Nitzan Pomerantz
105 lines
3.8 KiB
Python
105 lines
3.8 KiB
Python
#!/usr/bin/env python3
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"""
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Property Valuation Example
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This example shows how to find comparable properties and
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estimate the value of a property based on recent deals.
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"""
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from nadlan_mcp.govmap import GovmapClient
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def main():
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client = GovmapClient()
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# Property to value
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address = "רוטשילד 10 תל אביב"
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property_details = {
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"rooms": 3.5,
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"area": 85, # square meters
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"floor": 3,
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}
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print(f"Valuing property at: {address}")
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print("Property details:")
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print(f" Rooms: {property_details['rooms']}")
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print(f" Area: {property_details['area']}m²")
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print(f" Floor: {property_details['floor']}")
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print()
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try:
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# Get comparable deals with filters
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deals = client.find_recent_deals_for_address(
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address,
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years_back=2,
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radius=200, # Wider radius for more comparables
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)
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if not deals:
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print("No comparable deals found")
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return
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# Filter for similar properties
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comparables = client.filter_deals_by_criteria(
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deals,
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min_rooms=property_details["rooms"] - 0.5,
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max_rooms=property_details["rooms"] + 0.5,
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min_area=property_details["area"] * 0.85, # ±15%
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max_area=property_details["area"] * 1.15,
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min_floor=max(0, property_details["floor"] - 2),
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max_floor=property_details["floor"] + 2,
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)
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print(f"Found {len(comparables)} comparable properties\n")
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if not comparables:
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print("No close matches found. Try widening your criteria.")
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return
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# Calculate statistics on comparables
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stats = client.calculate_deal_statistics(comparables)
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print("=== Comparable Properties Analysis ===")
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print(f"Number of Comparables: {len(comparables)}")
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print("\nPrice Statistics:")
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print(f" Average Price: ₪{stats.mean_price:,.0f}")
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print(f" Median Price: ₪{stats.median_price:,.0f}")
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print(f" Price Range: ₪{stats.min_price:,.0f} - ₪{stats.max_price:,.0f}")
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if stats.mean_price_per_sqm:
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print("\nPrice per Square Meter:")
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print(f" Average: ₪{stats.mean_price_per_sqm:,.0f}/m²")
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print(f" Median: ₪{stats.median_price_per_sqm:,.0f}/m²")
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# Estimate property value
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estimated_value = stats.mean_price_per_sqm * property_details["area"]
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estimated_value_low = stats.percentile_25_price_per_sqm * property_details["area"]
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estimated_value_high = stats.percentile_75_price_per_sqm * property_details["area"]
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print("\n=== Estimated Property Value ===")
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print(f"Based on {property_details['area']}m² at ₪{stats.mean_price_per_sqm:,.0f}/m²:")
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print(f" Estimated Value: ₪{estimated_value:,.0f}")
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print(" Range (25th-75th percentile):")
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print(f" Low: ₪{estimated_value_low:,.0f}")
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print(f" High: ₪{estimated_value_high:,.0f}")
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# Show sample comparables
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print("\n=== Sample Comparables ===")
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for i, deal in enumerate(comparables[:5], 1):
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print(f"\n{i}. {deal.address_description or 'N/A'}")
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print(f" Date: {deal.deal_date}")
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print(f" Price: ₪{deal.deal_amount:,.0f}" if deal.deal_amount else " Price: N/A")
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print(f" Rooms: {deal.rooms}" if deal.rooms else " Rooms: N/A")
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print(f" Area: {deal.asset_area}m²" if deal.asset_area else " Area: N/A")
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if deal.price_per_sqm:
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print(f" Price/m²: ₪{deal.price_per_sqm:,.0f}")
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except ValueError as e:
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print(f"Error: {e}")
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except Exception as e:
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print(f"Unexpected error: {e}")
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if __name__ == "__main__":
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main()
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