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Spatial appreciation of the floor plan complexity in housing price appraisal

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  • Bae, Seongeun
  • An, Sihyun
  • Choi, Gahyun
  • Ahn, Kwangwon

Abstract

This study explores the relationship between structural feature of apartment interiors and housing prices across four different regions. This study first constructs floor plan complexity indices based on Shannon entropy and Delentropy and compares both entropy measures in seizing structural complexity of images. Subsequently, we adopt sophisticated instruments to scrutinize the linear and nonlinear effects of floor plan complexity on housing prices. A series of hedonic price models and machine learning models with the SHapley Additive exPlanation are utilized in capturing the impacts and importance of floor plan complexity indices on housing prices. This study exhibits three notable findings: (1) Delentropy can serve as a useful tool for capturing the structural complexity of floor plan images due to its capability of considering the spatial distribution of pixels; (2) floor plan complexity is significantly and positively associated with housing prices across all the surveyed areas; and (3) the importance of floor plan complexity varies upon local contexts. Our study proposes a quantification method that fully represents the images into the numerical data to analyze the relationship between floor plans and housing prices. This methodological approach serves as a significant analytical tool for site selection, enabling more informed decision-making through comprehensive market analysis in the context of real estate development. From a long-term perspective, local governments can support efficient management for the sustainable development of the real estate market.

Suggested Citation

  • Bae, Seongeun & An, Sihyun & Choi, Gahyun & Ahn, Kwangwon, 2026. "Spatial appreciation of the floor plan complexity in housing price appraisal," Chaos, Solitons & Fractals, Elsevier, vol. 203(C).
    • Handle: RePEc:eee:chsofr:v:203:y:2026:i:c:s0960077925015930
    DOI: 10.1016/j.chaos.2025.117580
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