Improving parameters
This commit is contained in:
Nitzan Pomerantz
+289
-69
@@ -112,46 +112,79 @@ def get_street_deals(polygon_id: str, limit: int = 100) -> str:
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return f"Error fetching street deals: {str(e)}"
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@mcp.tool()
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def find_recent_deals_for_address(address: str, years_back: int = 2) -> str:
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def find_recent_deals_for_address(address: str, years_back: int = 2, radius_meters: int = 30, max_deals: int = 200) -> str:
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"""Find recent real estate deals for a specific address.
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Args:
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address: The address to search for (in Hebrew or English)
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years_back: How many years back to search (default: 2)
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radius_meters: Search radius in meters from the address (default: 30)
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Small radius since street deals cover the entire street anyway
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max_deals: Maximum number of deals to return (default: 200)
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Returns:
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JSON string containing recent real estate deals for the address
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"""
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try:
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deals = client.find_recent_deals_for_address(address, years_back)
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deals = client.find_recent_deals_for_address(address, years_back, radius_meters, max_deals)
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if not deals:
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return f"No deals found for address '{address}'"
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# Calculate basic statistics
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# Calculate comprehensive statistics
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prices = [deal.get("dealAmount", 0) for deal in deals if deal.get("dealAmount")]
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areas = [deal.get("assetArea", 0) for deal in deals if deal.get("assetArea")]
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price_per_sqm_values = [deal.get("price_per_sqm", 0) for deal in deals if deal.get("price_per_sqm")]
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# Separate building, street and neighborhood deals for analysis
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building_deals = [deal for deal in deals if deal.get("deal_source") == "same_building"]
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street_deals = [deal for deal in deals if deal.get("deal_source") == "street"]
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neighborhood_deals = [deal for deal in deals if deal.get("deal_source") == "neighborhood"]
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stats = {
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"deal_breakdown": {
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"total_deals": len(deals),
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"same_building_deals": len(building_deals),
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"street_deals": len(street_deals),
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"neighborhood_deals": len(neighborhood_deals),
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"same_building_percentage": round((len(building_deals) / len(deals)) * 100, 1) if deals else 0,
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"street_emphasis_percentage": round((len(street_deals) / len(deals)) * 100, 1) if deals else 0,
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"neighborhood_percentage": round((len(neighborhood_deals) / len(deals)) * 100, 1) if deals else 0
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}
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}
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stats = {}
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if prices:
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stats["price_stats"] = {
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"average_price": sum(prices) / len(prices),
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"average_price": round(sum(prices) / len(prices), 0),
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"min_price": min(prices),
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"max_price": max(prices),
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"total_deals": len(prices)
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"median_price": sorted(prices)[len(prices)//2] if prices else 0,
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"total_volume": sum(prices)
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}
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if areas:
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stats["area_stats"] = {
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"average_area": sum(areas) / len(areas),
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"average_area": round(sum(areas) / len(areas), 1),
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"min_area": min(areas),
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"max_area": max(areas)
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"max_area": max(areas),
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"median_area": sorted(areas)[len(areas)//2] if areas else 0
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}
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if price_per_sqm_values:
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stats["price_per_sqm_stats"] = {
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"average_price_per_sqm": round(sum(price_per_sqm_values) / len(price_per_sqm_values), 0),
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"min_price_per_sqm": round(min(price_per_sqm_values), 0),
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"max_price_per_sqm": round(max(price_per_sqm_values), 0),
