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Investigating the Impact of Building Footprints on Valuation in Automated Valuation Models

Author

Listed:
  • Simon Thaler
  • Felipe Calainho
  • Marc Francke

Abstract

This study investigates the impact of incorporating building footprint images into Automated Valuation Models (AVMs) for improved property valuation accuracy. Traditional AVMs primarily rely on property characteristics, regional data, and historical transaction records, often overlooking the geometric and spatial features represented in building footprints. This research proposes integrating Convolutional Neural Networks (CNNs) to analyze building footprint images and refine AVM predictions by modeling residuals from a baseline AVM. The dataset includes Austrian residential properties, encompassing transaction prices, property attributes, and building footprint data. By leveraging CNNs, the study aims to capture hidden patterns related to building shape, layout, and surrounding spatial distribution, enhancing the understanding of factors influencing real estate prices. Anticipated results suggest that the inclusion of spatial data in AVMs can lead to more nuanced and accurate valuations, providing valuable insights for financial institutions and the real estate industry.

Suggested Citation

  • Simon Thaler & Felipe Calainho & Marc Francke, 2025. "Investigating the Impact of Building Footprints on Valuation in Automated Valuation Models," ERES eres2025_194, European Real Estate Society (ERES).
    • Handle: RePEc:arz:wpaper:eres2025_194
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    File URL: https://eres.architexturez.net/doc/oai-eres-id-eres2025-194
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    JEL classification:

    • R3 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location

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