Aligned
Zillow broadly agrees with the readable independent data.
If it is, it could be costing you tens of thousands of dollars — and you'd never know. We'll audit your Zestimate for feedback loops, data errors, and algorithmic penalties. If it's suppressing your home's value, we'll help you zillowmaxx it.
Built from a real investigation that uncovered a $220,000 Zestimate error on a single home — confirmed by Zillow support. See anonymized sample audits.
The act of systematically eliminating every data error, algorithmic penalty, and feedback loop suppressing your home's Zillow estimate — by correcting every datapoint, visual cue, and comp disadvantage dragging your property below its true market tier.
"She corrected the square footage, uploaded 20 interior photos, fixed the missing bedroom, filed a support ticket on the phantom sale — and her Zestimate jumped $95K in two weeks. Full zillowmaxxing."
What the Free Audit Can Find
The free audit does not sort homes into three vague buckets. It makes one of five plain-English calls, then shows which evidence families supported that call.
Zillow broadly agrees with the readable independent data.
There are clues, but not enough directional evidence to make a strong call.
Some evidence points toward Zillow running low, but the chain is not complete.
Multiple signals suggest stale or suppressed data may be reinforcing itself.
Independent sources suggest Zillow may be running high instead of low.
These are not diagnosis categories. They are the inputs we use to decide whether the Zestimate looks aligned, thin, suppressed, doom-looped, or inflated.
Sample premium outputs
These are anonymized, frozen audit examples from the test library. They show both sides of the product: sometimes the data tells a strong suppression story, and sometimes it says “slow down, this is only context.”
Premium term
The hyperlocal comp view. We start with comparable sales from the premium comp source, then show what changes when comps outside the subject municipality are removed.
Premium term
Compounded Exponential Discrepancy Chain: our read on whether stale facts, sale anchoring, valuation gaps, and history signals are reinforcing each other.
Anonymized northern NJ sample
Same-municipality comps were dramatically higher than the full comp pool, the neighbor gap was large, and sale anchoring made the feedback-loop narrative coherent.
Anonymized border-town sample
ClearComp found cross-municipality comp drag, but the public AVM stack stayed close to Zillow. The audit kept the diagnosis conservative instead of forcing a doom-loop story.
Zillow, in their own words
"The Zestimate relies heavily on recent transaction data. When a home sells, the algorithm anchors to that final sale price. If that sale price was indeed suppressed — whether by an older Zestimate, a lack of historical data, or other factors — the current Zestimate will reflect and reinforce that price point. It is a feedback loop."
— Zillow Support Agent, May 2026
We need the address to pull public records. You provide your Zestimate because Zillow won't let anyone check it automatically — not even us.
Feedback loops where low estimates cause low sales. Data errors where Zillow has the wrong facts about your home. Algorithm drops from model updates you were never told about. Cross-platform divergences that reveal when only Zillow sees a problem.
We tell you if your Zestimate is accurate, suppressed, or inflated. If it's hurting you, you get a personalized playbook of exactly what to fix, where to fix it, and how much it typically moves the needle. That's Zillowmaxxing.
Free. No account. No email. Just the truth about your Zestimate.
Questions, feedback, or a good Zillow story?
Have a question, a correction, or a Zestimate mystery worth sharing? Send it here. Your email is used only for a reply.
Diagnostic Confidence: (% — )
The Numbers
Zillow Zestimate
Entered by you from Zillow
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Last Sale Price
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Tax Assessment
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Structured facts, sale history, tax context, and property details when available
Comparable rows cleaned by municipality to detect cross-market comp bleed
Statistical agreement across all sources — how much do the models agree?
Flags whether stale facts, sale anchors, history, and valuation gaps are reinforcing each other
One-time payment. Instant results. Powered by premium records and comparable-sales data.
Valuation Consensus
Median Independent Estimate
Agreement Score
How closely all sources agree
Median vs. Zillow
Spread (Low to High)
Range across all models
CEDC Pattern Check
CEDC looks for a chain: stale facts, public-record drag, AVM gaps, sale-price anchoring, and Zestimate history reinforcing each other. One weird fact is a clue; a chain is the story.
Premium Valuations
Forecast Std Dev: — High confidence — Moderate confidence — Low confidence
ClearComp™ Report
The hyperlocal comp view that reflects how people actually buy homes.
Based on the premium comp source's individual comparable rows, filtered to the subject municipality.
comps came from outside : . Removing cross-market comps shifts the estimate by ().
These comps use list prices, not final sale prices. . Adjusted estimate: (+ sale-to-list adjustment).
Source: Redfin market data for ZIP
of comps are over 6 months old. In a market that's moved, these drag the estimate toward outdated prices.
All Comps Average
ClearComp™ Impact
Sale-Price Adjusted
| Address | City | Price | Dist. | Age | Match | |
|---|---|---|---|---|---|---|
If a simple API pulls comps from the wrong city, Zillow's neural network might be doing the same thing — but you'd never know, because they don't show you which comps they use.
Gap Analysis
Diagnostic Signals
Each signal is scored independently. More signals = higher confidence.
What This Means
Diagnosis Key
The Transparency Problem
We pulled data from every source that allows transparent access. The one number we couldn't verify independently? Your Zestimate. Because Zillow blocks all automated access to your data. Think about what that means.
What You Can Do
Already paid
If you find a Zestimate history table, a close neighbor, or corrected Zillow facts after the full audit runs, add them here and update this premium report using the same checkout session.
Check a neighbor's home, a home you're buying, or re-run this one after making changes.
Want to add context?
Every home has local quirks the audit may not fully see yet. If there’s useful context behind your result, send a note and we’ll read it.