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"median_price_per_sqm": round(sorted(price_per_sqm_values)[len(price_per_sqm_values)//2], 0) if price_per_sqm_values else 0
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}
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return json.dumps({
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"search_address": address,
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"years_back": years_back,
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"total_deals": len(deals),
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"search_parameters": {
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"address": address,
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"years_back": years_back,
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"radius_meters": radius_meters,
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"max_deals": max_deals
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},
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"market_statistics": stats,
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"deals": deals
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}, ensure_ascii=False, indent=2)
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@@ -177,9 +210,31 @@ def get_neighborhood_deals(polygon_id: str, limit: int = 100) -> str:
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if not deals:
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return f"No deals found for polygon ID {polygon_id}"
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# Add price per sqm calculation for each deal
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for deal in deals:
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price = deal.get('dealAmount', 0)
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area = deal.get('assetArea', 0)
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if isinstance(price, (int, float)) and isinstance(area, (int, float)) and area > 0:
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deal['price_per_sqm'] = round(price / area, 2)
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else:
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deal['price_per_sqm'] = None
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# Calculate basic statistics including price per sqm
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prices = [deal.get("dealAmount", 0) for deal in deals if deal.get("dealAmount")]
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price_per_sqm_values = [deal.get("price_per_sqm", 0) for deal in deals if deal.get("price_per_sqm")]
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stats = {}
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if price_per_sqm_values:
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stats["price_per_sqm_stats"] = {
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"average_price_per_sqm": round(sum(price_per_sqm_values) / len(price_per_sqm_values), 0),
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"min_price_per_sqm": round(min(price_per_sqm_values), 0),
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"max_price_per_sqm": round(max(price_per_sqm_values), 0)
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}
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return json.dumps({
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"total_deals": len(deals),
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"polygon_id": polygon_id,
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"market_statistics": stats,
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"deals": deals
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}, ensure_ascii=False, indent=2)
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@@ -188,94 +243,225 @@ def get_neighborhood_deals(polygon_id: str, limit: int = 100) -> str:
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return f"Error fetching neighborhood deals: {str(e)}"
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@mcp.tool()
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def analyze_market_trends(address: str, years_back: int = 3) -> str:
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"""Analyze market trends and price patterns for an area.
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def analyze_market_trends(address: str, years_back: int = 3, radius_meters: int = 300, max_deals: int = 500) -> str:
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"""Analyze market trends and price patterns for an area with comprehensive data.
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Args:
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address: The address to analyze trends around
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years_back: How many years of data to analyze (default: 3)
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radius_meters: Search radius in meters from the address (default: 300, larger for trend analysis)
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max_deals: Maximum number of deals to analyze (default: 500)
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Returns:
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JSON string containing market trend analysis including:
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- Price trends over time
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JSON string containing comprehensive market trend analysis including:
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- Detailed price trends over time
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- Average prices by property type
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- Market activity levels
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- Market activity levels and patterns
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- Price per square meter trends
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- Seasonal patterns
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- Market velocity indicators
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- Comparative neighborhood analysis
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"""
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try:
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# Get deals for the address
