Summary of Scalable Property Valuation Models Via Graph-based Deep Learning, by Enrique Riveros et al.
Scalable Property Valuation Models via Graph-based Deep Learning
by Enrique Riveros, Carla Vairetti, Christian Wegmann, Santiago Truffa, Sebastián Maldonado
First submitted to arxiv on: 10 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an innovative approach to enhance deep learning-based automated valuation models by incorporating an efficient graph representation of peer dependencies. This allows for capturing intricate spatial relationships between neighboring houses with similar features. Two novel graph neural network models are developed, employing different message passing algorithms: standard spatial graph convolutions and transformer graph convolutions. The proposed approach enables scalability in the modeling process. The evaluation is conducted using a proprietary dataset comprising approximately 200,000 houses in Santiago, Chile, showing significant improvements in house price prediction accuracy when utilizing transformer convolutional message passing layers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to make more accurate predictions about house prices. It does this by looking at relationships between neighboring homes and using special computer models called graph neural networks. These models are good at finding patterns in data that is connected, like houses that have similar features. The researchers use two different types of models to try and predict house prices better. They test their ideas on a big dataset of houses in Santiago, Chile, and find that it works really well. |
Keywords
» Artificial intelligence » Deep learning » Graph neural network » Transformer
