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Understanding the contribution of curb appeal to retail real estate values

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  • Julia Freybote
  • Lauren Simon
  • Lauren Beitelspacher

Abstract

The concept of curb appeal and its impact on property values has been largely neglected in the real estate literature. In the context of retail real estate, curb appeal represents the general attractiveness of a store as viewed from the sidewalk or parking lot that is expected to affect consumer patronage decisions and consequently property values. We first develop a measurement instrument for curb appeal and assess its validity using exploratory and confirmatory factor analysis. Our results suggest that curb appeal is multidimensional and consists of an atmospheric, architectural and authenticity dimension. Then, we use survey responses, transaction data and spatial regression to quantify the impact of curb appeal on sales prices. We find that the atmospheric and architectural dimensions have a significantly positive impact on sales prices. We also show that curb appeal dimensions are highly correlated with observable building features traditionally included in hedonic pricing models.

Suggested Citation

  • Julia Freybote & Lauren Simon & Lauren Beitelspacher, 2016. "Understanding the contribution of curb appeal to retail real estate values," Journal of Property Research, Taylor & Francis Journals, vol. 33(2), pages 147-161, April.
    • Handle: RePEc:taf:jpropr:v:33:y:2016:i:2:p:147-161
    DOI: 10.1080/09599916.2015.1135978
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    References listed on IDEAS

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    1. Turley, L. W. & Milliman, Ronald E., 2000. "Atmospheric Effects on Shopping Behavior: A Review of the Experimental Evidence," Journal of Business Research, Elsevier, vol. 49(2), pages 193-211, August.
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    6. Grossbart, Sanford & Hampton, Ronald & Rammohan, B. & Lapidus, Richard S., 1990. "Environmental dispositions and customer response to store atmospherics," Journal of Business Research, Elsevier, vol. 21(3), pages 225-241, November.
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    Cited by:

    1. Wayne Xinwei Wan & Thies Lindenthal, 2023. "Testing machine learning systems in real estate," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 51(3), pages 754-778, May.
    2. 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.
    3. 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.

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