Property Analytics Firm Builds Zillow and Realtor.com Data Pipeline for 2.4M Listings

About the Client

Client Overview

A New York-based property analytics startup building an AI-powered home valuation model, requiring daily structured data from major US real estate platforms to train and continuously improve their machine learning algorithms.

Industry: Real Estate / PropTech | Region: United States | Duration: 4 Weeks Build

The Challenge

What Problems Were They Facing?

• The AI valuation model required daily structured data from Zillow, Realtor.com, and Redfin covering property details, pricing history, neighborhood data, and market trends across all US states.
• Internal scraping attempts were blocked within days due to anti-bot protections, rate limiting, and constantly changing page structures on major real estate platforms.
• Data quality from alternative providers was inconsistent — missing fields, stale listings, and 15-20% error rates that degraded the AI model's prediction accuracy.
• The engineering team was spending 60% of their time maintaining scrapers instead of building the core AI product, delaying their Series A timeline.

Our Approach

How Actowiz Solved It

• Deployed Actowiz enterprise data pipeline extracting property listings, valuations, pricing history, and neighborhood data from Zillow, Realtor.com, and Redfin.
• Built a custom data schema matching the client's AI model requirements with 45+ structured fields per listing including coordinates, school ratings, and tax history.
• Implemented daily refresh cycles with automated data validation, deduplication, and quality scoring to ensure 99%+ data accuracy for ML training.
• Delivered data via cloud storage (AWS S3) and REST API in JSON format, integrated directly into the client's ML pipeline with zero manual intervention.
• Created a monitoring dashboard tracking coverage, freshness, accuracy, and delivery status across all data sources in real time.

The Results

Measurable Impact

2.4M

LISTINGS TRACKED

Daily monitoring of 2.4 million property listings across US markets

Daily

REFRESH RATE

Full dataset refresh every 24 hours with automated quality validation

4wk

PIPELINE BUILT

From scoping to production-ready data delivery in 4 weeks

99.2%

DATA ACCURACY

Verified accuracy rate across all extracted fields and platforms

60→5%

ENG TIME ON DATA

Engineering time spent on data reduced from 60% to 5% of total capacity

Client Feedback

Actowiz gave us back our engineering team. We went from spending most of our time fighting scrapers to spending it on our actual product. The data quality is better than anything we built internally.

CTO, Property Analytics Startup, New York

Key Takeaways

What Made This Work

• Outsourcing data extraction freed 55% of engineering capacity to focus on the core AI product rather than scraper maintenance.
• 99.2% data accuracy significantly improved the AI valuation model's prediction performance compared to prior data sources.
• 4-week deployment timeline enabled the startup to meet their Series A data milestones ahead of schedule.
• Automated daily delivery via S3 and API eliminated all manual data handling from the ML training pipeline.