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Valuing Curb Appeal

Author

Listed:
  • Erik B Johnson

    (University of Alabama)

  • Alan Tidwell

    (University of Alabama)

  • Sriram V Villupuram

    (University of Texas at Arlington)

Abstract

We recover the value of curb appeal in residential housing by using photos obtained from Google Street View, a deep learning classification algorithm and a variety of hedonic controls. We show that own property curb appeal is worth about twice that of an across the street neighbor. Together, neighbor and own property curb appeal together may account for up to 7% of a house’s sale price. The curb appeal premium is more pronounced during times of housing market weakness and greater in neighborhoods with high average curb appeal. Results are robust to a variety of spatial controls and curb appeal specifications.

Suggested Citation

  • Erik B Johnson & Alan Tidwell & Sriram V Villupuram, 2020. "Valuing Curb Appeal," The Journal of Real Estate Finance and Economics, Springer, vol. 60(1), pages 111-133, February.
    • Handle: RePEc:kap:jrefec:v:60:y:2020:i:1:d:10.1007_s11146-019-09713-z
    DOI: 10.1007/s11146-019-09713-z
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    References listed on IDEAS

    as
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    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Urban Umami or Urban Appakukan?: The Psychology of Streetscapes
      by Jason Barr in Skynomics Blog on 2020-10-22 12:34:19

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    Cited by:

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    2. Brian C. Albrecht & Shruti Rajagopalan, 2023. "Inframarginal externalities: COVID-19, vaccines, and universal mandates," Public Choice, Springer, vol. 195(1), pages 55-72, April.
    3. Patrick Gourley, 2021. "Curb appeal: how temporary weather patterns affect house prices," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 67(1), pages 107-129, August.
    4. Wan, Wayne Xinwei & Lindenthal, Thies, 2022. "Towards accountability in machine learning applications: A system-testing approach," ZEW Discussion Papers 22-001, ZEW - Leibniz Centre for European Economic Research.

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    Keywords

    Machine learning; Hedonic valuation;

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