Crexi Scraper — CRE Listings, Financials & Broker Leads

What is Crexi Scraper — CRE Listings, Financials & Broker Leads?

Crexi Scraper — CRE Listings, Financials & Broker Leads returns one dataset row per for-sale commercial property on Crexi: asking price, property type and subtypes, square footage, lot size, zoning, full address with county and coordinates, marketing description, listing flags and photos, plus the listing brokers as leads with brokerage, brokerage website, licenses, Crexi profile link and active listing count. CRE brokers, investors, analysts, lenders' business development teams and AI agents use it to source deals and build broker prospect lists by state, city and property type, or from specific listing URLs.

Why CRE teams use this Crexi scraper

• Broker leads built in. With includeBrokers on, brokers lists each listing agent with brokerageName, brokerageWebsite, licenses, profileUrl and numberOfActiveListings.
• Market filters. states, cities and propertyTypes focus the search, and sortBy orders it by relevance or price.
• Ready-to-map locations. fullAddress, county, stateCode, zip, latitude and longitude on every row.
• Listing signals. isNew, hasFlyer, hasOM, hasVideo, hasVirtualTour and isInOpportunityZone show how actively a listing is marketed.
• Detail on demand. enrichDetails adds subtypes, lot size, zoning, buildings, stories and the full marketing description.

Best fit for this Actor

• Best fit for broker prospecting and deal sourcing across Crexi's for-sale commercial listings.
• Designed for the public listing view. Crexi shows some values, such as cap rate, year built and APN, to signed-in users only, so in the public view those fields hold masked placeholders or stay empty.
• Use startUrls with listing URLs or bare asset IDs when you already know the properties.
• When you need ZIP, asking price or cap rate filters, use Crexi Scraper — Commercial Real Estate Listings instead.
• To cover the same market on another marketplace, continue with LoopNet Scraper and match on address.

Example workflow: from a market to a broker call list

A commercial lender's business development rep starts with states set to FL, cities set to Miami and propertyTypes set to Office and Retail, with enrichDetails and includeBrokers on and maxResults at 100. The dataset returns every listing with its brokers. They group rows by brokers[].publicProfileId, rank brokers by numberOfActiveListings, and keep those with several listings in their target size band. They export the brokers with brokerageWebsite and profileUrl to their CRM, then schedule the same input monthly to spot new listings and new brokers.

Input

Search a market:

{
  "states": ["FL"],
  "cities": ["Miami"],
  "propertyTypes": ["Office", "Retail"],
  "sortBy": "relevance",
  "includeUnpriced": true,
  "maxResults": 30,
  "enrichDetails": true,
  "includeBrokers": true
}

Fetch specific listings:

{
  "startUrls": ["https://www.crexi.com/properties/2726421/new-york-102-henry-street", "2586166"],
  "enrichDetails": true,
  "includeBrokers": true
}

Field	Type	What it controls
states	array	Two-letter state codes, the main location filter.
cities	array of strings	City names, most precise with the matching state.
propertyTypes	array	Retail, Office, Multifamily, Industrial, Land, Hospitality, Special Purpose, Mixed Use, Self Storage or Business for Sale.
sortBy	string	relevance, priceLowToHigh or priceHighToLow. Default relevance.
includeUnpriced	boolean	Keep listings without a public price. Default true.
maxResults	integer	Cap on listings saved; 0 collects every match. Default 30.
startUrls	array of strings	Crexi listing URLs or bare asset IDs.
enrichDetails	boolean	Add full property detail. Default true.
includeBrokers	boolean	Add broker lead records. Default true.
proxyConfiguration	object	Proxy settings. Default uses residential Apify Proxy.

Output

One row per listing.

Field	What it contains
id, name, url, status	Listing identity, link and status
propertyTypes, subtypes	Asset classes
askingPrice, askingPriceDisplay, isUnpriced	Price as a number and as shown
squareFootage, lotSizeSqFt, units, buildings, stories, zoning	Physical details, when listed
capRate, yearBuilt, apn	Values Crexi shows publicly for some listings, masked for others
address, city, county, state, stateCode, zip, fullAddress, latitude, longitude	Location
brokerageName, brokers	Brokerage and broker leads with website, licenses, profile URL and active listing count
marketingDescription, investmentHighlights	Marketing copy as plain text
isNew, isSold, isInOpportunityZone, hasFlyer, hasOM, hasVideo, hasVirtualTour	Listing flags
thumbnailUrl, numberOfImages	Photos
activatedOn, updatedOn, scrapedAt	Listing dates and collection time

Example enriched row (broker details are illustrative):

