Platform · Mobile.de

Mobile.de Data Scraping Services

Where the same model at different mileage is not the same product, so identity has to include continuous attributes.

Mobile.de data scraping is the automated collection of publicly visible used vehicle listing data for Germany — where mileage and age form part of product identity, so comparison is made within attribute bands rather than by model, alongside dealer versus private listing type and time-on-market.

Every other page here matches products by identifier. A used vehicle cannot be matched that way: the same model, year and trim at 40,000 and 140,000 kilometres are two different products with two different prices.

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mobilede_bands_2026-08-10.jsonl LIVE FEED

{"mobile_listing_id":"md-7712049", "make":"Example","model":"EX-300", "trim_normalised":"Sport", "year":2021,"age_band":"4-6y", "mileage_km":62400, "mileage_band":"50-75k", "band_definition":"age 4-6y | mileage 50-75k km", "asking_price":24900, "price_revisions":[{"price":26500,"date":"2026-06-20"}], "lister_type":"dealer", "dealer_name":"Example Autohaus GmbH", "first_seen":"2026-06-20", "days_on_market":51, "relisted":false} {"mobile_listing_id":"md-7719981", "mileage_km":141200, "mileage_band":"125-150k", "asking_price":13400, "lister_type":"private", "note":"same model — not comparable across bands"}

2 of 1,884,220 listing rows · run 2026-08-10relist linked 92.4% · bands stated per record · schema v1.8

Independence and trademarks. Actowiz Solutions is not affiliated with, endorsed by or connected to Mobile.de or its owners. Mobile.de and related marks belong to their respective owners, used here only to name the publicly accessible source this service collects from.

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Mobile.de at a glance

How we handle Mobile.de specifically

Platform-specific handling, not a generic retail template pointed at a different domain.

Platform: Mobile.de used and new vehicle listings in Germany
Identity problem: Continuous attributes are part of identity, not descriptors
How we handle it: Comparison within mileage, age and trim bands, with bands stated
Lister type: Dealer versus private listing distinguished
Lifecycle: Time on market with price revisions
Attributes: Structured vehicle specification where published
Refresh: Daily standard; sub-daily on priority segments
Region: Germany

Platform specifics

What makes used vehicle listing data structurally different

These are the reasons a Mobile.de dataset needs its own handling rather than a shared retail schema.

Continuous attributes belong in the identity, not the description

On a retailer, a product identifier resolves identity and attributes describe it. On used vehicles that breaks: mileage and age are not descriptors of one product, they are what makes two listings different products.

Why model-level matching fails

Price variance within a model is enormous, driven mostly by mileage and age.
A model-level average is meaningless, since it blends a nearly-new vehicle with a high-mileage one.
Trim and options matter substantially and are inconsistently described.
Condition and service history add variance that is only partly published.

We define comparison bands and deliver them explicitly: mileage_band, age_band and normalised trim, plus the raw values. Comparison happens within a band, and the band definition is in the scope document so it is inspectable rather than an internal choice.

We do not deliver a single price-per-model figure. It would be the most requested field and the least defensible one, because the variance it hides is larger than most of the differences clients want to detect.

Dealer and private listings price differently

The platform carries both dealer inventory and private seller listings, and they are different markets.

Dealers price with warranty, preparation and margin built in, so dealer prices sit above private ones for comparable vehicles.
Private listings carry more price dispersion, since sellers have less market information.
Time on market differs substantially between the two.
Merged, the price distribution is bimodal and any average describes neither.

We capture lister_type and keep the populations separable. For a dealer group benchmarking against competitors, dealer-only is the relevant population; for understanding what consumers ask, private matters. The same reasoning as franchise versus company-operated on Carrefour.

Time on market and price revisions, with a hard limit

Listing lifecycle is the strongest demand signal available here, and it needs continuity to be usable.

Days on market from first observation, with the caveat that pre-existing listings are archive-limited.
Price revisions with dates, showing how quickly a seller adjusts to lack of interest.
Relisting detection, since a vehicle can be relisted and reset its apparent time on market — the same problem we solve on Rightmove.

The limit

A disappeared listing is not a sale. It can be sold, withdrawn, relisted by a dealer, or moved to trade. We record disappeared_unconfirmed where no status is published and do not classify it. Transaction prices in this market are not published, and we do not estimate them.

Scope

What we collect on Mobile.de, and what we do not

The right column matters more than the left. Anyone can list fields; the limits are what tell you whether the dataset will hold up.

✅ What we collect

Mileage and age bands with raw values retained, and band definitions stated
Normalised trim alongside the published description
Comparison designed within bands, not across a model
Dealer versus private lister type, kept separable
Days on market with archive-limited flagging for pre-existing listings
Price revisions with dates
Relist detection with confidence, so time on market does not reset
Structured vehicle specification where published
Dealer business identity where displayed

❌ What we do not, and why

A single price-per-model figure, which hides more variance than it reveals
Any claim that a disappeared listing was sold
Achieved transaction prices, which are not published
Private seller personal details
Estimated condition or valuation figures

Core Mobile.de fields

The full dictionary is agreed during scoping. These are the fields specific to this platform.

