  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.
