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:
|
||||
return f"Error fetching street deals: {str(e)}"
|
||||
|
||||
@mcp.tool()
|
||||
def find_recent_deals_for_address(address: str, years_back: int = 2, radius_meters: int = 30, max_deals: int = 200) -> str:
|
||||
def find_recent_deals_for_address(address: str, years_back: int = 2, radius_meters: int = 30, max_deals: int = 50) -> str:
|
||||
"""Find recent real estate deals for a specific address.
|
||||
|
||||
Args:
|
||||
@@ -120,7 +120,7 @@ def find_recent_deals_for_address(address: str, years_back: int = 2, radius_mete
|
||||
years_back: How many years back to search (default: 2)
|
||||
radius_meters: Search radius in meters from the address (default: 30)
|
||||
Small radius since street deals cover the entire street anyway
|
||||
max_deals: Maximum number of deals to return (default: 200)
|
||||
max_deals: Maximum number of deals to return (default: 50, optimized for LLM token limits)
|
||||
|
||||
Returns:
|
||||
JSON string containing recent real estate deals for the address
|
||||
@@ -243,24 +243,17 @@ def get_neighborhood_deals(polygon_id: str, limit: int = 100) -> str:
|
||||
return f"Error fetching neighborhood deals: {str(e)}"
|
||||
|
||||
@mcp.tool()
|
||||
def analyze_market_trends(address: str, years_back: int = 3, radius_meters: int = 300, max_deals: int = 500) -> str:
|
||||
def analyze_market_trends(address: str, years_back: int = 3, radius_meters: int = 300, max_deals: int = 100) -> str:
|
||||
"""Analyze market trends and price patterns for an area with comprehensive data.
|
||||
|
||||
Args:
|
||||
address: The address to analyze trends around
|
||||
years_back: How many years of data to analyze (default: 3)
|
||||
radius_meters: Search radius in meters from the address (default: 300, larger for trend analysis)
|
||||
max_deals: Maximum number of deals to analyze (default: 500)
|
||||
max_deals: Maximum number of deals to analyze (default: 100, optimized for performance and token limits)
|
||||
|
||||
Returns:
|
||||
JSON string containing comprehensive market trend analysis including:
|
||||
- Detailed price trends over time
|
||||
- Average prices by property type
|
||||
- Market activity levels and patterns
|
||||
- Price per square meter trends
|
||||
- Seasonal patterns
|
||||
- Market velocity indicators
|
||||
- Comparative neighborhood analysis
|
||||
JSON string containing comprehensive market trend analysis (summarized data, not raw deals)
|
||||
"""
|
||||
try:
|
||||
# Get deals for the address with larger radius for trend analysis
|
||||
@@ -269,25 +262,20 @@ def analyze_market_trends(address: str, years_back: int = 3, radius_meters: int
|
||||
if not deals:
|
||||
return f"No deals found for comprehensive market analysis near '{address}'"
|
||||
|
||||
# Comprehensive analysis structure
|
||||
# Efficient analysis with reduced complexity
|
||||
from collections import defaultdict
|
||||
|
||||
yearly_data = defaultdict(list)
|
||||
monthly_data = defaultdict(list)
|
||||
property_types: Dict[str, List[Dict]] = defaultdict(list)
|
||||
property_types: Dict[str, List[float]] = defaultdict(list) # Store only prices for efficiency
|
||||
neighborhoods = defaultdict(list)
|
||||
quarterly_data = defaultdict(list)
|
||||
|
||||
# Process each deal for comprehensive analysis
|
||||
# Simplified processing - extract only essential data
|
||||
for deal in deals:
|
||||
date_str = deal.get('dealDate', '')
|
||||
if not date_str:
|
||||
continue
|
||||
|
||||
year = date_str[:4]
|
||||
month = date_str[:7] # YYYY-MM
|
||||
quarter = f"{year}-Q{((int(date_str[5:7]) - 1) // 3) + 1}" if len(date_str) >= 7 else None
|
||||
|
||||
price = deal.get('dealAmount')
|
||||
area = deal.get('assetArea')
|
||||
price_per_sqm = deal.get('price_per_sqm')
|
||||
@@ -295,172 +283,99 @@ def analyze_market_trends(address: str, years_back: int = 3, radius_meters: int
|
||||
neighborhood = deal.get('settlementNameHeb', deal.get('neighborhood', 'לא ידוע'))
|
||||
