Client's Goal: Real estate investors and realtors needed competitive intelligence across multiple markets without spending 15-20 hours weekly manually scrolling Zillow and copying data into spreadsheets.

Manual data collection limited coverage to 10-15 properties per hour

Standard scrapers couldn't access the "number of contacts" metric hidden in protected DOM elements

Zillow's changing page structure broke most automated tools

Investors missed opportunities because they couldn't process enough volume to spot patterns

Solution & Approach: I built a self-hosted scraping system on n8n that processes 50+ zip codes simultaneously in 2-3 minutes per execution. The workflow starts with a Postman webhook where users drop target zip codes and trigger a single batch process.

The system uses dual-layer scraping for reliability. Apify handles the initial sweep, pulling listing URLs, property data, and location details. Then Firecrawl acts as a redundancy layer, catching data that slips through when Zillow randomly changes its DOM structure. This eliminates the data gaps that plague single-scraper solutions.

Raw scraped data feeds into GPT-4o for standardization, formatting every datapoint consistently while keeping API costs under $20 monthly. The critical differentiator is a custom Python endpoint hosted on Replit using Selenium with CAPTCHA bypass. This endpoint accesses the "number of contacts" metric locked inside protected strong tags that standard scrapers can't reach. That number shows how many people contacted the property manager, revealing competition levels and interest patterns competitors never see.

Everything appends to a master Google Sheet with property type, full address variants, price, beds, baths, square footage, contacts, days on Zillow, broker details, and listing URLs. The system runs completely in the background with zero manual intervention.

Key Features: • Processes 50+ zip codes simultaneously in one batch execution • Dual-layer scraping (Apify + Firecrawl) eliminates data gaps from Zillow's changing structure • Custom Selenium endpoint extracts hidden "contacts" metric inaccessible to standard scrapers • GPT-4o standardizes all data for consistent formatting across thousands of listings • Self-hosted on n8n with zero platform fees, total monthly cost around $150

Results: Reduced data collection from 15-20 hours weekly to 2-3 minutes per execution. At $50/hour opportunity cost, that's $2,850 in monthly reclaimed time. Investors now process entire regional markets while handling normal work, accessing hidden contact volume data that reveals motivated sellers and overlooked opportunities. The system outputs clean, structured data ready for automated outreach workflows, CRM sync, and market analysis.

Ready to process entire markets while your competitors scroll one listing at a time? Let's build a scraping system that extracts the intelligence they're missing.

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