ST-RAP: A Spatio-Temporal Framework for Real Estate Appraisal
About
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
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Spatiotemporal forecasting | Mianyang 20 (train) | MAE1.535 | 12 | |
| Spatiotemporal forecasting | Zhuhai 20 Instances (train) | MAE6.69 | 12 | |
| Spatiotemporal forecasting | Mianyang 100 (train) | MAE1.449 | 12 | |
| Spatiotemporal forecasting | Mianyang 500 (train) | MAE1.068 | 12 | |
| Spatiotemporal forecasting | Shaoxing (train) | MAE2.675 | 12 | |
| Spatiotemporal forecasting | Shaoxing 20 Instances (train) | MAE3.471 | 12 | |
| Spatiotemporal forecasting | Shaoxing 100 Instances (train) | MAE3.065 | 12 | |
| Spatiotemporal forecasting | Zhuhai 100 (train) | MAE6.218 | 12 | |
| Spatiotemporal forecasting | Zhuhai (train) | MAE3.938 | 12 |
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