🏠 HomeMatch: AI-Powered Real Estate Assistant

HomeMatch is an AI-driven application that transforms the real estate search experience. Instead of scrolling through endless listings, users can chat with a conversational agent that learns their preferences and recommends properties tailored specifically to them.

🔗 Built for: Future Homes Realty
📌 Use Case: Personalized property discovery powered by Generative AI

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✨ Features

• Conversational Search – Find homes through natural dialogue rather than filters.
• Personalized Recommendations – Matches listings to your unique preferences (e.g., size, amenities, neighborhood style).
• Contextual Memory – Stores user responses in a vector database for tailored, evolving recommendations.
• Dynamic Listing Descriptions – Uses an LLM to generate persuasive, human-like property write-ups.

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🛠️ Tech Stack

• Language Models: OpenAI GPT-3.5 via langchain-openai
• Framework: LangChain
• Vector Database: ChromaDB with OpenAIEmbeddings
• Data Processing: pandas for CSV ingestion & formatting
• Environment: Jupyter Notebook (HomeMatch.ipynb)

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📂 Repository Structure

HomeMatch/
│── HomeMatch.ipynb       # Main notebook with full pipeline
│── listings.csv          # Sample real estate dataset
│── requirements.txt      # Dependencies
│── README.md             # Project documentation

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⚙️ How It Works

1. Data Ingestion

   • Listings from listings.csv are preprocessed into rich text features.

2. Vectorization

   • Listings + user answers are embedded into numerical vectors.
   • Stored in a Chroma vector database.

3. Conversational Interface

   • Users answer guided questions (size, amenities, location style, etc.).
   • Preferences are saved into the vector DB.

4. Recommendation Engine

   • The system retrieves the most relevant listings.
   • The LLM generates unique, human-friendly property descriptions personalized to the user.

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🚀 Getting Started

1. Clone the repo

git clone https://github.com/yourusername/HomeMatch.git
cd HomeMatch

2. Install dependencies

pip install -r requirements.txt

3. Set your API keys

Add your OpenAI key:

export OPENAI_API_KEY="your_api_key"

4. Run the notebook

jupyter notebook HomeMatch.ipynb

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📊 Example Interaction

User: “I want a suburban home, around 1200 sqft, with a backyard and pool, close to schools.”
HomeMatch:
“I’ve found a home in Suburban Sanctuary for $600,000. It has 4 bedrooms, 3 bathrooms, and a safe family-friendly neighborhood with nearby top-rated schools. The backyard includes space for gatherings and the area is known for its strong community watch.”

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✅ Future Improvements

• Integrate real-time MLS feeds for live data.
• Deploy a web UI with Streamlit or React.
• Add multi-modal search (images + text).
• Extend to rental listings and mortgage calculators.

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