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HabitatAgent: An End-to-End Multi-Agent System for Housing Consultation

Hongyang Yang, Yanxin Zhang, Yang She, Yue Xiao, Hao Wu, Yiyang Zhang, Jiapeng Hou, Rongshan Zhang

Abstract

Housing selection is a high-stakes and largely irreversible decision problem. We study housing consultation as a decision-support interface for housing selection. Existing housing platforms and many LLM-based assistants often reduce this process to ranking or recommendation, resulting in opaque reasoning, brittle multi-constraint handling, and limited guarantees on factuality. We present HabitatAgent, the first LLM-powered multi-agent architecture for end-to-end housing consultation. HabitatAgent comprises four specialized agent roles: Memory, Retrieval, Generation, and Validation. The Memory Agent maintains multi-layer user memory through internal stages for constraint extraction, memory fusion, and verification-gated updates; the Retrieval Agent performs hybrid vector--graph retrieval (GraphRAG); the Generation Agent produces evidence-referenced recommendations and explanations; and the Validation Agent applies multi-tier verification and targeted remediation. Together, these agents provide an auditable and reliable workflow for end-to-end housing consultation. We evaluate HabitatAgent on 100 real user consultation scenarios (300 multi-turn question--answer pairs) under an end-to-end correctness protocol. A strong single-stage baseline (Dense+Rerank) achieves 75% accuracy, while HabitatAgent reaches 95%.

HabitatAgent: An End-to-End Multi-Agent System for Housing Consultation

Abstract

Housing selection is a high-stakes and largely irreversible decision problem. We study housing consultation as a decision-support interface for housing selection. Existing housing platforms and many LLM-based assistants often reduce this process to ranking or recommendation, resulting in opaque reasoning, brittle multi-constraint handling, and limited guarantees on factuality. We present HabitatAgent, the first LLM-powered multi-agent architecture for end-to-end housing consultation. HabitatAgent comprises four specialized agent roles: Memory, Retrieval, Generation, and Validation. The Memory Agent maintains multi-layer user memory through internal stages for constraint extraction, memory fusion, and verification-gated updates; the Retrieval Agent performs hybrid vector--graph retrieval (GraphRAG); the Generation Agent produces evidence-referenced recommendations and explanations; and the Validation Agent applies multi-tier verification and targeted remediation. Together, these agents provide an auditable and reliable workflow for end-to-end housing consultation. We evaluate HabitatAgent on 100 real user consultation scenarios (300 multi-turn question--answer pairs) under an end-to-end correctness protocol. A strong single-stage baseline (Dense+Rerank) achieves 75% accuracy, while HabitatAgent reaches 95%.

Paper Structure

This paper contains 31 sections, 3 equations, 4 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Contributions.
  3. Related Work
  4. Conversational and Decision-Support Systems for Housing
  5. LLMs and Agents in Real-Estate Applications
  6. Multi-Agent LLM Systems and Graph-Grounded Retrieval
  7. Gap and Positioning of This Work
  8. Methodology
  9. Memory Agent: Verification-Gated Multi-Layer Memory
  10. Four-layer memory structure
  11. Toward context-aware conversational memory.
  12. Gated update policy
  13. Retrieval Agent: Adaptive Hybrid Vector--Graph Retrieval (GraphRAG)
  14. Adaptive retrieval router
  15. Graph-constrained retrieval
  16. ...and 16 more sections

Figures (4)

  • Figure 1: HabitatAgent overview. Four specialized agents---Memory, Retrieval, Generation, and Validation---are executed as a five-stage workflow. In particular, Stage 1a (constraint extraction), Stage 1b (memory fusion), and Stage 5 (memory update) are internal substeps of the Memory Agent.
  • Figure 2: Use case of HabitatAgent. A user query is processed through constraint extraction, hybrid retrieval (GraphRAG), evidence-grounded generation, and multi-tier validation to produce a verified recommendation.
  • Figure 3: Product Demo of Fangdongdong. Scan the QR code above, or search for "Fangdongdong" in WeChat to access the product and experience the real estate decision-making system powered by HabitatAgent.
  • Figure :