We extract commercial property listings, lease rates, sale comps, building specifications, and tenant directories from CoStar. 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 costar.com. All fields typed and schema-versioned.
"property_id": "PRP-839210", "address": "1200 17th St", "city": "Denver", "building_class": "A", "year_built": 1980, "gross_leasable_area": 250000, "property_type": "Office"
| # | property_id | address | city | state | zip_code | building_class |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Lease Listings objects from costar.com. All fields typed and schema-versioned.
"listing_id": "LST-99214", "suite_number": "Suite 400", "available_sqft": 5200, "lease_rate": 35.5, "lease_type": "NNN", "space_use": "Office", "date_available": "2024-09-01"
| # | listing_id | property_id | suite_number | floor | available_sqft | lease_rate |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Sale Comps objects from costar.com. All fields typed and schema-versioned.
"comp_id": "CMP-44182", "sale_price": 12500000, "sale_date": "2023-11-15", "cap_rate": 5.8, "price_per_sqft": 450.2, "days_on_market": 112, "buyer_name": "Apex Properties LLC"
| # | comp_id | property_id | sale_price | sale_date | cap_rate | price_per_sqft |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Tenant Data objects from costar.com. All fields typed and schema-versioned.
"tenant_id": "TNT-8821", "company_name": "TechFlow Solutions", "industry": "Software", "sqft_occupied": 12500, "lease_expiration": "2028-12-31", "employee_count": 85, "contact_name": "Sarah Jenkins"
| # | tenant_id | property_id | company_name | industry | sqft_occupied | lease_expiration |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Complete list of extractable fields for Market Analytics objects from costar.com. All fields typed and schema-versioned.
"submarket": "LoDo Denver", "inventory_sqft": 15400000, "vacancy_rate": 18.2, "asking_rent": 42.0, "rent_growth": -1.5, "under_construction_sqft": 450000, "market_cap_rate": 6.2
| # | submarket | inventory_sqft | under_construction_sqft | vacancy_rate | absorption_sqft | asking_rent |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
| 3 |
Our CoStar scraper handles every layer of the platform: property specifications, lease listings, and sale comps - with JavaScript rendering, session management, and anti-bot circumvention built in.
Extract building class, gross leasable area, year built, zoning, and lot size for any commercial property.
Capture suite-level availability, asking rates, lease types (NNN, Gross), and space use categories.
Extract historical transaction data, cap rates, price per square foot, and days on market for sold properties.
Map building occupants, industry classifications, occupied square footage, and lease expiration dates.
Track vacancy rates, inventory levels, rent growth, and net absorption metrics across defined submarkets.
Extract high-resolution property images, floor plan PDFs, and virtual tour links associated with listings.
Capture local zoning codes, parcel numbers, and transit scores for detailed site selection analysis.
Run one-off bulk exports or configure continuous pipelines at daily cadences with change-detection diffing.
Built-in residential proxy rotation and automated CAPTCHA solving to bypass aggressive rate limits.
Brief in. Clean data out.
Provide property IDs, zip codes, or submarket parameters. We design the extraction schema together.
We configure Scrapy / Playwright crawlers, proxy rotation, session management, and CAPTCHA handling for costar.com.
Schema validation, null-rate checks, and sample data review before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
CoStar invests heavily in scraping detection. Here is how we stay resilient and why teams choose managed infrastructure over DIY.
CoStar bot detection operates on TLS fingerprints, browser headers, and IP reputation. Our crawlers use residential ISP proxies with realistic browser fingerprints and full cookie session management, trained on real user behaviour patterns.
Property detail pages and interactive 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.
DOM structures change frequently. Our selector strategy uses multiple fallback chains per field, including CSS selectors, XPath, and text-pattern matching, so a layout change does not break your data pipeline overnight.
For large property catalogues, 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. 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, schema drift, and coverage drops, and respond before you notice. SLA uptime is contractual, not aspirational.
Private equity firms and REITs monitor cap rates, sale comps, and submarket trends to identify undervalued assets.
Commercial brokers track lease expirations and tenant movements to prospect for new representation opportunities.
Appraisers extract historical sale comps and current asking rents to build accurate valuation models for commercial assets.
Analysts track inventory levels, under-construction square footage, and vacancy rates to forecast market cycles.
Retailers analyse zoning data, transit scores, and nearby tenant mixes to select optimal new store locations.
Machine learning teams use structured property specifications and historical pricing to train automated valuation models.
"CoStar holds the definitive dataset for commercial real estate, but extracting structured intelligence requires bypassing the most aggressive bot mitigation in the industry."
Most teams underestimate the investment required: reliable CoStar scraping requires residential proxies, full JavaScript rendering, CAPTCHA handling, 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 costar.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 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 costar.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available information is generally permissible under applicable law, reinforced by the hiQ v. LinkedIn ruling. DataFlirt targets only public, non-authenticated property and listing data. We do not circumvent authentication walls to access subscriber-only data. Clients should review CoStar ToS 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. We monitor for 503 or CAPTCHA rate spikes in real time and trigger pool rotation or solver queues automatically.
Full market refreshes at daily cadence complete within a 6-12 hour window depending on submarket size. Historical snapshots are available from the day your pipeline is commissioned.
Yes. Every pipeline run produces timestamped snapshots. We maintain a time-series table per property for asking rents, available square footage, and vacancy status from the date your pipeline starts.
Our smallest packages start at a defined submarket list or specific property set with weekly delivery. For larger geographic areas or custom schema requirements, we price based on volume and delivery frequency.
Absolutely. We provide a sample run of up to 500 properties 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 dump or a continuous lease-monitoring feed across 50,000 buildings, we scope, build, and operate the pipeline. Tell us what you need.
