KazKN

Posted on May 31

How to Scrape LoopNet and Crexi Listings into One CRE Dataset

#webscraping #apify #realestate #automation

Quick answer

To scrape LoopNet and Crexi listings in a useful way, you need more than two raw scrapers. A practical workflow collects public commercial real estate listings from both sources, normalizes key fields, marks duplicate properties, exposes broker contact fields when the listing source provides them, and exports the result as CSV, Excel, JSON, or an API response.

Commercial Real Estate Brokerage Intel is an Apify actor built for this workflow: one run, two listing sources, one clean commercial real estate dataset.

Most CRE brokers already know the manual version of this workflow.

Open LoopNet. Search a market. Copy interesting listings. Open Crexi. Search the same market. Copy those listings too. Remove duplicate properties. Normalize cap rates. Check broker contacts. Export everything into a spreadsheet.

That process works once.

It becomes painful when you need to repeat it every day.

This guide shows how to think about a LoopNet scraper and Crexi scraper workflow on Apify, with the goal of producing structured commercial real estate listing data rather than another messy export.

What a useful CRE listing scraper should return

A commercial real estate listings scraper should be judged by the dataset it returns.

Useful fields include:

source_platform
listing_url
property_name
address
city
state
asset_class
asking_price_usd
building_size_sqft
lot_size_sqft
price_per_sqft
cap_rate_listed
cap_rate_normalized
cap_rate_estimated
cap_rate_source
noi_declared_usd
noi_implied_usd
noi_source
days_on_market
days_on_market_source
broker_name
broker_company
broker_phone
broker_email
also_listed_on

The important part is not only collecting fields. The important part is making them consistent enough to scan, filter, export, and send into another system.

Why LoopNet + Crexi is harder than one source

A basic LoopNet scraper or Crexi scraper only has to understand one source.

A combined workflow has to handle:

Different listing layouts
Different field names
Different price formats
Missing cap rates
Missing NOI
Public broker contacts that appear on one source but not the other
Duplicate properties across platforms
Incomplete square footage or lot-size data
Listings where the same value is expressed in different ways

This is why “scrape both sites” is not the same as “create a usable CRE dataset.”

For brokers and acquisition teams, the output needs to answer practical questions:

Is this property also listed somewhere else?
Is the cap rate declared by the source or estimated from other values?
How long has the listing been on market?
Which broker or brokerage is attached to the public listing?
Can I export this into Google Sheets, a CRM, or an underwriting model?

Example Apify input

Here is a simplified example of a market scan:

{
  "forSale": true,
  "forRent": false,
  "sourcesEnabled": ["loopnet", "crexi"],
  "city": "Austin",
  "state": "TX",
  "assetClasses": ["office", "retail"],
  "priceMin": 500000,
  "priceMax": 5000000,
  "maxResultsPerSource": 200
}

You can run the same workflow for Dallas, Phoenix, Miami, Chicago, Los Angeles, New York, or any supported market.

Step-by-step workflow

1. Choose the market

Start with one city and state.

Examples:

Austin, TX
Dallas, TX
Phoenix, AZ
Miami, FL
Chicago, IL

Focused runs are easier to validate before scaling to larger monitoring workflows.

2. Select sources

You can run:

LoopNet only
Crexi only
LoopNet and Crexi together

For a daily CRE deal-flow workflow, both sources are usually more useful because they help you spot broader inventory and cross-platform duplicates.

3. Apply listing filters

Useful filters include:

For sale vs for rent
Asset class
City and state
Minimum price
Maximum price
Maximum results per source

The goal is to keep each run targeted enough that the output is actionable.

4. Run the actor

The actor collects public listings, normalizes fields, and writes the result to an Apify dataset.

From Apify, users can export the dataset as:

CSV
Excel
JSON
API response

That makes the workflow useful for Google Sheets, Airtable, CRMs, dashboards, and internal underwriting tools.