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deals = client.find_recent_deals_for_address(address, years_back)
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# Get deals for the address with larger radius for trend analysis
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deals = client.find_recent_deals_for_address(address, years_back, radius_meters, max_deals)
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if not deals:
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return f"No deals found for market analysis near '{address}'"
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return f"No deals found for comprehensive market analysis near '{address}'"
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# Analyze trends by year
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# Comprehensive analysis structure
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from collections import defaultdict
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yearly_data = defaultdict(list)
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property_types: Dict[str, int] = defaultdict(int)
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neighborhoods = set()
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monthly_data = defaultdict(list)
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property_types: Dict[str, List[Dict]] = defaultdict(list)
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neighborhoods = defaultdict(list)
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quarterly_data = defaultdict(list)
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# Process each deal for comprehensive analysis
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for deal in deals:
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date_str = deal.get('dealDate', '')
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if date_str:
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year = date_str[:4]
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price = deal.get('dealAmount')
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area = deal.get('assetArea')
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prop_type = deal.get('assetTypeHeb', deal.get('propertyTypeDescription', 'Unknown'))
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neighborhood = deal.get('settlementNameHeb', deal.get('neighborhood'))
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if not date_str:
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continue
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if neighborhood:
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neighborhoods.add(neighborhood)
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year = date_str[:4]
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month = date_str[:7] # YYYY-MM
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quarter = f"{year}-Q{((int(date_str[5:7]) - 1) // 3) + 1}" if len(date_str) >= 7 else None
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if isinstance(price, (int, float)) and isinstance(area, (int, float)) and area > 0:
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yearly_data[year].append({
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'price': price,
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'area': area,
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'price_per_sqm': price / area
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})
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property_types[prop_type] += 1
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price = deal.get('dealAmount')
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area = deal.get('assetArea')
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price_per_sqm = deal.get('price_per_sqm')
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prop_type = deal.get('assetTypeHeb', deal.get('propertyTypeDescription', 'לא ידוע'))
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neighborhood = deal.get('settlementNameHeb', deal.get('neighborhood', 'לא ידוע'))
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deal_source = deal.get('deal_source', 'unknown')
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# Calculate yearly trends
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deal_data = {
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'price': price,
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'area': area,
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'price_per_sqm': price_per_sqm,
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'property_type': prop_type,
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'neighborhood': neighborhood,
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'deal_source': deal_source,
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'date': date_str
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}
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if isinstance(price, (int, float)) and isinstance(area, (int, float)) and area > 0:
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yearly_data[year].append(deal_data)
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monthly_data[month].append(deal_data)
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if quarter:
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quarterly_data[quarter].append(deal_data)
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property_types[prop_type].append(deal_data)
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neighborhoods[neighborhood].append(deal_data)
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# Calculate comprehensive yearly trends
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yearly_trends = {}
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for year, year_deals in yearly_data.items():
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if year_deals:
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prices = [d['price'] for d in year_deals if d['price']]
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price_per_sqm_vals = [d['price_per_sqm'] for d in year_deals if d['price_per_sqm']]
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areas = [d['area'] for d in year_deals if d['area']]
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building_deals = [d for d in year_deals if d['deal_source'] == 'same_building']
