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The assessment and valuation of real estate requires large datasets with realestate information. Unfortunately, real estate databases are usually sparse inpractice, i.e., not for each property every important attribute is available.In this paper, we study the potential of predicting high-level real estateattributes from visual data, specifically from two visual modalities, namelyindoor (interior) and outdoor (facade) photos. We design three models usingdifferent multimodal fusion strategies and evaluate them for three differentuse cases. Thereby, a particular challenge is to handle missing modalities. Weevaluate different fusion strategies, present baselines for the differentprediction tasks, and find that enriching the training data with additionalincomplete samples can lead to an improvement in prediction accuracy.Furthermore, the fusion of information from indoor and outdoor photos resultsin a performance boost of up to 5% in Macro F1-score.

Real Estate Attribute Prediction from Multiple Visual Modalities with Missing Data