Real Estate Expert

Real Estate Expert

Expert guidance for real estate systems, property management, Multiple Listing Service (MLS) integration, customer relationship management, virtual tours, and market analysis.

Core Concepts

Real Estate Systems

• Multiple Listing Service (MLS) integration
• Property Management Systems (PMS)
• Customer Relationship Management (CRM)
• Transaction management
• Document management
• Lease management
• Maintenance tracking

PropTech Solutions

• Virtual tours and 3D walkthroughs
• AI-powered property valuation
• Digital signatures and e-closing
• Smart home integration
• IoT sensors for properties
• Blockchain for title management
• Augmented reality for staging

Standards and Regulations

• RESO (Real Estate Standards Organization)
• Fair Housing Act compliance
• RESPA (Real Estate Settlement Procedures Act)
• Data privacy (GDPR, CCPA)
• ADA compliance for websites
• NAR Code of Ethics

Property Valuation and Analytics

import numpy as np
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.preprocessing import StandardScaler

class PropertyValuationSystem:
    """AI-powered property valuation"""

    def __init__(self):
        self.model = GradientBoostingRegressor(n_estimators=100)
        self.scaler = StandardScaler()
        self.trained = False

    def train_model(self, training_data: List[dict]):
        """Train valuation model on historical data"""
        features = []
        prices = []

        for property_data in training_data:
            feature_vector = self._extract_features(property_data)
            features.append(feature_vector)
            prices.append(property_data['sold_price'])

        X = np.array(features)
        y = np.array(prices)

        # Scale features
        X_scaled = self.scaler.fit_transform(X)

        # Train model
        self.model.fit(X_scaled, y)
        self.trained = True

    def estimate_value(self, property_data: dict) -> dict:
        """Estimate property value"""
        if not self.trained:
            return {'error': 'Model not trained'}

        features = self._extract_features(property_data)
        features_scaled = self.scaler.transform([features])

        estimated_value = self.model.predict(features_scaled)[0]

        # Calculate confidence interval (simplified)
        confidence_range = estimated_value * 0.1  # ±10%

        return {
            'estimated_value': estimated_value,
            'confidence_interval': {
                'lower': estimated_value - confidence_range,
                'upper': estimated_value + confidence_range
            },
            'price_per_sqft': estimated_value / property_data['square_feet']
        }

    def _extract_features(self, property_data: dict) -> List[float]:
        """Extract features for valuation model"""
        return [
            property_data['square_feet'],
            property_data['bedrooms'],
            property_data['bathrooms'],
            property_data['lot_size'],
            property_data['year_built'],
            property_data.get('garage_spaces', 0),
            property_data.get('stories', 1),
            1 if property_data.get('has_pool', False) else 0,
            1 if property_data.get('has_fireplace', False) else 0,
            property_data.get('neighborhood_score', 50)  # 0-100 scale
        ]

class MarketAnalytics:
    """Real estate market analytics"""

    def calculate_market_trends(self, sales_data: List[dict]) -> dict:
        """Calculate market trends and statistics"""
        if not sales_data:
            return {'error': 'No sales data available'}

        # Calculate metrics
        prices = [s['price'] for s in sales_data]
        days_on_market = [s['days_on_market'] for s in sales_data]

        median_price = np.median(prices)
        avg_price = np.mean(prices)
        avg_days_on_market = np.mean(days_on_market)

        # Calculate price trends (compare recent vs older data)
        recent_data = sales_data[-30:]  # Last 30 sales
        older_data = sales_data[-60:-30]  # Previous 30 sales

        if len(recent_data) > 0 and len(older_data) > 0:
            recent_avg = np.mean([s['price'] for s in recent_data])
            older_avg = np.mean([s['price'] for s in older_data])
            price_change = ((recent_avg - older_avg) / older_avg) * 100
        else:
            price_change = 0

        # Market health indicator
        if avg_days_on_market < 30:
            market_health = "Hot"
        elif avg_days_on_market < 60:
            market_health = "Balanced"
        else:
            market_health = "Slow"

        return {
            'median_price': median_price,
            'average_price': avg_price,
            'average_days_on_market': avg_days_on_market,
            'price_trend_percentage': price_change,
            'market_health': market_health,
            'total_sales': len(sales_data)
        }

    def calculate_inventory_metrics(self, active_listings: List[Property]) -> dict:
        """Calculate inventory and absorption metrics"""
        total_listings = len(active_listings)

        # Calculate average price
        avg_price = np.mean([float(p.listing_price) for p in active_listings])

        # Calculate months of inventory (simplified)
        # Would need sales velocity for accurate calculation
        months_of_inventory = 6.0  # Placeholder

        return {
            'total_active_listings': total_listings,
            'average_listing_price': avg_price,
            'months_of_inventory': months_of_inventory,
            'market_condition': 'Balanced' if 4 <= months_of_inventory <= 6 else
                              'Seller' if months_of_inventory < 4 else 'Buyer'
        }

Lease Management

@dataclass
class Lease:
    """Rental lease agreement"""
    lease_id: str
    property_id: str
    tenant_name: str
    tenant_contact: dict
    start_date: datetime
    end_date: datetime
    monthly_rent: Decimal
    security_deposit: Decimal
    status: str  # 'active', 'expired', 'terminated'
    auto_renew: bool

@dataclass
class MaintenanceRequest:
    """Maintenance request for property"""
    request_id: str
    property_id: str
    tenant_name: str
    category: str  # 'plumbing', 'electrical', 'hvac', etc.
    priority: str  # 'low', 'medium', 'high', 'emergency'
    description: str
    submitted_date: datetime
    status: str  # 'open', 'in_progress', 'completed'
    assigned_to: Optional[str]

class PropertyManagementSystem:
    """Property management for landlords and property managers"""

    def __init__(self):
        self.leases = {}
        self.maintenance_requests = []
        self.rent_payments = []

    def create_lease(self, lease_data: dict) -> Lease:
        """Create new lease agreement"""
        lease_id = self._generate_lease_id()

        lease = Lease(
            lease_id=lease_id,
            property_id=lease_data['property_id'],
            tenant_name=lease_data['tenant_name'],
            tenant_contact=lease_data['tenant_contact'],
            start_date=lease_data['start_date'],
            end_date=lease_data['end_date'],
            monthly_rent=Decimal(str(lease_data['monthly_rent'])),
            security_deposit=Decimal(str(lease_data['security_deposit'])),
            status='active',
            auto_renew=lease_data.get('auto_renew', False)
        )

        self.leases[lease_id] = lease

        # Schedule rent payment reminders
        self._schedule_rent_reminders(lease)

        return lease

    def record_rent_payment(self,
                           lease_id: str,
                           amount: Decimal,
                           payment_date: datetime,
                           payment_method: str) -> dict:
        """Record rent payment"""
        lease = self.leases.get(lease_id)
        if not lease:
            return {'error': 'Lease not found'}

        payment = {