Real Estate Shark

Real Estate Shark

Trit: MINUS (-1) — Validation/​Analysis
Color: #DA9E5A
URI: skill://real-estate-shark#DA9E5A

Purpose

Transform single property/​neighborhood images into photorealistic 3D Gaussian Splats using Apple's ml-sharp, enabling virtual walkthroughs from a single photograph. Bridge to PropertyPriceOracle for valuation analytics.

When to Use

• Converting property listing photos to 3D walkthroughs
• Neighborhood visualization from street-level imagery
• Real estate due diligence with 3D spatial analysis
• Virtual staging and property tours from single images

Dependencies

• apple/​ml-sharp — SHARP monocular view synthesis
• PropertyPriceOracle — Addictive market dynamics valuation
• Gay.jl GF(3) coloring for property classification

Quick Start

# Activate ml-sharp environment
cd ~/​ies/​ml-sharp && source .venv/​bin/​activate

# Single image → 3D Gaussian Splat (<1 second on GPU)
sharp predict -i property_photo.jpg -o splats/ --device mps

# With video rendering (CUDA only)
sharp predict -i photos/ -o splats/ --render --device cuda

# Render from existing splats
sharp render -i splats/ -o videos/

Architecture

┌─────────────────┐     ┌──────────────────┐     ┌─────────────────┐
│  Property Image │────▶│  SHARP (ml-sharp)│────▶│  3DGS .ply file │
│  (single photo) │     │  ViT + SPN + UNet│     │  (63MB splat)   │
└─────────────────┘     └──────────────────┘     └────────┬────────┘
                                                          │
                        ┌──────────────────┐              │
                        │ PropertyPriceOracle│◀────────────┘
                        │ • get_price()     │     Spatial analysis
                        │ • get_volatility()│     informs valuation
                        │ • get_sentiment() │
                        └──────────────────┘

GF(3) Balanced Triad

Output Format

SHARP produces 3DGS .ply files compatible with:

• gsplat renderers
• Standard 3DGS viewers
• OpenCV coordinate convention (x right, y down, z forward)

Performance

Example: Neighborhood Batch Processing

from pathlib import Path
import subprocess

def shark_neighborhood(image_dir: Path, output_dir: Path, device: str = "mps"):
    """Convert neighborhood photos to 3D splats."""
    cmd = [
        "sharp", "predict",
        "-i", str(image_dir),
        "-o", str(output_dir),
        "--device", device
    ]
    subprocess.run(cmd, check=True)
    return list(output_dir.glob("*.ply"))

# Usage
splats = shark_neighborhood(
    Path("~/​listings/​123_main_st/​photos"),
    Path("~/​listings/​123_main_st/​splats")
)
print(f"Generated {len(splats)} 3D property splats")

Integration with PropertyPriceOracle

from addictive_market_dynamics import PropertyPriceOracle

# Initialize oracle with base property values
oracle = PropertyPriceOracle({
    1: 450_000,  # 123 Main St
    2: 525_000,  # 456 Oak Ave
    3: 380_000,  # 789 Pine Rd
})

# Get current valuation with market dynamics
for prop_id in [1, 2, 3]:
    price = oracle.get_price(prop_id)
    vol = oracle.get_volatility(prop_id)
    sentiment = oracle.get_sentiment(prop_id)
    cascade = oracle.get_cascade_state(prop_id)
    
    print(f"Property {prop_id}: ${price:,.0f} (vol={vol:.2%}, {cascade})")

References

• Apple ml-sharp
• arXiv:2512.10685 — Sharp Monocular View Synthesis
• Project Page