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street_deals = [d for d in year_deals if d['deal_source'] == 'street']
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neighborhood_deals = [d for d in year_deals if d['deal_source'] == 'neighborhood']
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yearly_trends[year] = {
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"average_price": sum(d['price'] for d in year_deals) / len(year_deals),
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"min_price": min(d['price'] for d in year_deals),
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"max_price": max(d['price'] for d in year_deals),
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"average_area": sum(d['area'] for d in year_deals) / len(year_deals),
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"average_price_per_sqm": sum(d['price_per_sqm'] for d in year_deals) / len(year_deals),
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"deal_count": len(year_deals)
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"deal_count": len(year_deals),
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"same_building_deals_count": len(building_deals),
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"street_deals_count": len(street_deals),
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"neighborhood_deals_count": len(neighborhood_deals),
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"same_building_percentage": round((len(building_deals) / len(year_deals)) * 100, 1),
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"street_deals_percentage": round((len(street_deals) / len(year_deals)) * 100, 1),
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"average_price": round(sum(prices) / len(prices), 0) if prices else 0,
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"median_price": round(sorted(prices)[len(prices)//2], 0) if prices else 0,
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"min_price": min(prices) if prices else 0,
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"max_price": max(prices) if prices else 0,
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"price_std_dev": round((sum([(p - sum(prices)/len(prices))**2 for p in prices]) / len(prices))**0.5, 0) if len(prices) > 1 else 0,
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"average_area": round(sum(areas) / len(areas), 1) if areas else 0,
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"average_price_per_sqm": round(sum(price_per_sqm_vals) / len(price_per_sqm_vals), 0) if price_per_sqm_vals else 0,
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"median_price_per_sqm": round(sorted(price_per_sqm_vals)[len(price_per_sqm_vals)//2], 0) if price_per_sqm_vals else 0,
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"total_market_volume": sum(prices) if prices else 0
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}
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# Calculate price trend direction
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# Calculate quarterly trends for seasonality analysis
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quarterly_trends = {}
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for quarter, quarter_deals in quarterly_data.items():
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if quarter_deals:
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prices = [d['price'] for d in quarter_deals if d['price']]
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price_per_sqm_vals = [d['price_per_sqm'] for d in quarter_deals if d['price_per_sqm']]
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quarterly_trends[quarter] = {
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"deal_count": len(quarter_deals),
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"average_price": round(sum(prices) / len(prices), 0) if prices else 0,
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"average_price_per_sqm": round(sum(price_per_sqm_vals) / len(price_per_sqm_vals), 0) if price_per_sqm_vals else 0
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}
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# Property type analysis
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property_type_analysis = {}
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for prop_type, type_deals in property_types.items():
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if type_deals:
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prices = [d['price'] for d in type_deals if d['price']]
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price_per_sqm_vals = [d['price_per_sqm'] for d in type_deals if d['price_per_sqm']]
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areas = [d['area'] for d in type_deals if d['area']]
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property_type_analysis[prop_type] = {
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"deal_count": len(type_deals),
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"market_share_percentage": round((len(type_deals) / len(deals)) * 100, 1),
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"average_price": round(sum(prices) / len(prices), 0) if prices else 0,
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"average_area": round(sum(areas) / len(areas), 1) if areas else 0,
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"average_price_per_sqm": round(sum(price_per_sqm_vals) / len(price_per_sqm_vals), 0) if price_per_sqm_vals else 0
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}
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# Neighborhood comparison analysis
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neighborhood_analysis = {}
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for neighborhood, neighborhood_deals in neighborhoods.items():
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if neighborhood_deals and len(neighborhood_deals) >= 3: # Only include neighborhoods with sufficient data
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prices = [d['price'] for d in neighborhood_deals if d['price']]
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price_per_sqm_vals = [d['price_per_sqm'] for d in neighborhood_deals if d['price_per_sqm']]
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neighborhood_analysis[neighborhood] = {
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"deal_count": len(neighborhood_deals),
