I asked Gemini what my Parkton home was worth.

The answer looked impressive.

It pulled together public estimates, tax data, property facts, and market context.

But as a Maryland Certified Residential Appraiser with 36 years of local experience, I saw the problem immediately.

The AI was not wrong because it was lazy.

It was limited because the underlying data was inconsistent.

And that is exactly why automated valuations can become dangerous when people treat them like real appraisals.

The Algorithm Spread

The major consumer valuation tools showed a wide range:

• Redfin Estimate: approximately $745,000

• Zillow Zestimate: approximately $687,000

• Realtor.com estimate/assessment reference: approximately $664,000

That is a spread of roughly $81,000.

For a homeowner, buyer, estate executor, divorcing spouse, or financial planner, that is not a minor difference.

That is real money.

Why the Algorithms Disagreed

The biggest issue was not the neighborhood.

It was not the lot size.

It was not even the age of the home.

The biggest issue was square footage.

One source appeared to calculate the property using approximately 2,204 square feet of living area, while other public-facing sources reflected approximately 3,107 square feet.

That is a difference of more than 900 square feet.

When an automated valuation model starts with inconsistent square footage, every calculation after that becomes unstable.

What AI Cannot See Clearly

AI can summarize public data.

It can compare online records.

It can identify broad market patterns.

But it cannot reliably inspect or interpret:

• finished basement quality

• walk-out lower-level utility

• exterior decking condition

• pool condition and market appeal

• layout functionality

• renovation quality

• site utility

• privacy

• street appeal

• micro-market demand

Those are not just details.

Those are value factors.

The Parkton Problem

Northern Baltimore County is not a simple spreadsheet market.

A property in Parkton can vary dramatically in appeal based on lot utility, setting, school district perception, condition, updates, privacy, functional layout, and buyer demand.

Automated systems often struggle with these nuances because they are trying to compare public data points, not interpret real-world market behavior.

The Appraiser’s Verdict

The AI answer was useful as a research starting point.

But it was not a valuation.

A true valuation requires verified data, relevant comparable sales, property-specific analysis, local market judgment, and a defensible conclusion.

That is the difference between an online estimate and a professional appraisal.

The Bigger Lesson

AI is powerful.

But AI is not magic.

When the public data is inconsistent, the AI answer inherits the inconsistency.

That is why experienced professionals still matter.

The future does not belong to people who ignore AI.

It belongs to people who know how to use AI without surrendering judgment to it.

Final Thought

AI can gather the clues.

Experience knows which clues matter.

That is the heart of The AI Visibility Engine.

Your expertise becomes more valuable, not less, when you know how to structure it, test it, and explain it in a world where AI is becoming the first place people ask questions.

Ed Drost is Baltimore’s Trusted Appraiser, an AI Architect, and creator of The AI Visibility Engine — a practical authority system that helps experienced professionals turn what they know into the answer AI recommends.

With 36 years of Baltimore-area appraisal experience, Ed bridges real-world expertise with AI-powered visibility, helping professionals build content, systems, and authority assets that make them easier to find, trust, cite, and recommend in the AI era.