Data science is new ways of thinking, new tools, computation, and visualization.

For valuation, it is simply optimizing the best of appraiser judgment and computer power.

Traditional appraisal theory is good.  It is a subset of economic theory.  What is different is the available data and algorithms to select and predict from that data.  Also different is the ability for appraisers to better fill market needs with new services.  Teaching the data science approach builds on what we already know, tweaked to revise old ways of thinking, old forms, and spreadsheets boxes.

We learn to provide understandable analytic results, in place of an opinion (everyone has one).

As I started this blog, it seemed difficult to provide more than two or three reasons how I ended up teaching only data science for valuers.  We call it Evidence Based Valuation (EBV)©.  Then my list grew.

Needed.              The traditional point value opinion is obsolete.  Analysis, not prose is needed.

Fun.                       The mental switch to measuring markets instead of comparing comps is a joy.

Passionate.          It is much more fun to deliver a bulletproof product, instead of having to be defensive.

Focused.              The goal is a measurably reliable result, not an ambiguous “worthy of belief” opinion.

Modern.              Today’s valuation tools integrate with today’s client systems.

Easier.                  Data science critical reasoning and clear tools are more straightforward to work with.

Attractive.          Visuals, including graphs, tell the story with color and shape and comparison

Robust.                Results with visuals are understandable by a wide range of client competence.

Intuitive.             Once learned, it eliminates some of the “common sense” ambiguities.

Hope.                   Provides a forward path for the appraisal profession, not living in the past.

Rewarding.         Brings a sense of new accomplishment, not defending outdated ‘process.’

Inclusive.            Clarifies when old ‘sparse data’ thinking is still necessary.

Incremental       Builds on old education and practical experience.

Natural                 Intuitive thinking parallels the reality of “appraisal is market analysis.”

Complete.          Avoids the fundamental problem of ‘missing data’ with traditional comparable selection.

Freeing.               Empowers the unlearning of old unnecessary methods and habits.

Clear                     Unclutters and dispels old ambiguities in standards and regulations.

Focused.              Gets to the real issue of analytics:  risk/reliability of the value conclusion.

Enabling.             Allows and promotes future growth in valuation theory and practice.

Professional.     Puts the profession into the lead, rather than reacting to client and regulator edicts.

Aligned.               Brings the profession to the real goal of serving the public good.

For me, I get great satisfaction from helping a profession which has been so good to me.  I was blessed with accidental knowledge of a variety of fields which later came together to be called ‘data science.’