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"average_price": round(sum(prices) / len(prices), 0) if prices else 0,
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"average_price_per_sqm": round(sum(price_per_sqm_vals) / len(price_per_sqm_vals), 0) if price_per_sqm_vals else 0
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}
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# Market trend direction analysis
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price_trend_analysis = {}
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years_sorted = sorted(yearly_trends.keys())
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if len(years_sorted) >= 2:
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first_year_avg = yearly_trends[years_sorted[0]]['average_price_per_sqm']
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last_year_avg = yearly_trends[years_sorted[-1]]['average_price_per_sqm']
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first_year_data = yearly_trends[years_sorted[0]]
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last_year_data = yearly_trends[years_sorted[-1]]
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trend_percentage = ((last_year_avg - first_year_avg) / first_year_avg) * 100
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trend_direction = "rising" if trend_percentage > 5 else "declining" if trend_percentage < -5 else "stable"
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# Price trend
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first_year_avg = first_year_data['average_price_per_sqm']
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last_year_avg = last_year_data['average_price_per_sqm']
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price_trend_analysis = {
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"trend_direction": trend_direction,
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"trend_percentage": round(trend_percentage, 1),
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"first_year": years_sorted[0],
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"last_year": years_sorted[-1],
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"first_year_avg_price_per_sqm": round(first_year_avg, 0),
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"last_year_avg_price_per_sqm": round(last_year_avg, 0)
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if first_year_avg > 0:
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price_trend_percentage = ((last_year_avg - first_year_avg) / first_year_avg) * 100
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price_trend_direction = "עולה" if price_trend_percentage > 5 else "יורד" if price_trend_percentage < -5 else "יציב"
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# Volume trend
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first_year_volume = first_year_data['deal_count']
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last_year_volume = last_year_data['deal_count']
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volume_trend_percentage = ((last_year_volume - first_year_volume) / first_year_volume) * 100 if first_year_volume > 0 else 0
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volume_trend_direction = "עולה" if volume_trend_percentage > 10 else "יורד" if volume_trend_percentage < -10 else "יציב"
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price_trend_analysis = {
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"price_trend_direction": price_trend_direction,
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"price_trend_percentage": round(price_trend_percentage, 1),
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"volume_trend_direction": volume_trend_direction,
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"volume_trend_percentage": round(volume_trend_percentage, 1),
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"analysis_period": f"{years_sorted[0]} - {years_sorted[-1]}",
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"first_year_avg_price_per_sqm": round(first_year_avg, 0),
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"last_year_avg_price_per_sqm": round(last_year_avg, 0),
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"total_price_change": round(last_year_avg - first_year_avg, 0),
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"annualized_price_growth": round(price_trend_percentage / len(years_sorted), 1)
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}
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# Market velocity indicators
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market_velocity = {
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"average_deals_per_month": round(len(deals) / (years_back * 12), 1),
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"peak_activity_quarter": max(quarterly_trends.keys(), key=lambda q: quarterly_trends[q]['deal_count']) if quarterly_trends else None,
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"lowest_activity_quarter": min(quarterly_trends.keys(), key=lambda q: quarterly_trends[q]['deal_count']) if quarterly_trends else None
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}
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# Price distribution analysis
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all_prices_per_sqm = [deal.get('price_per_sqm', 0) for deal in deals if deal.get('price_per_sqm')]
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price_distribution = {}
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if all_prices_per_sqm:
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sorted_prices = sorted(all_prices_per_sqm)
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price_distribution = {
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"25th_percentile": round(sorted_prices[len(sorted_prices)//4], 0),
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"75th_percentile": round(sorted_prices[3*len(sorted_prices)//4], 0),
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"price_range_iqr": round(sorted_prices[3*len(sorted_prices)//4] - sorted_prices[len(sorted_prices)//4], 0),
|
||||
"coefficient_of_variation": round((yearly_trends[years_sorted[-1]]['price_std_dev'] / yearly_trends[years_sorted[-1]]['average_price']) * 100, 1) if years_sorted and yearly_trends[years_sorted[-1]]['average_price'] > 0 else 0
|
||||
}
|
||||
|
||||
return json.dumps({
