LoopNet + Crexi Listings API - Commercial Real Estate Market File

Pull public commercial real estate listings from LoopNet and Crexi, then export one clean dataset for first-pass market scans, broker research, underwriting prep, CRM enrichment, or daily monitoring.

This repository is a public integration and workflow hub for the Commercial Real Estate Brokerage Intel Apify Actor. It is built for developers, CRE brokers, analysts, investors, and operators who want structured listing intelligence without manually rebuilding spreadsheets from multiple portals.

Why this exists

Commercial real estate teams often start research in two tabs: LoopNet and Crexi.

The manual workflow usually looks like this:

1. Search a market on LoopNet.
2. Repeat the same search on Crexi.
3. Copy listings into a spreadsheet.
4. Remove duplicate properties.
5. Normalize price, rent, cap rate, square footage, and days on market.
6. Find broker names and companies.
7. Rebuild the same file tomorrow.

The actor turns that into one API call.

It is not trying to replace every enterprise CRE platform. It is designed for the first-pass market file: a clean, exportable dataset that helps you move from "what is listed?" to "which deals should I inspect?" faster.

What you get

Need	Output
LoopNet listings data	Public LoopNet listing rows in a normalized schema
Crexi listings data	Public Crexi listing rows in the same schema
Commercial real estate API	JSON, CSV, Excel, Google Sheets, CRM, or database-ready records
Cross-platform deduplication	also_listed_on, also_listed_on_text, and stable dedup keys
Cap rate data API	Listed, implied, recomputed, or estimated cap-rate fields with provenance
Days-on-market data	days_on_market when the source exposes usable date signals
CRE broker leads	Broker name, company, phone, and email when publicly exposed
Daily monitoring	Emit only newly seen listings for a saved market scan
Transaction tracking	Detect removed, sold, leased, or changed listings across runs

Quick start

1. Run on Apify

Open the actor:

apify.com/kazkn/commercial-real-estate-brokerage-intel

Use this input for a quick Austin market scan:

{
  "city": "Austin",
  "state": "TX",
  "sourcesEnabled": ["loopnet", "crexi"],
  "transactionTypes": ["sale"],
  "assetClasses": ["office", "retail"],
  "priceMin": 500000,
  "priceMax": 5000000,
  "maxResultsPerSource": 200,
  "deduplicate": true,
  "normalizeCapRate": true,
  "includeListingDetails": false
}

2. Export the dataset

After the run finishes, export from Apify Storage as:

• CSV for Excel or Google Sheets
• JSON for API workflows
• XLSX for spreadsheet review
• API dataset items for apps, CRMs, or warehouses

3. Enable broker enrichment when needed

For pure market scans, keep includeListingDetails disabled.

For broker lead research, enable it:

{
  "city": "Dallas",
  "state": "TX",
  "sourcesEnabled": ["loopnet", "crexi"],
  "transactionTypes": ["sale"],
  "assetClasses": ["office", "industrial", "retail"],
  "maxResultsPerSource": 1000,
  "includeListingDetails": true
}

Listing detail enrichment is best-effort and only fills broker phone/email when the source publicly exposes those fields.

API examples

This repo includes ready-to-use examples:

File	Use case
examples/01-curl-quickstart.sh	Trigger a run from the terminal
examples/02-python-market-scan.py	Run a market scan and export CSV
examples/03-node-daily-monitoring.mjs	Schedule-style daily monitoring input
examples/04-google-sheets-export.py	Convert dataset items into a Sheets-ready CSV
examples/05-broker-lead-list.py	Build a broker outreach review file

All examples read APIFY_TOKEN from the environment.

export APIFY_TOKEN=<YOUR_APIFY_TOKEN>

Get your token from Apify Console.

Python example

from apify_client import ApifyClient

client = ApifyClient("YOUR_APIFY_TOKEN")

run = client.actor("kazkn/commercial-real-estate-brokerage-intel").call(run_input={
    "city": "Dallas",
    "state": "TX",
    "sourcesEnabled": ["loopnet", "crexi"],
    "transactionTypes": ["sale"],
    "assetClasses": ["office", "retail", "industrial"],
    "maxResultsPerSource": 500,
    "deduplicate": True,
    "normalizeCapRate": True,
    "includeListingDetails": False
})

items = client.dataset(run["defaultDatasetId"]).list_items().items
print(f"Fetched {len(items)} commercial real estate listings")

Node.js example

import { ApifyClient } from "apify-client";

const client = new ApifyClient({ token: process.env.APIFY_TOKEN });

const run = await client.actor("kazkn/commercial-real-estate-brokerage-intel").call({
  city: "Phoenix",
  state: "AZ",
  sourcesEnabled: ["loopnet", "crexi"],
  transactionTypes: ["sale"],
  assetClasses: ["office", "retail"],
  maxResultsPerSource: 250,
  deduplicate: true,
  normalizeCapRate: true,
});

const { items } = await client.dataset(run.defaultDatasetId).listItems();
console.log(items.slice(0, 5));

