We extract Top Safety Pick awards, crash test metrics, headlight ratings, and HLDI loss statistics from IIHS. 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 Overview objects from iihs.org. All fields typed and schema-versioned.
"make": "Subaru", "model": "Outback", "year": 2023, "vehicle_class": "Midsize SUV", "top_safety_pick_plus": true, "overall_evaluation": "Good"
| # | make | model | year | vehicle_class | top_safety_pick | top_safety_pick_plus |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Crashworthiness objects from iihs.org. All fields typed and schema-versioned.
"small_overlap_front_driver": "Good", "moderate_overlap_front": "Good", "side_updated": "Acceptable", "roof_strength": "Good", "head_restraints_seats": "Good", "structure_safety_cage": "Good"
| # | vehicle_id | small_overlap_front_driver | small_overlap_front_passenger | moderate_overlap_front | side_original | side_updated |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Crash Avoidance objects from iihs.org. All fields typed and schema-versioned.
"headlights_evaluation": "Good", "front_crash_prevention_v2v": "Superior", "front_crash_prevention_v2p_day": "Advanced", "seat_belt_reminders": "Marginal", "standard_equipment": true, "system_name": "EyeSight"
| # | vehicle_id | headlights_evaluation | front_crash_prevention_v2v | front_crash_prevention_v2p_day | front_crash_prevention_v2p_night | seat_belt_reminders |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for LATCH Ease of Use objects from iihs.org. All fields typed and schema-versioned.
"latch_overall_rating": "Good+", "lower_anchors_too_deep": false, "hardware_confusing": false, "tether_anchor_confusing": false, "total_latch_positions": 3, "center_latch_available": true
| # | vehicle_id | latch_overall_rating | lower_anchors_too_deep | lower_anchors_force_required | hardware_confusing | tether_anchor_confusing |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for HLDI Losses objects from iihs.org. All fields typed and schema-versioned.
"collision_loss": 112, "property_damage_loss": 98, "comprehensive_loss": 105, "personal_injury_protection": 85, "medical_payment": 88, "bodily_injury": 92
| # | vehicle_id | collision_loss | property_damage_loss | comprehensive_loss | personal_injury_protection | medical_payment |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our IIHS scraper translates complex HTML tables, dynamic filters, and historical rating changes into a unified, queryable dataset for automotive analytics.
Identify Top Safety Pick and Top Safety Pick+ winners across all years, including the specific criteria met for each award.
Extract granular ratings for small overlap, moderate overlap, side impact, roof strength, and head restraints.
Capture evaluations for vehicle-to-vehicle and vehicle-to-pedestrian front crash prevention systems, including day and night performance.
Parse detailed headlight performance metrics, noting differences across trim levels and option packages.
Extract ease-of-use ratings for child seat attachment hardware, including anchor depth, force requirements, and potential confusion points.
Scrape relative risk scores for collision, property damage, comprehensive, PIP, medical payment, and bodily injury claims.
Maintain a full history of rating changes and test updates as IIHS evolves its evaluation criteria over time.
Map safety equipment and ratings to specific vehicle trims, handling complex conditional logic presented on the IIHS site.
Run continuous pipelines that only emit records when new test results or updated ratings are published.
Brief in. Clean data out.
Provide vehicle makes, models, years, or specific vehicle classes. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, handle dynamic filters, and parse nested rating tables on iihs.org.
Schema validation, null-rate checks, and normalisation of historical rating scales before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Extracting structured data from IIHS requires parsing deeply nested HTML tables and normalising evolving rating scales. Here is how we maintain data integrity.
IIHS uses JavaScript-heavy dropdowns and dynamic routing to filter vehicles by make, model, and year. Our Playwright integration executes these flows natively to ensure complete catalogue coverage without missing hidden variants.
Crashworthiness details are often buried in expanding HTML tables with merged cells and inconsistent DOM layouts. We use bespoke XPath rules and structural mapping to flatten these tables into strict relational schemas.
IIHS frequently updates its test protocols, such as introducing the updated side impact test. We standardise these metrics, explicitly separating 'original' and 'updated' test results so longitudinal analysis remains accurate.
Headlight and crash prevention ratings often vary by trim or option package. Our extraction logic parses the conditional text ('Applies to models built after...', 'When equipped with...') and maps it directly to the relevant trim designations.
When IIHS redesigns a vehicle page or introduces a new rating category, our observability stack flags the schema drift immediately. We update extraction rules before your downstream models encounter null values.
Actuaries ingest HLDI loss metrics and crashworthiness ratings to refine premium pricing models and risk assessments.
Car research websites aggregate IIHS Top Safety Pick awards and crash test data to enrich vehicle detail pages.
Corporate fleet managers filter available vehicles against strict safety thresholds, ensuring compliance with internal safety mandates.
Automotive OEMs track how rival models perform in specific tests like the updated moderate overlap front evaluation.
Researchers correlate historical IIHS ratings with real-world fatality statistics to evaluate the effectiveness of new safety standards.
Analysts track the adoption rate of standard front crash prevention systems across different vehicle segments over time.
"IIHS provides the definitive benchmark for vehicle safety in North America, but its data is locked in complex web views rather than accessible APIs."
Building a reliable parser for IIHS requires handling nested HTML tables, dynamic JavaScript filters, and constantly evolving rating criteria. DataFlirt manages this complexity entirely, delivering a clean, structured dataset of crash metrics and HLDI loss statistics directly to your warehouse. You focus on risk modelling, not DOM parsing.
Everything supported by our iihs.org 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 deduplication. Playwright navigates the JavaScript-heavy vehicle selection filters and dynamic tables.
Bespoke parsing modules map IIHS's evolving HTML table structures into strict relational data models, handling merged cells and nested sub-metrics.
Pipelines run on AWS Lambda and ECS. Airflow manages scheduling and dependency tracking, ensuring data is delivered reliably on your cadence.
Data delivered to where your team already works — no new tooling required.
About iihs.org scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available safety ratings and test results from IIHS is generally permissible under applicable web scraping laws. DataFlirt extracts only public, non-authenticated data. Clients should review IIHS terms of service and consult legal counsel for their specific commercial use cases.
IIHS frequently introduces new tests (like the updated side test). Our pipeline maps these as distinct schema fields rather than overwriting historical data, ensuring longitudinal analysis remains accurate. We monitor schema drift and update parsers promptly.
Yes. IIHS often assigns different headlight or crash avoidance ratings based on specific trims or optional equipment. We parse this conditional text and map it accurately to the relevant vehicle configurations.
Yes. We extract the full suite of HLDI relative risk scores, including collision, property damage, comprehensive, personal injury protection, medical payment, and bodily injury metrics.
We can configure pipelines to run weekly or monthly to capture new vehicle test publications. Change detection ensures you only receive records for newly tested or updated vehicles.
Yes. We can execute a full historical scrape of all available makes, models, and years currently published on the IIHS website to seed your database before transitioning to a continuous update schedule.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a complete historical dump of vehicle safety ratings or continuous monitoring for new crash test results, we scope, build, and operate the pipeline. Tell us what you need.
