Especially when holding a scalpel or during a closing argument.

Special life hack; Do not talk to robots.

Can’t wait until they start trying legal cases using their algorithms. Better yet, performing heart and brain surgeries using robots to perform the procedures. Yes, folks, coming soon to your neighborhood legal and medical practices that don’t actually require human beings.

The letter that wrote might as well have been on some obscure blog that no one reads. It was not shocking and didn’t “do” anything. Just my port right here. It conveys a message but nothing from it will directly happen.

The Institute has been “knee-jerking” their way to obscurity. Shame their leadership is so arrogant they can’t see they’ve been driving themselves and the profession off a cliff. What they are trying to do now is too little, too late.

Duh, the fact that this is now only being discussed after decades of including the census tract in the report is the most amazing aspect of this entire discussion. Simply, including the census tract number in the appraisal report opens up the underwriting process to the same if not worse types of racial profiling and biases Appraisers are being accused of. At least the suspicion of Appraiser bias can be challenged but the automated underwriting tools are not transparent enough for any to be challenged or pushed against by the underwriter or anyone else.

If the census tract number is being used strictly for data collection and statistical analysis of housing activity, that is perfectly fine. However, for the census tract data to have any bearing on the valuation process by formulating adjustments, selecting comparables, or measuring the reliability of the appraisal report undermines the objective of fair lending and fair housing as doing so essentially automates redlining and unfair lending.

The below link; ‘Data analytical models for loan treatment.’ There are new strategies of multi dimensional consideration for every data factor available to assimilate, considerations for lenders which may run beyond the scope of an individuals credit score and an individual home.

Basically if a person is part of a socio economic group of persons whom have higher tendencies to default, be cash poor, spend money unwisely, or somehow be ‘abusive’ to the lending systems (read the stereo type labels they categorically assign people), then that person is treated the same as everyone else around them, even if they do not exhibit those same behaviors personally. They are part of the clustered identification group.

What used to be proprietary behind the scenes operational data utilized by upper tier money managers is now available for public research, because of the way someone monetized the process by way of patenting and selling a software. Want to know the details of how lenders make decisions? And that’s just one find. What else is out there?

Corelogic/FNC patent on appraisal report auto review. Expanded patent history, they share the tools with lenders.
https://patents.google.com/patent/US8244563B2/en?q=(Current+Assignee:+fannie+mae)&oq=Current+Assignee:++fannie+mae+inc

Systems in place to predict borrowers behavior based on statistical data of millions of loans, thousands of factors, including activity not related to lending, and geographic data. Corelogic. ‘Data analytic models for loan treatment.’
https://patents.google.com/patent/US8775300B2/en?q=(~patent%2fUS20160078573A1)
_________________________

The classifier stage may be characterized as personalization modeling. When making personalization models, the developers model the actual players, not the target events. In the real estate transaction, that means the borrower, the property, the loan product and the local real estate market are modeled, not the results provided in the second stage (e.g., default, prepayment, or loan modification failure, per se)./ Personalization models provide information about behavior of the player, such as the borrower. Whereas a typical score predicts only one kind of target event (e.g. FICO, default), the personalization models simultaneously predict a portfolio of target events across a wide range of borrower, asset and local market behaviors.

Once the factors have been established, the factors may be used to segregate borrowers into groups, each with a specific profile, containing borrower’s with that profile. Since each factor is an information rich vector describing a particular dimension of the borrower’s behavior potential, the position of any specific group of borrower’s within the array of factors conveys a rich, multi-dimensional view of a specific kind of borrower

The valuation metrics are used for calculating the raw net present value and the revised net present value. A raw net present value is calculated solely upon the outputs of the model and the economic assumptions relative to the borrower’s expected behavior and the property’s projected values.
____________________________

And now we know why lenders deny more loans among some socio economic groups of people and not others. This is a social credit score system. The concept of ‘default rate clusters’. Image attached.

This tool identifies fraudulent activity. The term; ‘specific geographical data’, does that mean the census tract?
https://patents.google.com/patent/US20110173116A1/en?q=(~patent%2fUS20160078573A1)
The mortgage application data may include property valuation information and geographic information. For example, the model or models 115 may be configured to output scores and/or other risk indicators based in part on geographic default risk information.

Why do you think they’re redoing the forms?

Oh I do understand that, but I feel I have to assume they are using all the same data. I get that one does the underwriting and one produces the value, but is illogical to view them as working harmoniously in sink? Would this no complete the closed network system they are hoping for, with one major flaw, the closed network would be prone to error with the lack of new data could it not spiral? Meaning the AVM is just there to validate the CU, or the other way around.

CU is not the same thing as the AVM that issues waivers. They may rely upon some of the same data and analysis, but they are different tools.

Awesome, thank you for the catch. More eyes are better.

Spencer Paul, review that last sentence and it’s good. There is a spelling error and I believe there is an incorrect word or two. But it’s good.

It would have to be. If they are using that information to arrive at the CBG adjustments, then it would only be logical that it was being used to locate sales the CU would prefer you use in order to lower the risk score, or in otherwards lower the value. They are the culprits and the one’s that are gaslighting the media about appraiser’s are doing when they are doing it.

It’s right out there in the open, even Fannie admits it in their AVM patents.

https://patents.google.com/patent/US20160078573A1/en?#:~:text=Under%20the%20%E2%80%9CTract,in%20valuation%20model.

“Under the “Tract” mode, the comparable analysis application will look for comparable sales in the Census Tract of the subject property, and all contiguous Census Tracts. Because the Census Bureau has tried to identify homogenous areas in the process of defining a Census Tract, this mode is believed to provide an easy but effective method of identifying relevant comparable sales to be used in valuation model.”

From the Census Bureau.

“Designed to be relatively homogeneous units with respect to population characteristics…” i.e. racial makeup.

https://www.census.gov/data/data-tools/survey-explorer/geo.html#:~:text=Census%20Tract,-A%20small%2C%20relatively&text=Glossary%20G%2D11%20Designed%20to,by%20any%20subcounty%20geographic%20entity.

Is this the same AVM that issues waivers? Nobody knows, but we fair housing groups should be encouraged to ask these questions.