Is traditional appraisal theory different from modern analytics theory?

This Modernizing Appraisal series is about the analysis process, not FannieMae and FreddieMac reporting requirements. The process is identical:   1) The problem;  2) The data;  3) Predict or adjust;  4) Communicate.  We continue here on #1 – defining the  problem to be solved.     Read the earlier parts of this series here.

In this series, we have seen the culture and heritage of traditional appraisal.  We have overviewed the logic of asset valuation – regardless of the methods used.

In this edition, we consider today’s reality:  Artificial intelligence, and its dependence on data science.  Here we define data science as based on today’s data, computation, and expert interface agents.

Data science provides us with the ideal toward the goals of valuation:  trueness, sureness, and usefulness.  Artificial intelligence provides us with an efficient way to meld the science with the needed professional judgment.  Yes – art merged with science!

Of the four “chunks” of modern analyses:  problem, data, prediction, delivery – we continue on the first chunk:  identify and demarcate the problem to be solved.  (We use the word “chunk” to highlight the each chunk has a distinct goal, methodology, and tools).

Traditional appraisal’s goal is an opinion “worthy of belief.”  The “Scope of Work Rule” in USPAP is three sections:  “Problem identification,” “Scope of Work Acceptability.” And “Disclosure Obligations.”

Overall, it requires the extent of property identification, inspection, “type and extent” of data researched and analyses applied.  It details also:  intended users, use, value definition, effective date, subject and “relevant” characteristics, and assignment conditions.

Scope of work acceptability “meets or exceeds” expectations of intended users, and what appraiser peers actions would be.

Disclosure is solely toward the client and intended users to “understand the scope of work performed.”

Evidence Based Valuation (EBV)^© is the application of data science concepts and principles to valuation.  In general, it is similar to the requirements for traditional appraisals as above.  However, the principles of client expectation and peers’ actions are definitely not a stated requirement.

The focus of EBV is toward the ideal.  It explicitly recognizes the need and value of human expert input.

Science is defined as systematic study by an expert in the scientific method and in the field of study.  Let me repeat:  An expert in:  the scientific method AND the field of study!  Data refers to raw facts, figures, and observations, like as numbers, words, or images.  Data does not provide insight until it is processed or analyzed to provide meaningful insights or context – “useful information.”

Data science, applied to valuation we refer to as EBV (Evidence Based Valuation)^©.  EBV provides added results beyond just a point estimate of value, and maximizes reproducibility, a core point of the scientific method.

In the next post, we will continue and expand on the how EBV clarifies the role of the expert – the appraiser-asset-analyst.  This focus includes “unmentionable” parts of traditional appraisal, and how they are explicitly stated in the first “chunk” of the development process.

“A problem well-stated, is half solved.”  (Charles F. Kettering, per Copilot)