Credit Repair Statistics: Real Data From 1,031 Credit Reports (2026)

Most credit repair statistics online are recycled guesses. These are not. Every number in this report was computed directly from the operational database of the founder’s Texas credit repair practice: 1,031 full three-bureau credit report files containing 64,500 bureau tradelines (28,510 merged accounts), plus 4,421 report pulls tracked between January 2024 and August 2026. No estimates, no third-party surveys — real reports from real consumer files, aggregated and anonymized.

Key Findings at a Glance

Every figure below was computed from the same first-party dataset described in the methodology. Sample size (n) is stated for each group because the denominators differ — some metrics are measured per report, some per account, some per report pull.

Ultra Dispute Credit Report Field Analysis, January 2024 – August 2026. Source: anonymized operational data, Best Texas Credit Pros.

Metric	Value	Base (n)
Sample and scope
Three-bureau credit report files fully parsed	1,031	—
Bureau-level tradelines parsed	64,500	—
Merged (cross-bureau deduplicated) accounts	28,510	—
Historical report pulls tracked	4,421	—
Client records in the source database	3,028	—
Date range covered	Jan 2024 – Aug 2026	—
Derogatory items on the file
Reports with at least one derogatory account	73.6%	1,031 reports
Reports with at least one charge-off	51.7%	1,031 reports
Reports with at least one collection	49.1%	1,031 reports
Reports showing any late-payment history	65.0%	1,031 reports
Derogatory accounts per affected report	8.3 avg / 4 median	reports with ≥1 derogatory
Collection accounts per client who had any	2.7 avg	reports with ≥1 collection
Distinct merged accounts per report	27.7 avg	1,031 reports
Charged-off accounts, total count	2,134	—
Collection accounts, total count	1,384	—
Cross-bureau disagreement on the same account
Different account condition (open / paid / closed / derogatory)	35.4%	15,927 multi-bureau accounts
Different current balance	6.5%	15,927 multi-bureau accounts
Different payment status	5.9%	15,927 multi-bureau accounts
Different date opened	91.0%	15,927 multi-bureau accounts
Score spread between bureaus
Average highest-to-lowest bureau score spread	30.3 pts (median 22)	3,655 pulls, all 3 scores
Pulls with a spread of 20+ points	54.7%	3,655 pulls
Pulls with a spread of 40+ points	27.4%	3,655 pulls
Average three-bureau score	628	3,655 pulls
Observed score change over time
Average change in three-bureau average score	+21.3 pts (median +22)	330 clients, avg 321-day window
Clients who ended the window higher	65.2%	330 clients
Clients who did not end the window higher	34.8%	330 clients

Score-change figures are observational, not a performance claim. See limitations.

Where Does This Data Come From?

The dataset is the founder’s own credit repair operation: 3,028 client records, 1,031 fully parsed three-bureau credit report files, and 4,421 historical report pulls spanning January 2024 through August 2026. Statistics were computed programmatically across every tradeline field — account conditions, pay statuses, balances, dates, and bureau-level scores — then aggregated with no personal information retained.

Ultra Dispute was built inside a working credit repair business — Best Texas Credit Pros — and this is the anonymized shape of what that business actually sees when it imports a new client’s Experian, Equifax, and TransUnion reports. Each report file is a merged three-bureau snapshot in the same TrueLink/Metro 2-derived structure that credit monitoring services deliver, which is what lets us compare how the three bureaus report the same account side by side.

How Many Negative Items Does a Typical Credit Repair Client Have?

73.6% of the 1,031 client credit reports contained at least one derogatory account. 49.1% had at least one collection, 51.7% had at least one charge-off, and 65.0% showed late-payment history. When derogatory accounts were present, clients averaged 8.3 of them; the median report carried 4 derogatory accounts across bureaus.

The average report in the dataset contained 27.7 distinct accounts once the three bureaus’ tradelines were merged. Among clients who had collections at all, the average was 2.7 collection accounts — collections rarely travel alone, because one defaulted debt frequently spawns multiple entries as it is assigned, sold, and resold. Charge-offs were slightly more common than collections (2,134 charged-off accounts versus 1,384 collection accounts in total), and late-payment history was the most widespread derogatory of all.

The practical takeaway for anyone learning how to remove collections or address a charge-off: a professional’s first audit of a new file is rarely about one bad account. It is about four to eight of them, each needing its own analysis and dispute basis.

How Often Do the Three Bureaus Disagree About the Same Account?

Constantly. Among the 15,927 accounts in this dataset reported by two or more bureaus, 35.4% showed a different account condition (open, paid, closed, derogatory) depending on which bureau you asked, 6.5% showed a different current balance, 5.9% showed a different payment status, and 91.0% recorded a different date opened.

