  This study presents a new user experience in apartment searches using
functionality and comfort as query items. This study has three technical
contributions. First, we present a new dataset on the perceived functionality
and comfort scores of residential floor plans using nine question statements
about the level of comfort, openness, privacy, etc. Second, we propose an
algorithm to predict the scores from the floor plan images. Lastly, we
implement a new apartment search system and conduct a large-scale usability
study using crowdsourcing. The experimental results show that our apartment
search system can provide a better user experience. To the best of our
knowledge, this study is the first work to propose a highly accurate prediction
model for the subjective functionality and comfort of apartments using machine
learning.
