ST-RAP: A Spatio-Temporal Framework for Real Estate Appraisal
In this paper, we introduce ST-RAP, a novel Spatio-Temporal framework for Real estate APpraisal. ST-RAP employs a hierarchical architecture with a heterogeneous graph neural network to encapsulate temporal dynamics and spatial relationships simultaneously. Through comprehensive experiments on a large-scale real estate dataset, ST-RAP outperforms previous methods, demonstrating the significant benefits of integrating spatial and temporal aspects in real estate appraisal. Our code and dataset are available at https://github.com/dojeon-ai/STRAP.
Code (1)
Tasks
Graph Neural NetworkMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
MugRep: A Multi-Task Hierarchical Graph Representation Learning Framework for Real Estate Appraisal
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…
Decision MakingGraph Representation LearningMulti-Task LearningRepresentation LearningMeta-Transfer Learning Empowered Temporal Graph Networks for Cross-City Real Estate Appraisal
Real estate appraisal is important for a variety of endeavors such as real estate deals, investment analysis, and real property taxation. Recently, deep learning has shown great promise for real estate appraisal by harne…
Meta-LearningMulti-Task LearningTransfer LearningImproving Real Estate Appraisal with POI Integration and Areal Embedding
Despite advancements in real estate appraisal methods, this study primarily focuses on two pivotal challenges. Firstly, we explore the often-underestimated impact of Points of Interest (POI) on property values, emphasizi…
feature selectionSpatial InterpolationMultimodal Machine Learning for Real Estate Appraisal: A Comprehensive Survey
Real estate appraisal has undergone a significant transition from manual to automated valuation and is entering a new phase of evolution. Leveraging comprehensive attention to various data sources, a novel approach to au…
SurveyHSTFL: A Heterogeneous Federated Learning Framework for Misaligned Spatiotemporal Forecasting
Spatiotemporal forecasting has emerged as an indispensable building block of diverse smart city applications, such as intelligent transportation and smart energy management. Recent advancements have uncovered that the pe…
energy managementFederated LearningRepresentation LearningTime Series+1