Developers building pricing scrapers, automated repricing tools, or valuation pipelines around public eBay sales listings face an invisible data bias. According to study data published by CompsAPI on October 10, 2026, web scraping tools reading public eBay sold pages consistently retrieve original asking prices rather than the negotiated amounts buyers actually paid for items closed through the Best Offer feature.
How Public Sold Pages Obscure Actual Transaction Prices
When an eBay listing concludes through an accepted Best Offer, the public web interface displays the original asking price while omitting the finalized transaction figure. Because the actual settlement amount is kept off the public document and is absent from front-end page markup, HTML scrapers and price ingestion pipelines receive well-formed, plausible numerical data that is fundamentally incorrect.
Because two separate scraping tools reading the same public page yield identical results, pricing pipelines falsely confirm agreement without triggering parser errors or status code alerts. The underlying data discrepancy cannot be resolved by refining DOM selectors or increasing request frequency, as the true settlement figure is entirely absent from the public page surface.
Measuring the Scale of Best Offer Price Inflation
To measure the discrepancy across product categories, CompsAPI analyzed 5,859 completed Best Offer transactions across 5,840 distinct listings gathered from 93 targeted searches over a 90-day period ending October 10, 2026. Comparing matched listing identifiers where both asking and accepted prices were known, the analysis revealed that 99.0% of completed Best Offer listings displayed an inflated figure on public pages, with 0.7% matching within a half-percent margin and 0.4% reading lower.
The study recorded a median overstatement of 14.9%, while the money-weighted inflation across all analyzed transaction volume reached 14.2%. The median accepted price stood at $240 compared to a median asking price of $284. At the top 90th percentile, one in ten negotiated sales showed public asking prices overstated by more than 40.8%. All 12 sample categories exhibited money-weighted overstatements above 11%, with high-value items showing pronounced gaps, such as graded trading cards at 17.3% and fine jewelry at 16.8%.
Why Unadjusted Comp Data Skews Automated Repricing and Machine Learning
This structural data gap presents serious implications for engineering teams operating automated commerce engines, inventory appraisal systems, and financial models. Because accepted Best Offers are virtually never settled above the seller's asking price, the error is strictly one-sided. Unlike symmetric Gaussian noise in data collection, these discrepancies do not average out over large sample sizes; instead, every unadjusted listing comp pushes price estimates consistently higher.
For automated repricing algorithms, deriving target listing prices from public comps causes bots to set prices above true market clearing levels. As a result, inventory remains unsold despite software logic designed to list items just below perceived market values. For automated procurement workflows, algorithms risk evaluating buying ceilings based on non-existent profit margins. Furthermore, machine learning models trained on raw public sales feeds end up learning artificial price distributions that fail to reflect actual consumer willingness to pay. Developer teams relying on secondary market sales data must account for offer status or integrate direct API endpoints to prevent compounding estimation errors.
AI-generated editorial illustration; not a documentary photograph.
Source: Your eBay sold-price data is 14.9% too high and the response looks fine · eBay Best Offer Prices Read 14.9% High on the Public Sold Page · CompsAPI
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