|
||||
"analysis_address": address,
|
||||
"analysis_period_years": years_back,
|
||||
"total_deals_analyzed": len(deals),
|
||||
"neighborhoods": list(neighborhoods),
|
||||
"property_types": dict(property_types),
|
||||
"analysis_parameters": {
|
||||
"address": address,
|
||||
"analysis_period_years": years_back,
|
||||
"search_radius_meters": radius_meters,
|
||||
"max_deals_analyzed": max_deals
|
||||
},
|
||||
"market_overview": {
|
||||
"total_deals_analyzed": len(deals),
|
||||
"unique_neighborhoods": len(neighborhoods),
|
||||
"unique_property_types": len(property_types),
|
||||
"data_coverage_years": len(yearly_trends)
|
||||
},
|
||||
"yearly_trends": yearly_trends,
|
||||
"price_trend_analysis": price_trend_analysis
|
||||
"quarterly_trends": quarterly_trends,
|
||||
"property_type_analysis": property_type_analysis,
|
||||
"neighborhood_comparison": neighborhood_analysis,
|
||||
"market_trend_analysis": price_trend_analysis,
|
||||
"market_velocity_indicators": market_velocity,
|
||||
"price_distribution_analysis": price_distribution,
|
||||
"detailed_insights": {
|
||||
"most_active_property_type": max(property_type_analysis.keys(), key=lambda pt: property_type_analysis[pt]['deal_count']) if property_type_analysis else None,
|
||||
"highest_value_property_type": max(property_type_analysis.keys(), key=lambda pt: property_type_analysis[pt]['average_price_per_sqm']) if property_type_analysis else None,
|
||||
"most_expensive_neighborhood": max(neighborhood_analysis.keys(), key=lambda n: neighborhood_analysis[n]['average_price_per_sqm']) if neighborhood_analysis else None,
|
||||
"deal_source_breakdown": f"Same building: {len([d for d in deals if d.get('deal_source') == 'same_building'])}, Street: {len([d for d in deals if d.get('deal_source') == 'street'])}, Neighborhood: {len([d for d in deals if d.get('deal_source') == 'neighborhood'])}"
|
||||
}
|
||||
}, ensure_ascii=False, indent=2)
|
||||
|
||||
except Exception as e:
|
||||
@@ -302,27 +488,47 @@ def compare_addresses(addresses: List[str]) -> str:
|
||||
if deals:
|
||||
prices = [deal.get("dealAmount", 0) for deal in deals if deal.get("dealAmount")]
|
||||
areas = [deal.get("assetArea", 0) for deal in deals if deal.get("assetArea")]
|
||||
price_per_sqm_values = [deal.get("price_per_sqm", 0) for deal in deals if deal.get("price_per_sqm")]
|
||||
building_deals = [deal for deal in deals if deal.get("deal_source") == "same_building"]
|
||||
street_deals = [deal for deal in deals if deal.get("deal_source") == "street"]
|
||||
neighborhood_deals = [deal for deal in deals if deal.get("deal_source") == "neighborhood"]
|
||||
|
||||
comparison = {
|
||||
"address": address,
|
||||
"total_deals": len(deals),
|
||||
"same_building_deals": len(building_deals),
|
||||
"street_deals": len(street_deals),
|
||||
"neighborhood_deals": len(neighborhood_deals),
|
||||
"same_building_percentage": round((len(building_deals) / len(deals)) * 100, 1) if deals else 0,
|
||||
"street_emphasis_percentage": round((len(street_deals) / len(deals)) * 100, 1) if deals else 0,
|
||||
"price_stats": {
|
||||
"average_price": sum(prices) / len(prices) if prices else 0,
|
||||
"average_price": round(sum(prices) / len(prices), 0) if prices else 0,
|
||||
"min_price": min(prices) if prices else 0,
|
||||
"max_price": max(prices) if prices else 0
|
||||
},
|
||||
"area_stats": {
|
||||
"average_area": sum(areas) / len(areas) if areas else 0,
|
||||
"average_area": round(sum(areas) / len(areas), 1) if areas else 0,
|
||||
"min_area": min(areas) if areas else 0,
|
||||
"max_area": max(areas) if areas else 0
|
||||
},
|
||||
"price_per_sqm_stats": {
|
||||
"average_price_per_sqm": round(sum(price_per_sqm_values) / len(price_per_sqm_values), 0) if price_per_sqm_values else 0,
|
||||
"min_price_per_sqm": round(min(price_per_sqm_values), 0) if price_per_sqm_values else 0,
|
||||
"max_price_per_sqm": round(max(price_per_sqm_values), 0) if price_per_sqm_values else 0
|
||||
}
|
||||
}
|
||||
else:
|
||||
comparison = {
|
||||
"address": address,
|
||||
"total_deals": 0,
|
||||
"same_building_deals": 0,
|
||||
"street_deals": 0,
|
||||
"neighborhood_deals": 0,
|
||||
"same_building_percentage": 0,
|
||||
"street_emphasis_percentage": 0,
|
||||
"price_stats": {},
|
||||
"area_stats": {}
|
||||
"area_stats": {},
|
||||
"price_per_sqm_stats": {}
|
||||
}
|
||||
|
||||
comparisons.append(comparison)
|
||||
@@ -334,13 +540,27 @@ def compare_addresses(addresses: List[str]) -> str:
|
||||
"error": str(e)
|
||||
})
|
||||
|
||||
# Rank addresses by average price
|
||||
valid_comparisons = [c for c in comparisons if c.get("price_stats", {}).get("average_price", 0) > 0]
|
||||
valid_comparisons.sort(key=lambda x: x["price_stats"]["average_price"], reverse=True)
|
||||
# Rank addresses by average price per sqm
|
||||
valid_comparisons = []
|
||||
for comparison in comparisons:
|
||||
if (isinstance(comparison, dict) and
|
||||
"price_per_sqm_stats" in comparison and
|
||||
isinstance(comparison["price_per_sqm_stats"], dict) and
|
||||
comparison["price_per_sqm_stats"].get("average_price_per_sqm", 0) > 0):
|
||||
valid_comparisons.append(comparison)
|
||||
|
||||
# Sort by price per sqm
|
||||
def get_price_per_sqm(comp: dict) -> float:
|
||||
price_stats = comp.get("price_per_sqm_stats", {})
|
||||
if isinstance(price_stats, dict):
|
||||
return price_stats.get("average_price_per_sqm", 0)
|
||||
return 0
|
||||
|
||||
valid_comparisons.sort(key=get_price_per_sqm, reverse=True)
|
||||
|
||||
return json.dumps({
|
||||
"addresses_compared": len(addresses),
|
||||
"ranking_by_average_price": valid_comparisons,
|
||||
"ranking_by_average_price_per_sqm": valid_comparisons,
|
||||
"all_results": comparisons
|
||||
}, ensure_ascii=False, indent=2)
|
||||
|
||||
|
||||
+119
-22
@@ -225,18 +225,24 @@ class GovmapClient:
|
||||
logger.error(f"Error parsing JSON response: {e}")
|
||||
return []
|
||||
|
||||
def find_recent_deals_for_address(self, address: str, years_back: int = 2) -> List[Dict[str, Any]]:
|
||||
def find_recent_deals_for_address(self, address: str, years_back: int = 2,
|
||||
radius: int = 30, max_deals: int = 200) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Find all relevant real estate deals for a given address from the last few years.
|
||||
|
||||
This is the main use case function that ties everything together.
|
||||
Street deals include deals from the same building which get highest priority.