deal_source = deal.get('deal_source', 'unknown')
|
||||
|
||||
deal_data = {
|
||||
'price': price,
|
||||
'area': area,
|
||||
'price_per_sqm': price_per_sqm,
|
||||
'property_type': prop_type,
|
||||
'neighborhood': neighborhood,
|
||||
'deal_source': deal_source,
|
||||
'date': date_str
|
||||
}
|
||||
if isinstance(price, (int, float)) and isinstance(area, (int, float)) and area > 0 and isinstance(price_per_sqm, (int, float)):
|
||||
yearly_data[year].append({
|
||||
'price': price, 'area': area, 'price_per_sqm': price_per_sqm, 'deal_source': deal_source
|
||||
})
|
||||
property_types[prop_type].append(price_per_sqm)
|
||||
neighborhoods[neighborhood].append(price_per_sqm)
|
||||
|
||||
if isinstance(price, (int, float)) and isinstance(area, (int, float)) and area > 0:
|
||||
yearly_data[year].append(deal_data)
|
||||
monthly_data[month].append(deal_data)
|
||||
if quarter:
|
||||
quarterly_data[quarter].append(deal_data)
|
||||
property_types[prop_type].append(deal_data)
|
||||
neighborhoods[neighborhood].append(deal_data)
|
||||
|
||||
# Calculate comprehensive yearly trends
|
||||
# Calculate streamlined yearly trends
|
||||
yearly_trends = {}
|
||||
for year, year_deals in yearly_data.items():
|
||||
if year_deals:
|
||||
prices = [d['price'] for d in year_deals if d['price']]
|
||||
price_per_sqm_vals = [d['price_per_sqm'] for d in year_deals if d['price_per_sqm']]
|
||||
areas = [d['area'] for d in year_deals if d['area']]
|
||||
prices = [d['price'] for d in year_deals]
|
||||
price_per_sqm_vals = [d['price_per_sqm'] for d in year_deals]
|
||||
building_deals = [d for d in year_deals if d['deal_source'] == 'same_building']
|
||||
street_deals = [d for d in year_deals if d['deal_source'] == 'street']
|
||||
neighborhood_deals = [d for d in year_deals if d['deal_source'] == 'neighborhood']
|
||||
|
||||
yearly_trends[year] = {
|
||||
"deal_count": len(year_deals),
|
||||
"same_building_deals_count": len(building_deals),
|
||||
"street_deals_count": len(street_deals),
|
||||
"neighborhood_deals_count": len(neighborhood_deals),
|
||||
"same_building_percentage": round((len(building_deals) / len(year_deals)) * 100, 1),
|
||||
"street_deals_percentage": round((len(street_deals) / len(year_deals)) * 100, 1),
|
||||
"average_price": round(sum(prices) / len(prices), 0) if prices else 0,
|
||||
"median_price": round(sorted(prices)[len(prices)//2], 0) if prices else 0,
|
||||
"min_price": min(prices) if prices else 0,
|
||||
"max_price": max(prices) if prices else 0,
|
||||
"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,
|
||||
"average_area": round(sum(areas) / len(areas), 1) if areas else 0,
|
||||
"average_price_per_sqm": round(sum(price_per_sqm_vals) / len(price_per_sqm_vals), 0) if price_per_sqm_vals else 0,
|
||||
"median_price_per_sqm": round(sorted(price_per_sqm_vals)[len(price_per_sqm_vals)//2], 0) if price_per_sqm_vals else 0,
|
||||
"total_market_volume": sum(prices) if prices else 0
|
||||
"same_building_deals": len(building_deals),
|
||||
"street_deals": len(street_deals),
|
||||
"avg_price": round(sum(prices) / len(prices), 0),
|
||||
"avg_price_per_sqm": round(sum(price_per_sqm_vals) / len(price_per_sqm_vals), 0),
|
||||
"min_price_per_sqm": round(min(price_per_sqm_vals), 0),
|
||||
"max_price_per_sqm": round(max(price_per_sqm_vals), 0),
|
||||
"total_volume": sum(prices)
|
||||
}
|
||||
|
||||
# Calculate quarterly trends for seasonality analysis
|
||||
quarterly_trends = {}
|
||||
for quarter, quarter_deals in quarterly_data.items():
|
||||
if quarter_deals:
|
||||
prices = [d['price'] for d in quarter_deals if d['price']]
|
||||
price_per_sqm_vals = [d['price_per_sqm'] for d in quarter_deals if d['price_per_sqm']]
|
||||
|
||||
quarterly_trends[quarter] = {
|
||||
"deal_count": len(quarter_deals),
|
||||
"average_price": round(sum(prices) / len(prices), 0) if prices else 0,
|
||||
"average_price_per_sqm": round(sum(price_per_sqm_vals) / len(price_per_sqm_vals), 0) if price_per_sqm_vals else 0
|
||||
}
|
||||
|
||||
