Nitzan Pomerantz
99 lines
3.4 KiB
Python
99 lines
3.4 KiB
Python
#!/usr/bin/env python3
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"""
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Investment Comparison Example
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This example compares multiple neighborhoods to help identify
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the best investment opportunities based on various metrics.
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"""
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from nadlan_mcp.govmap import GovmapClient
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def analyze_location(client, address, years=2):
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"""Analyze a single location and return key metrics."""
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try:
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deals = client.find_recent_deals_for_address(address, years_back=years, radius=150)
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if not deals:
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return None
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stats = client.calculate_deal_statistics(deals)
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activity = client.calculate_market_activity_score(deals)
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liquidity = client.get_market_liquidity(deals)
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investment = client.analyze_investment_potential(deals)
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return {
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"address": address,
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"deal_count": len(deals),
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"avg_price": stats.mean_price,
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"avg_price_per_sqm": stats.mean_price_per_sqm,
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"activity_score": activity.activity_score,
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"activity_level": activity.activity_level,
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"liquidity_score": liquidity.liquidity_score,
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"investment_score": investment.investment_score,
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"price_trend": investment.price_trend,
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"appreciation_rate": investment.price_appreciation_rate,
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"volatility": investment.volatility_score,
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}
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except Exception as e:
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print(f"Error analyzing {address}: {e}")
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return None
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def main():
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client = GovmapClient()
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# Addresses to compare
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locations = [
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"רוטשילד 1 תל אביב", # Rothschild, Tel Aviv - Prestigious
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"ז'בוטינסקי 1 רמת גן", # Jabotinsky, Ramat Gan - Business district
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"הרצל 1 חיפה", # Herzl, Haifa - Northern city
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]
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print("=== Investment Comparison ===\n")
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print("Analyzing multiple neighborhoods...\n")
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results = []
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for location in locations:
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print(f"Analyzing: {location}")
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result = analyze_location(client, location, years=3)
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if result:
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results.append(result)
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print()
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if not results:
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print("No data available for comparison")
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return
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# Display comparison table
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print("\n" + "=" * 100)
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print("COMPARISON SUMMARY")
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print("=" * 100)
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for result in results:
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print(f"\n{result['address']}")
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print("-" * 80)
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print(f" Deal Count: {result['deal_count']}")
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print(f" Avg Price: ₪{result['avg_price']:,.0f}")
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if result['avg_price_per_sqm']:
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print(f" Avg Price/m²: ₪{result['avg_price_per_sqm']:,.0f}")
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print(f" Activity: {result['activity_score']:.0f}/100 ({result['activity_level']})")
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print(f" Liquidity: {result['liquidity_score']:.0f}/100")
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print(f" Investment Score: {result['investment_score']:.0f}/100")
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print(f" Price Trend: {result['price_trend']}")
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print(f" Appreciation: {result['appreciation_rate']:.2f}%/year")
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print(f" Volatility: {result['volatility']:.0f}/100")
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# Find best investment based on investment score
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best_investment = max(results, key=lambda x: x["investment_score"])
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print("\n" + "=" * 100)
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print("RECOMMENDATION")
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print("=" * 100)
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print(f"\nBest Investment Opportunity: {best_investment['address']}")
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print(f"Investment Score: {best_investment['investment_score']:.0f}/100")
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print(f"Price Appreciation: {best_investment['appreciation_rate']:.2f}%/year")
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if __name__ == "__main__":
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main()
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