Methodology version 1.0Last reviewed: 2026-05-06

Our methodology

A history report is only as trustworthy as the sources behind it. This page explains where Machinetrail's data comes from, how we validate it, and how we stitch it into a single VIN/PIN-keyed view. Buyers, dealers, and lenders can use this page to satisfy due diligence and audit requirements.

We organize our coverage into four data categories. Each category below lists representative public sources and the analytical approach we use to turn raw feeds into a buyer-grade signal. We update sources continuously and publish refresh timestamps on individual reports.

Registry data

Registration history is the spine of any history report. Machinetrail synthesizes public agency data from national vehicle and machinery registries to reconstruct ownership, country-of-origin, and de-registration timelines.

Representative sources

Latvia — CSDD / VTUA tractor registry
Finland — Traficom vehicle registry
Switzerland — ASTRA federal registry
Luxembourg — SNCA vehicle registry
Netherlands — RDW open data

Analytical approach

We normalize identifiers across registries (VIN, PIN, serial number, frame number), reconcile spelling and transliteration variations, and emit one canonical machine record per physical asset. Where a registry exposes registration timestamps we store them; where it does not, we stitch sequence from auction and customs data.

Recall data

Recalls and safety notices are the single most under-checked risk in used-equipment buying. Machinetrail synthesizes public agency data from regulators on both sides of the Atlantic.

Representative sources

United States — CPSC SaferProducts API
European Union — Safety Gate (formerly RAPEX) machinery alerts
Germany — KBA Kraftfahrt-Bundesamt recall database

Analytical approach

Each recall notice is parsed for affected make, model, model-year range, and component. We match recalls to canonical machines via an engine-family crosswalk (Nebraska × EPA) so a buyer sees only recalls that actually apply to the unit they are evaluating, not the whole product family.

Theft data

A stolen tractor or excavator is the worst possible outcome for an unwary buyer — title doesn't transfer and law enforcement will recover the asset. Machinetrail synthesizes public agency data from national and supranational policing sources.

Representative sources

National police stolen-vehicle databases (multiple EU member states)
Europol property-crime threat reports
NER — National Equipment Register (United States)
TER — TheftEquipment Register (United Kingdom)

Analytical approach

Stolen records are matched on VIN/PIN and on (make, model, year, color) tuples to catch identifier-mangled listings. Every theft hit links back to the source registry record and includes the date the theft was reported, so buyers can see whether a flag is fresh or historical.

Auction & price data

Fair market value is the difference between a good deal and an overpay. Machinetrail synthesizes public auction-platform data and dealer-listing feeds to produce price comparables grounded in actual transactions, not asking prices.

Representative sources

Mascus — pan-European used-machinery marketplace
Ritchie Bros — global heavy-equipment auctioneer
Klaravik, Troostwijk, and other regional auction houses

Analytical approach

We separate sold-price from asking-price wherever the source distinguishes them, normalize to EUR with the auction-date FX rate, and adjust for hours and condition where disclosed. The resulting distribution is what powers the fair-market-value range shown on each report.

Data corrections and disputes

If you believe a record on Machinetrail is incorrect — for example a theft flag that has been resolved, or a recall that does not apply to your serial number — contact us and we will investigate against source-of-truth registries within five business days.

Learn more about Machinetrail