AI Rehab Estimation¶
Photo-based cost prediction for renovation budgets
What Is AI Rehab Estimation?¶
AI Rehab Estimation uses computer vision to analyze property photos and generate rehab cost estimates without requiring a contractor walkthrough.
graph TB
subgraph Input["Input"]
P1[Kitchen Photo]
P2[Bathroom Photo]
P3[Exterior Photo]
P4[Property Details]
end
subgraph Vision["AI Vision Analysis"]
V[Vision AI
GPT-4V or Claude Vision] D[Condition Detection] S[Scope Identification] end subgraph Cost["Cost Engine"] L[Local Pricing Data] C[Cost Calculator] R[Risk Adjustment] end subgraph Output["Output"] O[Estimated Rehab 38500
Range 32K-45K
Confidence 75%] end P1 & P2 & P3 & P4 --> V V --> D --> S S --> L --> C --> R --> O style V fill:#3b82f6,color:#fff style D fill:#3b82f6,color:#fff style S fill:#3b82f6,color:#fff style L fill:#6366f1,color:#fff style C fill:#6366f1,color:#fff style R fill:#6366f1,color:#fff style O fill:#22c55e,color:#fff
GPT-4V or Claude Vision] D[Condition Detection] S[Scope Identification] end subgraph Cost["Cost Engine"] L[Local Pricing Data] C[Cost Calculator] R[Risk Adjustment] end subgraph Output["Output"] O[Estimated Rehab 38500
Range 32K-45K
Confidence 75%] end P1 & P2 & P3 & P4 --> V V --> D --> S S --> L --> C --> R --> O style V fill:#3b82f6,color:#fff style D fill:#3b82f6,color:#fff style S fill:#3b82f6,color:#fff style L fill:#6366f1,color:#fff style C fill:#6366f1,color:#fff style R fill:#6366f1,color:#fff style O fill:#22c55e,color:#fff
How It Works¶
Step 1: Photo Upload¶
User uploads property photos (minimum 5, recommended 15+):
| Photo Type | Required | Purpose |
|---|---|---|
| Kitchen (multiple angles) | Yes | Cabinets, counters, appliances |
| Bathrooms (each) | Yes | Fixtures, tile, vanity |
| Living areas | Yes | Flooring, walls, ceilings |
| Bedrooms | Recommended | Flooring, closets, windows |
| Exterior (front, back, sides) | Yes | Roof, siding, foundation |
| Mechanicals (HVAC, water heater) | Recommended | System age/condition |
| Basement/attic | If applicable | Foundation, insulation |
Step 2: AI Vision Analysis¶
The AI analyzes each photo for condition indicators:
graph LR
subgraph Kitchen["Kitchen Analysis"]
K1[Cabinets] --> K1R[Dated oak
Replace 8-12K] K2[Counters] --> K2R[Laminate
Replace 2-4K] K3[Appliances] --> K3R[Working
Keep or 2-3K] K4[Flooring] --> K4R[Vinyl
Replace 1.5-2.5K] end style K1 fill:#3b82f6,color:#fff style K2 fill:#3b82f6,color:#fff style K3 fill:#3b82f6,color:#fff style K4 fill:#3b82f6,color:#fff style K1R fill:#eab308,color:#000 style K2R fill:#eab308,color:#000 style K3R fill:#22c55e,color:#fff style K4R fill:#eab308,color:#000
Replace 8-12K] K2[Counters] --> K2R[Laminate
Replace 2-4K] K3[Appliances] --> K3R[Working