|
||||
|
||||
Args:
|
||||
address: The address to search for
|
||||
years_back: How many years back to search (default: 2)
|
||||
radius: Search radius in meters for initial coordinate search (default: 30)
|
||||
Small radius since street deals cover the entire street anyway
|
||||
max_deals: Maximum number of deals to return (default: 200)
|
||||
|
||||
Returns:
|
||||
List of deals found for the address area
|
||||
List of deals found for the address area, with same building deals prioritized first,
|
||||
then street deals, then neighborhood deals
|
||||
|
||||
Raises:
|
||||
ValueError: If address cannot be found or processed
|
||||
@@ -268,10 +274,11 @@ class GovmapClient:
|
||||
raise ValueError("Invalid coordinate format in autocomplete result")
|
||||
|
||||
point = (float(coords[0]), float(coords[1]))
|
||||
search_address_normalized = address.lower().strip()
|
||||
logger.info(f"Found coordinates: {point}")
|
||||
|
||||
# Step 2: Get deals by radius to find polygon IDs
|
||||
nearby_deals = self.get_deals_by_radius(point, radius=30) # Slightly larger radius
|
||||
nearby_deals = self.get_deals_by_radius(point, radius=radius)
|
||||
|
||||
# Extract unique polygon IDs
|
||||
polygon_ids = set()
|
||||
@@ -288,46 +295,136 @@ class GovmapClient:
|
||||
end_date_str = end_date.strftime('%Y-%m')
|
||||
|
||||
# Step 4: Get street and neighborhood deals for each polygon
|
||||
all_deals = []
|
||||
# Prioritize: same building (0) > street deals (1) > neighborhood deals (2)
|
||||
building_deals = []
|
||||
street_deals = []
|
||||
neighborhood_deals = []
|
||||
seen_deals = set() # For deduplication
|
||||
|
||||
for polygon_id in polygon_ids:
|
||||
try:
|
||||
# Get street deals
|
||||
street_deals = self.get_street_deals(
|
||||
polygon_id, limit=50,
|
||||
# Get street deals first (higher priority)
|
||||
current_street_deals = self.get_street_deals(
|
||||
polygon_id, limit=max_deals // 2, # Allocate more to street deals
|
||||
start_date=start_date_str, end_date=end_date_str
|
||||
)
|
||||
|
||||
# Get neighborhood deals
|
||||
neighborhood_deals = self.get_neighborhood_deals(
|
||||
polygon_id, limit=50,
|
||||
# Get neighborhood deals (lower priority)
|
||||
current_neighborhood_deals = self.get_neighborhood_deals(
|
||||
polygon_id, limit=max_deals // 4, # Allocate less to neighborhood deals
|
||||
start_date=start_date_str, end_date=end_date_str
|
||||
)
|
||||
|
||||
# Combine deals
|
||||
combined_deals = street_deals + neighborhood_deals
|
||||
|
||||
# Add to results with deduplication
|
||||
for deal in combined_deals:
|
||||
# Create a unique identifier for the deal
|
||||
# Process street deals and separate building deals
|
||||
for deal in current_street_deals:
|
||||
deal_id = f"{deal.get('dealId', '')}{deal.get('address', '')}{deal.get('dealDate', '')}"
|
||||
|
||||
if deal_id not in seen_deals:
|
||||
seen_deals.add(deal_id)
|
||||
deal['source_polygon_id'] = polygon_id # Add source for reference
|
||||
all_deals.append(deal)
|
||||
deal['source_polygon_id'] = polygon_id
|
||||
deal['deal_source'] = 'street'
|
||||
|
||||
# Check if this is from the same building
|
||||
deal_address = deal.get('address', '').lower().strip()
|
||||
if self._is_same_building(search_address_normalized, deal_address):
|
||||
deal['deal_source'] = 'same_building'
|
||||
deal['priority'] = 0 # Highest priority
|
||||
building_deals.append(deal)
|
||||
else:
|
||||
deal['priority'] = 1 # Street deals priority
|
||||
street_deals.append(deal)
|
||||
|
||||
# Add neighborhood deals with lowest priority
|
||||
for deal in current_neighborhood_deals:
|
||||
deal_id = f"{deal.get('dealId', '')}{deal.get('address', '')}{deal.get('dealDate', '')}"
|
||||
if deal_id not in seen_deals:
|
||||
seen_deals.add(deal_id)
|
||||
deal['source_polygon_id'] = polygon_id
|
||||
deal['deal_source'] = 'neighborhood'
|
||||
