Automated real estate lead sourcing

What it solves

Checking Idealista and Fotocasa daily for interesting listings or price drops is repetitive work that rarely gets done with the consistency it needs. This workflow automates that tracking: an always-updated opportunity database, without depending on expensive commercial tools.

How it’s built

The system runs on its own, on scheduled cycles:

1. Every two weeks it starts by itself and defines which area and portals to scan.
2. It downloads the results pages from Idealista and Fotocasa through a proxy service that avoids blocks.
3. It extracts the essentials from each listing: price, square meters, rooms, phone number, and advertiser type.
4. It normalizes listings from both portals into a common format.
5. It compares each listing against the historical record to tell whether it’s the same property seen before — even if published on the other portal — or a new one, and detects price drops.
6. It stores everything in a centralized database: one record per property and a price-drop history.

Why it’s interesting

The delicate part isn’t the scraping — it’s deduplication: recognizing that the same flat listed on two portals, with different photos and different copy, is the same property. That cross-referencing is what turns a pile of HTML into a living database of sourcing opportunities, fed by itself, with no scraping license fees and no premium CRM. The same result as a closed platform, built with open-source and low-cost tools.

The workflow, in the open

The full workflow is published on GitHub: you can download it, import it into your own n8n and see exactly how it’s built, node by node → scraping-inmuebles.json