Keep or 2-3K] K4[Flooring] --> K4R[Vinyl
Replace 1.5-2.5K] end style K1 fill:#3b82f6,color:#fff style K2 fill:#3b82f6,color:#fff style K3 fill:#3b82f6,color:#fff style K4 fill:#3b82f6,color:#fff style K1R fill:#eab308,color:#000 style K2R fill:#eab308,color:#000 style K3R fill:#22c55e,color:#fff style K4R fill:#eab308,color:#000
Step 3: Condition Scoring¶
Each room/component gets a condition score:
| Score | Condition | Typical Action |
|---|---|---|
| 90-100 | Excellent | Keep as-is |
| 70-89 | Good | Minor updates |
| 50-69 | Fair | Moderate rehab |
| 30-49 | Poor | Major rehab |
| 0-29 | Failed | Full replacement |
Step 4: Cost Calculation¶
AI applies local pricing data to identified scope:
📸 Kitchen Assessment
────────────────────────
Cabinets: REPLACE $9,500 (±$2,000)
Counters: REPLACE $3,200 (±$800)
Appliances: KEEP $0
Flooring: REPLACE $2,100 (±$500)
Lighting: UPDATE $450 (±$100)
────────────────────────
Kitchen Total: $15,250 (Range: $12,250 - $18,250)
📸 Bathroom 1 Assessment
────────────────────────
Vanity: REPLACE $1,200 (±$300)
Toilet: KEEP $0
Tub/Shower: REGLAZE $350 (±$50)
Tile: KEEP $0
Flooring: REPLACE $800 (±$200)
────────────────────────
Bathroom 1 Total: $2,350 (Range: $1,850 - $2,850)
... (continues for all areas)
Example Analysis¶
Input: Distressed Property Photos¶
Property: 3bd/1.5ba, 1,400 sqft, built 1955
AI Analysis Output¶
pie title Rehab Budget Breakdown
"Kitchen" : 15250
"Bathrooms" : 5200
"Flooring" : 4800
"Paint & Drywall" : 3500
"Windows" : 2800
"Exterior" : 4000
"HVAC" : 0
"Electrical" : 1500
"Contingency" : 5550
OVERALL ESTIMATE: $42,600¶
| Category | Condition | Action | Estimate | Range |
|---|---|---|---|---|
| Kitchen | Poor | Full remodel | $15,250 | $12-18K |
| Bathroom 1 | Fair | Update fixtures | $2,350 | $2-3K |
| Bathroom 2 | Poor | Full remodel | $2,850 | $2-4K |
| Flooring | Poor | Replace all | $4,800 | $4-6K |
| Paint/Drywall | Fair | Full repaint | $3,500 | $3-4K |
| Windows | Fair | Replace 4 of 8 | $2,800 | $2-4K |
| Exterior | Fair | Paint + repairs | $4,000 | $3-5K |
| HVAC | Good | Keep existing | $0 | $0 |
| Electrical | Fair | Panel upgrade | $1,500 | $1-2K |
| Contingency (15%) | - | - | $5,550 | - |
| TOTAL | - | - | $42,600 | $36-50K |
Confidence Level: 72%
⚠️ AI Limitations Detected: - Roof condition unclear from photos (add inspection) - Foundation not visible (recommend inspection) - HVAC age not confirmed (verify before closing)
AI Vision Technology¶
How Vision AI Works¶
sequenceDiagram
participant U as User
participant API as Tuff Flips
participant V as Vision AI
participant P as Pricing Engine
participant DB as Local Costs DB
U->>API: Upload 15 photos
API->>V: Analyze images
V->>V: Detect: cabinets, counters, flooring...
