Commercial Real Estate Data Extraction Services

Commercial Real Estate (CRE) Data Extraction Services

The commercial real estate market demands data at speed and scale. Whether you are a private equity acquisition team screening cap rates, a REIT benchmarking industrial rents, or a PropTech startup building an automated valuation model, our managed CRE extraction pipelines give you structured, query-ready data without the overhead of expensive CoStar or Reonomy subscriptions.

CRE Asset Classes & Platforms We Extract

We extract commercial listings across all major asset types and marketplaces:

• Office Buildings: Class A/B/C office space, occupancy rates, lease type (NNN / Full Service), floor plate sizes.
• Retail & Shopping Centers: Anchored shopping centers, strip malls, NNN net lease properties, tenant rosters.
• Industrial & Logistics Warehouses: Clear height, loading docks, data center land, industrial outdoor storage (IOS).
• Multi-Family Apartments: 5+ unit apartment complexes, door counts, average rent per unit, cap rates.
• Self-Storage & Hospitality: Occupancy rates, ADR (average daily rate), RevPAR metrics for hotel and motel assets.
• Land & Development Sites: Commercial acreage, FAR ratios, zoning overlays, entitlement status.

Structured Commercial Data Fields

DIY CRE Scraping vs. WebScrapingHub Managed API

Key Use Cases for Commercial Real Estate Data

CoStar & Reonomy Alternative Solution

Instead of locking your firm into expensive multi-year database contracts, WebScrapingHub builds custom CRE scrapers that deliver exact property listings matching your target geographic markets, asset classes, and deal criteria on a pay-as-you-go basis.

Our CRE extraction service covers LoopNet, Crexi, commercial county assessor portals, and municipal permit registries - sources that CoStar and Reonomy often aggregate with a significant markup. By extracting directly from primary sources, you receive raw, unfiltered data before it reaches proprietary databases.

Commercial Lease Agreement Data Extraction

Commercial lease contracts and rent rolls contain vital financial intelligence locked inside lengthy, unstructured PDF documents, scanned Offering Memorandums (OMs), and municipal lease registry filings. Our commercial lease data extraction services use advanced OCR parsing and AI-driven document intelligence to convert complex commercial leases into structured, standardized datasets.

Key Lease Metrics & Contract Data Points We Extract

Case Study: PE Firm Automates CRE Deal Screening

How to Extract LoopNet & Crexi Data with Python

Below is a Python sample demonstrating how to extract commercial listings with cap rates and broker contacts via our managed CRE data API:

import requests, pandas as pd

API = "https://api.webscrapinghub.com/v1/real-estate/cre"
payload = {
    "api_key":    "YOUR_API_KEY",
    "source":     "loopnet",       # or "crexi"
    "asset_type": "industrial",
    "state":      "TX",
    "min_cap_rate": 6.5,
    "extract_broker_contacts": True,
    "extract_om_pdf": True
}

data = requests.post(API, json=payload).json()
rows = [{
    "Property":    i.get("name"),
    "Address":     i.get("address"),
    "Cap_Rate":    i.get("cap_rate"),
    "NOI":         i.get("noi"),
    "Sqft":        i.get("building_sqft"),
    "Broker_Name": i.get("broker", {}).get("name"),
    "Broker_Phone":i.get("broker", {}).get("phone")
} for i in data.get("listings", [])]

pd.DataFrame(rows).to_csv("cre_loopnet_texas.csv", index=False)
print(f"Extracted {len(rows)} CRE listings.")