deal['priority'] = 2 # Lowest priority
|
||||
neighborhood_deals.append(deal)
|
||||
|
||||
except Exception as e:
|
||||
logger.warning(f"Error processing polygon {polygon_id}: {e}")
|
||||
continue
|
||||
|
||||
# Step 5: Sort by date (newest first)
|
||||
all_deals.sort(key=lambda x: x.get('dealDate', ''), reverse=True)
|
||||
# Step 5: Combine and prioritize: building deals first, then street, then neighborhood
|
||||
all_deals = building_deals + street_deals + neighborhood_deals
|
||||
|
||||
logger.info(f"Found {len(all_deals)} total deals for address: {address}")
|
||||
# Use stable sort: first by date (newest first), then by priority
|
||||
# Since Python's sort is stable, the second sort maintains date order within each priority
|
||||
all_deals.sort(key=lambda x: x.get('dealDate', '1900-01-01'), reverse=True) # Newest first
|
||||
all_deals.sort(key=lambda x: x.get('priority', 3)) # Priority first (0=building, 1=street, 2=neighborhood)
|
||||
|
||||
# Limit to max_deals
|
||||
if len(all_deals) > max_deals:
|
||||
all_deals = all_deals[:max_deals]
|
||||
|
||||
# Add price per square meter calculation
|
||||
for deal in all_deals:
|
||||
price = deal.get('dealAmount', 0)
|
||||
area = deal.get('assetArea', 0)
|
||||
if isinstance(price, (int, float)) and isinstance(area, (int, float)) and area > 0:
|
||||
deal['price_per_sqm'] = round(price / area, 2)
|
||||
else:
|
||||
deal['price_per_sqm'] = None
|
||||
|
||||
logger.info(f"Found {len(all_deals)} total deals for address: {address} "
|
||||
f"(Building: {len(building_deals)}, Street: {len(street_deals)}, Neighborhood: {len(neighborhood_deals)})")
|
||||
return all_deals
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Error in find_recent_deals_for_address: {e}")
|
||||
raise
|
||||
|
||||
def _is_same_building(self, search_address: str, deal_address: str) -> bool:
|
||||
"""
|
||||
Check if a deal is from the same building as the search address.
|
||||
|
||||
Args:
|
||||
search_address: The normalized search address (lowercase, stripped)
|
||||
deal_address: The normalized deal address (lowercase, stripped)
|
||||
|
||||
Returns:
|
||||
True if likely the same building, False otherwise
|
||||
"""
|
||||
if not search_address or not deal_address:
|
||||
return False
|
||||
|
||||
# Exact match
|
||||
if search_address == deal_address:
|
||||
return True
|
||||
|
||||
# Extract key components for comparison
|
||||
def extract_address_parts(addr: str) -> tuple:
|
||||
"""Extract street name and number from address"""
|
||||
# Remove common prefixes/suffixes and normalize
|
||||
addr_clean = addr.replace('רח\'', '').replace('רחוב', '').replace('שד\'', '').replace('שדרות', '')
|
||||
addr_clean = addr_clean.replace(' ', ' ').strip()
|
||||
|
||||
# Try to extract number and street name
|
||||
parts = addr_clean.split()
|
||||
if len(parts) >= 2:
|
||||
# Look for number (could be at start or end)
|
||||
for i, part in enumerate(parts):
|
||||
if part.isdigit() or any(c.isdigit() for c in part):
|
||||
number = part
|
||||
street_parts = parts[:i] + parts[i+1:]
|
||||
street_name = ' '.join(street_parts).strip()
|
||||
return (street_name, number)
|
||||
|
||||
return (addr_clean, '')
|
||||
|
||||
search_street, search_number = extract_address_parts(search_address)
|
||||
deal_street, deal_number = extract_address_parts(deal_address)
|
||||
|
||||
# Same street and same number = same building
|
||||
if (search_street and deal_street and search_number and deal_number and
|
||||
search_street == deal_street and search_number == deal_number):
|
||||
return True
|
||||
|
||||
# Check if one address is contained in the other (for different formats of same address)
|
||||
if len(search_address) > 5 and len(deal_address) > 5:
|
||||
if search_address in deal_address or deal_address in search_address:
|
||||
return True
|
||||
|
||||
return False
|
||||
Reference in New Issue
Block a user