V->>V: Score condition: 0-100
V-->>API: Identified scope + conditions
API->>P: Request pricing
P->>DB: Lookup local costs
DB-->>P: Cleveland, OH pricing
P-->>API: Itemized estimate
API-->>U: Full rehab report
Vision AI Capabilities¶
| Detection Type | Examples | Accuracy |
|---|---|---|
| Material ID | Wood vs laminate, tile vs vinyl | 85% |
| Condition | Dated, worn, damaged, modern | 80% |
| Style | Builder-grade, mid-range, high-end | 75% |
| Age | 1960s vs 1990s vs 2020s | 70% |
| Issues | Water damage, cracks, stains | 75% |
Limitations¶
| Limitation | Mitigation |
|---|---|
| Can't see behind walls | Flag for inspection |
| Roof condition from ground only | Recommend drone/inspection |
| HVAC age from label only | Ask user to input |
| Foundation often not visible | Recommend inspection |
| Photo quality affects accuracy | Require minimum resolution |
Pricing Database¶
Local Cost Data¶
AI pulls from our pricing database (500+ line items):
| Category | Item | Unit | Min | Typical | Max |
|---|---|---|---|---|---|
| Kitchen | Cabinets (10 LF) | LF | $150 | $225 | $400 |
| Kitchen | Countertops (granite) | SF | $45 | $65 | $100 |
| Kitchen | Appliance package | Set | $1,500 | $2,500 | $5,000 |
| Bathroom | Vanity + top | Each | $300 | $600 | $1,200 |
| Bathroom | Toilet | Each | $150 | $250 | $500 |
| Flooring | LVP | SF | $3 | $5 | $8 |
| Flooring | Hardwood refinish | SF | $3 | $4.50 | $6 |
| Paint | Interior (per room) | Room | $200 | $350 | $600 |
| HVAC | Furnace + AC | System | $4,000 | $6,500 | $10,000 |
| Roof | Shingle (per sq) | Sq | $300 | $450 | $700 |
Data Sources: - Cameron's SOW pricing template - HomeAdvisor/Thumbtack contractor data - Local contractor quotes (Cleveland focus) - User-submitted actual costs
Accuracy Validation¶
How Accurate Is AI Rehab Estimation?¶
| Metric | Target | Current Status |
|---|---|---|
| Within ±20% of actual | 80% of estimates | Planned |
| Within ±30% of actual | 95% of estimates | Planned |
| False confidence | <5% | Planned |
Improving Accuracy¶
graph TB
subgraph Feedback["Feedback Loop"]
E[AI Estimate]
A[Actual Costs
user submits] C[Compare] T[Train Model] end E --> A A --> C C --> T T --> E style E fill:#3b82f6,color:#fff style A fill:#f97316,color:#fff style C fill:#6366f1,color:#fff style T fill:#22c55e,color:#fff
user submits] C[Compare] T[Train Model] end E --> A A --> C C --> T T --> E style E fill:#3b82f6,color:#fff style A fill:#f97316,color:#fff style C fill:#6366f1,color:#fff style T fill:#22c55e,color:#fff
Every completed deal improves the model:
- User enters actual rehab costs
- System compares to original estimate
- Model adjusts for systematic errors
- Cleveland-specific accuracy improves over time
Who Offers This Today?¶
Competitor Landscape¶
| Company | Photo-Based Estimates | Accuracy Claim |
|---|---|---|
| Handoff.ai | Yes | ~±25% |
| Flipster | Basic | Unknown |
| DealCheck | No | N/A |
| FlipperForce | No | N/A |
| PropStream | No | N/A |
Handoff.ai is the closest competitor with photo-based analysis.
Our Advantage: - Integrated with Cameron's SOW methodology - Cleveland market-specific pricing - User feedback loop for continuous improvement - Combined with Deal Review AI scoring
Implementation Roadmap¶
| Phase | Milestone | Complexity | Timeline |
|---|---|---|---|
| Phase 3 | Manual SOW Builder | Done | - |
| Phase 3 | Basic photo condition hints | Medium | Q3 2026 |
| Future | Full vision-based estimation | Very High | 2027 |
| Future | Automated accuracy validation | High | 2027 |
Technical Requirements¶
| Component | Technology | Cost |
|---|---|---|
| Vision AI | GPT-4V or Claude Vision | $0.01-0.03/image |
| Image storage | Cloudflare R2 | ~$5/mo |
| Pricing database | PostgreSQL | Included |
| Training data | User submissions | Free |
Estimated cost per estimate: $0.15-0.50 (15-30 photos)
AI Rehab by Tier¶
| Feature | Free | Solo | Pro | Team |
|---|---|---|---|---|
| Manual SOW Builder | ❌ | ✅ | ✅ | ✅ |
| Photo condition hints | ❌ | ❌ | ✅ | ✅ |
| Full AI estimation | ❌ | ❌ | ❌ | ✅ (Beta) |
| Accuracy feedback | ❌ | ❌ | ✅ | ✅ |
AI Rehab Estimation is a Phase 3/Future feature
Currently available: Manual SOW Builder with 500+ line items
See AI Roadmap for development timeline