Why Your Zillow Zestimate May Be Wrong (And What to Use Instead)

In 30 years of acquiring and underwriting investment properties, I've seen every valuation mistake in the book. I've bought over 16,000 single-family homes—renovated and leased up about half of them—and fully underwritten more than 500,000 properties. One lesson comes up again and again: never trust a single AVM.

If you've ever bought, sold, or even daydreamed about real estate, you've probably checked a Zillow Zestimate. Over 100 million people do every month. It's fast, it's free, and it feels authoritative—that bold number sitting right at the top of every listing.

But here's what most people don't realize: Zillow themselves publishes data showing their estimates can be off by 5% or more on over half of all homes. For a $500,000 property, that's a $25,000 swing. When you're underwriting deals at scale, that error compounds fast.

What 500,000 Underwritten Properties Taught Me About AVM Accuracy

Early in my career, I learned to cross-reference every automated estimate against actual comps, boots-on-the-ground assessments, and multiple data sources. Not because any single AVM was "bad"—but because they all have blind spots.

Zillow publishes a metric called the "median error rate." As of their latest data, the nationwide median error for on-market homes is around 2.4%, but for off-market homes—the ones investors like me target—it jumps to 7.5% or higher.

On a portfolio of 100 acquisitions, that error can mean millions in overpayment. I've seen it happen to institutional buyers who got lazy with diligence.

Why Single-Source AVMs Fail Investors

The core problem isn't that Zillow is bad—it's that any single AVM has inherent limitations. Automated Valuation Models rely on:

• Recent comparable sales — sparse in rural or unique markets
• Public records — often outdated or incomplete
• User-submitted data — which homeowners can edit themselves

Redfin's estimates use different data. CoreLogic's models weight factors differently. Quantarium takes yet another approach. Each has markets where it excels and markets where it struggles.

When I was scaling acquisitions, we quickly learned that a Zestimate might nail condos in Phoenix but miss badly on rural acreage in Tennessee. Relying on just one source is like getting a medical diagnosis from a single doctor and skipping the second opinion.

The Consensus Approach: How We Actually Valued Properties at Scale

When you're underwriting thousands of properties, you can't manually comp every one. But you also can't blindly trust a single number. The solution: multi-source consensus.

We'd pull 5-9 different AVM estimates and look for agreement. When they clustered within 3% of each other, we had high confidence. When they diverged by 15%, that was a red flag demanding deeper diligence—or a pass on the deal entirely.

The spread itself is information. Tight consensus means the property is "readable" by algorithms—typically a standard home in a liquid market. Wide divergence often means something unusual: a unique property type, sparse comps, or data issues that warrant investigation.

What to Check Before You Trust Any Estimate

Whether you use AVMLens or run your own comparisons, here's the checklist I still use:

• Pull multiple sources — At minimum, compare Zillow, Redfin, and Realtor.com
• Check the confidence score — Most AVMs publish this; low confidence = proceed carefully
• Verify recent comps manually — AVMs can miss recent sales or use bad comps
• Adjust for condition — AVMs can't see deferred maintenance or recent renovations (I've renovated 8,000+ homes—condition matters)
• Factor in geo-risks — Proximity to power lines, flood zones, cell towers. AVMs ignore these; buyers don't.

The Bottom Line

Zillow Zestimates aren't worthless—they're a starting point. But after underwriting half a million properties, I can tell you: treating any single AVM as gospel is how investors overpay or miss red flags entirely.

The institutional approach—aggregating multiple sources, measuring consensus, and flagging divergence—used to require a team and expensive data subscriptions. It doesn't anymore.