Online real estate platforms have become significant marketplaces facilitating users’ search for an apartment or a house. Yet it remains challenging to accurately appraise a property’s value. Prior works have primarily studied real estate valuation based on hedonic price models that take structured data into account while accompanying unstructured data is typically ignored. In this study, we investigate to what extent an automated visual analysis of apartment floor plans on online real estate platforms can enhance hedonic rent price appraisal. We propose a tailored two-staged deep learning approach to learn price-relevant aesthetics of floor plans from historical price data. Subsequently, we integrate the floor plan predictions into hedonic rent price models that account for both structural and locational characteristics of an apartment. Our empirical analysis based on a unique dataset of 9,174 real estate listings suggests that there is an underutilization of the available data in current hedonic models. We find that (1) the aesthetics of floor plans have significant explanatory power regarding rent prices – even after controlling for structural and locational apartment characteristics, and (2) harnessing floor plans results in an up to 10.56% lower out-of-sample prediction error. We further find that floor plans yield a particular high gain in predictive performance for older and smaller apartments. Altogether, our empirical findings contribute to the existing research body by establishing the link between visual aesthetics of floor plans and real estate prices. Moreover, our approach has important implications for online real estate platforms, which can use our findings to enhance user experience in their real estate listings.

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