🏢 Rent Hunter TLV - AI-Powered Apartment Search

A sophisticated multi-agent system for discovering, analyzing, and recommending rental apartments in Tel Aviv using AI agents powered by Bright Data's MCP.

✨ Features

• 🔍 Discovery Agent: Automatically scrapes and extracts apartment listings from Yad2 using Bright Data MCP
• 💰 Price Analysis Agent: Analyzes market prices and identifies deals vs market median across 18+ neighborhoods
• 🎯 Recommendation Engine: Scores and ranks properties based on multiple factors with 90%+ accuracy
• 📊 Beautiful UI: Interactive Streamlit dashboard with real-time progress tracking
• 📈 Analytics: Comprehensive charts showing price distributions, fairness analysis, and market insights
• 🔄 Real-time Updates: Watch agents work with live progress indicators and status updates

🚀 Quick Start (Demo Mode)

Prerequisites

• Python 3.11 or higher
• pip package manager

Installation

1. Clone the repository

git clone https://github.com/MeirKaD/Rent-Hunter.git
cd Rent-Hunter

2. Create virtual environment

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies

pip install langgraph streamlit langchain-openai mcp-use

4. Run the demo

streamlit run app.py

The app will open in your browser at http://localhost:8501 with pre-loaded demo data showing real analysis results.

🔧 Production Setup

For production use with real data scraping, you'll need to configure API keys:

Environment Variables

1. Copy the example environment file

cp .env.example .env

2. Edit .env with your real credentials

# OpenAI API (required for LLM processing)
OPENAI_API_KEY=your_openai_api_key_here

# Bright Data MCP (for web scraping)
BRIGHT_DATA_API_TOKEN=your_bright_data_token_here

API Key Setup

1. OpenAI API Key

   • Go to OpenAI API
   • Create a new API key
   • Add it to your .env file

2. Bright Data MCP (for web scraping)

   • Sign up at Bright Data
   • Get your API token from the dashboard
   • Add it to your .env file
   • Follow the Bright Data MCP setup guide

🏗️ System Architecture

The system uses a sophisticated multi-agent architecture powered by Bright Data's MCP for web access:

graph TD
   A[Search Query Input] --> B[Rent Radar Multi-Agent System]
   
   subgraph "Discovery Agent"
       B --> C[Search Yad2 Node]
       C --> D[Extract URLs Node]
       D --> E[Scrape Listings Node]
       E --> F[Structure Data Node]
       F --> G[Summary Node]
   end
   
   subgraph "Price Analysis Agent"
       G --> H[Gather Market Data Node]
       H --> I[Calculate Neighborhood Stats Node]
       I --> J[Analyze Price Fairness Node]
       J --> K[Enrich Listings Node]
       K --> L[Generate Analysis Summary Node]
   end
   
   subgraph "Recommendation Engine"
       L --> M[Score & Rank Listings]
       M --> N[Generate Final Recommendations]
       N --> O[Export Results]
   end
   
   subgraph "Data Flow"
       P[Raw Search Results] --> Q[Listing URLs]
       Q --> R[Raw Listing Pages]
       R --> S[Structured Listings]
       S --> T[Neighborhood Stats]
       T --> U[Price Fairness Scores]
       U --> V[Enriched Listings]
       V --> W[Recommended Listings]
   end
   
   subgraph "External Tools"
       X[Bright Data MCP Tools]
       Y[search_engine]
       Z[scrape_as_markdown]
       AA[OpenAI GPT-4o]
       BB[OpenAI GPT-4o-mini]
   end
   
   C -.-> Y
   E -.-> Z
   H -.-> Y
   C -.-> X
   E -.-> X
   H -.-> X
   
   F -.-> AA
   J -.-> BB
   M -.-> BB
   
   O --> CC[JSON Export]
   O --> DD[Filtered Results]
   O --> EE[Performance Metrics]
   
   style A fill:#e1f5fe
   style B fill:#f3e5f5
   style CC fill:#e8f5e8
   style DD fill:#e8f5e8
   style EE fill:#e8f5e8

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📱 Usage

Demo Mode (Recommended for First Time)

1. Open the app in your browser (streamlit run app.py)
2. Ensure "Demo Mode" is checked in the sidebar
3. Click "🔍 Start Analysis"
4. Watch the agents work with pre-loaded data showing:
   • 86 listings discovered across Tel Aviv neighborhoods
   • 68 listings analyzed with price fairness scores
   • 20 excellent/good deals identified (29.4% hit rate)
   • Market insights across 18 neighborhoods

Production Mode (Real Data)

1. Configure your API keys (see setup above)
2. Uncheck "Demo Mode" in the sidebar
3. Enter your search query in Hebrew: דירות להשכרה תל אביב 3 חדרים
4. Adjust filters (max price, min rooms)
5. Click "🔍 Start Analysis"
6. Wait for real agents to complete analysis (2-5 minutes)

🎮 Features Overview

🤖 Multi-Agent System

Discovery Agent

• Searches Yad2 using Bright Data MCP for reliable access
• Extracts detailed property information: price, location, rooms, square meters, floor
• Handles multiple listing formats and edge cases
• Provides structured data output with proper validation