5. Review the dataset

When checking the output, focus on the fields that change the workflow:

cap_rate_normalized
cap_rate_source
days_on_market
broker_name
broker_company
broker_phone
broker_email
also_listed_on

These fields are what turn raw listing scraping into brokerage intelligence.

Handling cap rate and NOI correctly

Cap rate data can be messy.

Some listings provide a cap rate directly. Some provide NOI but not cap rate. Some provide neither. A useful cap rate data API should preserve that distinction.

For example:

{
  "cap_rate_listed": 6.25,
  "cap_rate_normalized": 6.25,
  "cap_rate_source": "listed",
  "noi_declared_usd": 312500,
  "noi_source": "listed"
}

If a value is estimated, the dataset should make that clear:

{
  "cap_rate_estimated": 5.8,
  "cap_rate_source": "estimated",
  "noi_implied_usd": 290000,
  "noi_source": "implied"
}

That matters because brokers and analysts should not mix source-declared figures with derived figures without knowing the difference.

Handling days on market

Days on market commercial real estate data is useful for prioritizing outreach.

Recent listings may signal fresh opportunities. Older listings may signal stale inventory, pricing pressure, or a broker who may be more open to a conversation.

When the source exposes usable listing-date context, the actor returns:

days_on_market
days_on_market_source

If the source does not expose enough information, the dataset should not pretend that it does.

Handling broker contacts

A broker contact scraper should be transparent about what it can collect.

Commercial Real Estate Brokerage Intel returns broker names, brokerage companies, phone numbers, and emails when those details are publicly exposed by the source listing.

It is useful for CRE broker leads and outreach review, but it should be treated as public listing data, not private enrichment.

Handling duplicates across LoopNet and Crexi

LoopNet vs Crexi data often overlaps.

The same property can appear on both platforms, sometimes with slightly different titles, prices, or broker details.

A combined scraper should mark duplicate signals so the team does not chase the same property twice.

Example:

{
  "source_platform": "loopnet",
  "also_listed_on": ["crexi"],
  "also_listed_on_text": "crexi"
}

This is one of the main reasons to use a combined commercial real estate API instead of separate exports.

Practical use cases

This workflow is useful for:

Daily CRE deal-flow monitoring
Broker lead lists
Acquisition screening
Market scans by city and asset class
Cap rate comparison
Days-on-market tracking
CSV exports into Google Sheets
CRM enrichment with public listing context
Internal dashboards for active commercial real estate listings

FAQ

Can I scrape LoopNet and Crexi together?

Yes. A combined Apify actor can collect public listing data from both sources and return one dataset with source platform, normalized fields, and duplicate signals.

Is this a LoopNet scraper or a Crexi scraper?

It can work as both. Commercial Real Estate Brokerage Intel is designed as a LoopNet scraper, Crexi scraper, commercial real estate listings scraper, and lightweight commercial real estate API.

Does the actor include broker contact data?

It can include broker name, broker company, phone, and email when those details are publicly exposed by the listing source.

Can I export the results?

Yes. Apify datasets can be exported as CSV, Excel, JSON, or accessed through the API.

Is this a CoStar alternative?

It is a lightweight CoStar alternative for one specific workflow: public listing monitoring from LoopNet and Crexi. It is not positioned as a full enterprise research suite.

Final workflow

The repeatable workflow is simple:

Choose a market
Select LoopNet, Crexi, or both
Apply filters
Run the actor
Review cap rates, days on market, duplicate signals, and broker contacts
Export to CSV, JSON, Google Sheets, a CRM, or a dashboard
Schedule the same run daily or weekly

If your team lives in LoopNet and Crexi, this turns manual listing research into a repeatable CRE data pipeline.

Try Commercial Real Estate Brokerage Intel on Apify:
https://apify.com/kazkn/commercial-real-estate-brokerage-intel?fpr=8fp2od

Watch the 2-minute demo:
https://youtu.be/-9rSWW3B4ms

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