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Modern art meets machine learning: advancing price predictions with visual features
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| 1.85 MB | Adobe PDF |
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Resumo(s)
This thesis explores machine learning's (ML) potential in art price prediction by integrating
traditional artwork features with visual data from high-quality images. Using a dataset of
modernist artworks auctioned by Christie’s (2007-2024), the study employs hedonic
regression, advanced ML, and Convolutional Neural Networks. Results show ML models
outperform traditional methods, revealing biases in auction house estimates. Visual features
are relevant predictors of price and improve predictions modestly. Auction house valuation
biases when combined with ML modes can be interpreted and subsequently mitigated. These
findings highlight the promise of data-driven, scalable art valuation frameworks, advancing
both academic research and industry practice.
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Alternative finance Art valuation Modern art Machine learning Forecasting Regression Convolutional neural network