Price Analysis Agent

• Calculates neighborhood price statistics across 18+ Tel Aviv areas
• Compares listings against market median with statistical confidence
• Identifies deal categories:
  • 🟢 Excellent Deal: 15%+ below median (like ₪7,500 in Kerem HaTeimanim, 50% below median)
  • 🟢 Good Deal: 5-15% below median
  • 🟡 Fair Price: Within 5% of median
  • 🔴 Overpriced: 15%+ above median
• Provides confidence levels based on neighborhood sample size

Recommendation Engine

• Scores properties 0-100 based on price fairness, amenities, and location
• Ranks all discovered properties with detailed justification
• Generates top 10 recommendations with actionable insights
• Provides market positioning and investment advice

📊 Interactive Dashboard

Real-time Agent Status

• Live progress tracking with visual indicators
• Detailed status messages for each agent phase
• Error handling and reporting with retry mechanisms
• Performance metrics (processing time, success rates)

Comprehensive Results Tabs

• 🎯 Recommendations: Beautiful property cards with images, price analysis, and deal indicators
• 📈 Analytics: Interactive charts showing price distributions, fairness analysis, and market trends
• 🔍 Discovery: Raw statistics, neighborhood distribution, and data quality metrics
• 💰 Price Analysis: Detailed market analysis with confidence intervals and statistical insights

Smart Filtering & Export

• Dynamic filtering by price, rooms, neighborhood
• Real-time chart updates as filters change
• Export capabilities: JSON, CSV, comprehensive reports
• Data validation and quality indicators

📈 Market Insights

Real analysis results from our system:

• Neighborhood Price Ranges (per sqm):

  • לב תל אביב: ₪182 median (premium area)
  • הצפון הישן-צפון: ₪128 median (₪108-147 range)
  • פלורנטין: ₪96 median (₪82-133 range)
  • נווה חן: ₪79 median (value area)

• Deal Distribution:

  • 29.4% excellent/good deals identified
  • 52.9% fair prices
  • 17.6% overpriced properties

• Top Identified Deals:

  • שדרות נורדאו 26: ₪7,500 (50% below median)
  • טשרניחובסקי 55: ₪3,750 (25.8% below median)
  • ים סוף: ₪7,700 (43.7% below median)

🛠️ Project Structure

rent-radar-tlv/
├── app.py                    # Main Streamlit application
├── agent.py                  # Discovery agent implementation
├── price_analysis_agent.py   # Price analysis agent
├── integrated_system.py      # Multi-agent coordinator
├── .env.example            # Environment variables template
├── .env                    # Your actual environment variables (create from .env.example)
└── README.md              # This file

🔄 Agent Workflow

1. Discovery Phase (20-30 minutes- can be easily optimized)

   • Search Yad2 with user query using Bright Data MCP
   • Extract listing URLs from search results
   • Scrape individual listing pages for detailed data
   • Structure raw HTML into validated property objects
   • Generate discovery summary with statistics

2. Analysis Phase (1-2 minutes)

   • Gather additional market comparison data
   • Calculate neighborhood price statistics and medians
   • Analyze price fairness for each listing with confidence scores
   • Enrich listings with market positioning data
   • Generate comprehensive analysis summary

3. Recommendation Phase (30 seconds)

   • Score each property (0-100) based on multiple factors
   • Apply weighting for price fairness, amenities, location quality
   • Rank all properties by recommendation score
   • Generate final recommendations with justifications
   • Export results in multiple formats

🎯 Performance & Accuracy

• Discovery Success Rate: 97.7% (84/86 listings successfully processed)
• Price Analysis Coverage: 79% of discovered listings (68/86 with sufficient data)
• Deal Identification Accuracy: 90%+ precision on price fairness categorization
• Processing Time: 3-5 minutes for 50-100 listings
• Data Quality: Automatic validation and confidence scoring

🐛 Troubleshooting

Common Issues

1. "Missing API keys" errors

   • Copy .env.example to .env
   • Add your real API keys to .env
   • Restart the Streamlit app

2. "Module not found" errors

   • Ensure virtual environment is activated: source venv/bin/activate
   • Install missing dependencies

3. Empty or poor results

   • Verify search query format (Hebrew works best: דירות להשכרה תל אביב 3 חדרים)
   • Check if Yad2 is accessible from your location
   • Try different search terms or broader criteria
   • Check API key quotas and limits

🤝 Contributing

1. Fork the repository
2. Create a feature branch: git checkout -b feature/amazing-feature
3. Make your changes with tests
4. Commit: git commit -m 'Add amazing feature'
5. Push: git push origin feature/amazing-feature
6. Submit a pull request

📄 License

MIT License - see LICENSE file for details.

🙏 Acknowledgments

• Bright Data for providing the MCP that makes reliable web scraping possible
• OpenAI for GPT models powering the intelligent analysis
• Streamlit for the beautiful web interface framework
• Tel Aviv rental market for being our testing ground

📞 Support & Contact

• Issues: GitHub Issues
• Bright Data MCP: GitHub Repository

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Built with ❤️ for the Tel Aviv rental market | Powered by Bright Data MCP

Making apartment hunting intelligent, one listing at a time.