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

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