Fort Mason: A Rental Market in One Sheet, Collected by n8n by Andrés Sepulveda MoralesFort Mason: A Rental Market in One Sheet, Collected by n8n by Andrés Sepulveda Morales

Fort Mason: A Rental Market in One Sheet, Collected by n8n

Andrés Sepulveda Morales

Andrés Sepulveda Morales

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Following a rental market by hand means opening listing after listing and copying the same details into a spreadsheet, again every time the market moves. Fort Mason wanted that work done by a workflow instead: for each ZIP code, the rentals on the market and the details of each one, collected as repeated runs, because how a market changes over time says more than one snapshot. I built it in n8n in a one-week sprint.
A few blocks of Zillow results: a lot to collect by hand, and it changes every day.
A few blocks of Zillow results: a lot to collect by hand, and it changes every day.
Still copying data from one site into a spreadsheet? I build automations that collect it, clean it and put it where your team already works. Book a call at cal.com/andres-sepulveda-morales or email andres@redmage.cc.

A whole market, one comparable row per unit

The workflow takes a ZIP code, collects the rental listings for it and writes them to Google Sheets. What comes back from the source is nested JSON, shaped one way for a single rental and another for a building with several units. Code nodes in n8n handle both, splitting a building into its units and pulling the same fields from each, so every row means the same thing and a market can be sorted, filtered and compared at a glance. I tested it on two ZIP codes, one in Ohio and one in Fort Collins, Colorado.
The workflow for one ZIP code in n8n.
The workflow for one ZIP code in n8n.
Sample output for a ZIP code in Ohio, one row per listing.
Sample output for a ZIP code in Ohio, one row per listing.

A source that actually answers

Zillow turns most automated requests away; the general-purpose scraping services I tried came back with 403 errors. I tested three, Scrapeless, Scrape.do and Apify, and Apify's Zillow detail scraper returned the most complete listing data, so I built the workflow on it. It runs on the client's own Apify key, so the account, the usage and the data stay theirs.
Parsing the specifics out of the raw listing data.
Parsing the specifics out of the raw listing data.

Knowing which numbers exist before you plan around them

Testing three sources also mapped what public listing data can and cannot supply. Some numbers a team might want, such as how many people have contacted a listing, are not in any public feed I could reach, and Apify has since confirmed its scraper cannot extract them. Zillow's terms of use also restrict automated collection. For a pipeline a business depends on, check field by field what a source exposes before building, and use an official feed or licensed data where one exists.
Want the data you check every week to show up on its own? Tell me where it lives and where you need it. I'll map what can be automated and build it. Book a call at cal.com/andres-sepulveda-morales or email andres@redmage.cc.
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Posted Oct 12, 2026

An n8n workflow that collects a ZIP code's rental listings through Apify into Google Sheets, one comparable row per unit.

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Timeline

Nov 14, 2025 - Nov 14, 2025

Clients

Fort Mason