It’s not me, I’m not biased!

Is it me, the model, or the data? A great problem with legacy appraisal is that bias is about personal bias, rather than analytical bias. USPAP (Uniform Standards of Professional Appraisal Practice) only addresses human bias.  Similarly, The Appraisal of Real Estate by the Appraisal Institute only addresses analytical bias in the context of random sample statistics, and the assumption that multiple regression is used.  Neither of these are practical for the usual nature of the appraisal problem.

Analytical bias

Three sources of analytical bias are worth noting.  Reliable valuation assumes:  1) the right question is addressed; 2) the data is relevant; 3) the right analytic model (algorithm) is applied.  Analytical bias is the focus of many of the posts on this blog, and it is an important topic in the TAAR (The Asset Analyst Report), available as a paid subscription.  You may also refer to my peer-reviewed articles in The Appraisal Journal.

Intentional bias

Most appraisers who wish to “hit the number” know that the best way is not through large adjustments, but it is by selecting data biased toward the intentionally biased goal “value.”  Intentional bias requires a set of skills which are convincing and credible – “worthy of belief.”  Personal bias can rely on known analytical biases in model selection, data selection, “adjustments,” and even reconciliation/reporting.  Where you find intentional bias, you will often find a client/system similarly motivated to “make the deal.”

Intentional bias can come from motives of money, security, or just plain seeking approval.

Unintentional bias

There are many sources.  Unintentional bias can be from ignorance, bad training, or human response bias (psychology – having to do with how the human brain is wired).

Ignorance itself can come from different directions.  It can be simply be from a fresh newbie, who has had little or no training.  The training itself may be wrong or purposeless.  Much of the “statistical” education for appraisers is itself at issue.  Some is wrong.  Some assumes random sampling inferential statistics (of little or no use for valuation).  The lack of proper use of graphs is glaring.

Bad training itself comes from the passing of theoretical “knowledge.”  This is basically stuff taught by those who do not actually apply modern analytics software, visual methods, or simple algorithm modeling.  This is in addition to mis-intended statistical education, or reverence for traditional ‘judgment-based’ appraisal model. This outdated model blocks the light of science, given today’s technology.  Science is the systematic study of the world through observation or experiment.  Today’s technology enables evidence-based methods, replacing outdated ‘trust me’ methods.

In my opinion, there is a glaring lack of training in the proper use of judgment, given the computation, visualization, and dashboard analytics possible today.

Human response bias is a large topic of its own.  There are two or three dozen forms of human bias, each of which arises unconsciously.  Human bias can be extremely useful in a tribal primal setting.  It can be quite damaging in a setting where ethical behavior is required.  One example of this is called “anchoring.”  Anchoring means the subconscious mind simply attaches to the first number brought to awareness.  (Like a sale price . . .)

In my opinion, the appraisal profession will not advance until the psychological aspects and analytical aspects of bias are directly addressed.  Admonitions to “be careful” will not do.