Summary of A Multi-modal Deep Learning Based Approach For House Price Prediction, by Md Hasebul Hasan et al.
A Multi-Modal Deep Learning Based Approach for House Price Prediction
by Md Hasebul Hasan, Md Abid Jahan, Mohammed Eunus Ali, Yuan-Fang Li, Timos Sellis
First submitted to arxiv on: 9 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 This paper proposes a multi-modal deep learning approach to predict house prices accurately by incorporating various attributes, including textual and visual features. The authors leverage different types of data, such as raw house attributes, geo-spatial neighborhood, textual descriptions, and images, to learn a joint embedding representation of the house. A downstream regression model is then used to predict the house price from this jointly learned embedding vector. Experimental results show that the inclusion of text embeddings from house advertisement descriptions and image embeddings from house pictures significantly improves the accuracy of house price predictions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to predict house prices by considering many different factors, like what’s inside the house, where it is located, and even pictures and words about the house. The researchers use a special kind of computer learning called deep learning to find patterns in lots of data, including text and images. They show that this approach can make more accurate predictions than previous methods. This could be helpful for people who want to buy or sell houses, as well as real estate companies trying to understand the housing market. |
Keywords
» Artificial intelligence » Deep learning » Embedding » Multi modal » Regression
