Examples and code quality

examples/valuation.py

@@ -0,0 +1,102 @@
+#!/usr/bin/env python3
+"""
+Property Valuation Example
+
+This example shows how to find comparable properties and
+estimate the value of a property based on recent deals.
+"""
+
+from nadlan_mcp.govmap import GovmapClient
+
+
+def main():
+    client = GovmapClient()
+
+    # Property to value
+    address = "רוטשילד 10 תל אביב"
+    property_details = {
+        "rooms": 3.5,
+        "area": 85,  # square meters
+        "floor": 3,
+    }
+
+    print(f"Valuing property at: {address}")
+    print(f"Property details:")
+    print(f"  Rooms: {property_details['rooms']}")
+    print(f"  Area: {property_details['area']}m²")
+    print(f"  Floor: {property_details['floor']}")
+    print()
+
+    try:
+        # Get comparable deals with filters
+        deals = client.find_recent_deals_for_address(
+            address, years_back=2, radius=200  # Wider radius for more comparables
+        )
+
+        if not deals:
+            print("No comparable deals found")
+            return
+
+        # Filter for similar properties
+        comparables = client.filter_deals_by_criteria(
+            deals,
+            min_rooms=property_details["rooms"] - 0.5,
+            max_rooms=property_details["rooms"] + 0.5,
+            min_area=property_details["area"] * 0.85,  # ±15%
+            max_area=property_details["area"] * 1.15,
+            min_floor=max(0, property_details["floor"] - 2),
+            max_floor=property_details["floor"] + 2,
+        )
+
+        print(f"Found {len(comparables)} comparable properties\n")
+
+        if not comparables:
+            print("No close matches found. Try widening your criteria.")
+            return
+
+        # Calculate statistics on comparables
+        stats = client.calculate_deal_statistics(comparables)
+
+        print("=== Comparable Properties Analysis ===")
+        print(f"Number of Comparables: {len(comparables)}")
+        print(f"\nPrice Statistics:")
+        print(f"  Average Price: ₪{stats.mean_price:,.0f}")
+        print(f"  Median Price: ₪{stats.median_price:,.0f}")
+        print(f"  Price Range: ₪{stats.min_price:,.0f} - ₪{stats.max_price:,.0f}")
+
+        if stats.mean_price_per_sqm:
+            print(f"\nPrice per Square Meter:")
+            print(f"  Average: ₪{stats.mean_price_per_sqm:,.0f}/m²")
+            print(f"  Median: ₪{stats.median_price_per_sqm:,.0f}/m²")
+
+            # Estimate property value
+            estimated_value = stats.mean_price_per_sqm * property_details["area"]
+            estimated_value_low = stats.percentile_25_price_per_sqm * property_details["area"]
+            estimated_value_high = stats.percentile_75_price_per_sqm * property_details["area"]
+
+            print(f"\n=== Estimated Property Value ===")
+            print(f"Based on {property_details['area']}m² at ₪{stats.mean_price_per_sqm:,.0f}/m²:")
+            print(f"  Estimated Value: ₪{estimated_value:,.0f}")
+            print(f"  Range (25th-75th percentile):")
+            print(f"    Low: ₪{estimated_value_low:,.0f}")
+            print(f"    High: ₪{estimated_value_high:,.0f}")
+
+        # Show sample comparables
+        print(f"\n=== Sample Comparables ===")
+        for i, deal in enumerate(comparables[:5], 1):
+            print(f"\n{i}. {deal.address_description or 'N/A'}")
+            print(f"   Date: {deal.deal_date}")
+            print(f"   Price: ₪{deal.deal_amount:,.0f}" if deal.deal_amount else "   Price: N/A")
+            print(f"   Rooms: {deal.rooms}" if deal.rooms else "   Rooms: N/A")
+            print(f"   Area: {deal.asset_area}m²" if deal.asset_area else "   Area: N/A")
+            if deal.price_per_sqm:
+                print(f"   Price/m²: ₪{deal.price_per_sqm:,.0f}")
+
+    except ValueError as e:
+        print(f"Error: {e}")
+    except Exception as e:
+        print(f"Unexpected error: {e}")
+
+
+if __name__ == "__main__":
+    main()