TY - GEN
T1 - Graph Neural Network Based Living Comfort Prediction Using Real Estate Floor Plan Images
AU - Kitabayashi, Ryota
AU - Narahara, Taro
AU - Yamasaki, Toshihiko
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/12/13
Y1 - 2022/12/13
N2 - In recent years, machine learning has been widely used in the real estate field. However, most of these previous studies have been limited to analysis based on objective perspectives, such as analysis of the structure of the floor plan and rent estimation. On the other hand, we focus on the subjective "living comfort"of real estate properties and aim to predict people's impressions of properties based on information obtained from floor plan images. Specifically, by using deep learning to analyze floor plan images and graph structures reflecting the floor plans, it becomes possible to predict the attractiveness of each property in terms of spaciousness, modernity, privacy, and so on. As a result of the experiments, the effectiveness of using both the floor plan image and the corresponding graph structure for prediction was confirmed.
AB - In recent years, machine learning has been widely used in the real estate field. However, most of these previous studies have been limited to analysis based on objective perspectives, such as analysis of the structure of the floor plan and rent estimation. On the other hand, we focus on the subjective "living comfort"of real estate properties and aim to predict people's impressions of properties based on information obtained from floor plan images. Specifically, by using deep learning to analyze floor plan images and graph structures reflecting the floor plans, it becomes possible to predict the attractiveness of each property in terms of spaciousness, modernity, privacy, and so on. As a result of the experiments, the effectiveness of using both the floor plan image and the corresponding graph structure for prediction was confirmed.
KW - graph neural networks (GNN)
KW - living comfort
KW - real estate floor plans
UR - http://www.scopus.com/inward/record.url?scp=85145769562&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85145769562&partnerID=8YFLogxK
U2 - 10.1145/3551626.3564970
DO - 10.1145/3551626.3564970
M3 - Conference contribution
AN - SCOPUS:85145769562
T3 - Proceedings of the 4th ACM International Conference on Multimedia in Asia, MMAsia 2022
BT - Proceedings of the 4th ACM International Conference on Multimedia in Asia, MMAsia 2022
PB - Association for Computing Machinery, Inc
T2 - 4th ACM International Conference on Multimedia in Asia, MMAsia 2022
Y2 - 13 December 2022 through 16 December 2022
ER -