Deal Sourcing | Real Estate Automation

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Commercial Real Estate

Automated CRE Deal Sourcing & Lead Pipeline

Public listing aggregation for a multi-market CRE acquisitions team across Zillow, LoopNet, MLS, and more.

Real Estate automation case study featuring data entry and integration.

Hours Saved/Day per Analyst

+42%

More Opportunities Reviewed

Missed Listings in Target Zips

Overview

A commercial real estate acquisitions team operating across five U.S. states faced the daily challenge of sourcing deals from a fragmented landscape of listing platforms. Their analysts manually searched Zillow, LoopNet, Realtor.com, Redfin, and multiple regional MLS feeds every morning, copying listing data into spreadsheets, cross-referencing for duplicates, and attempting to identify promising opportunities before competitors. The process consumed the majority of each analyst's day, leaving little time for the actual analysis work that drives investment decisions. Properties frequently appeared on multiple platforms with inconsistent data, creating confusion and wasted effort.

Business Context

In commercial real estate acquisitions, speed matters enormously. Properties in target markets often receive multiple offers within days of listing. The team was losing deals not because of pricing or terms, but simply because they discovered opportunities too late. Their best analysts were spending seven hours daily on data entry rather than analysis, creating a morale problem and limiting the team's capacity to scale. The leadership recognized that their competitive advantage should be analytical insight, not data collection speed, but the current process made that impossible. They engaged ZapWizards to build an automated deal sourcing system that would eliminate the data collection bottleneck entirely.

How We Built It

We built a comprehensive scraping and data aggregation system that runs continuously across all target platforms. Custom Python scrapers handle the technical complexity of extracting data from sites that don't offer APIs — including rotating proxy infrastructure to avoid rate limiting and sophisticated anti-blocking logic that adapts to platform changes. The system captures complete listing details including price, rent comps, property images, location data, and listing history. A critical normalization layer identifies duplicate listings across platforms using fuzzy matching on address, price, and property characteristics, ensuring analysts see each property only once. Clean data flows directly into the team's CRM with automatic deal tagging, stage assignment, and routing to the appropriate analyst based on property type and geography. A custom Retool dashboard provides pipeline visibility with saved searches, comp analysis, and filter capabilities. When a new property matches saved criteria, the system automatically generates sales material including a PDF summary and comp analysis that analysts can immediately share internally.

Challenges

No API access for most real estate listing sites

Duplication of listings across platforms

Manual searching, copying, and comparing comps

No integration with HubSpot / Monday

Slow creation of sales material for internal discussions

What We Delivered

Custom scraping system for Zillow, LoopNet, Realtor, MLS (rotating proxies, anti-blocking logic)

Automated ingestion of listing details: price, rent comps, images, location

Normalization & deduplication across platforms

Direct push into CRM with deal tags, stage assignment, and analyst routing

Retool dashboard for pipeline visibility, comps, filters, and saved searches

Automatic sales material generation (PDF + summary)

Tech Stack

Python scrapers, Bright Data, Make.com, HubSpot API, Retool, Google Cloud, OpenAI

Results

Hours Saved/Day per Analyst

+42%

More Opportunities Reviewed

Missed Listings in Target Zips

Strategic Impact

The transformation freed up seven hours per analyst per day — time that now goes to actual deal analysis and relationship building with brokers who bring off-market opportunities. The team reviews 42% more opportunities than before because automated sourcing catches every listing in their target zip codes within hours of posting. Zero missed listings means they're now seeing deals their competitors don't find until days later, giving them a meaningful first-mover advantage in competitive markets. The consistency of normalized data has eliminated the comparison problems that previously plagued analysis — analysts can now trust that they're comparing apples to apples across platforms. Perhaps most importantly, the team can scale their acquisitions capacity without proportionally adding headcount, fundamentally changing the economics of their business. The system has also improved deal velocity, as automatic sales material generation means promising properties can be circulated internally within minutes of discovery.