This is the single most important finding in the dataset. Consumers assume the three bureaus hold one synchronized record; in this data, more than a third of multi-bureau accounts had bureaus disagreeing about the account’s basic condition. Not every discrepancy is a legal violation — open-date differences, in particular, are often day-level recording artifacts — but a condition or balance that changes depending on the bureau is exactly the kind of concrete, checkable inconsistency that gives a dispute a factual basis under the FCRA’s accuracy requirements.

It is also why field-level, cross-bureau analysis matters more than template letters. A generic “this account is wrong” letter ignores the strongest evidence sitting in plain sight: the bureaus contradicting each other. This is the defect class that Metro 2 analysis is designed to surface — and at 15,927 multi-bureau accounts, no human is auditing it by hand.

How Far Apart Are Credit Scores Across Bureaus?

Across 3,655 report pulls where all three bureau scores were present, the average spread between a consumer’s highest and lowest bureau score was 30.3 points (median 22). 54.7% of pulls showed a spread of 20 points or more, and 27.4% showed 40 points or more. The average three-bureau score in the dataset was 628.

A 30-point average gap means “what’s your credit score?” has no single answer — and it is one more consequence of the reporting inconsistencies above: bureaus scoring different underlying data produce different numbers. For a credit repair professional, a wide spread is a diagnostic signal worth investigating: it often means one bureau is carrying a derogatory or an error the other two are not.

Do Credit Scores Change Over the Course of Credit Repair?

Among 330 clients in this dataset with score history spanning at least 90 days (average tracking window: 321 days), the average three-bureau score change was +21.3 points and the median was +22. 65.2% of tracked clients ended the period with a higher average score than they started with — and 34.8% did not.

Read this honestly: these are observational numbers from one practice, not a promise. Score movement over months reflects everything happening in a consumer’s file — successful disputes, but also the consumer’s own payment behavior, balances, aging of negative items, and new credit activity. No credit repair outcome is guaranteed, and federal law (CROA) prohibits anyone from promising specific results. We publish the distribution, including the third of clients whose scores did not rise, because real data includes the part marketing usually deletes.

What Do These Numbers Mean for Dispute Strategy?

Three conclusions follow directly from the data: audit every account across all three bureaus (35.4% of multi-bureau accounts disagree on condition alone), expect multi-item files (median 4, average 8.3 derogatory accounts when present), and prioritize documented inconsistencies over template letters — the contradictions are already sitting in the file.

At 27.7 accounts per report and thousands of field-level comparisons per client, this audit is software work. It is the reason Ultra Dispute’s engine parses every tradeline field on all three reports and flags cross-bureau contradictions automatically — the dispute grounds shown above are found by comparison, not by guesswork. The 609 letter myth persists precisely because it promises a shortcut around this audit; the data says the audit is where the leverage actually lives.

Operationally, findings like these are an argument for two things. The first is field-level auditing across all three bureaus rather than a manual skim, which is what Metro 2 compliance dispute software automates by comparing every reported value against the standard. The second is systematic follow-up: with this many items per file and multiple rounds each, a credit repair CRM software that tracks outcomes per tradeline is what keeps rounds two and three from being written blind.

Methodology

This is a first-party census of one credit repair practice’s own case files, not a survey and not a sample of the general population. Everything published here was computed programmatically from stored three-bureau report files; nothing was estimated, modeled, or sourced from a third party.

Source and population

The data comes from the operational database of Best Texas Credit Pros, the working Texas credit repair company where Ultra Dispute was built. The database held 3,028 client records at the time of computation; 1,031 of those had at least one fully parsed three-bureau credit report file, and those 1,031 files are the analysis population for every report-level statistic. The population is therefore people who sought credit repair help — not a random sample of U.S. consumers.

Time period

Report files and report pulls span January 2024 through August 2026. All statistics were computed in August 2026.

Unit of analysis

Three different denominators are used, and each figure states which one applies:

• Report file (n = 1,031) — one merged three-bureau snapshot per client, in the same TrueLink/Metro 2-derived structure that credit monitoring services deliver. Used for prevalence figures such as “73.6% had at least one derogatory account.”
• Account (64,500 bureau tradelines → 28,510 merged accounts) — a merged account is the same underlying tradeline as reported by one, two, or three bureaus. The 15,927 accounts carried by two or more bureaus are the only ones eligible for cross-bureau comparison, so they are the denominator for every disagreement rate.
• Report pull (n = 4,421; 3,655 with all three bureau scores present) — one retrieval event. Used for score-spread figures, which require all three scores in the same pull.

