TL;DR
- Manual 3-criteria stack: 1.2% conversion, $2,800 CPD
- AI-scored lists (general model): 2.1% conversion, $1,400 CPD
- AI-scored lists (personalized model): 2.8% conversion, $890 CPD
- Hidden Gems (AI-identified non-obvious patterns): 2.9% conversion, 35-45% of deals
Rule-Based vs Pattern-Based Targeting
The Manual Stacking Approach
You define criteria: absentee owner + tax delinquent 2+ years + equity 60%+. The system returns matching properties.
Strengths: - Understandable logic - Control over criteria - Works at small scale
Limitations: - Same 3-5 variables as every competitor - Can't weight relative importance - Misses non-obvious patterns - Static - doesn't learn
The Predictive Approach
The system analyzes thousands of closed investor deals, identifies which variables (and combinations) actually correlate with sales, weights them by importance, and scores all properties.
Example of what it finds:
| Pattern | Manual Stacking | Predictive Model |
|---|---|---|
| "Absentee owner" | Equal weight | 2.3% weight (weak signal alone) |
| "Tax delinquent 3+ years" | Equal weight | 8.7% weight (strong signal) |
| "7-12 year ownership tenure" | Not included | 6.2% weight (counter-intuitive peak) |
| "Mortgage refinanced in last 3 years" | Not included | 4.8% weight (often means life change) |
Manual stacking treats all criteria equally. Predictive models weight them by actual predictive power.
How Predictive Models Work
Training Phase
1. Historical Deal Data Collect 10,000+ successful investor acquisitions in the target market.
Client Results
“BuyBox IQ showed me that my best deals came from a specific ownership tenure window (7-12 years) that I was actively excluding. I was filtering for 15+ year owners assuming they'd be more motivated. The data said otherwise - 7-12 year owners with recent mortgage activity converted at 3.2% vs 1.8% for my old criteria.”
— Tampa investor, 84 deals/year
2. Feature Engineering Transform raw data into 50-100+ analyzable signals: - Ownership tenure (not just yes/no - specific ranges) - Tax payment patterns (not just delinquent - trajectory) - Life event proximity signals - Behavioral clusters
3. Pattern Discovery Machine learning identifies which features (and combinations) correlate with sales: - Some obvious: pre-foreclosure = high probability - Some counter-intuitive: 7-12 year tenure peaks higher than 15+ year
4. Weight Calibration Assign predictive weights based on correlation strength and uniqueness.
Scoring Phase
1. Apply Model to Current Database Score every property in target market against trained model.
2. Generate Probability Scores Each property gets a 0-100 score representing sell likelihood.
3. Prioritize by Score Top 10-20% scores become your marketing list.
What the Model Actually Weighs
Owner Signals (35% of total weight)
| Signal | Weight | Why It Matters |
|---|---|---|
| Ownership tenure (specific ranges) | 6.2% | 7-12 years peaks in most markets |
| Owner age/life stage estimates | 4.8% | Life transitions correlate with sales |
| Out-of-state distance (miles) | 3.9% | 100+ miles converts 2.3x better |
| Portfolio size | 2.1% | Single-property owners more motivated |
| Address change recency | 2.8% | Recent move = property burden |
Financial Signals (30% of total weight)
| Signal | Weight | Why It Matters |
|---|---|---|
| Tax delinquency (duration) | 8.7% | 3+ years = strong motivation |
| Mortgage behavior patterns | 4.8% | Recent refi often precedes life change |
| Lien sequence timing | 3.2% | Multiple liens in sequence = distress |
| Equity trajectory | 2.4% | Rising equity + distress = deal math works |
Property Signals (20% of total weight)
| Signal | Weight | Why It Matters |
|---|---|---|
| Vacancy indicators | 5.1% | Utility patterns, mail forwarding |
| Maintenance proxies | 3.8% | Permit gaps, code history |
| Assessment anomalies | 2.2% | Mismatch = unaddressed issues |
Behavioral Signals (15% of total weight)
| Signal | Weight | Why It Matters |
|---|---|---|
| Prior listing history | 4.2% | Expired 60+ days = open to alternatives |
| Inquiry activity | 2.8% | Responded to prior marketing |
| Price reduction patterns | 2.1% | Multiple reductions = motivation |
Hidden Gems: The Non-Obvious Advantage
Hidden Gems are properties that score high on predictive probability but don't match traditional distress criteria.
Example Hidden Gem Patterns
Pattern 1: Life Stage Transition - Owner age estimate: 62-68 - Ownership tenure: 18-25 years - No distress signals - Recent mail forwarding to adult child's address - *Sell probability: 3.1% (vs 0.4% for age cohort baseline)*
Pattern 2: Accidental Landlord Exit - Owner-occupied converted to rental 2-4 years ago - Out of state (moved for job) - Single rental property in portfolio - PM company changed in last year - *Sell probability: 2.7%*
Pattern 3: Pre-Distress Behavioral Cluster - Utility reduction (but not shutoff) - Insurance carrier change (often to cheaper) - Property tax payment pattern change (late but paying) - *Sell probability: 2.4%*
Hidden Gems Performance
| Metric | Traditional Distress | Hidden Gems |
|---|---|---|
| Conversion rate | 3.2% | 2.9% |
| Mail competition | High (15+ investors) | Low (2-3 investors) |
| CPD | $1,800 | $980 |
| % of total deals | 55-65% | 35-45% |
Hidden Gems often have LOWER CPD than traditional distress because of reduced competition.
Generic vs Personalized Models
Generic Predictive Model
Trained on general market data. Same model for all users.
Performance: 2.1% conversion, $1,400 CPD
Limitation: Doesn't account for YOUR specific deal patterns.
Personalized Model (BuyBox IQ)
Trained on YOUR closed deals + market data. Learns what YOU actually close.
Performance: 2.8% conversion, $890 CPD
Advantage: Finds patterns specific to your operation: - Your geographic sweet spots - Your price range patterns - Your seller profile preferences (often subconscious)
The Three-Score System
BuyBox IQ delivers three scores per property:
| Score | Definition | Use Case |
|---|---|---|
| Likely Deal Score | Probability any investor closes | Market heat indicator |
| Buy Box Score | Match to your stated criteria | Confirms obvious fits |
| 8020REI Score | Probability YOU close this deal | Primary targeting score |
The 8020REI Score combines market probability with your personal pattern match.