LoopNet + Crexi Scraper · Commercial Real Estate Listings · CoStar Alternative

🏢 The autonomous commercial real estate API that aggregates LoopNet and Crexi listings, normalizes cap rates, and deduplicates cross-platform. $5 per 1,000 results — 1/40th of CoStar's $40k/yr median.

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💰 Why CRE brokers switch to this

CoStar gates the U.S. commercial real estate listings market behind $14k–$40k/year per seat. Every existing scraper on Apify Store covers a single source (LoopNet OR Crexi). None normalize cap rates. None track days-on-market. None deduplicate cross-platform.

This actor does. And it runs fully autonomously, 24/7.

💵 Annual cost	🏢 Coverage	📊 Intelligence layer
This actor ~$1,500/yr (typical Pro broker use)	LoopNet + Crexi	✅ Cap rate, DOM, dedup
CoStar Suite entry	$14,000–$20,000/yr	⚠️ Web UI only, no API
CoStar Suite median	$40,000/yr	⚠️ Multi-seat
Reonomy / Compstak	$5,000–$18,000/yr	Off-market focus

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⚡ Quick output preview

{
  "source": "loopnet",
  "source_listing_id": "29769721",
  "address": {
    "street": "2921 E 17th St",
    "city": "Austin",
    "state": "TX",
    "zip": "78702",
    "lat": 30.278541,
    "lng": -97.709889
  },
  "asset_class": "office",
  "sub_type": "Loft/Creative Space",
  "sqft": 7500,
  "asking_price_usd": 2500000,
  "cap_rate_listed": null,
  "cap_rate_normalized": 7.5,
  "cap_rate_estimated": true,
  "price_per_sqft": 333,
  "days_on_market": 32,
  "status": "active",
  "broker": {
    "name": "Isaac Gutierrez",
    "company": "ECR",
    "phone": null
  },
  "also_listed_on": [],
  "photo_urls": ["https://images1.loopnet.com/i2/.../900x675/image.jpg"],
  "description": "Creative office building in the East Austin submarket available for sale."
}

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🎯 Quick start

🏙️ Search by city + state (most common)

{
  "city": "Austin",
  "state": "TX",
  "sourcesEnabled": ["loopnet", "crexi"],
  "assetClasses": ["office", "retail"],
  "priceMin": 500000,
  "priceMax": 5000000,
  "maxResults": 200
}

🔄 Daily monitoring — only NEW listings

{
  "city": "Dallas",
  "state": "TX",
  "monitoringMode": true,
  "maxResults": 500
}

Run on a daily schedule. The actor maintains an internal snapshot per query. Only listings not seen in previous runs are emitted.

📊 Track CLOSED deals

{
  "city": "Phoenix",
  "state": "AZ",
  "transactionTrackingMode": true
}

Emits ONLY listings that disappeared since the last run (likely sold/leased) or whose status flipped to sold / under_contract. Each item carries transaction_tracking.close_status, close_detected_at, previous_price.

🔗 Paste specific listing URLs

{
  "startUrls": [
    {"url": "https://www.loopnet.com/Listing/12345678/example-property/"},
    {"url": "https://www.crexi.com/properties/87654321/example-listing"}
  ]
}

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🏢 Who uses this

Persona	Use case
💼 CRE investment broker	Daily morning brief: new sub-$5M listings in target market
🏛️ REIT acquisition team	Track multifamily + retail across 5 metros, normalize cap rates
🔍 ETA / search fund	Identify owner-operated deals, dedupe LoopNet+Crexi to skip duplicate underwriting
📈 CRE consulting firm	Build normalized comp datasets for client reports
🏰 Family office	Filter for opportunity-zone office, weekly digest
🏦 Lender / appraiser	Days-on-market by asset class for valuation models

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📊 The intelligence layer (the actual moat)

Cap-rate normalization

Listing data is inconsistent across platforms. Some declare cap rate, others NOI only, auctions and unpriced listings have neither. The actor handles all 4 cases:

Source data	Action	Output cap_rate_estimated
NOI + price both declared	Recompute cap = NOI/price × 100	false ✅
Only cap_rate declared	Pass-through, derive implied NOI	false ✅
Neither declared, price known	Estimate with asset-class median (US Q1 2024-25)	true ⚠️
No price + no cap_rate	—	null

Asset-class medians used:

Class	Median	Class	Median
🏠 Multifamily	5.4%	🛍️ Retail	6.8%
🏭 Industrial	5.9%	🏢 Office	7.5%
🏗️ Mixed-use	6.5%	🎯 Specialty	7.0%
🏨 Hotel	8.5%

🔄 Cross-platform deduplication

Property listed on both LoopNet AND Crexi appears in BOTH datasets. The actor:

1. Computes a stable dedup_key = hash(normalized_street + city + state + sqft_bucket + asset_class)
2. Groups listings by key
3. Picks the most complete record as primary (highest non-null field count)
4. Marks also_listed_on: ["crexi"] on the LoopNet primary (or vice versa)
5. Outputs one deduplicated record per property

📌 No other Apify actor does this. It's the difference between raw scrape and intelligence.

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💰 Pricing — fully transparent

Pay-per-event on Apify Store. New Apify users get $5 platform credit on signup (~1,000 listings test on this actor included).

