GoodCarBadCar Automotive Sales Data

GoodCarBadCar Automotive Sales Data is a long-running archive of new-vehicle unit sales, organised into market hubs for the United States (2006-2026), Canada (2012-2025), China (2015-2022) and Europe (2012-2022), plus a Rest of World section carrying more than 180 country pages, regional hubs and worldwide rollups. Every hub cuts the same volume three ways - by automaker, by brand and by model - and segment reports rank nameplates within pickups, SUV size classes, cars, commercial vans and sports cars.

That combination is rarer than it sounds. Registration series from trade associations measure the European market well but rarely reach brand-by-model depth, and government statistics stop at industry production indexes. This is the catalog's retail-side counterweight: who actually sold how many units, of which nameplate, in which market, month by month and year by year.

Get a sample of this dataset - name the market and the cut you need, and real rows come back before you commit to a feed.

Three row shapes cover everything the archive publishes - a by-manufacturer entry, a by-brand entry and a by-model entry. Their anatomy, with values left as placeholders:

• market : us
• period : <calendar year>
• cut : by-manufacturer
• automaker : <corporate grouping such as VW Group>
• sales_units : <reported units for the period>

The by-brand and by-model shapes swap automaker for brand, then add model on top:

• market : <hub market>
• period : <month or year>
• brand : <brand from the A-Z list>
• model : <nameplate, by-model cut only>
• sales_units : <reported units>

The placeholders are deliberate: the public record documents the cuts, not the cell values, and Datadory does not print numbers it cannot stand behind. Ask for a sample with your market named, and the same shapes come back with reported units filled in.

Seven fields carry every ranking table in the archive, reconstructed from the published table structures - manufacturer hubs, the brand A-Z list, model rankings and the segment reports. The dictionary below is complete for those four families of tables.

• Geography - dedicated hubs for the US, Canada, Europe and China, plus a Rest of World section of more than 180 country pages with regional hubs and worldwide rollups by region and country.
• Temporal - US figures run 2006 through 2026, Canada 2012 through 2025, Europe 2012 through 2022 and China 2015 through 2022; annual year-hub pages sit alongside month-cut views within each hub.
• Granularity - four cuts of the same volume: automaker group, brand, individual nameplate, and segment (commercial vans, compact/midsize/small cars, small/midsize/large SUVs, pickups, sports cars).

Depth is not uniform across markets - the US archive is the deepest, and the Europe and China hubs currently stop at 2022. If your window depends on a specific market, say so in the sample request and the answer comes back with that hub's actual span.

API, files, or your warehouse. Daily, weekly, or hourly.

Ranking tables arrive flattened into analysis-ready rows - one observation per automaker-brand-model-market-period combination - so the directory's HTML-table shape never reaches your pipeline.

• Market-share tracking - the automaker and brand cuts give group-level and marque-level share in the same market, so a Stellantis number reads at group scale or unwinds brand by brand.
• Competitive intelligence - dedicated tracker pages exist for Toyota, VW Group, Stellantis, Tesla, GM, Ford, Hyundai, BMW, Honda, Nissan, Mazda, Subaru, Volvo, JLR and the Renault-Nissan-Mitsubishi Alliance, which makes single-OEM run-rate monitoring a lookup rather than a rebuild.
• Model-line and assortment planning - by-model rankings crossed with segment reports show where nameplates win inside pickups, three SUV size classes, cars, vans and sports cars.
• Cross-market screening - Rest of World country pages let a team benchmark its home market against 180-plus others before deciding where an expansion case holds water.
• Citable reporting - year-hub pages such as '2026 U.S Auto Sales Figures - By Manufacturer' give journalists and analysts a stable, nameable artifact to cite.

Competitive-intelligence and product teams get the OEM-tracking backbone the role is built on - share by group, by brand and by nameplate without rebuilding a directory each Monday. Journalists and academics get citable, long-span archives: two decades of US figures is enough runway for trend claims that survive review. Investors and quants read the brand and model cuts as demand signals - a nameplate's trajectory is often the earliest honest indicator an OEM's product cycle gives. Market researchers get cross-market breadth for category sizing beyond the usual EU-and-US pairing. Data scientists get the retail-demand join key: these volumes enrich EPA fuel-economy configurations, Eurostat production indexes and NHTSA vPIC entity resolution into models that finally see demand, not just specifications.

Four things, all knowable upfront. First, the source directory does not name its own upstream providers anywhere on its sales-data pages, so figures are best treated as that directory's editorial compilation - teams with audit requirements typically reconcile a sample against OEM press releases. Second, the field dictionary is reconstructed from the published table structures rather than a formal data dictionary, so definitions are structural inferences confirmed at first delivery. Third, hub depth varies: Europe and China currently end at 2022, Canada at 2025, and whether they extend is exactly what a sample request settles for your markets. Fourth, period labeling follows the year-hub convention ('2026 U.S Auto Sales Figures - By Manufacturer'), so pipelines should expect mixed annual and monthly period forms until normalized.