TM Investment Group
AI-Native Deal Sourcing
US real estate investment group sourcing motivated sellers, undervalued listings, and off-market-style opportunities from noisy public signals.
deal flow
ROI
qualified opportunities
less manual research
From manual market watching to always-on opportunity detection.
AI-native deal sourcing
+185%
deal flow
3.8x
ROI
327
qualified opportunities
76%
less research time
TM Investment Group needed to find motivated sellers and undervalued properties before the rest of the market reacted.
Manual research meant scanning thousands of posts, listings, and data points every day. The team could miss signals simply because the market produced more information than humans could review.
REPCONN built an AI-native sourcing operation that mines opportunities, scores investment fit, detects motivated-seller patterns, and alerts the team in real time.
Thousands
Posts and listings watched without manual scanning
327
Qualified opportunities surfaced in 3 months
<33min
Average opportunity alert delivery time
23%
Lower acquisition prices through earlier signals
Information advantage was buried inside market noise.
The market moved faster than manual research
Staff had to monitor thousands of posts, listings, and property signals daily. Valuable opportunities could disappear before the team found them.
Motivated-seller signals were buried
Price drops, stale listings, agent patterns, seller urgency, and undervaluation signals were spread across noisy public data sources.
Speed-to-contact mattered
In real estate investing, being first to identify and contact a motivated seller can shape the economics of the entire deal.
Manual filtering limited deal volume
The team needed more qualified opportunities without adding headcount to repetitive research, data extraction, and early screening.
Our Solution
One sourcing layer across listings, signals, scoring, and alerts.
REPCONN connected listing monitoring, contact extraction, motivated-seller pattern recognition, investment criteria matching, and real-time alerting into one managed deal sourcing workflow.
System 01
Multi-Source Opportunity Mining
REPCONN built a sourcing operation that monitors listing sites, social signals, and investment-platform data to identify opportunities before they become obvious.
What changed
Listing and property signal monitoring
Social and platform opportunity mining
Geographic targeting and filtering
Structured organization for review
Deal sourcing engine
Market feeds
The system watches listing sites, social signals, and investment platforms for new potential opportunities.
System 02
Redfin AI Intelligence
The system watches listings, extracts structured property fields, and evaluates undervaluation, days-on-market, pricing changes, and motivated-seller patterns.
What changed
Automated listing monitoring
Undervaluation detection
Days-on-market and price signal extraction
Investment-criteria matching
System 03
Opportunity Scoring and Alerts
Qualified opportunities are scored and routed to the investment team with context, so the team reviews actionable leads instead of raw market noise.
What changed
Explainable scoring factors
Real-time opportunity alerts
Human review before outreach
Feedback loops for scoring improvement
System 04
Secure Data Pipelines
REPCONN structured the sourcing workflow around secure data pipelines, controlled access, public-data handling discipline, and US data-handling standards.
What changed
Encrypted lead and property records
Controlled access for investment users
Rate-limited collection workflows
Audit trails for AI-generated recommendations
Redfin and market-signal monitoring behind the alert layer.
The sourcing workflow monitors listings, extracts individual property URLs, parses investment fields, applies AI scoring, and records structured opportunities for review.
8.7%
Actionable opportunity rate from screened signals
<33min
Average opportunity alert delivery time
65%
Faster response time to opportunities
4-5
Successful transactions per 200 prospects
Sourcing map
Listings
Redfin feed
Listing pages are monitored and queued for structured extraction.
More deal flow, faster response, and better acquisition economics.
Deal flow
The AI-native sourcing operation increased the deal evaluation pipeline by surfacing more actionable opportunities.
ROI
The system produced a 3.8x return by shifting repetitive market research into an always-on sourcing workflow.
Qualified opportunities
327 qualified investment opportunities were identified in the first three months.
Less manual research time
The team spent far less time manually checking posts and listings, freeing time for evaluation and negotiation.
Lower acquisition prices
AI-identified opportunities helped the team reach sellers earlier, improving acquisition economics.
Actionable opportunity rate
Social and listing monitoring produced an 8.7% actionable opportunity rate from screened signals.
Secure pipelines for public-market deal intelligence.
The system was built with encrypted lead and property records, secure API connections, controlled access for investment users, rate-limited collection, and US data-handling standards.
Encrypt
Contact and property details are stored through secured pipelines.
Control
Investment users access only the sourcing data they need.
Respect
Collection focuses on public information with rate-limited workflows.
Audit
Opportunity scoring, alerts, and review decisions are logged.
Special thanks to Marc Del Priore and Mitchel Kida from TM Investment Group
“Thanks to REPCONN, our workflow has become so much more efficient. Their AI tools take care of data collection, saving us hours each day, and the quality of leads we get is consistently high.”
Deal sourcing stopped being manual research. The system watches the market continuously and surfaces opportunities on its own.
Confidentiality Notice
Specific data sources, lead records, scoring rules, investment criteria, and internal workflow details are not published. Metrics represent rounded public-facing outcomes from the engagement.
Get Started
Want your team to find better deals faster?
We assess your current sourcing workflow, identify where AI-native systems will have the highest impact, and build the operating layer around your market, criteria, and data controls.