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ST-RAP: A Spatio-Temporal Framework for Real Estate Appraisal

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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.

Hojoon Lee, Hawon Jeong, Byungkun Lee, Kyungyup Lee, Jaegul Choo• 2023

Related benchmarks

TaskDatasetResultRank
Spatiotemporal forecastingMianyang 20 (train)
MAE1.535
12
Spatiotemporal forecastingZhuhai 20 Instances (train)
MAE6.69
12
Spatiotemporal forecastingMianyang 100 (train)
MAE1.449
12
Spatiotemporal forecastingMianyang 500 (train)
MAE1.068
12
Spatiotemporal forecastingShaoxing (train)
MAE2.675
12
Spatiotemporal forecastingShaoxing 20 Instances (train)
MAE3.471
12
Spatiotemporal forecastingShaoxing 100 Instances (train)
MAE3.065
12
Spatiotemporal forecastingZhuhai 100 (train)
MAE6.218
12
Spatiotemporal forecastingZhuhai (train)
MAE3.938
12
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