# MugRep: A Multi-Task Hierarchical Graph Representation Learning Framework for Real Estate Appraisal

arXiv: https://arxiv.org/abs/2107.05180 · HTML: https://mlsanity.com/paper/2107.05180/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 47 references that are in the map.
- First posted on arXiv 12 Jul 2021.

## Builds on (most cited first, 15 of 15)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Neural Message Passing for Quantum Chemistry](https://mlsanity.com/paper/1704.01212/) (2017)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [A simple neural network module for relational reasoning](https://mlsanity.com/paper/1706.01427/) (2017)
- [Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems](https://mlsanity.com/paper/1905.04413/) (2019)
- [Joint Air Quality and Weather Prediction Based on Multi-Adversarial Spatiotemporal Networks](https://mlsanity.com/paper/2012.15037/) (2020)
- [Intelligent Electric Vehicle Charging Recommendation Based on Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2102.07359/) (2021)
- [Take a Look Around: Using Street View and Satellite Images to Estimate House Prices](https://mlsanity.com/paper/1807.07155/) (2018)
- [Vision-based Real Estate Price Estimation](https://mlsanity.com/paper/1707.05489/) (2017)
- [Image Based Appraisal of Real Estate Properties](https://mlsanity.com/paper/1611.09180/) (2016)
- [Out-of-Town Recommendation with Travel Intention Modeling](https://mlsanity.com/paper/2101.12555/) (2021)
- [Crowdfunding Dynamics Tracking: A Reinforcement Learning Approach](https://mlsanity.com/paper/1912.12016/) (2019)

## Built on by (most cited first, 20 of 20)

- [UUKG: Unified Urban Knowledge Graph Dataset for Urban Spatiotemporal Prediction](https://mlsanity.com/paper/2306.11443/) (2023)
- [Irregular Traffic Time Series Forecasting Based on Asynchronous Spatio-Temporal Graph Convolutional Network](https://mlsanity.com/paper/2308.16818/) (2023)
- [Time-IMM: A Dataset and Benchmark for Irregular Multimodal Multivariate Time Series](https://mlsanity.com/paper/2506.10412/) (2025)
- [A Contextual Master-Slave Framework on Urban Region Graph for Urban Village Detection](https://mlsanity.com/paper/2211.14633/) (2022)
- [Interpretable Cascading Mixture-of-Experts for Urban Traffic Congestion Prediction](https://mlsanity.com/paper/2406.12923/) (2024)
- [Look Around! A Neighbor Relation Graph Learning Framework for Real Estate Appraisal](https://mlsanity.com/paper/2212.12190/) (2022)
- [ST-RAP: A Spatio-Temporal Framework for Real Estate Appraisal](https://mlsanity.com/paper/2308.10609/) (2023)
- [DoRA: Domain-Based Self-Supervised Learning Framework for Low-Resource Real Estate Appraisal](https://mlsanity.com/paper/2309.00855/) (2023)
- [When Graph Neural Network Meets Causality: Opportunities, Methodologies and An Outlook](https://mlsanity.com/paper/2312.12477/) (2023)
- [HiFGL: A Hierarchical Framework for Cross-silo Cross-device Federated Graph Learning](https://mlsanity.com/paper/2406.10616/) (2024)
- [NRFormer: Nationwide Nuclear Radiation Forecasting with Spatio-Temporal Transformer](https://mlsanity.com/paper/2410.11924/) (2024)
- [Spatial-Temporal Mixture-of-Graph-Experts for Multi-Type Crime Prediction](https://mlsanity.com/paper/2409.15764/) (2024)
- [Activity-aware Human Mobility Prediction with Hierarchical Graph Attention Recurrent Network](https://mlsanity.com/paper/2210.07765/) (2022)
- [HSTFL: A Heterogeneous Federated Learning Framework for Misaligned Spatiotemporal Forecasting](https://mlsanity.com/paper/2409.18482/) (2024)
- [Improving Real Estate Appraisal with POI Integration and Areal Embedding](https://mlsanity.com/paper/2311.11812/) (2023)
- [Hierarchical Structure Sharing Empowers Multi-task Heterogeneous GNNs for Customer Expansion](https://mlsanity.com/paper/2410.22089/) (2024)
- [Multimodal Machine Learning for Real Estate Appraisal: A Comprehensive Survey](https://mlsanity.com/paper/2503.22119/) (2025)
- [Meta-Transfer Learning Powered Temporal Graph Networks for Cross-City Real Estate Appraisal](https://mlsanity.com/paper/2410.08947/) (2024)
- [Sub-City Real Estate Price Index Forecasting at Weekly Horizons Using Satellite Radar and News Sentiment](https://mlsanity.com/paper/2602.18572/) (2026)
- [Retrieval-Enhanced Real Estate Appraisal](https://mlsanity.com/paper/2603.12986/) (2026)

## Abstract (arXiv)

Real estate appraisal refers to the process of developing an unbiased opinion for real property's market value, which plays a vital role in decision-making for various players in the marketplace (e.g., real estate agents, appraisers, lenders, and buyers). However, it is a nontrivial task for accurate real estate appraisal because of three major challenges: (1) The complicated influencing factors for property value; (2) The asynchronously spatiotemporal dependencies among real estate transactions; (3) The diversified correlations between residential communities. To this end, we propose a Multi-Task Hierarchical Graph Representation Learning (MugRep) framework for accurate real estate appraisal. Specifically, by acquiring and integrating multi-source urban data, we first construct a rich feature set to comprehensively profile the real estate from multiple perspectives (e.g., geographical distribution, human mobility distribution, and resident demographics distribution). Then, an evolving real estate transaction graph and a corresponding event graph convolution module are proposed to incorporate asynchronously spatiotemporal dependencies among real estate transactions. Moreover, to further incorporate valuable knowledge from the view of residential communities, we devise a hierarchical heterogeneous community graph convolution module to capture diversified correlations between residential communities. Finally, an urban district partitioned multi-task learning module is introduced to generate differently distributed value opinions for real estate. Extensive experiments on two real-world datasets demonstrate the effectiveness of MugRep and its components and features.