How each measure was computed

• Derogatory prevalence — a report counts once if any account on it carries a derogatory condition, collection, charge-off, or late-payment history. Per-report averages for derogatory count are conditional: they are computed only over reports that had at least one, so the average of 8.3 (median 4) describes affected files, not all 1,031.
• Cross-bureau disagreement — for each multi-bureau account, the value of a given field (account condition, current balance, payment status, date opened) is compared across the bureaus reporting it. If the values are not identical, that account is counted as disagreeing on that field. Fields are evaluated independently, so the four rates are not additive.
• Score spread — the highest bureau score minus the lowest bureau score within a single pull.
• Score change — the change in a client’s three-bureau average score between their first and last pull, restricted to the 330 clients with at least 90 days of score history (average tracking window 321 days).

Privacy

All statistics are aggregate. No names, addresses, Social Security numbers, account numbers, creditor-identifying client details, or any other personal information were retained in, or are derivable from, the published figures. No individual file is described.

Limitations — read these before citing

• Not representative of all consumers. These are files belonging to people who hired a credit repair company, which skews the sample toward damaged credit (average three-bureau score 628). Do not present these rates as U.S. population rates.
• One practice, one state. The data comes from a single Texas company’s client base. Regional creditor mix and client demographics may not generalize.
• A discrepancy is not automatically an error or a violation. Cross-bureau differences are measured facts about what the bureaus reported; whether any given difference is inaccurate under the FCRA is a case-by-case determination. Date-opened differences in particular (91.0%) are frequently day-level recording artifacts and should not be characterized as a 91% error rate.
• Score change is observational, not attributed. The +21.3-point average reflects everything happening in those files over roughly a year — disputes, the consumer’s own payment behavior and balances, aging of negative items, and new credit — not the isolated effect of any product or service. 34.8% of tracked clients did not end higher, and that figure is published for the same reason the positive one is. Federal law (CROA) prohibits promising specific credit repair results, and nothing here should be read as such a promise.
• Bureau coverage varies by file. Not every account is reported by all three bureaus, which is why account-level rates use the 15,927 multi-bureau subset rather than all 28,510 merged accounts.
• Not independently audited. These are self-computed first-party figures published with their derivation stated. Methodology questions and requests for additional cuts of the aggregate data are welcome at the contact below.

Cite This Study

Buyers using this data to evaluate tooling can pair it with our weighted credit dispute software comparison scorecard, which turns capability claims into a single score per platform.

Related reading

• How to Remove Collections From Your Credit Report
• What Is a Charge-Off (And How to Remove It)?
• How Long Do Negative Items Stay on a Credit Report?
• Metro 2 Disputes Explained
• 609 Dispute Letters: The Truth
• Credit repair glossary: 60 key terms defined

Run your credit repair business on Ultra Dispute

Ultra Dispute is credit repair business software built inside the practice that produced this data. It imports three-bureau reports, runs the field-level cross-bureau audit described above on every tradeline automatically, and generates specific, compliant dispute letters from what it finds. Join the launch list — launching Fall 2026.

Credit Repair Statistics FAQ

How many disputes does a typical credit repair client need?

In this dataset, reports containing derogatory accounts averaged 8.3 of them (median 4), and each derogatory account may be reported by up to three bureaus — so a typical file involves a dozen or more individual bureau-level disputes across multiple rounds, not one letter.

What percentage of credit reports contain errors?

We can only publish what we measured: among accounts reported by two or more bureaus in this dataset, 35.4% showed a different account condition and 6.5% a different balance across bureaus. Not every inconsistency is an error, but each is a concrete discrepancy worth investigating. (The FTC’s landmark study separately found about 1 in 5 consumers had a confirmed error on at least one report.)

Which is more common — collections or charge-offs?

Charge-offs, narrowly: 51.7% of reports in this dataset had at least one charge-off versus 49.1% with at least one collection. In absolute counts, charged-off accounts outnumbered collection tradelines 2,134 to 1,384.

Why are my three credit scores different?

Because the bureaus hold different data: in this dataset the average gap between a consumer’s highest and lowest bureau score was 30.3 points, and 35.4% of multi-bureau accounts showed a different account condition per bureau. Different underlying records produce different scores.

Is this data independently verified?

It is first-party operational data, computed programmatically from the founder’s practice database and published with its limitations stated. It is not a randomized study of all consumers — it describes people who sought credit repair help, which skews the sample toward damaged files (average score 628).

How should I cite this study?

Attribute to Ultra Dispute and link to this page, e.g. “Ultra Dispute. (2026). Credit repair statistics: Real data from 1,031 credit reports. https://ultradispute.com/blog/credit-repair-dispute-statistics/”. The aggregate statistics and tables are released under CC BY 4.0, so they may be reused commercially with attribution. Full APA, MLA, plain-text and HTML citation formats are in the Cite This Study section, and the sample sizes and limitations you should quote alongside them are in the Methodology.