I built a $5/1k-listing CRE data API because CoStar is overkill for first-pass scans
Hey IH,
I’m building in a niche I didn’t expect to get this obsessed with: commercial real estate data.
The pattern I kept seeing was simple:
Commercial real estate brokers often start with the same two tabs open: LoopNet and Crexi.
If they need a full enterprise-grade data platform, there’s CoStar. But for a quick first-pass market scan, that can feel like using a Bloomberg Terminal just to check one ticker.
The expensive part is not only the subscription.
It is also the manual cleanup after the search:
the same listing appears on multiple portals
cap rates are formatted differently or missing
days-on-market is not always easy to compare
broker contacts are scattered
the output still has to be rebuilt into a spreadsheet
So I built an Apify actor for it.
It takes a CRE market search and turns public LoopNet + Crexi listings into one cleaner dataset:
deduped listings
source provenance
normalized cap-rate / days-on-market context
broker contacts when visible
also_listed_on signals
CSV / Excel / JSON / API export
The pricing angle is the wedge:
roughly $5 / 1,000 listings.
The goal is not to replace CoStar.
The goal is to give brokers, investors, and analysts a much cheaper first-pass file before they spend time or money on deeper research.
For devs here, the interesting part is the product shape.
This is not a generic scraper with a pretty README. It is a vertical workflow product:
Input: market + filters
Process: collect, normalize, dedupe, enrich
Output: a dataset that can go straight into Excel, Sheets, a CRM, or an API workflow
I also chose Apify instead of building a full SaaS dashboard first because it gives me hosting, runs, datasets, billing, API access, and marketplace discovery out of the box.
That let me test the workflow before building a whole app around it.
The honest limitations:
it only uses public listing data
broker contacts appear only when available
cap rates / NOI need to be clear when they are estimated vs declared
dedupe is useful, but never magic
it is a first-pass market scan, not a full proprietary CRE intelligence platform
What I’m trying to validate now:
Would CRE people rather see:
a Dallas sample dataset
an Austin / Phoenix sample dataset
a daily monitoring workflow
a Google Sheets / CRM export tutorial
a pure API workflow for analysts
And for the devs / data founders here:
Would you keep this as a marketplace actor, or use the actor as the backend and build a dedicated SaaS UI on top?
Actor is here if anyone wants to see the current version:
https://apify.com/kazkn/commercial-real-estate-brokerage-intel?fpr=8fp2od
Happy to share a sample output if useful.
Mostly looking for honest feedback on the positioning:
Is “low-cost first-pass CRE market scan” clear enough, or should I frame it more directly as a CoStar-light workflow for brokers?
Built almost exactly this for federal legislation -- same wedge: the Federal Register is 300 pages/day (the CoStar equivalent for regulatory data), trade associations charge $3k+/year for filtered digests, and we are building the cheap-first-pass version for small business owners who need alerts when relevant bills move.
On your SaaS vs actor question: we validated with just a landing page and a Google Form before writing code. The Apify-first approach you took is more honest because it processes real data -- landing pages with zero traffic tell you nothing about whether the workflow is useful.
On framing: "CoStar-light workflow for brokers" is cleaner than "first-pass market scan". The specificity of the job does more work than the feature name. Same lesson we learned going from "federal bill tracker" to "alerts for small businesses when relevant bills move through committee".
One extra note: I’m intentionally avoiding the “generic scraper” positioning.
The people I’m trying to reach don’t wake up wanting a scraper.
They want a cleaner market file, faster broker research, and a cheaper way to monitor public listings before committing to deeper paid tools.
That distinction seems small, but I think it changes the whole product.
I agree with that distinction.
The part I'd be careful with is that "cleaner market file," "faster broker research," and "cheaper monitoring" are still different jobs.
They all make sense, but they can pull the product toward different buyers and different moments of use.
That's why I think the workflow question matters more than the scraper question.
The product already sounds useful. The harder decision is which use case should carry the whole thing first.
That’s a really good point.
I think the first use case should probably be the “cleaner market file” workflow.
The monitoring angle is useful, but it feels like a second step once someone trusts the output. And “faster broker research” is the broader benefit, not necessarily the first concrete job.
So the clearest starting point might be:
“I need a clean first-pass market file from LoopNet + Crexi for a specific market, without paying enterprise-platform prices.”
Then the proof asset becomes simple: Dallas / Austin / Phoenix sample datasets with the exact fields, duplicates, source provenance, cap-rate / DOM context, and broker contacts.
That feels easier to evaluate than a generic scraper or a vague productivity promise.
Appreciate the push. It helps narrow the positioning a lot.
Yes, that’s the cleaner starting point.
Send me your email and I’ll write the tighter positioning path properly instead of stretching this thread.