. You need to understand MARS, probably how to program in R or Python - and be able to create a workflow to handle the tedious work - without making significant errors. Not easy.
The tedious work after you have done all the work to create a good price model is:
1. Calculation of value contributions for all measured variables for all MLS comparables used in the regression - you may have hundreds.
2. Calculation residuals for all comparables
3. Ranking of all comparables by residual.
4. Calculation of Residual (CQA) Scores for all comparables
5. Manually find the rank of the subject property in the sorted comparables and assign it a Residual (CQA) Score.
6. Rerun the dataset to get the adjustments for the URAR. These may be for variables for which there are no slots in the URAR - so you have to set up methods to aggregate the adjustments into URAR fields in an Excel spreadsheet.
7. Now calculate all adjusted sales prices for all MLS comparables (hundreds).
8. Select the best comparables for the Sales Grid - move them to the top of the list. Let's say the top 12.
9. Run the next stage program - which will then extract the data for chosen comps to a spreadsheet for upload into Alamode.
10. Upload into Alamode and fine-tune adjustments for the unmeasured variables, breaking the residual adjustment into separate unmeasured variable adjustments for attributes such as condition, quality, functional utility, view, design, etc..
It is a lot of work that must be done precisely and it will give you the same adjusted sale price for each comparable - to the dollar (only because of rounding - otherwise to the cent). So, this has to be done programmatically.
I would just comment,
(1) Sure, theory, and protocols are one thing, practice is another.
(2) You would expect the theory to be correct, but it is trash. SCA depends on the Principle of Substitution - which the AI has apparently spent years and hundreds of thousands of dollars lobbying for. The Principle of Substitution, the foundation of the SCA, is trash and so is the technique of matched pairs analysis which builds on it. Why is the Principle of Substitution trash? - Because it only works if all of your significant variables have good measures. The issue of unmeasured variables such as condition, quality, aesthetics/design, functional utility, view, etc.,, is treated as if it were not important - yet in many areas such as the SF Bay Area, it is indeed very important. Secondly, the matched pairs technique is trash because it pulls adjustments out of mid-air. An adjustment is a difference. So, if you have adjustment A, there should be some S (subject attribute value) and C (comparable attribute value) so that A=S-C. Why do you need S and C? - These are the value contributions that go into the final sale price in the case of the comparable or the estimated sale price in the case of the subject. You need to go through the determination of value contributions to get adjustments - that is "having your feet on the ground" - your support. (One can also mention that matched pairs invariably disregards the set of unmeasured attributes as if they were not important - or assumes they are the same between comparables when they are not - and finding a good match just based on measured variables is difficult enough ...) - Now a good regression technique like MARS, capable of providing high R2 values with other useful features, can get you the value contributions of significant measured variables (it determines which ones are significant) - but it can only give you the total value of all unmeasured variables (from the residual difference between the estimate based on measured variables and the actual price of each comparable) - which, as it fortunately, turns out - is good enough. So, the theory and protocols could be corrected. The caveat being, a much higher level skill set is needed to put that theory into practice. You need to understand MARS, probably how to program in R or Python - and be able to create a workflow to handle the tedious work - without making significant errors. Not easy.
The tedious work after you have done all the work to create a good price model is:
1. Calculation of value contributions for all measured variables for all MLS comparables used in the regression - you may have hundreds.
2. Calculation residuals for all comparables
3. Ranking of all comparables by residual.
4. Calculation of Residual (CQA) Scores for all comparables
5. Manually find the rank of the subject property in the sorted comparables and assign it a Residual (CQA) Score.
6. Rerun the dataset to get the adjustments for the URAR. These may be for variables for which there are no slots in the URAR - so you have to set up methods to aggregate the adjustments into URAR fields in an Excel spreadsheet.
7. Now calculate all adjusted sales prices for all MLS comparables (hundreds).
8. Select the best comparables for the Sales Grid - move them to the top of the list. Let's say the top 12.
9. Run the next stage program - which will then extract the data for chosen comps to a spreadsheet for upload into Alamode.
10. Upload into Alamode and fine-tune adjustments for the unmeasured variables, breaking the residual adjustment into separate unmeasured variable adjustments for attributes such as condition, quality, functional utility, view, design, etc..
It is a lot of work that must be done precisely and it will give you the same adjusted sale price for each comparable - to the dollar (only because of rounding - otherwise to the cent). So, this has to be done programmatically.