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

Hojun Lee, Hoon Han (), Chris Pettit, Qishuo Gao and Vivien Shi
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Hojun Lee: University of New South Wales
Hoon Han: 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

The Annals of Regional Science, 2024, vol. 72, issue 2, No 12, 579-599

Abstract: 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.

Keywords: C31; O18; C31; R32 (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s00168-023-01212-7

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