Event	Price	When
🚀 Actor Start	$0.05 / run	Once per scrape job
📍 Result ⭐	$0.005 / listing	Each unique listing returned (primary cost)
📋 Listing detail enrichment	$0.003 / listing	Only when includeListingDetails: true (NOI, photos, year built)

💵 Realistic cost estimates

Volume	Without details	With details
100 listings (test)	$0.55	$0.85
1,000 listings (city scan)	$5.05	$8.05
10,000 listings (multi-city)	$50.05	$80.05
25,000 listings/mo (active broker)	~$125/mo	~$200/mo

📈 At 25K listings/mo the actor costs ~$1,500/yr vs CoStar's $40,000/yr median. ROI = 27×.

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✅ Key features

• 🔄 Cross-platform aggregation — LoopNet + Crexi
• 📊 Cap-rate normalization with NOI fallback + asset-class medians
• 🔁 Cross-platform deduplication with stable dedup_key
• ⏱️ Days-on-market index auto-computed
• 🆕 Monitoring mode — only new listings since last run
• 🤝 Transaction tracking — only closed deals (sold / leased / under contract)
• 📦 Portfolio expansion — multi-property listings split into individual items
• 🎛️ 20+ filters — price, cap rate, sqft, asset class, sub-type, state, city
• 📁 Apify dataset views — Overview / Financial / Broker contacts pre-configured
• 🌍 Multi-locale parsing — handles English + French, ranges (1.5K-13K SF), K/M multipliers
• 🤖 Fully autonomous — runs on Apify residential proxy, no human ritual

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❓ FAQ

Q: How is this different from existing LoopNet / Crexi scrapers on Apify? A: Other scrapers cover a single source and return raw data. This covers BOTH, normalizes cap rates, deduplicates cross-platform, computes days-on-market. The intelligence layer is the value.

Q: How fresh is the data? A: As fresh as LoopNet/Crexi public listings — typically updated every 24–48h on the source side. With monitoring mode, you're notified within 1 day of a new listing.

Q: Can I get NOI and cap rate that aren't publicly listed? A: When the source platform exposes them, yes. When not (~40% of listings), the actor estimates with asset-class medians, clearly flagged with cap_rate_estimated: true. Medians refreshed quarterly.

Q: Are broker contact emails / phones included? A: Phone and email when available on source (Crexi exposes more contact info than LoopNet). Use ethically — direct outreach is governed by your local CRE broker license rules.

Q: Can I use this for off-market data? A: No, on-market only. For off-market, look at Reonomy or CompStak (different price tier).

Q: What about Ten-X auctions? A: Out of scope — Ten-X auction listings are gated (bidder registration required). The actor flags listings transitioning to auction status via status: "under_contract" when source exposes it.

Q: Can I run this on a schedule? A: Yes — Apify scheduler. Recommended: daily 6am ET in monitoringMode: true for new-listing alerts. Combine with Apify webhooks → Slack / Zapier / your CRM.

Q: How does dedup handle two listings with same address but different asset class? A: Dedup key includes asset_class, so a "Mixed Use" record and a "Multifamily" record at the same address remain separate items.

Q: What about international listings? A: USA + Canada (LoopNet covers both). International CRE markets not currently supported.

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📋 Output schema

Each dataset row follows this schema:

{
  source: "loopnet" | "crexi",
  source_listing_id: string,
  source_url: string,
  scraped_at: string,                 // ISO8601

  address: {
    raw: string,
    street: string | null,
    city: string | null,
    state: string | null,             // 2-letter US/CA code
    zip: string | null,
    country: "US" | "CA",
    lat: number | null,
    lng: number | null
  },

  asset_class:
    | "office" | "retail" | "industrial" | "multifamily"
    | "land" | "hotel" | "mixed-use" | "specialty" | "unknown",
  sub_type: string | null,
  sqft: number | null,
  units: number | null,
  year_built: number | null,
  lot_size_sqft: number | null,

  asking_price_usd: number | null,
  noi_usd: number | null,
  cap_rate_listed: number | null,
  cap_rate_normalized: number | null,
  cap_rate_estimated: boolean,
  price_per_sqft: number | null,

  listed_at: string | null,
  days_on_market: number | null,
  status:
    | "active" | "under_contract" | "sold" | "leased"
    | "removed" | "off_market" | "unknown",

  broker: {
    name: string | null,
    company: string | null,
    phone: string | null,
    email: string | null,
    profile_url: string | null
  },

  dedup_key: string,                  // stable hash for dedup
  also_listed_on: ("loopnet" | "crexi")[],

  photo_urls: string[],
  description: string | null,

  // Only when transactionTrackingMode=true
  transaction_tracking?: {
    close_status: "sold" | "leased" | "under_contract" | "removed" | "pending",
    close_detected_at: string,
    first_seen_at: string,
    last_seen_at: string,
    previous_price: number | null
  }
}

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🛠️ Technical notes

• Stack: Apify SDK v3 + Crawlee v3.16, TypeScript ESM, Node 22
• Source extraction: pds.loopnet.com mobile API + api.crexi.com JSON
• Anti-blocking: Apify residential proxies, auto-rotated
• Performance: HTTP-only for normal usage, sub-10s runs for 50 listings
• Cap-rate medians: refreshed quarterly from Marcus & Millichap + CBRE Cap Rate Survey
• Address normalization: USPS-style street parsing + sqft buckets for dedup

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📞 Contact

Issues, feature requests, custom feeds → open an issue on Apify Store or contact KazKN directly.

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🏢 Built to help SMB CRE brokers stop overpaying for data they couldn't differentiate themselves with anyway.