# Property type analysis
|
||||
# Streamlined property type analysis (top 5 only)
|
||||
property_type_analysis = {}
|
||||
for prop_type, type_deals in property_types.items():
|
||||
if type_deals:
|
||||
prices = [d['price'] for d in type_deals if d['price']]
|
||||
price_per_sqm_vals = [d['price_per_sqm'] for d in type_deals if d['price_per_sqm']]
|
||||
areas = [d['area'] for d in type_deals if d['area']]
|
||||
|
||||
for prop_type, prices_per_sqm in property_types.items():
|
||||
if len(prices_per_sqm) >= 2: # Only include types with multiple deals
|
||||
property_type_analysis[prop_type] = {
|
||||
"deal_count": len(type_deals),
|
||||
"market_share_percentage": round((len(type_deals) / len(deals)) * 100, 1),
|
||||
"average_price": round(sum(prices) / len(prices), 0) if prices else 0,
|
||||
"average_area": round(sum(areas) / len(areas), 1) if areas else 0,
|
||||
"average_price_per_sqm": round(sum(price_per_sqm_vals) / len(price_per_sqm_vals), 0) if price_per_sqm_vals else 0
|
||||
"deal_count": len(prices_per_sqm),
|
||||
"avg_price_per_sqm": round(sum(prices_per_sqm) / len(prices_per_sqm), 0)
|
||||
}
|
||||
|
||||
# Neighborhood comparison analysis
|
||||
# Keep only top 5 property types by deal count
|
||||
property_type_analysis = dict(sorted(property_type_analysis.items(),
|
||||
key=lambda x: x[1]['deal_count'], reverse=True)[:5])
|
||||
|
||||
# Streamlined neighborhood analysis (top 5 only)
|
||||
neighborhood_analysis = {}
|
||||
for neighborhood, neighborhood_deals in neighborhoods.items():
|
||||
if neighborhood_deals and len(neighborhood_deals) >= 3: # Only include neighborhoods with sufficient data
|
||||
prices = [d['price'] for d in neighborhood_deals if d['price']]
|
||||
price_per_sqm_vals = [d['price_per_sqm'] for d in neighborhood_deals if d['price_per_sqm']]
|
||||
|
||||
for neighborhood, prices_per_sqm in neighborhoods.items():
|
||||
if len(prices_per_sqm) >= 3: # Minimum 3 deals for statistical significance
|
||||
neighborhood_analysis[neighborhood] = {
|
||||
"deal_count": len(neighborhood_deals),
|
||||
"average_price": round(sum(prices) / len(prices), 0) if prices else 0,
|
||||
"average_price_per_sqm": round(sum(price_per_sqm_vals) / len(price_per_sqm_vals), 0) if price_per_sqm_vals else 0
|
||||
"deal_count": len(prices_per_sqm),
|
||||
"avg_price_per_sqm": round(sum(prices_per_sqm) / len(prices_per_sqm), 0)
|
||||
}
|
||||
|
||||
# Market trend direction analysis
|
||||
price_trend_analysis = {}
|
||||
# Keep only top 5 neighborhoods by deal count
|
||||
neighborhood_analysis = dict(sorted(neighborhood_analysis.items(),
|
||||
key=lambda x: x[1]['deal_count'], reverse=True)[:5])
|
||||
|
||||
# Simple trend analysis
|
||||
years_sorted = sorted(yearly_trends.keys())
|
||||
trend_analysis = {}
|
||||
if len(years_sorted) >= 2:
|
||||
first_year_data = yearly_trends[years_sorted[0]]
|
||||
last_year_data = yearly_trends[years_sorted[-1]]
|
||||
first_year = yearly_trends[years_sorted[0]]
|
||||
last_year = yearly_trends[years_sorted[-1]]
|
||||
|
||||
# Price trend
|
||||
first_year_avg = first_year_data['average_price_per_sqm']
|
||||
last_year_avg = last_year_data['average_price_per_sqm']
|
||||
if first_year['avg_price_per_sqm'] > 0:
|
||||
price_change = ((last_year['avg_price_per_sqm'] - first_year['avg_price_per_sqm']) / first_year['avg_price_per_sqm']) * 100
|
||||
volume_change = ((last_year['deal_count'] - first_year['deal_count']) / first_year['deal_count']) * 100 if first_year['deal_count'] > 0 else 0
|
||||
|
||||
if first_year_avg > 0:
|
||||
price_trend_percentage = ((last_year_avg - first_year_avg) / first_year_avg) * 100
|
||||
price_trend_direction = "עולה" if price_trend_percentage > 5 else "יורד" if price_trend_percentage < -5 else "יציב"
|
||||
|
||||
# Volume trend
|
||||
first_year_volume = first_year_data['deal_count']
|
||||
last_year_volume = last_year_data['deal_count']
|
||||
volume_trend_percentage = ((last_year_volume - first_year_volume) / first_year_volume) * 100 if first_year_volume > 0 else 0