Output fields

Typical dataset columns include:

Field	Meaning
source	loopnet or crexi
source_listing_id	Source-specific listing ID
listing_url	Public listing URL
transaction_type	sale or lease
address_full	Normalized address text
city, state, zip	Location fields when available
asset_class	Office, retail, industrial, multifamily, land, hotel, mixed-use, specialty
asking_price_usd	Sale asking price when exposed
lease_rate / rent fields	Lease rent fields when exposed by the source
sqft	Building or listing square footage when available
cap_rate_listed	Source-listed cap rate
cap_rate_normalized	Comparable cap rate field
cap_rate_source	Declared, recomputed, implied, or estimated
noi_usd	NOI value used for cap-rate calculations
noi_source	Declared, implied, or estimated
days_on_market	Days on market when a usable listing date exists
broker_name	Broker name when public
broker_company	Brokerage company when public
broker_phone	Broker phone when public
broker_email	Broker email when public
also_listed_on	Other source where the same property appears
enrichment_status	Whether a row is search-only, detail-enriched, or broker-limited

See sample-data/example-output.json and sample-data/dallas-market-file-sample.csv for example rows.

Pricing examples

The actor uses pay-per-event pricing on Apify:

Event	Cost
Actor start	$0.05 per run
Result	$5.00 per 1,000 returned listings
Listing detail enrichment	$3.00 per 1,000 enriched listings

Example run costs:

Scenario	Approx. cost
100 listings, no details	$0.55
1,000 listings, no details	$5.05
1,000 listings with details	$8.05
5,000 listings, no details	$25.05

Apify also gives new users free monthly platform credits, which are useful for testing small market scans.

Practical workflows

First-pass market file

Use the actor to create a quick market file for Dallas, Austin, Phoenix, Miami, Chicago, Los Angeles, or any other US city.

Recommended input:

{
  "city": "Dallas",
  "state": "TX",
  "sourcesEnabled": ["loopnet", "crexi"],
  "transactionTypes": ["sale"],
  "assetClasses": ["office", "retail", "industrial"],
  "maxResultsPerSource": 1000,
  "deduplicate": true,
  "normalizeCapRate": true
}

Broker lead research

Enable listing details to enrich the dataset with broker fields where public:

{
  "city": "Austin",
  "state": "TX",
  "sourcesEnabled": ["loopnet", "crexi"],
  "transactionTypes": ["sale"],
  "assetClasses": ["office", "retail"],
  "maxResultsPerSource": 500,
  "includeListingDetails": true
}

Daily CRE deal flow dashboard

Use monitoringMode on an Apify schedule:

{
  "city": "Phoenix",
  "state": "AZ",
  "sourcesEnabled": ["loopnet", "crexi"],
  "transactionTypes": ["sale"],
  "assetClasses": ["office", "industrial"],
  "maxResultsPerSource": 1000,
  "monitoringMode": true
}

Guides

• How to deduplicate LoopNet and Crexi listings
• Build a daily CRE deal flow dashboard
• Broker lead workflow from public CRE listings
• Cost, data quality, and what this actor is not

Video demo

Watch the demo: LoopNet + Crexi to one clean commercial real estate dataset.

Related links

• Commercial Real Estate Brokerage Intel on Apify
• Actor API documentation
• Apify platform
• LoopNet
• Crexi
• KazKN on Apify

FAQ

Is this a CoStar replacement?

No. It is better to position it as a lightweight first-pass market-file workflow. It helps you collect, normalize, export, and monitor public on-market listings from LoopNet and Crexi. It does not replace every proprietary research, comp, ownership, lease, or analytics feature in an enterprise CRE platform.

Does it return broker emails and phones?

Only when the source publicly exposes them. The actor never invents broker contacts. Rows include broker fields and enrichment status so users can see what was available.

Can I use this as a commercial real estate API?

Yes. You can run it through Apify API, Python, Node.js, cURL, Apify schedules, webhooks, Make, Zapier, Google Sheets, Airtable, or your own backend.

Can I run both LoopNet and Crexi in the same job?

Yes. Set sourcesEnabled to ["loopnet", "crexi"]. If both sources expose enough listings, maxResultsPerSource: 1000 can return up to 1,000 LoopNet rows plus 1,000 Crexi rows before deduplication.

Is the data always complete?

No public listing source is perfectly complete. Some rows are search-only, some are detail-enriched, and some broker contact fields are blocked or not exposed. The actor is designed to return the maximum usable dataset without pretending unavailable fields exist.

Support

• Open an issue in this repo for integration examples, feature requests, or documentation gaps.
• Run the actor on Apify: commercial-real-estate-brokerage-intel

Built by KazKN.