Property Scoring & Comps Automation
Analyzed 10x more deals with same team size
Client / Context
A small real estate investment group focused on single-family rentals and small multifamily properties. The three-person acquisitions team was analyzing 50-100 potential deals per month manually.
The Problem
Each property analysis took 2-3 hours: pulling comparable sales, estimating rehab costs, calculating potential rent, and scoring against investment criteria. The team could only thoroughly analyze a fraction of available opportunities. They were missing good deals simply because they couldn't process them fast enough.
Our Approach
We built an end-to-end automation that takes a property address, pulls all relevant data from multiple sources, generates a comp analysis, estimates ARV (after-repair value) and rent, and produces a scorecard based on the client's investment criteria. The goal was to reduce initial analysis time from hours to minutes.
The Solution
Created an n8n workflow that integrates with property data APIs, MLS data (via a data partner), and county records. The workflow uses GPT-4 to analyze property descriptions and photos for condition assessment. Outputs are pushed to a custom Notion database with all analysis data, supporting documents, and an investment recommendation.
The Property Scoring Model
Properties are scored on a 0-100 scale across five dimensions:
Cash Flow Potential (30 points max)
- Estimated rent vs. acquisition cost
- Operating expense ratio
- Market rent growth trends
Appreciation Potential (20 points max)
- Market price trends
- Neighborhood development indicators
- School district ratings
Risk Factors (20 points max, inverse scoring)
- Property condition indicators
- Days on market
- Price reduction history
- Flood/environmental risks
Execution Complexity (15 points max, inverse scoring)
- Estimated rehab scope
- Permitting requirements
- Tenant situation (if occupied)
Strategic Fit (15 points max)
- Geographic concentration goals
- Property type preferences
- Portfolio balance considerations
AI-Assisted Condition Assessment
One innovative aspect of this solution is the use of GPT-4 Vision to analyze listing photos and estimate property condition. The model provides:
- Overall condition rating (1-10)
- Estimated major repairs needed
- Curb appeal assessment
- Red flags (visible damage, deferred maintenance)
This doesn't replace in-person inspection but provides valuable early screening data.
Results
- Analysis time reduced from 2-3 hours to 8-10 minutes per property
- Deal flow capacity increased from 50-100 to 500+ properties per month
- Consistent scoring methodology across all team members
- Closed 3 additional deals in first quarter (attributed to increased capacity)
- Better documentation for investor reporting
Stack Used
Want Results Like These?
Let's discuss how we can help you achieve similar outcomes.