|
||||
volume_trend_direction = "עולה" if volume_trend_percentage > 10 else "יורד" if volume_trend_percentage < -10 else "יציב"
|
||||
|
||||
price_trend_analysis = {
|
||||
"price_trend_direction": price_trend_direction,
|
||||
"price_trend_percentage": round(price_trend_percentage, 1),
|
||||
"volume_trend_direction": volume_trend_direction,
|
||||
"volume_trend_percentage": round(volume_trend_percentage, 1),
|
||||
"analysis_period": f"{years_sorted[0]} - {years_sorted[-1]}",
|
||||
"first_year_avg_price_per_sqm": round(first_year_avg, 0),
|
||||
"last_year_avg_price_per_sqm": round(last_year_avg, 0),
|
||||
"total_price_change": round(last_year_avg - first_year_avg, 0),
|
||||
"annualized_price_growth": round(price_trend_percentage / len(years_sorted), 1)
|
||||
trend_analysis = {
|
||||
"price_trend_percentage": round(price_change, 1),
|
||||
"volume_trend_percentage": round(volume_change, 1),
|
||||
"first_year_avg_price_per_sqm": first_year['avg_price_per_sqm'],
|
||||
"last_year_avg_price_per_sqm": last_year['avg_price_per_sqm']
|
||||
}
|
||||
|
||||
# Market velocity indicators
|
||||
market_velocity = {
|
||||
"average_deals_per_month": round(len(deals) / (years_back * 12), 1),
|
||||
"peak_activity_quarter": max(quarterly_trends.keys(), key=lambda q: quarterly_trends[q]['deal_count']) if quarterly_trends else None,
|
||||
"lowest_activity_quarter": min(quarterly_trends.keys(), key=lambda q: quarterly_trends[q]['deal_count']) if quarterly_trends else None
|
||||
}
|
||||
|
||||
# Price distribution analysis
|
||||
all_prices_per_sqm = [deal.get('price_per_sqm', 0) for deal in deals if deal.get('price_per_sqm')]
|
||||
price_distribution = {}
|
||||
if all_prices_per_sqm:
|
||||
sorted_prices = sorted(all_prices_per_sqm)
|
||||
price_distribution = {
|
||||
"25th_percentile": round(sorted_prices[len(sorted_prices)//4], 0),
|
||||
"75th_percentile": round(sorted_prices[3*len(sorted_prices)//4], 0),
|
||||
"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 summarized analysis (NO raw deals to save tokens)
|
||||
return json.dumps({
|
||||
"analysis_parameters": {
|
||||
"address": address,
|
||||
"analysis_period_years": years_back,
|
||||
"search_radius_meters": radius_meters,
|
||||
"max_deals_analyzed": max_deals
|
||||
"years_analyzed": years_back,
|
||||
"radius_meters": radius_meters,
|
||||
"deals_analyzed": len(deals)
|
||||
},
|
||||
"market_overview": {
|
||||
"total_deals_analyzed": len(deals),
|
||||
"unique_neighborhoods": len(neighborhoods),
|
||||
"unique_property_types": len(property_types),
|
||||
"data_coverage_years": len(yearly_trends)
|
||||
"market_summary": {
|
||||
"total_deals": len(deals),
|
||||
"years_with_data": len(yearly_trends),
|
||||
"unique_property_types": len(property_type_analysis),
|
||||
"unique_neighborhoods": len(neighborhood_analysis)
|
||||
},
|
||||
"yearly_trends": yearly_trends,
|
||||
"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'])}"
|
||||
"top_property_types": property_type_analysis,
|
||||
"top_neighborhoods": neighborhood_analysis,
|
||||
"trend_analysis": trend_analysis,
|
||||
"key_insights": {
|
||||
"most_active_year": max(yearly_trends.keys(), key=lambda y: yearly_trends[y]['deal_count']) if yearly_trends else None,
|
||||
"highest_avg_price_year": max(yearly_trends.keys(), key=lambda y: yearly_trends[y]['avg_price_per_sqm']) if yearly_trends else None,
|
||||
"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'])}"
|
||||
}
|
||||
}, ensure_ascii=False, indent=2)
|
||||
|
||||
|
||||
@@ -226,7 +226,7 @@ class GovmapClient:
|
||||
return []
|
||||
|
||||
def find_recent_deals_for_address(self, address: str, years_back: int = 2,
|
||||
radius: int = 30, max_deals: int = 200) -> List[Dict[str, Any]]:
|
||||
radius: int = 30, max_deals: int = 50) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
Find all relevant real estate deals for a given address from the last few years.
|
||||
|
||||
|
||||
Reference in New Issue
Block a user