# A Multi-Modal Deep Learning Based Approach for House Price Prediction

arXiv: https://arxiv.org/abs/2409.05335 · HTML: https://mlsanity.com/paper/2409.05335/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 42 references that are in the map.
- First posted on arXiv 9 Sep 2024.

## Builds on (most cited first, 10 of 10)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [Supervision Exists Everywhere: A Data Efficient Contrastive Language-Image Pre-training Paradigm](https://mlsanity.com/paper/2110.05208/) (2021)
- [Self-supervised learning of visual features through embedding images into text topic spaces](https://mlsanity.com/paper/1705.08631/) (2017)
- [Vision-based Real Estate Price Estimation](https://mlsanity.com/paper/1707.05489/) (2017)
- [Boosting House Price Predictions using Geo-Spatial Network Embedding](https://mlsanity.com/paper/2009.00254/) (2020)
- [House Price Prediction Using LSTM](https://mlsanity.com/paper/1709.08432/) (2017)

## Built on by (most cited first, 2 of 2)

- [Sub-City Real Estate Price Index Forecasting at Weekly Horizons Using Satellite Radar and News Sentiment](https://mlsanity.com/paper/2602.18572/) (2026)
- [Representation Risk in Pretrained Image Encoders](https://mlsanity.com/paper/2609.35470/) (2026)

## Abstract (arXiv)

Accurate prediction of house price, a vital aspect of the residential real estate sector, is of substantial interest for a wide range of stakeholders. However, predicting house prices is a complex task due to the significant variability influenced by factors such as house features, location, neighborhood, and many others. Despite numerous attempts utilizing a wide array of algorithms, including recent deep learning techniques, to predict house prices accurately, existing approaches have fallen short of considering a wide range of factors such as textual and visual features. This paper addresses this gap by comprehensively incorporating attributes, such as features, textual descriptions, geo-spatial neighborhood, and house images, typically showcased in real estate listings in a house price prediction system. Specifically, we propose a multi-modal deep learning approach that leverages different types of data to learn more accurate representation of the house. In particular, we learn a joint embedding of raw house attributes, geo-spatial neighborhood, and most importantly from textual description and images representing the house; and finally use a downstream regression model to predict the house price from this jointly learned embedding vector. Our experimental results with a real-world dataset show that the text embedding of the house advertisement description and image embedding of the house pictures in addition to raw attributes and geo-spatial embedding, can significantly improve the house price prediction accuracy. The relevant source code and dataset are publicly accessible at the following URL: https://github.com/4P0N/mhpp