Visualizing the Importance of Floor-Plan Image Features in Rent-Prediction Models

Hattori, Ryosuke; Okamoto, Koji; Shibata, Atsushi

doi:10.1109/scisisis50064.2020.9322769

Cited by 1 publication

(2 citation statements)

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Section: B Real Estate Tasks Using Floor Planmentioning

confidence: 99%

“…Floor plan images have also proved to be useful for rent price prediction [12]- [14]. In [12], [13], it was demonstrated that conventional bag-of-features (BoFs) [15] has the potential to achieve lower-error prediction with smaller variance, although the contribution of BoFs was smaller than that of other apartment attribute information. In [14], image features were applied to hedonic price models [16] to predict the apartment rent price after controlling for locational and structural characteristics of an apartment.…”

Section: B Real Estate Tasks Using Floor Planmentioning

confidence: 99%

See 1 more Smart Citation

Subjective Functionality and Comfort Prediction for Apartment Floor Plans and Its Application to Intuitive Online Property Search

Narahara

Yamasaki

2023

IEEE Trans. Multimedia

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This paper presents a new user experience for online apartment search using functionality and comfort as query items. Specifically, it 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 is the first work to propose a highly accurate machine learning model for predicting the subjective functionality and comfort of apartments.

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Section: B Real Estate Tasks Using Floor Planmentioning

confidence: 99%