  Access graphs that indicate adjacency relationships from the perspective of
flow lines of rooms are extracted automatically from a large number of floor
plan images of a family-oriented rental apartment complex in Osaka Prefecture,
Japan, based on a recently proposed access graph extraction method with slight
modifications. We define and implement a graph convolutional network (GCN) for
access graphs and propose a model to estimate the real estate value of access
graphs as the floor plan value. The model, which includes the floor plan value
and hedonic method using other general explanatory variables, is used to
estimate rents and their estimation accuracies are compared. In addition, the
features of the floor plan that explain the rent are analyzed from the learned
convolution network. Therefore, a new model for comprehensively estimating the
value of real estate floor plans is proposed and validated. The results show
that the proposed method significantly improves the accuracy of rent estimation
compared to that of conventional models, and it is possible to understand the
specific spatial configuration rules that influence the value of a floor plan
by analyzing the learned GCN.
