Appraisals as a Chain of Thought

The Six-Step Appraisal Process as a Reasoning Framework

The Appraisal Institute teaches a six-step valuation process. Each step contributes a critical link in the reasoning chain:

1. Identify the problem
2. Determine the scope of work
3. Collect data and describe the property
4. Analyze the data (including market analysis and highest and best use)
5. Apply the approaches to value
6. Reconcile the indications and report the value

This isn't merely a workflow checklist. It is a structured reasoning model. Each conclusion must follow logically from the step before it.

That is exactly what "chain-of-thought" represents in AI systems.

1. Identifying the Problem: Defining the Question

All credible reasoning begins with clarity.

Problem identification establishes:

• intended use
• intended users
• type and definition of value
• effective date
• relevant property characteristics and rights
• any extraordinary assumptions or hypothetical conditions

If this foundation is wrong—or vague—everything downstream is weakened.

AI prompt engineers say something similar: if you cannot define the task precisely, you cannot trust the output. In both domains, the reasoning chain begins with a simple question: "What, exactly, are we solving for?"

2. Determining the Scope of Work: Designing the Reasoning Plan

After defining the problem, the appraiser establishes the scope of work—the analytical plan necessary to produce credible results for the assignment.

This includes deciding:

• what data must be collected and verified
• what analyses are appropriate
• which approaches to value are applicable
• the level of detail needed

It is reasoning about the reasoning to follow.

In AI terms, this is like selecting the strategy: will the system perform a quick heuristic, a deep multi-step analysis, or a full set of approaches with explicit justification for any omissions?

The scope of work is the appraisal profession's explicit commitment to how the problem will be thought through.

3. Collecting Data and Describing the Property: Supplying the Inputs

With the plan established, the appraiser gathers the information required for analysis:

• neighborhood and market area data
• site and improvement characteristics
• legal and regulatory information
• comparable sales and rentals
• income and expense patterns, when relevant

This is the "input provisioning" stage—the point where the reasoning engine receives the facts it will work from.

AI models rely on context in the same way: without relevant, accurate, and properly scoped inputs, even a perfectly structured reasoning process produces weak results.

4. Analyzing the Data: Market Analysis and Highest and Best Use

Data is not analysis.

In step four, the appraiser interprets the information collected, including:

• market supply, demand, competition, and trends
• highest and best use as though vacant
• highest and best use as improved

Highest and best use functions as a logical filter. The tests of legal permissibility, physical possibility, financial feasibility, and maximum productivity narrow the solution space to only those uses that are supportable.

AI systems follow a similar pattern—applying constraints before generating an output.

Here, the appraiser effectively states: "These are the conditions within which the rest of the analysis makes sense."

5. Applying the Approaches to Value: Parallel Chains of Reasoning

With the groundwork established, the appraiser develops the applicable approaches to value. Each approach is its own structured reasoning path:

Cost Approach

• estimate land value
• estimate replacement or reproduction cost
• estimate and allocate depreciation
• conclude an indication of value

Sales Comparison Approach

• select and verify comparable sales
• analyze elements of comparison
• make adjustments
• reconcile to an indicated value

Income Approach

• analyze income streams and lease data
• develop stabilized income and expenses
• derive capitalization or discount rates
• value the cash flows

Every step should be transparent and supportable. The reader should be able to follow the chain from market evidence to the indicated value.

This is the essence of chain-of-thought: breaking complex reasoning into explicit, understandable steps that connect evidence to conclusions.

6. Reconciling and Reporting: Synthesizing the Chain of Thought

Finally, the appraiser:

• reconciles the value indications from each approach
• evaluates the quality and reliability of the evidence
• considers consistency with highest and best use
• reports a supported opinion of value appropriate to the assignment

Reconciliation is not averaging—it is judgment.

In AI reasoning, this is the final synthesis step: multiple reasoning paths are evaluated and resolved into one coherent conclusion, with an explanation of why that conclusion is most credible.

The reporting stage then communicates that reasoning with clarity for intended users.

Appraisers Were Doing Chain-of-Thought Before It Had a Name

What the AI community now calls "chain-of-thought," appraisers have long described as:

• logical narrative
• scope of work explanation
• support for adjustments
• transparent analysis
• reasoned reconciliation

In other words: Explain what you did, why you did it, and how it leads to a credible conclusion. That is chain-of-thought.

A Modern Profession With Time-Tested Logic

As valuation practice incorporates advanced analytics, workflow automation, and AI-assisted tools, the underlying cognitive framework remains unchanged.

The six-step valuation process is more than a methodology—it is a thinking model.

Appraisal has always been a profession built on transparent, step-by-step reasoning.

Modern AI is simply giving new language to what appraisers have practiced all along: structured, defensible chain-of-thought.