# A Comparison of Multi-View Learning Strategies for Satellite Image-Based Real Estate Appraisal

arXiv: https://arxiv.org/abs/2105.04984 · HTML: https://mlsanity.com/paper/2105.04984/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 25 references that are in the map.
- First posted on arXiv 11 May 2021.

## Builds on (most cited first, 8 of 8)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [A Survey on Multi-view Learning](https://mlsanity.com/paper/1304.5634/) (2013)
- [A Survey of Multi-View Representation Learning](https://mlsanity.com/paper/1610.01206/) (2016)
- [Take a Look Around: Using Street View and Satellite Images to Estimate House Prices](https://mlsanity.com/paper/1807.07155/) (2018)
- [Vision-based Real Estate Price Estimation](https://mlsanity.com/paper/1707.05489/) (2017)
- [Beyond Spatial Auto-Regressive Models: Predicting Housing Prices with Satellite Imagery](https://mlsanity.com/paper/1610.04805/) (2016)
- [Location, location, location: Satellite image-based real-estate appraisal](https://mlsanity.com/paper/2006.11406/) (2020)

## Built on by (most cited first, 1 of 1)

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

## Abstract (arXiv)

In the house credit process, banks and lenders rely on a fast and accurate estimation of a real estate price to determine the maximum loan value. Real estate appraisal is often based on relational data, capturing the hard facts of the property. Yet, models benefit strongly from including image data, capturing additional soft factors. The combination of the different data types requires a multi-view learning method. Therefore, the question arises which strengths and weaknesses different multi-view learning strategies have. In our study, we test multi-kernel learning, multi-view concatenation and multi-view neural networks on real estate data and satellite images from Asheville, NC. Our results suggest that multi-view learning increases the predictive performance up to 13% in MAE. Multi-view neural networks perform best, however result in intransparent black-box models. For users seeking interpretability, hybrid multi-view neural networks or a boosting strategy are a suitable alternative.