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Machine learning approach to residential valuation: a convolutional neural network model for geographic variation

Author

Listed:
  • Hojun Lee

    (University of New South Wales)

  • Hoon Han

    (University of New South Wales
    University of New South Wales)

  • Chris Pettit

    (University of New South Wales)

  • Qishuo Gao

    (University of New South Wales)

  • Vivien Shi

    (University of New South Wales)

Abstract

Geographic location and neighbourhood attributes are major contributors to residential property values. Automated valuation models (AVM) often use hedonic pricing with location and neighbourhood attributes in the form of numeric and categorical variables. This paper proposed a novel approach to automated property valuation using a machine learning model with a convolutional neural network (CNN), fully connected neural network layers with numeric and categorical variables. In this study we compare the results of a fused model, which treat geographical data as an input with the performance of the baseline neural network model with only numerically or categorically represented data. Furthermore, the residential valuation by the proposed fused model was tested with actual sold price data in Greater Sydney, Australia. The study found that the fused model produced valuations with a significantly lower mean absolute percentage error (MAPE) (8.71%) than the MAPE of the baseline model (11.59%). The results show that the fused model with CNN significantly improves the accuracy for residential valuation, reducing spatial information loss by data manipulation and distance calibration.

Suggested Citation

  • Hojun Lee & Hoon Han & Chris Pettit & Qishuo Gao & Vivien Shi, 2024. "Machine learning approach to residential valuation: a convolutional neural network model for geographic variation," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 72(2), pages 579-599, February.
    • Handle: RePEc:spr:anresc:v:72:y:2024:i:2:d:10.1007_s00168-023-01212-7
    DOI: 10.1007/s00168-023-01212-7
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    References listed on IDEAS

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    1. Mulley, Corinne & Ma, Liang & Clifton, Geoffrey & Yen, Barbara & Burke, Matthew, 2016. "Residential property value impacts of proximity to transport infrastructure: An investigation of bus rapid transit and heavy rail networks in Brisbane, Australia," Journal of Transport Geography, Elsevier, vol. 54(C), pages 41-52.
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    More about this item

    Keywords

    C31; O18; C31; R32;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • R32 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - Other Spatial Production and Pricing Analysis

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