@inproceedings{shen-etal-2026-propgenie,
    title = "{P}rop{G}enie: A Multi-Agent Conversational Framework for Real Estate Assistance",
    author = "Shen, Chang  and
      Yuan, Shaozu  and
      Wu, Kuizong  and
      Xu, Long  and
      Chen, Meng",
    editor = "Croce, Danilo  and
      Leidner, Jochen  and
      Moosavi, Nafise Sadat",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 3: System Demonstrations)",
    month = mar,
    year = "2026",
    address = "Rabat, Marocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-demo.3/",
    pages = "33--45",
    ISBN = "979-8-89176-382-1",
    abstract = "In this paper, we present PropGenie, a novel multi-agent framework based on large language models (LLMs) to deliver comprehensive real estate assistance in real-world scenarios. PropGenie coordinates eight specialized sub-agents, each tailored for distinct tasks, including search and recommendation, question answering, financial calculations, and task execution. To enhance response accuracy and reliability, the system integrates diverse knowledge sources and advanced computational tools, leveraging structured, unstructured, and multimodal retrieval-augmented generation techniques. Experiments on real user queries show that PropGenie outperforms both a general-purpose LLM (OpenAI{'}s o3-mini-high) and a domain-specific chatbot (Realty AI{'}s Madison) in real estate scenarios. We hope that PropGenie serves as a valuable reference for future research in broader AI-driven applications."
}