CRE Listing Intelligence Series
- Best CoStar alternatives for small CRE brokers
- How to scrape LoopNet and Crexi listings into one CRE dataset
- Commercial real estate API for brokers: cheap CoStar alternative
- I Pulled 1,234 Dallas CRE Listings from LoopNet + Crexi. Deduping Was the Real Problem.
- LoopNet vs Crexi: Which Commercial Real Estate Platform Is Better?
- CoStar vs LoopNet vs Crexi: Which CRE Platform Should Brokers Use?
- How to Export Commercial Real Estate Listings to CSV from LoopNet + Crexi
If you work with commercial real estate listings, the hard part is not always finding properties.
The hard part is turning listings into a file someone can actually use.
Two tabs are open:
Then the manual work starts:
- copy listing URLs
- normalize property names and addresses
- remove duplicates
- compare asking price and rent fields
- check whether cap rate is stated or missing
- look for broker names and companies
- keep source links so someone can verify the row later
That is the difference between a listing portal and a market file.
One helps you browse.
The other helps you review.
What a useful CRE CSV actually needs
A basic export with only title, price, and URL is usually not enough.
For first-pass commercial real estate research, I want each row to answer a few questions quickly:
| Question | Field examples |
|---|---|
| What is the asset? | address, city, state, asset class, subtype |
| Where did it come from? | source, listing URL, source ID |
| Is it sale or lease? | transaction type, asking price, rent per sqft/year |
| Is the financial context usable? | cap rate, cap-rate source, NOI when available |
| Is it fresh or stale? | days on market, listed date, enrichment status |
| Who is attached to it? | broker name, broker company, public phone/email when available |
| Is it duplicated? | also listed on, duplicate signals, dedup key |
The important part is not making the file look clean.
The important part is making the row explain itself.
Why LoopNet + Crexi can be messy together
Using both sources is useful because coverage is not identical.
But combining them creates a second problem: the same property can appear in slightly different ways.
For example:
123 Main St
123 Main Street
123 Main St., Suite 400
123 Main Street - Office Building
Technically, those are different strings.
In a market file, they may be the same property.
That is why a good commercial real estate CSV export should preserve source links and duplicate signals instead of pretending the data is perfect.
A cleaner workflow
The workflow I like is:
- Pick a market: Dallas, Los Angeles, Houston, Phoenix, Austin, etc.
- Pick transaction type: sale, lease, or both.
- Pick asset classes: office, retail, industrial, multifamily.
- Pull public listing rows from LoopNet and Crexi.
- Normalize common fields into one schema.
- Add duplicate signals across sources.
- Add cap-rate and days-on-market context where available.
- Keep broker fields best-effort, not overpromised.
- Export to CSV, Excel, JSON, or API.
The output should be boring in the best way:
address_full
city
state
source
transaction_type
asset_class
asking_price_usd
asking_rent_per_sqft_per_year_usd
cap_rate_normalized
cap_rate_source
days_on_market
broker_name
broker_company
broker_phone
broker_email
also_listed_on_text
enrichment_status
listing_url
Example Apify input
I packaged this workflow as an Apify actor because Apify already gives you runs, datasets, schedules, exports, and API access without building a full app first.
Example input:
{
"city": "Dallas",
"state": "TX",
"sourcesEnabled": ["loopnet", "crexi"],
"transactionTypes": ["sale", "lease"],
"assetClasses": ["office", "retail", "industrial", "multifamily"],
"maxResultsPerSource": 500,
"includeListingDetails": true,
"deduplicate": true,
"normalizeCapRate": true,
"monitoringMode": false,
"downloadImages": false
}
The actor is here:
Commercial Real Estate Brokerage Intel
It is designed for first-pass public listing research, not as a full CRE platform replacement.
CSV vs API
CSV is usually the right first export.
It is easy to review, send, upload to Google Sheets, or drop into a CRM.
API access becomes useful when the workflow repeats:
- daily CRE deal-flow monitoring
- internal dashboards
- lead routing
- enrichment pipelines
- market-by-market research
That is why I think of the product as a market-file engine first, and an API second.
The buyer usually wants the file.
The developer wants the repeatable workflow behind it.
A few caveats
Public listing data is not perfect.
Broker phone and email fields depend on what the source exposes publicly. Cap rates can be missing or estimated. Days-on-market may not exist for every row. Some rows should stay marked as search-only if detail enrichment is blocked or unavailable.
That is why I like exposing fields such as:
cap_rate_source
enrichment_status
also_listed_on_text
listing_url
Hiding uncertainty makes the output look cleaner.
Showing uncertainty makes it more useful.
FAQ
Can you export LoopNet and Crexi listings to CSV?
You can build a workflow that turns public LoopNet and Crexi listing searches into a structured CSV, but the quality depends on normalization, deduplication, and source provenance.
Is there a commercial real estate listings API?
There are multiple real estate data APIs, but if your workflow is specifically public LoopNet + Crexi first-pass research, an Apify actor can be a lightweight way to produce CSV, Excel, JSON, and API outputs from the same run.
What fields should a CRE market file include?
At minimum: address, city, state, source, listing URL, transaction type, asset class, price or rent, square footage, cap-rate context, days-on-market when available, broker fields when public, duplicate signals, and enrichment status.
Should this replace deeper underwriting or paid CRE platforms?
No. This is better used before deeper analysis: a first-pass market file that helps decide which listings deserve attention.
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