We extract commercial properties for sale and lease, auction schedules, cap rates, zoning details, and broker intelligence from Crexi. 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 For Sale Listings objects from crexi.com. All fields typed and schema-versioned.
"property_id": "PRP-849201", "title": "Downtown Retail Center", "property_type": "Retail", "price": 4500000.0, "cap_rate": 6.5, "noi": 292500.0, "occupancy": 95.0, "apn": "042-192-04-000"
| # | property_id | title | property_type | sub_type | price | cap_rate |
|---|---|---|---|---|---|---|
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Complete list of extractable fields for Lease Listings objects from crexi.com. All fields typed and schema-versioned.
"property_id": "LSE-392011", "space_available_sqft": 12500, "min_divisible_sqft": 2500, "lease_rate": 24.5, "lease_type": "NNN", "space_use": "Medical Office", "condition": "White Box", "date_available": "2026-08-01"
| # | property_id | title | space_available_sqft | min_divisible_sqft | max_contiguous_sqft | lease_rate |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Auction Properties objects from crexi.com. All fields typed and schema-versioned.
"auction_id": "AUC-99382", "starting_bid": 1500000.0, "reserve_met": false, "auction_start_date": "2026-09-15T14:00:00Z", "auction_end_date": "2026-09-17T14:00:00Z", "deposit_required": 50000.0, "current_bid": 1650000.0
| # | auction_id | property_id | title | starting_bid | reserve_met | auction_start_date |
|---|---|---|---|---|---|---|
| 1 | ||||||
| 2 | ||||||
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Complete list of extractable fields for Broker Intelligence objects from crexi.com. All fields typed and schema-versioned.
"broker_id": "BRK-4829", "name": "Sarah Jenkins", "agency": "CBRE", "active_listings_count": 14, "specialties": "['Industrial', 'Logistics']", "licenses": "['DRE 01928374']", "regions_served": "['Southern California']"
| # | broker_id | name | agency | title | phone_number | |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Complete list of extractable fields for Demographics & Traffic objects from crexi.com. All fields typed and schema-versioned.
"property_id": "PRP-849201", "radius_miles": 3.0, "population": 142890, "median_income": 84500.0, "projected_growth": 2.4, "traffic_count": 34500, "walk_score": 82, "transit_score": 65
| # | property_id | radius_miles | population | median_income | average_age | households |
|---|---|---|---|---|---|---|
| 1 | ||||||
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Our Crexi scraper navigates map-based search interfaces, extracts nested financial models, and normalises lease rates across millions of commercial assets.
Extract specific data models for retail, industrial, office, multifamily, and special purpose properties.
Capture Cap Rate, Net Operating Income (NOI), Gross Rent Multiplier (GRM), and occupancy percentages.
We intercept Crexi map tile APIs to extract properties by polygon, radius, or specific MSA boundaries.
Monitor starting bids, auction windows, deposit requirements, and reserve status for distressed assets.
Standardise Triple Net (NNN), Modified Gross (MG), and Full Service Gross (FSG) lease structures.
Extract Assessor's Parcel Numbers, zoning codes, lot dimensions, and year-built metadata.
Extract listing agents, brokerage firms, contact numbers, and active listing portfolios.
Pull 1-mile, 3-mile, and 5-mile radius demographic models and traffic counts attached to listings.
Track when properties move from Active to Under Contract, Sold, or Off-Market.
Brief in. Clean data out.
Provide target MSAs, asset classes, or specific brokerages. We design the extraction schema together.
We configure Scrapy crawlers, map API interception, and session management for crexi.com.
Schema validation, null-rate checks, and lease rate normalisation before full launch.
JSON / CSV / Parquet pushed to your S3 bucket, BigQuery dataset, or Snowflake stage on agreed cadence.
Commercial real estate platforms use complex map interfaces and gated interactions. Here is how we extract the underlying data.
Crexi limits traditional pagination in favour of map-based browsing. We intercept the backend GraphQL and REST APIs driving the map layers, allowing us to query by specific bounding boxes and extract thousands of points without manual zooming.
Property details are rendered client-side via React. We parse the underlying Next.js hydration state (JSON embedded in the DOM) to extract cap rates and NOI directly, bypassing the need for fragile CSS selectors.
Broker contact details and offering memorandums often require a click to reveal. Our Playwright nodes simulate user interaction patterns to expose phone numbers and email addresses while managing session cookies.
Lease terms on Crexi are highly variable. We parse and normalise text strings to categorise leases into NNN, MG, or FSG, and convert monthly vs annual rates into a unified annualised metric.
Crexi removes sold properties from primary search results. We maintain historical hashes of all seen APNs and query them directly to determine if an asset has sold, expired, or been delisted.
Private equity firms monitor cap rates and NOI across specific MSAs to identify mispriced assets and yield opportunities.
National brokerages track competitor listing volume, time-on-market, and asset class dominance by region.
Commercial appraisers build automated valuation models (AVMs) using active listing prices, APN data, and zoning codes.
Lenders, title companies, and contractors extract broker contact details and new listings to pitch commercial services.
Corporate expansion teams analyse traffic counts, demographic models, and lease rates to determine new store locations.
Real estate software platforms backfill their databases with active inventory and historical auction data.
"Crexi centralises the commercial real estate market, but extracting structured financial models and zoning data requires bypassing complex map-layer APIs."
Most teams underestimate the investment required: reliable Crexi scraping requires intercepting map-bound API responses, rendering React hydration states, and managing session cookies to reveal broker details. DataFlirt absorbs that complexity so your engineers can focus on yield analysis - not infrastructure.
Everything supported by our crexi.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 map API pagination. Playwright handles JavaScript rendering and interaction flows for gated contact details.
We maintain pools of residential ISP proxies across US regions to prevent IP bans while querying Crexi backend APIs at high concurrency.
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 crexi.com scraping, legality, and pipeline operations.
Ask us directly →Scraping publicly available real estate listings is generally permissible. DataFlirt targets only public, non-authenticated property data, broker details, and auction schedules. We do not bypass NDA walls or extract PRO-only comp data. Clients should review Crexi ToS and consult legal counsel for specific use cases.
We intercept the underlying API requests that populate the map tiles. By supplying specific polygon coordinates or bounding boxes, we can extract all properties within a target MSA without relying on brittle UI automation.
Yes. While phone numbers and emails are often hidden behind a 'click to reveal' button, our pipeline uses Playwright to simulate the necessary interactions and extract the contact details into the structured payload.
Yes. We maintain a state database of all seen APNs and property IDs. Subsequent pipeline runs check these IDs to determine if a property has dropped in price, gone under contract, or been removed from the market.
We can extract public flyers and brochures attached to listings. However, access to the Due Diligence Vault, which contains detailed OMs and rent rolls, typically requires an executed NDA and manual approval by the listing broker, which we do not automate.
For targeted MSAs or specific asset classes, we can run daily or hourly pipelines. Full national sweeps of all active inventory typically run on a weekly cadence to manage compute costs and avoid rate limits.
20-minute scoping call. Pilot dataset within the week. Production within two. Whether you need a one-off export of industrial assets in Texas or a continuous feed of national multifamily listings - we scope, build, and operate the pipeline. Tell us what you need.
