LoopNet + Crexi Scraper for CRE Listings
Turn LoopNet + Crexi searches into one CRE dataset
I have been building Apify Store actors, and my newest one targets a narrow B2B workflow: commercial real estate listing research.
The problem:
CRE brokers often monitor LoopNet and Crexi manually. They search both portals, copy listings into spreadsheets, remove duplicates, compare cap rates, check days on market, look for broker contacts, and then export the useful rows into a CRM or underwriting workflow.
I built an Apify actor to turn that into one structured dataset.
What it returns:
Public LoopNet + Crexi listing rows
Duplicate signals across platforms
Cap rate / NOI context
Days-on-market fields when available
Broker name/company
Public phone/email when exposed by the source
CSV / Excel / JSON / API export
The unexpected lessons:
The output schema is the product
If the dataset is not easy to scan, filter, and export, the scraper does not matter.
Niche positioning beats generic naming
"Real estate scraper" is too generic. "LoopNet + Crexi public listing workflow for CRE brokers" is clearer.
Marketplace unit economics are brutal
If a user runs too broadly and the actor takes too long, margin disappears. Runtime control, result limits, and pricing events matter a lot.
Users care about proof
A short demo video and concrete output columns convert better than a long feature list.
The hardest part is trust
For fields like cap rate and NOI, users need to know whether values are declared by the source or estimated. Mixing those without provenance makes the dataset less trustworthy.
The actor: https://apify.com/kazkn/commercial-real-estate-brokerage-intel?fpr=8fp2od
Demo: https://youtu.be/TVAcUSzaS7w
I am now working on distribution: SEO, articles, LinkedIn, Reddit carefully, and maybe Product Hunt after a better landing page.
Curious: for niche B2B tools, would you prioritize more integrations first, or more market-specific proof like example datasets for Dallas/Austin/Phoenix?
5 Comments
I’d prioritize market-specific proof before more integrations.
For a CRE broker, another integration is useful later, but an example Dallas or Austin dataset creates trust immediately. It lets them see the exact rows, fields, duplicates, cap rate context, broker info, and provenance before they spend time testing the actor.
The product here is not really “scraping.” It is structured listing intelligence they can drop into a brokerage or underwriting workflow.
So I’d probably test proof assets by market:
Dallas sample dataset
Austin sample dataset
Phoenix sample dataset
Then use each one as the basis for very targeted LinkedIn outreach to brokers and analysts in that market.
Happy to put a tighter version in writing if useful. I’d map the proof assets, first broker segment, LinkedIn outreach angle, and a 7-day distribution test for this actor.
This is a really helpful way to frame it.
I agree that market-specific proof is probably more valuable than adding more integrations right now. A broker does not just want to hear that the actor pulls public listings. They want to see a real Dallas, Austin, or Phoenix output and immediately understand the rows, fields, duplicate signals, cap rate context, broker contacts, and source provenance.
I also like your point that the product is not really “scraping,” but structured listing intelligence that can fit into a brokerage or underwriting workflow.
I’m going to prioritize sample datasets by market first, then use each one for targeted outreach to brokers and analysts in that market.
And yes, I’d definitely be interested in your tighter version: proof assets, first broker segment, LinkedIn outreach angle, and a 7-day distribution test.
Yes, this is exactly the kind of thing I’d make concrete before more outreach.
The risk is that brokers hear “scraper” and mentally file it as a tool. The proof asset has to make it feel like market-ready listing intelligence they can actually use.
Drop your email and I’ll send over a tighter version. I’d keep it focused on the market-specific proof assets, first broker segment, LinkedIn angle, and the 7-day test path.
Appreciate it, that framing is really useful.
You can send it through DM here or use the contact link in my bio.
Just to be transparent, I’m not looking to hire paid consulting at this stage. I’m mainly collecting sharp feedback and executing in-house for now.
But if you’re open to sharing the tighter version as feedback, I’d definitely read it carefully.
Totally fair, Yorick.
If you’re executing in-house for now, I’d keep going with the market-specific proof asset direction and see how brokers react to real samples.
I’ll hold off on the tighter written version since that’s the part I’d normally make into a paid pass.
Good luck with the outreach.