We extract property details, nightly rates, availability windows, and amenity lists from Motel6. 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 Property Details objects from motel6.com. All fields typed and schema-versioned.
"property_id": "M6_1034", "name": "Motel 6 Dallas - Market Center", "address": "1625 Regal Row", "city": "Dallas", "state": "TX", "zip_code": "75247", "latitude": 32.8184, "longitude": -96.8732, "pet_friendly": true
| # | property_id | name | address | city | state | zip_code |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Room Rates objects from motel6.com. All fields typed and schema-versioned.
"property_id": "M6_1034", "date": "2026-08-15", "room_type": "1 Full Bed Non-Smoking", "base_rate": 54.99, "taxes": 7.15, "total_rate": 62.14, "currency": "USD", "refundable": true, "ada_accessible": false
| # | property_id | date | room_type | base_rate | taxes | total_rate |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Amenities objects from motel6.com. All fields typed and schema-versioned.
"property_id": "M6_1034", "has_pool": true, "has_wifi": true, "laundry_facilities": true, "truck_parking": true, "free_coffee": true, "interior_corridors": false
| # | property_id | has_pool | has_wifi | laundry_facilities | truck_parking | free_coffee |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Availability objects from motel6.com. All fields typed and schema-versioned.
"property_id": "M6_1034", "check_in_date": "2026-08-15", "check_out_date": "2026-08-16", "length_of_stay": 1, "room_type": "2 Full Beds Non-Smoking", "is_available": true, "remaining_inventory": 4, "rate_code": "BAR"
| # | property_id | check_in_date | check_out_date | length_of_stay | room_type | is_available |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Location Context objects from motel6.com. All fields typed and schema-versioned.
"property_id": "M6_1034", "airport_distance_miles": 4.5, "highway_exit": "I-35E Exit 431", "nearby_attractions": "['Dallas Market Center', 'American Airlines Center']", "dining_options": "["Denny's", 'Waffle House']", "region": "Southwest", "neighborhood": "Stemmons Corridor"
| # | property_id | airport_distance_miles | highway_exit | nearby_attractions | public_transit_access | dining_options |
|---|---|---|---|---|---|---|
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Our Motel6 scraper captures nationwide budget lodging data: property geocodes, dynamic nightly rates, availability calendars, and amenity matrices - with session management and proxy rotation built in.
Addresses, geocodes, total room counts, and contact information mapped for all 1,400+ North American locations.
Capture base rates, local taxes, fees, and final totals across multiple dates and room configurations.
Monitor 30, 60, or 90-day availability windows to track inventory depletion and peak season booking trends.
Differentiate rates and inventory between King, Double, Smoking, Non-Smoking, and ADA accessible rooms.
Extract property-level features including outdoor pools, truck parking availability, guest laundry, and free morning coffee.
Identify pet-friendly locations, maximum pet limits, and associated fees or restrictions per property.
Group and analyse rates by state, city, or highway corridor to understand regional pricing strategies.
Track rate variations for AARP, military, senior, and corporate discount codes applied at checkout.
Run one-off bulk exports or configure continuous pipelines at hourly, daily, or real-time cadences with change-detection diffing.
Brief in. Clean data out.
Provide target cities, highway corridors, or specific property IDs. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for motel6.com.
Schema validation, null-rate checks, price-outlier detection, and sample rate checks before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Travel booking engines deploy rate-limiting and bot-protection to prevent scraping. Here is how we maintain steady extraction.
Booking engines block data centre IPs aggressively. Our crawlers use US-based residential ISP proxies with realistic browser fingerprints, randomised request timing, and full cookie session management - trained on real user behaviour patterns.
Rather than scraping raw HTML, we reverse-engineer the internal XHR endpoints Motel6 uses for rate and availability checks. This reduces payload size, speeds up extraction, and provides cleaner JSON responses.
Pricing queries often require valid session tokens generated on the homepage. We manage token lifecycles, refreshing them automatically before they expire to ensure uninterrupted rate extraction.
For large property networks, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs - reducing compute cost, storage bloat, 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 - and respond before you notice. SLA uptime is contractual, not aspirational.
Competing economy hotel chains monitor Motel6 base rates and promotional discounts to dynamically adjust their own pricing.
Online travel agencies integrate direct pricing data to verify parity and identify margin opportunities.
Commercial real estate investors evaluate nightly rates and occupancy proxies to estimate property valuation and market health.
Trucking and logistics companies map properties with truck parking to optimise driver rest stops and lodging budgets.
Analysts track budget lodging trends, seasonal rate fluctuations, and regional demand shifts across North America.
Hospitality brands benchmark amenity offerings, pet policies, and ADA compliance against the Motel6 portfolio.
"Motel6 provides a critical baseline for North American budget lodging rates - but querying 1,400 properties daily requires dedicated infrastructure."
Most teams underestimate the investment required: reliable travel scraping requires residential proxies, session token management, daily selector maintenance, and anomaly monitoring. DataFlirt absorbs that complexity so your engineers can focus on the analysis - not the infrastructure.
Everything supported by our motel6.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, deduplication, and retry logic. Playwright handles JavaScript rendering, cookie sessions, and interaction flows. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across US/CA regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
Pipelines run on AWS Lambda (burst) and ECS (sustained). 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 motel6.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available pricing and availability data is generally permissible under applicable law. DataFlirt targets only public, non-authenticated property and rate data. We do not circumvent authentication walls or extract personal user data. Clients should review target site ToS and consult legal counsel for specific use cases.
We use US-based residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. We monitor for 429/503 rate spikes in real time and distribute load across broader IP pools automatically.
Real-time streaming pipelines achieve sub-30-minute latency for price and availability signals on a defined property set. Full network refreshes across all 1,400+ locations complete within a 2-4 hour window depending on date-range depth.
Yes. We can configure the pipeline to check rates for specific future dates, rolling windows (e.g., next 30, 60, or 90 days), or specific seasonal events across target properties.
Our smallest packages start at daily rate extraction for up to 250 properties. For the entire North American portfolio or high-frequency intra-day checks, we price based on compute volume and delivery frequency. Contact us with your use case for a scoped quote.
Absolutely. We provide a sample run of up to 50 properties across a 7-day booking window as part of the pre-engagement scoping process - so you can validate schema fit, field completeness, and data quality before signing any contract.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off property location dump or a continuous price-monitoring feed across 1,400 locations - we scope, build, and operate the pipeline. Tell us what you need.