{
  "id": 2196935,
  "name": "2175 SW 1st St",
  "url": "https://www.crexi.com/properties/2196935/florida-2175-sw-1st-st",
  "status": "Active",
  "propertyTypes": ["Business for Sale", "Retail"],
  "subtypes": ["Business Only", "Restaurant"],
  "askingPrice": 125000,
  "askingPriceDisplay": "$125,000",
  "squareFootage": 10419,
  "lotSizeSqFt": "15,920",
  "zoning": "6100",
  "address": "2175 SW 1st St",
  "city": "Miami",
  "county": "Miami-Dade County",
  "state": "Florida",
  "stateCode": "FL",
  "zip": "33135",
  "latitude": 25.7715933,
  "longitude": -80.2303667,
  "fullAddress": "2175 SW 1st St, Miami, FL 33135",
  "brokerageName": "Example Realty Partners",
  "brokers": [
    {
      "name": "Maria Lopez",
      "brokerageName": "Example Realty Partners",
      "brokerageWebsite": "https://www.example.com/",
      "licenses": ["1234567"],
      "publicProfileId": "marialopez",
      "profileUrl": "https://www.crexi.com/profile/marialopez",
      "numberOfActiveListings": 5
    }
  ],
  "isInOpportunityZone": false,
  "isNew": false,
  "isSold": false,
  "hasFlyer": true,
  "hasOM": false,
  "numberOfImages": 40,
  "marketingDescription": "Fully equipped corner restaurant with parking and seating, sold with its equipment and transferable licenses.",
  "activatedOn": "2025-10-04T14:14:41Z",
  "updatedOn": "2026-05-04T12:02:40.625Z",
  "scrapedAt": "2026-10-01T12:16:20.423Z"
}

Pricing

This Actor uses Pay per event plus Apify platform usage. Each listing saved is one Listing scraped event, and each listing enriched with full detail or broker data adds one Detail + broker enrichment event. Every run also has one Actor Start event plus the platform and proxy usage of the run. For example, 100 listings with enrichment and brokers are 100 Listing scraped events plus 100 enrichment events and the start event. Switch off enrichDetails and includeBrokers for listing-card rows only. The live Pricing tab on this page shows the current event prices.

API example

curl -X POST "https://api.apify.com/v2/acts/khadinakbar~crexi-scraper/run-sync-get-dataset-items?token=YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"states": ["TX"], "cities": ["Austin"], "propertyTypes": ["Office"], "maxResults": 25, "includeBrokers": true}'

from apify_client import ApifyClient

client = ApifyClient("YOUR_API_TOKEN")
run = client.actor("khadinakbar/crexi-scraper").call(run_input={"states": ["CA"], "propertyTypes": ["Multifamily"], "maxResults": 50})
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    for broker in item.get("brokers") or []:
        print(item["name"], broker["name"], broker.get("profileUrl"))

Use with AI agents (MCP)

Connect the Actor to Claude, ChatGPT or Cursor through Apify MCP and ask:

List 30 office and retail properties for sale in Miami on Crexi, and give me the brokers with the most active listings and their brokerage websites.

The agent sets states, cities, propertyTypes, includeBrokers and maxResults, reads rows with get-dataset-items, groups by broker, and cites each listing url. Each listing is one event plus one enrichment event when enriched, so maxResults keeps the cost of the request predictable.

Best results

Starting situation	Helpful action	Expected outcome
Broker prospecting	Keep includeBrokers on and group by publicProfileId	One lead per broker with listing counts
Fast market scan	Switch off enrichDetails and includeBrokers	Lean listing rows
A specific city	Pair cities with its states code	A focused submarket
Priced deals first	Set includeUnpriced to false and sortBy to priceLowToHigh	Comparable asking prices
Known listings	Paste URLs or asset IDs into startUrls	Exactly those properties

Builder's note

I built this one around the broker, because in commercial real estate the listing agent is often the real lead: each broker record carries a brokerage website and an active listing count so you can tell a busy specialist from a one-off seller. I kept location filters to state and city because they map cleanly to how Crexi organizes its market.

FAQ

Does it return broker emails or phone numbers?

It returns broker name, brokerage, brokerage website, licenses and Crexi profile link, which Crexi shows publicly.

Does it cover lease listings?

It covers for-sale listings.

Why is yearBuilt a row of asterisks?

Crexi reserves some details for signed-in users and masks them in the public view.

Responsible use

This Actor collects publicly available Crexi data. Broker names and licenses are personal data under laws such as the GDPR and CCPA, so contact brokers with a lawful basis, follow anti-spam rules, and respect Crexi's terms of service. Crexi is a trademark of its owner; this independent Actor is not affiliated with or endorsed by Crexi.