Optimization because of long running times
This commit is contained in:
Nitzan Pomerantz
+70
-155
@@ -112,7 +112,7 @@ 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, radius_meters: int = 30, max_deals: int = 200) -> str:
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def find_recent_deals_for_address(address: str, years_back: int = 2, radius_meters: int = 30, max_deals: int = 50) -> str:
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"""Find recent real estate deals for a specific address.
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Args:
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@@ -120,7 +120,7 @@ def find_recent_deals_for_address(address: str, years_back: int = 2, radius_mete
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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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max_deals: Maximum number of deals to return (default: 50, optimized for LLM token limits)
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Returns:
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JSON string containing recent real estate deals for the address
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@@ -243,24 +243,17 @@ 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, radius_meters: int = 300, max_deals: int = 500) -> str:
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def analyze_market_trends(address: str, years_back: int = 3, radius_meters: int = 300, max_deals: int = 100) -> 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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max_deals: Maximum number of deals to analyze (default: 100, optimized for performance and token limits)
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Returns:
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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 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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JSON string containing comprehensive market trend analysis (summarized data, not raw deals)
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"""
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try:
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# Get deals for the address with larger radius for trend analysis
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@@ -269,25 +262,20 @@ def analyze_market_trends(address: str, years_back: int = 3, radius_meters: int
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if not deals:
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return f"No deals found for comprehensive market analysis near '{address}'"
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# Comprehensive analysis structure
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# Efficient analysis with reduced complexity
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from collections import defaultdict
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yearly_data = defaultdict(list)
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monthly_data = defaultdict(list)
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property_types: Dict[str, List[Dict]] = defaultdict(list)
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property_types: Dict[str, List[float]] = defaultdict(list) # Store only prices for efficiency
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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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# Simplified processing - extract only essential data
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for deal in deals:
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date_str = deal.get('dealDate', '')
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if not date_str:
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continue
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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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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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@@ -295,172 +283,99 @@ def analyze_market_trends(address: str, years_back: int = 3, radius_meters: int
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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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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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if isinstance(price, (int, float)) and isinstance(area, (int, float)) and area > 0 and isinstance(price_per_sqm, (int, float)):
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yearly_data[year].append({
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'price': price, 'area': area, 'price_per_sqm': price_per_sqm, 'deal_source': deal_source
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})
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property_types[prop_type].append(price_per_sqm)
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neighborhoods[neighborhood].append(price_per_sqm)
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# Calculate comprehensive yearly trends
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# Calculate streamlined 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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prices = [d['price'] for d in year_deals]
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price_per_sqm_vals = [d['price_per_sqm'] for d in year_deals]
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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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"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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"same_building_deals": len(building_deals),
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"street_deals": len(street_deals),
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"avg_price": round(sum(prices) / len(prices), 0),
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"avg_price_per_sqm": round(sum(price_per_sqm_vals) / len(price_per_sqm_vals), 0),
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"min_price_per_sqm": round(min(price_per_sqm_vals), 0),
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"max_price_per_sqm": round(max(price_per_sqm_vals), 0),
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"total_volume": sum(prices)
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}
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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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# Streamlined property type analysis (top 5 only)
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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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for prop_type, prices_per_sqm in property_types.items():
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if len(prices_per_sqm) >= 2: # Only include types with multiple deals
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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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"deal_count": len(prices_per_sqm),
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"avg_price_per_sqm": round(sum(prices_per_sqm) / len(prices_per_sqm), 0)
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}
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# Neighborhood comparison analysis
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# Keep only top 5 property types by deal count
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property_type_analysis = dict(sorted(property_type_analysis.items(),
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key=lambda x: x[1]['deal_count'], reverse=True)[:5])
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# Streamlined neighborhood analysis (top 5 only)
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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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for neighborhood, prices_per_sqm in neighborhoods.items():
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if len(prices_per_sqm) >= 3: # Minimum 3 deals for statistical significance
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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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"deal_count": len(prices_per_sqm),
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"avg_price_per_sqm": round(sum(prices_per_sqm) / len(prices_per_sqm), 0)
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}
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# Market trend direction analysis
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price_trend_analysis = {}
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# Keep only top 5 neighborhoods by deal count
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neighborhood_analysis = dict(sorted(neighborhood_analysis.items(),
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key=lambda x: x[1]['deal_count'], reverse=True)[:5])
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# Simple trend analysis
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years_sorted = sorted(yearly_trends.keys())
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trend_analysis = {}
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if len(years_sorted) >= 2:
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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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first_year = yearly_trends[years_sorted[0]]
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last_year = yearly_trends[years_sorted[-1]]
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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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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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if first_year['avg_price_per_sqm'] > 0:
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price_change = ((last_year['avg_price_per_sqm'] - first_year['avg_price_per_sqm']) / first_year['avg_price_per_sqm']) * 100
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volume_change = ((last_year['deal_count'] - first_year['deal_count']) / first_year['deal_count']) * 100 if first_year['deal_count'] > 0 else 0
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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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trend_analysis = {
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"price_trend_percentage": round(price_change, 1),
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"volume_trend_percentage": round(volume_change, 1),
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"first_year_avg_price_per_sqm": first_year['avg_price_per_sqm'],
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"last_year_avg_price_per_sqm": last_year['avg_price_per_sqm']
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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),
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"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
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}
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# Return summarized analysis (NO raw deals to save tokens)
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return json.dumps({
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"analysis_parameters": {
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"address": address,
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"analysis_period_years": years_back,
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"search_radius_meters": radius_meters,
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"max_deals_analyzed": max_deals
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"years_analyzed": years_back,
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"radius_meters": radius_meters,
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"deals_analyzed": len(deals)
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},
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"market_overview": {
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"total_deals_analyzed": len(deals),
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"unique_neighborhoods": len(neighborhoods),
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"unique_property_types": len(property_types),
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"data_coverage_years": len(yearly_trends)
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"market_summary": {
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"total_deals": len(deals),
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"years_with_data": len(yearly_trends),
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"unique_property_types": len(property_type_analysis),
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"unique_neighborhoods": len(neighborhood_analysis)
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},
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"yearly_trends": yearly_trends,
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"quarterly_trends": quarterly_trends,
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"property_type_analysis": property_type_analysis,
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"neighborhood_comparison": neighborhood_analysis,
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"market_trend_analysis": price_trend_analysis,
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"market_velocity_indicators": market_velocity,
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"price_distribution_analysis": price_distribution,
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"detailed_insights": {
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"most_active_property_type": max(property_type_analysis.keys(), key=lambda pt: property_type_analysis[pt]['deal_count']) if property_type_analysis else None,
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"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,
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"most_expensive_neighborhood": max(neighborhood_analysis.keys(), key=lambda n: neighborhood_analysis[n]['average_price_per_sqm']) if neighborhood_analysis else None,
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"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'])}"
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"top_property_types": property_type_analysis,
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"top_neighborhoods": neighborhood_analysis,
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"trend_analysis": trend_analysis,
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"key_insights": {
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"most_active_year": max(yearly_trends.keys(), key=lambda y: yearly_trends[y]['deal_count']) if yearly_trends else None,
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"highest_avg_price_year": max(yearly_trends.keys(), key=lambda y: yearly_trends[y]['avg_price_per_sqm']) if yearly_trends else None,
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"deal_source_summary": f"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'])}"
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}
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}, ensure_ascii=False, indent=2)
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