With modernized methods, appraisers can gain respect, income, and provide a genuine public service.
This Modernizing Appraisal series is about the analysis process, not the reporting revisions –(New URAR, UAD3.6). We step through: scope, data, and ”adjustments.” It sets today’s path to competence, using data science with artificial intelligence. Read the earlier parts of this series here.
To modernize we can outline legacy practices, and compare them to an outline of optimal practices – given today’s process technologies, data, and expert interface opportunities.
In brief, legacy appraisal delivers a personal/expert opinion of a point value. Modernized valuation delivers an analytical result.
“Appraisal” is tightly defined in USPAP (Uniform Standards of Professional Appraisal Practice) as an opinion. Specifically, as “the act or process of developing an opinion of value,” or “an opinion.”
Contrarily, the common colloquial industry use of the word means the report.
This equivocation of word meaning is the core of much confusion in the industry. The provider group drives the motor, transmission, and steering. The user group sees a fast, cheap, pretty car.
So how do we contrast traditional appraisal to modern asset analysis? The underlying process is identical! 1) ID the problem; 2) Get the data; 3) Predict or adjust; 4) Deliver opinion or results.
What’s different is modern expert judgment, and computation (or Ai) interface.
Legacy practice: 1) Do scope of Work; 2) Select and improve comparables; 3) Adjust for subject differences (and reconcile remaining differences); 4) Deliver opinion report.
Modernized practice: 1) Define problem, & preliminary data analysis (PDA); 2) Delineate competitive market segment (CMS)©; 3) Apply predictive models/algorithms; 4) Deliver reproducible result.
We will be exploring in depth these differences and how appraisers can gain respect, increase income, and provide superior product in the future.
Let’s summarize here, the future role of expertise, judgment, and appraiser competence.
- Problem identification will apply judgment of the overall data to be used. We call this the ADF (Assignment Data Frame). Its reduction to the most relevant data set requires appraiser expertise and experience. It includes the optimal size of the data set as well as its components. Also, similar to established practice, definitions, assumptions, and path description go here.
- Data selection includes decisioning balancing the amount of relevant data available to the result reliability desired. Thinking and analysis may vary. Where data is sparse or poor in quality, analyst/appraiser judgment is needed. “Results may vary.”
Picking 4 or 5 comps is quite different from optimizing the available and relevant data set, given its size and quality.
- Adjustment is quite different from prediction modeling. “Adjusting comps” recognized that with sparse data, judgment and experience are the best method. Prediction recognizes that use of complete, near-complete, or sparse data requires analytic tools to give clear results.
- Reporting traditionally required a fixed form, or a structured narrative. Modern data-science based delivery integrates flexible analyses and flexible delivery. Data science provides options and additional services and products which may be provided by analyst-appraisers.
The combination of competent analyst modeling, computer algorithms, via visual (graphical/statistical) methods is the best solution. It beats automatic (AVM) valuation, and is superior to obsolete “pick comps, make adjustments” routines.