We extract JDM vehicle listings, dealer inventories, pricing signals, and inspection metrics from Goo-Net. Delivered as clean JSON, CSV, or Parquet to S3, BigQuery, or Snowflake on your cadence.
Structured, schema-consistent data across all major object types — delivered clean, typed, and ready to query.
Complete list of extractable fields for Vehicle Listings objects from goo-net.com. All fields typed and schema-versioned.
"listing_id": "700020834130230514001", "make": "Toyota", "model": "Supra", "year": 1998, "price_jpy": 8500000, "mileage_km": 112450, "transmission": "MT", "colour": "White", "repair_history": false
| # | listing_id | title | make | model | year | price_jpy |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Pricing & Valuations objects from goo-net.com. All fields typed and schema-versioned.
"listing_id": "700020834130230514001", "base_price_jpy": 8500000, "total_price_jpy": 8750000, "tax_included": true, "recycling_fee": 12500, "maintenance_fee": 45000, "price_timestamp": "2026-05-12T09:14:00Z"
| # | listing_id | base_price_jpy | total_price_jpy | monthly_installment | tax_included | recycling_fee |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Goo Kantei (Inspections) objects from goo-net.com. All fields typed and schema-versioned.
"listing_id": "700020834130230514001", "exterior_rating": "4", "interior_rating": "4", "repair_flag": false, "inspection_date": "2023-04-15", "certificate_url": "https://www.goo-net.com/kantei/700020834130230514001.pdf", "odometer_verified": true
| # | listing_id | exterior_rating | interior_rating | repair_flag | inspector_notes | inspection_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Dealer Intelligence objects from goo-net.com. All fields typed and schema-versioned.
"dealer_id": "0208341", "dealer_name": "JDM Motors Tokyo", "location_pref": "Tokyo", "location_city": "Setagaya", "rating": 4.8, "review_count": 142, "inventory_size": 45, "business_hours": "10:00-19:00"
| # | dealer_id | dealer_name | location_pref | location_city | rating | review_count |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Search Rankings objects from goo-net.com. All fields typed and schema-versioned.
"keyword": "Skyline GT-R", "prefecture": "Osaka", "position": 1, "listing_id": "700020834130230514005", "promoted_flag": true, "price": 14500000, "mileage": 85000, "scraped_at": "2026-05-12T09:14:33Z"
| # | keyword | prefecture | position | listing_id | promoted_flag | price |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our Goo-Net scraper handles every layer of the platform: vehicle specifications, dealer inventories, pricing models, and Goo Kantei inspection reports - with JavaScript rendering and Japan-localised session management built in.
Make, model, year, chassis code, engine displacement, transmission type, colour, and mileage - scraped at the listing level.
Capture base price, total on-road price, tax inclusions, recycling fees, and maintenance costs in JPY - timestamped per crawl.
Extract exterior ratings, interior ratings, Shaken validity dates, repair history flags, and verified odometer readings.
Dealer name, location, contact details, user ratings, and full active inventory counts for every seller on the platform.
Track organic vs promoted position for any vehicle model or keyword across all 47 Japanese prefectures.
Capture high-resolution vehicle image URLs, interior shots, and Goo Kantei certificate links for offline processing.
Requests routed through Japanese residential proxies to ensure native pricing, local inventory visibility, and bot circumvention.
Run one-off bulk exports or configure continuous pipelines at hourly or daily cadences with change-detection diffing.
Monitor price drops, days-on-market, and inventory turnover rates across specific JDM chassis codes.
Brief in. Clean data out.
Provide vehicle models, prefectures, dealer IDs, or search URLs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, Japanese proxy rotation, and session management for goo-net.com.
Schema validation, null-rate checks, price-outlier detection, and sample vehicle records before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting data from Japanese automotive marketplaces requires specific infrastructure. Here is how we stay resilient.
Goo-Net restricts access and alters content for non-Japanese IP addresses. Our crawlers use Japanese residential ISP proxies with realistic browser fingerprints to ensure accurate, local-market data extraction.
Vehicle search results, image galleries, and dealer contact details are heavily JavaScript-rendered. We run full Playwright browser sessions to capture data that headless HTTP clients miss entirely.
Japanese web layouts often use complex table structures and irregular DOM nesting. Our selector strategy uses multiple fallback chains per field so a layout change does not break your data pipeline.
For large vehicle catalogues, we maintain a hash index of last-seen values per listing. Subsequent runs only push diffs, reducing compute cost and downstream processing load.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, price outliers, schema drift, and coverage drops.
JDM vehicle exporters monitor pricing and inventory across prefectures to identify arbitrage opportunities for global markets.
Automotive pricing platforms ingest historical Goo-Net data to build depreciation curves and residual value models.
Dealership groups track competitor inventory size, pricing strategies, and days-on-market to optimise their own listings.
Machine learning teams use structured vehicle features and Goo Kantei inspection scores to train automated valuation models.
Global automotive classifieds aggregate JDM stock levels and dealer details to enrich their own platform supply.
Auto finance and insurance firms monitor repair history frequencies and Shaken validity trends to assess vehicle risk profiles.
"Goo-Net contains the definitive dataset for the Japanese domestic automotive market, but accessing it at scale requires specialised infrastructure."
Most teams underestimate the investment required: reliable Goo-Net scraping requires Japanese residential proxies, full JavaScript rendering, daily selector maintenance for complex DOMs, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis.
Everything supported by our goo-net.com scraper — rendered SPA elements, auth walls, rate-limit evasion and beyond.
Open-source tooling on proven cloud infra — no vendor lock-in, full observability.
Scrapy handles crawl orchestration and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows for complex search interfaces.
We maintain pools of Japanese residential ISP proxies. Rotation happens per-request to ensure local market visibility and prevent IP blocking.
Pipelines run on AWS Lambda and ECS. Airflow handles scheduling, dependency management, and SLA alerting. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About goo-net.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information is generally permissible. DataFlirt targets only public, non-authenticated vehicle, pricing, and dealer data. We do not extract personal data or circumvent authentication walls. Clients should review Goo-Net terms of service and consult legal counsel for specific use cases.
We use Japanese residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We monitor for rate spikes in real time and trigger pool rotation automatically.
We extract the raw Japanese text exactly as it appears on the DOM. DataFlirt focuses on reliable extraction infrastructure; text translation and normalisation are typically handled downstream by the client.
Real-time streaming pipelines achieve sub-60-minute latency for specific search parameters. Full catalogue refreshes at daily cadence complete within a 6-12 hour window depending on scale.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series record per listing for price drops and availability from the date your pipeline starts.
Our smallest packages start at a defined search scope (typically 5,000-20,000 listings) with weekly delivery. For larger catalogues, we price based on volume and delivery frequency.
Absolutely. We provide a sample run of up to 500 vehicle listings as part of the pre-engagement scoping process so you can validate schema fit and data quality.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off JDM inventory dump or a continuous price-monitoring feed across 400K listings - we scope, build, and operate the pipeline. Tell us what you need.
