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Extracting real estate values of rental apartment floor plans using graph convolutional networks

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  • Atsushi Takizawa

Abstract

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. 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.

Suggested Citation

  • Atsushi Takizawa, 2024. "Extracting real estate values of rental apartment floor plans using graph convolutional networks," Environment and Planning B, , vol. 51(6), pages 1195-1209, July.
    • Handle: RePEc:sae:envirb:v:51:y:2024:i:6:p:1195-1209
    DOI: 10.1177/23998083231213894
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    References listed on IDEAS

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    1. James Valente & ShanShan Wu & Alan Gelfand & C. F. Sirmans, 2005. "Apartment Rent Prediction Using Spatial Modeling," Journal of Real Estate Research, Taylor & Francis Journals, vol. 27(1), pages 105-136, January.
    2. Xiaolu Gao & Yasushi Asami & Yanmin Zhou & Toru Ishikawa, 2013. "Preferences for Floor Plans of Medium-Sized Apartments: A Survey Analysis in Beijing, China," Housing Studies, Taylor & Francis Journals, vol. 28(3), pages 429-452, April.
    3. Edward L. Glaeser & Michael Scott Kincaid & Nikhil Naik, 2018. "Computer Vision and Real Estate: Do Looks Matter and Do Incentives Determine Looks," NBER Working Papers 25174, National Bureau of Economic Research, Inc.
    4. James Valente & ShanShan Wu & Alan Gelfand & C.F. Sirmans, 2005. "Apartment Rent Prediction Using Spatial Modeling," Journal of Real Estate Research, American Real Estate Society, vol. 27(1), pages 105-136.
    5. Allen C. Goodman & Thomas G. Thibodeau, 2007. "The Spatial Proximity of Metropolitan Area Housing Submarkets," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 35(2), pages 209-232, June.
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