AI Land & New-Home Community Scout
900+ communities mapped, 2,000+ land deals ranked by AI
A real-estate firm tracked new-home communities by hand and judged every land listing one by one. I built two AI scrapers on n8n and Airtable: one reads home-builder websites on a schedule and keeps a map of new communities up to date, the other pulls new land listings, estimates how many lots each could hold and ranks it, so the team knows which ones are worth a call.
By Jin Longyu·Published
The context
Communities by hand
Hundreds of new-home communities checked manually on builder websites.
Listings one by one
Every land listing read and judged by hand.
Noisy data
Duplicates and listings that didn't fit slipped into the searches.
How I built it
Builder scraper
n8n and Firecrawl read builder websites on a schedule, place each community on a map and flag likely duplicates for review.
Deal scraper
New land listings are classified and ranked High, Medium, Low or Can't tell; the rules live in Airtable, so the team can retune them without a developer.
Lot estimator
An estimate of how many lots fit on the usable part of each site.
Guardrails in code
Hard limits are checked in code, not left to the model, and the filters were tested on sample listings.
Map and run reports
An Airtable map and a summary email after every run.
Airtable
n8n
Firecrawl
