Property Scoring Algorithms

Mis à jour le 2026-04-22Confiance : high

property-scoringreal-estate-analysismulti-criteria-decisionweighted-scoringterrain-valuationvalue-decomposition

Mathematical approaches for systematically evaluating and ranking real estate properties based on multiple criteria. Essential for automated property search systems where dozens of listings need objective comparison.

Multi-Criteria Decision Framework

Property evaluation involves balancing competing factors:

Primary Criteria:

Location desirability and proximity to amenities
Price relative to budget constraints
Property size (both building and land)
Condition and renovation requirements

Secondary Criteria:

Future value potential
Neighborhood development trends
Environmental factors
Specific use case requirements (garden space, privacy, etc.)

Terrain-Focused Scoring Model

Advanced scoring for properties where land value dominates decision-making:

def calculate_terrain_focused_score(property_data, criteria):
    # Terrain size weighted heavily (50% of total score)
    terrain_score = score_terrain_size(
        actual=property_data.terrain_m2,
        minimum=criteria.min_terrain_m2,
        ideal=criteria.ideal_terrain_m2
    )
    
    # Price efficiency (30% weight)
    price_score = score_price_efficiency(
        price=property_data.price,
        max_budget=criteria.max_budget,
        terrain_m2=property_data.terrain_m2
    )
    
    # Location match (20% weight)
    location_score = score_location_preference(
        property_city=property_data.city,
        preferred_areas=criteria.preferred_cities
    )
    
    total_score = (terrain_score * 0.5 + 
                  price_score * 0.3 + 
                  location_score * 0.2)
    return min(10, total_score)

Value Decomposition Methodology

Separating land value from building value for better analysis:

Theoretical Pricing Model:

def decompose_property_value(property_data, land_rate_per_m2=50):
    terrain_value = property_data.terrain_m2 * land_rate_per_m2
    building_value = property_data.price - terrain_value
    
    # Calculate efficiency metrics
    land_efficiency = terrain_value / property_data.price
    building_price_per_m2 = building_value / property_data.surface_house_m2
    
    return {
        'terrain_value': terrain_value,
        'building_value': building_value,
        'land_efficiency': land_efficiency,
        'building_price_per_m2': building_price_per_m2
    }

Scoring Function Patterns

Non-linear Terrain Scoring: Properties below minimum requirements score exponentially lower:

def score_terrain_size(actual_m2, minimum_m2, ideal_m2):
    if actual_m2 < minimum_m2:
        # Exponential penalty below minimum
        ratio = actual_m2 / minimum_m2
        return max(0, ratio ** 2 * 3)  # Max 3 points if below minimum
    
    if actual_m2 >= ideal_m2:
        return 10  # Perfect score at ideal size or above
    
    # Linear interpolation between minimum and ideal
    excess = actual_m2 - minimum_m2
    ideal_excess = ideal_m2 - minimum_m2
    return 3 + (excess / ideal_excess) * 7  # Scale from 3 to 10

Budget Efficiency Scoring:

def score_price_efficiency(price, max_budget, terrain_m2):
    if price > max_budget:
        return 0  # Immediately disqualify over-budget properties
    
    # Reward properties that leave budget room
    budget_efficiency = (max_budget - price) / max_budget
    
    # Also consider price per m² of terrain
    price_per_m2 = price / terrain_m2
    market_rate = 200  # Reference rate for comparison
    terrain_efficiency = max(0, (market_rate - price_per_m2) / market_rate)
    
    return (budget_efficiency + terrain_efficiency) * 5  # Scale to 0-10

Self-Sufficiency Integration

Scoring based on practical land use requirements:

def score_self_sufficiency_potential(terrain_m2, household_size=5):
    # Permaculture space requirements per person
    permaculture_per_person = 200  # m² per person
    required_space = household_size * permaculture_per_person
    
    if terrain_m2 < required_space:
        return terrain_m2 / required_space * 3  # Partial capability
    
    # Bonus for excess space (livestock, storage, workshops)
    excess_ratio = terrain_m2 / required_space
    return min(10, 3 + (excess_ratio - 1) * 7)  # Diminishing returns

Dynamic Criteria Weighting

Allowing users to adjust scoring importance:

class ScoringCriteria:
    def __init__(self):
        self.terrain_weight = 0.5
        self.price_weight = 0.3
        self.location_weight = 0.2
        self.condition_weight = 0.0  # Optional criteria
        
    def calculate_weighted_score(self, scores):
        total_weight = sum([
            self.terrain_weight,
            self.price_weight,
            self.location_weight,
            self.condition_weight
        ])
        
        weighted_sum = (
            scores['terrain'] * self.terrain_weight +
            scores['price'] * self.price_weight +
            scores['location'] * self.location_weight +
            scores['condition'] * self.condition_weight
        )
        
        return (weighted_sum / total_weight) * 10  # Normalize to 0-10

Historical Score Evolution

Tracking how property scores change over time:

def update_property_score_history(property_id, new_score):
    # Track score changes due to:
    # - Market condition updates
    # - Criteria preference changes
    # - New comparable properties
    # - External data integration (DVF, infrastructure)
    pass