We extract carrier pricing, coverage limits, deductibles, and policy terms across auto, home, and commercial lines from CoverHound. 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 Auto Insurance Quotes objects from coverhound.com. All fields typed and schema-versioned.
"carrier_name": "Progressive", "monthly_premium": 142.5, "coverage_type": "Standard", "bodily_injury_limit": "50k/100k", "property_damage_limit": "50k", "comprehensive_deductible": 500, "zip_code": "90210", "quote_timestamp": "2023-10-24T14:32:00Z"
| # | carrier_name | monthly_premium | six_month_premium | coverage_type | bodily_injury_limit | property_damage_limit |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Homeowners Policies objects from coverhound.com. All fields typed and schema-versioned.
"carrier_name": "State Farm", "annual_premium": 1250.0, "dwelling_coverage": 350000, "personal_property": 175000, "liability_limit": 300000, "deductible": 1000, "bundle_discount": true, "zip_code": "30301"
| # | carrier_name | annual_premium | dwelling_coverage | personal_property | liability_limit | loss_of_use |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Cyber & Commercial objects from coverhound.com. All fields typed and schema-versioned.
"carrier_name": "Chubb", "policy_type": "Cyber Liability", "annual_premium": 2400.0, "aggregate_limit": 1000000, "deductible": 2500, "business_class": "Technology", "employee_count_tier": "10-49", "state": "CA"
| # | carrier_name | policy_type | annual_premium | aggregate_limit | per_occurrence_limit | deductible |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Carrier Metadata objects from coverhound.com. All fields typed and schema-versioned.
"carrier_name": "Geico", "am_best_rating": "A++", "naic_code": "41491", "support_phone": "800-207-7847", "year_founded": 1936, "lines_offered": "['Auto', 'Home', 'Renters']", "claims_url": "https://www.geico.com/claims/"
| # | carrier_name | am_best_rating | naic_code | support_phone | claims_url | year_founded |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Location Pricing Index objects from coverhound.com. All fields typed and schema-versioned.
"zip_code": "33101", "state": "FL", "city": "Miami", "avg_auto_premium": 215.0, "min_auto_premium": 185.0, "carrier_count": 8, "risk_tier": "High", "scraped_at": "2023-10-24T15:00:00Z"
| # | zip_code | state | city | avg_auto_premium | min_auto_premium | avg_home_premium |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our CoverHound scraper navigates multi-step quote funnels, handles dynamic form states, and normalises carrier data across all coverage lines. Delivered ready for analysis.
We orchestrate multi-page form submissions to generate valid quotes without triggering bot protection or fraud alerts.
Extract monthly, six-month, and annual premium variations across carriers for identical risk profiles.
Standardise bodily injury, property damage, and liability limits across different carrier formats into clean tabular data.
Support for auto, homeowners, renters, and commercial lines, mapping specific attributes for each policy type.
Capture premium changes across different deductible levels to model price elasticity.
Extract AM Best ratings, financial stability indicators, and customer satisfaction scores presented alongside quotes.
Route requests through ZIP-code specific residential proxies to capture accurate regional pricing.
Maintain session cookies and CSRF tokens across complex SPA quote flows.
Run recurring pipelines to track premium changes over time across target ZIP codes.
Identify and emit only the premiums and coverage limits that have changed since the last pipeline run.
Brief in. Clean data out.
Provide target ZIP codes, vehicle profiles, home specifications, or business types. We design the extraction schema together.
We configure Playwright form-fillers, state management, residential proxies, and CAPTCHA solvers for coverhound.com.
Schema validation, premium outlier detection, and coverage limit normalisation checks before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
CoverHound uses dynamic single-page applications and strict session management. Here is how we extract data reliably.
Insurance quoting requires filling 5-10 pages of dynamic forms. We use Playwright to simulate human typing, handle dropdowns, and maintain session state across the entire funnel.
Insurance rates are highly localised. We route requests through residential proxies matching the target ZIP code to prevent geographic blocking and ensure accurate premium generation.
Every carrier presents coverage limits and deductibles differently. Our pipeline normalises these outputs into a consistent schema, making cross-carrier comparison immediate.
CoverHound relies heavily on session cookies and CSRF tokens to prevent automated quoting. Our infrastructure manages token lifecycles to maintain valid sessions through to the final quote page.
We monitor extracted premiums against historical baselines. If a form error causes a carrier to return a generic base rate rather than a tailored quote, our system flags it for review.
Insurance carriers monitor competitor premiums across different risk profiles and regions to adjust their own underwriting models.
Analysts track which carriers are quoting aggressively in specific ZIP codes to identify regional expansion strategies.
Data science teams use historical premium data across millions of quote permutations to train proprietary pricing models.
Independent brokerages use aggregate quote data to understand market averages and advise clients on policy renewals.
Correlate premium spikes in specific ZIP codes with environmental data (wildfires, floods) to map carrier risk appetite.
Startups use coverage limits and deductible structures to design competitive alternative insurance products.
"CoverHound aggregates pricing across the fragmented insurance market, but accessing those premiums programmatically requires navigating complex multi-step quote funnels."
Most data teams fail at insurance scraping because they cannot maintain state across complex dynamic forms. DataFlirt orchestrates full Playwright sessions, injecting realistic payload data and managing session cookies to extract accurate premiums without triggering fraud detection.
Everything supported by our coverhound.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 concurrency and scheduling. Playwright executes the complex JavaScript required to navigate CoverHound's multi-step quote forms and render final carrier pricing.
We maintain pools of US residential ISP proxies. Requests are routed through IPs matching the target ZIP code to ensure accurate geographic pricing and prevent blocking.
Pipelines run on AWS ECS for sustained form-filling tasks. Airflow handles scheduling across thousands of ZIP code permutations. All state stored in managed Postgres.
Data delivered to where your team already works — no new tooling required.
About coverhound.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly accessible quote data is generally permissible. DataFlirt targets only non-authenticated, aggregate pricing data using synthetic risk profiles. We do not extract PII or interact with payment gateways. Clients should review CoverHound's ToS and consult legal counsel for specific use cases.
We use Playwright to simulate user interaction, injecting predefined synthetic profiles (vehicle details, home specs) into the forms. Our state management handles the necessary cookies and tokens to reach the final carrier comparison page.
Yes. You provide the list of target ZIP codes and risk profiles. We route the requests through residential proxies located in or near those ZIP codes to ensure the quotes returned are geographically accurate.
Pipelines can be configured to run daily, weekly, or monthly depending on your requirements. Given the time required for form filling, large-scale national ZIP code runs typically complete within a 24-48 hour window.
We deliver in JSON, CSV, XLS, and Parquet. Data can be pushed directly to AWS S3, BigQuery, Snowflake, or delivered via Webhook and API.
Our minimum engagement typically starts at 5,000 ZIP code / profile permutations per month. We price based on the complexity of the form fills and the volume of quotes extracted.
Yes. We provide a sample run of up to 50 ZIP code permutations during the scoping process so you can validate the schema, carrier coverage, and premium accuracy before committing.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need to track auto premiums across 10,000 ZIP codes or monitor commercial lines pricing — we scope, build, and operate the pipeline. Tell us what you need.
