  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 designs 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 9174 real
estate listings suggests that current hedonic models underutilize the available
data. We find that (1) the visual design of floor plans has 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 particularly high gain in prediction performance
for older and smaller apartments. Altogether, our empirical findings contribute
to the existing research body by establishing the link between the visual
design 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.