Field	What it is on this platform
mobile_listing_id	Listing identifier, the record key
make / model / trim_normalised	Vehicle identity with trim normalised
year / age_band	Registration year and derived age band
mileage_km / mileage_band	Raw mileage and derived band, both delivered
band_definition	The band boundaries used, so comparison is inspectable
asking_price / price_revisions	Current asking price and revision history with dates
lister_type	dealer or private, kept separable
dealer_name	Dealer business identity where displayed
first_seen / days_on_market / archive_limited	Lifecycle with the archive caveat flagged
relisted / relist_confidence	Relist detection so time on market does not reset
specification_attributes	Fuel, transmission, power and options where published

Use cases

What teams do with Mobile.de data

Band-level price benchmarking

Comparison within mileage, age and trim bands with band definitions stated produces price benchmarks that survive the variance a model-level average hides.

Dealer competitive analysis

Dealer and private populations are kept separable, so a dealer group benchmarks against comparable dealer inventory rather than against private asking prices.

Demand inference from time on market

Days on market with price revisions and relist detection shows which bands move quickly and which require repricing, the strongest available demand signal.

Depreciation curve construction

Raw mileage and age with prices across bands support depreciation analysis by model and trim, built from asking prices with that basis stated.

The 24-hour sample — run on your sources, not ours

Send us a Mobile.de item or category list. We run real collection against it and return the output within 24 hours, with the platform-specific fields populated so you can check them yourself rather than take our word for it.

Real extraction from your actual sources
Returned within 24 hours
Coverage and QA note included
You keep the data either way
No card, no trial clock
Named engineer on the call

Get my free sample Book a 20-min scoping call Reply within one business day. Reference calls available under NDA.

How we engage

Three ways to engage us

Same collection pipeline and QA underneath. The difference is who holds the schedule and how the data reaches you.

Managed service (most common)

We own the collection, the QA and the delivery. You receive clean data on a schedule and never touch a scraper.

Dedicated engineer assigned to your account
Site changes fixed by us, not reported to you
Scheduled delivery to your warehouse or S3
Named contact on Slack or email

Best fit: Teams who need the data, not the infrastructure.

API access

The same collection pipeline exposed as an authenticated REST endpoint your systems query directly.

On-demand and scheduled endpoints
Rate limits agreed to your load profile
Sandbox keys for integration testing
Versioned schema with deprecation notice

Best fit: Product and engineering teams building on live data.

One-time or project extraction

A defined pull for a specific question — market sizing, diligence, a pitch, a one-off audit.

Fixed scope agreed in writing upfront
Single delivery with full QA report
Methodology documented for your records
Converts to managed if you want continuity

Best fit: Research, strategy and diligence work with a deadline.

Pricing

Every engagement is quoted individually, because the honest answer depends on your scope: how many sources, how many records, how often, and how the data reaches you. We scope it with you, run a free pilot on your own sources, and then quote a fixed monthly figure — no per-request metering and no overage billing when volumes move. Request a quote and you will have a number after one call.

Mobile.de is usually collected alongside its competitors

Almost nobody buys a single platform in isolation. Mobile.de data becomes useful when it sits next to the competitor set on one schema, refreshed on one schedule, so a price index or availability comparison is genuinely like-for-like.

That is what automotive & ev data covers, and a Mobile.de-only engagement can be expanded into it without rebuilding. If you already know you need several platforms, start there instead — it is the same pipeline and usually the better scoping conversation.

FAQ

Mobile.de data scraping: frequently asked questions

Platform-specific questions, including what cannot be collected here.

Because the variance within a model is enormous and driven mostly by mileage and age. A model-level average blends a nearly-new vehicle with a high-mileage one, hiding more variance than most clients want to detect.

It would be the most requested field and the least defensible. We deliver bands with their definitions stated so comparison happens between comparable vehicles.

With you, during scoping, and the definitions are recorded in the scope document and delivered on every record as band_definition.

That matters because band choice affects every conclusion. Having it inspectable means you can disagree with it rather than inheriting an internal choice you cannot see.

Because they are different markets. Dealers price with warranty, preparation and margin built in, so dealer prices sit above private ones for comparable vehicles, and private listings carry much more dispersion.

Merged, the distribution is bimodal and any average describes neither population.

No. It can be sold, withdrawn, relisted by a dealer, or moved to trade. We record disappeared_unconfirmed where no status is published and do not classify it.

Transaction prices are not published in this market and we do not estimate them. Asking price is what we collect and we never present it as achieved.

Yes, with relist detection and a confidence score, so a relisted vehicle does not reset its apparent time on market.

It is the same problem we solve on Rightmove: without linkage, a vehicle for sale eight months looks new because a dealer refreshed the listing last week.

We quote individually. Drivers are segment scope, whether both dealer and private populations are needed, refresh frequency, and whether historical backfill is required for depreciation work.

One scoping call, a free pilot on your own segments within 24 hours, then a fixed monthly quote. Request a quote.

See real Mobile.de data before you commit to anything

Send us an item or category list. We return the output within 24 hours with the platform-specific fields populated.

Get a free sample Talk to an engineer

Free pilot, no card, no obligation. If we cannot collect a field you need on this platform, the sample shows you that too.

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