We extract property listings, lease rates, sale prices, zoning details, broker intelligence, and transaction history from LoopNet. 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 loopnet.com. All fields typed and schema-versioned.
"listing_id": "28394012", "address": "1200 Westlake Ave N", "property_type": "Office", "building_size_sqft": 45000, "year_built": 2008, "zoning_code": "C1-65", "apn_parcel_id": "192830-0192", "parking_ratio": 3.5
| # | listing_id | address | city | state | zip_code | property_type |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Sale & Lease Pricing objects from loopnet.com. All fields typed and schema-versioned.
"listing_type": "Lease", "lease_rate_annual": 42.5, "lease_type": "NNN", "space_available_sqft": 12500, "min_divisible_sqft": 2500, "cam_charges": 8.5, "date_available": "2024-09-01"
| # | listing_id | listing_type | sale_price | cap_rate | net_operating_income | lease_rate_annual |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Broker & Contact objects from loopnet.com. All fields typed and schema-versioned.
"broker_name": "Sarah Jenkins", "brokerage_firm": "CBRE", "broker_phone": "206-555-0192", "license_number": "WA-99201", "broker_profile_url": "https://www.loopnet.com/broker/sarah-jenkins/19283", "days_on_market": 45
| # | listing_id | broker_name | brokerage_firm | broker_phone | broker_email | license_number |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Building Amenities objects from loopnet.com. All fields typed and schema-versioned.
"traffic_count": 28400, "frontage": "250 ft on Westlake Ave", "dock_doors": 0, "drive_in_doors": 1, "cooling": "Central HVAC", "sprinkler_system": "Wet"
| # | listing_id | amenities_list | frontage | traffic_count | power_supply | dock_doors |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Transaction History objects from loopnet.com. All fields typed and schema-versioned.
"sale_date": "2018-11-14", "sale_price": 14500000, "buyer_name": "Westlake Holdings LLC", "seller_name": "Pacific Properties Trust", "document_number": "20181114001923", "sale_condition": "Standard"
| # | listing_id | sale_date | sale_price | buyer_name | seller_name | recording_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
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| 3 |
Our LoopNet scraper handles every layer of the platform: property details, dynamic lease pricing, broker intelligence, and transaction history, with JavaScript rendering and anti-bot circumvention built in.
Address, building size, lot size, year built, zoning codes, and every metadata field LoopNet surfaces, scraped at the individual listing level.
Capture sale prices, cap rates, NOI, NNN lease rates, CAM charges, and space availability, timestamped per crawl.
Extract broker names, brokerage firms, contact numbers, license details, and profile URLs for every listing.
Extract latitude and longitude coordinates along with APN parcel IDs to map properties accurately in your GIS systems.
Capture URLs for property photos, floor plans, offering memorandums, and virtual tours attached to the listing.
Track market inventory by scraping search results across specific MSAs, property types, and price tiers.
Extract specialized fields like clear height, dock doors, power supply, and traffic counts for industrial and retail spaces.
Capture past sale dates, prices, and buyer or seller entities where publicly listed on the property record.
Run one-off bulk exports or configure continuous pipelines at daily or weekly cadences with change-detection diffing.
Brief in. Clean data out.
Provide target MSAs, property types, or specific listing URLs. We design the extraction schema together.
We configure Scrapy and Playwright crawlers, proxy rotation, and CAPTCHA handling for loopnet.com.
Schema validation, null-rate checks, and sample data reviews before full launch.
JSON, CSV, or Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
LoopNet invests heavily in scraping detection. Here is how we stay resilient, and why teams choose managed infrastructure over DIY.
LoopNet uses strict bot detection based on IP reputation and browser fingerprints. Our crawlers use residential ISP proxies with realistic browser fingerprints and full cookie session management, trained on real user behaviour patterns.
LoopNet search results and property maps are heavily JavaScript-rendered. We run full Playwright browser sessions with JavaScript execution and lazy-load triggering, capturing data that headless HTTP clients miss entirely.
LoopNet caps search results at a fixed number of pages. We bypass this by segmenting large MSAs into micro-grids based on latitude and longitude, ensuring 100% extraction coverage without hitting pagination walls.
For large market monitors, we maintain a hash index of last-seen values per field. Subsequent runs only push diffs, reducing compute cost and downstream processing load. You get a clean changelog rather than full re-dumps.
Every run emits structured logs to our observability stack. We alert on null-rate spikes, layout changes, and coverage drops, responding before you notice.
Private equity and REITs monitor cap rates, asking prices, and NOI across MSAs to identify undervalued assets.
Commercial brokers track days on market and expiring listings to target owners for representation.
Appraisers and analysts aggregate lease rates and NNN charges to establish accurate market comps.
Retail brands analyze traffic counts, frontage, and zoning to evaluate new store locations at scale.
Valuation firms use historical transaction data and current asking prices to build automated valuation models.
PropTech companies map zoning codes and parcel data against active listings to identify redevelopment opportunities.
"LoopNet holds the definitive commercial real estate inventory for the US market, but extracting structured cap rates and lease terms requires a resilient pipeline."
Most teams underestimate the investment required: reliable LoopNet scraping requires residential proxies, full JavaScript rendering for map interfaces, CAPTCHA handling, and daily selector maintenance. DataFlirt absorbs that complexity so your engineers can focus on the analysis, not the infrastructure.
Everything supported by our loopnet.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 deduplication. Playwright handles JavaScript rendering, cookie sessions, and map interactions. Combined via scrapy-playwright middleware.
We maintain pools of residential ISP proxies across US regions. Rotation happens per-request with sticky sessions where required. IP score monitoring prevents blacklisted pool contamination.
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 loopnet.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information from LoopNet is generally permissible under applicable law, reinforced by the hiQ v. LinkedIn ruling. DataFlirt targets only public, non-authenticated property and broker data. We do not extract personal data or circumvent authentication walls. Clients should review LoopNet Terms of Service and consult legal counsel for specific use cases.
We use residential ISP proxies, full Playwright browser sessions with realistic fingerprints, and request timing modelled on human behaviour. Our selectors have multi-layer fallback chains so DOM changes do not break the pipeline.
Yes. We can configure pipelines to target specific Metropolitan Statistical Areas, cities, zip codes, or custom geographic bounding boxes based on your exact requirements.
Market-wide refreshes at weekly or daily cadences complete within defined SLA windows. We track the last_updated timestamp provided by the platform to ensure you have the most current listing status.
No. DataFlirt only extracts publicly visible data on LoopNet. We do not bypass paywalls or extract proprietary CoStar analytics that require a paid subscription.
Absolutely. We provide a sample run of up to 500 listings 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 market extraction or a continuous property monitor across all major MSAs, we scope, build, and operate the pipeline. Tell us what you need.
