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Valuing Curb Appeal


  • Erik B Johnson

    (University of Alabama)

  • Alan Tidwell

    (University of Alabama)

  • Sriram V Villupuram

    (University of Texas at Arlington)


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

    1. Kelejian, Harry H & Prucha, Ingmar R, 1999. "A Generalized Moments Estimator for the Autoregressive Parameter in a Spatial Model," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 40(2), pages 509-533, May.
    2. Kelejian, Harry H & Prucha, Ingmar R, 1998. "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 99-121, July.
    3. Kelejian, Harry H. & Prucha, Ingmar R., 2010. "Specification and estimation of spatial autoregressive models with autoregressive and heteroskedastic disturbances," Journal of Econometrics, Elsevier, vol. 157(1), pages 53-67, July.
    4. Irani Arraiz & David M. Drukker & Harry H. Kelejian & Ingmar R. Prucha, 2010. "A Spatial Cliff‐Ord‐Type Model With Heteroskedastic Innovations: Small And Large Sample Results," Journal of Regional Science, Wiley Blackwell, vol. 50(2), pages 592-614, May.
    5. Edward L. Glaeser & Michael Scott Kincaid & Nikhil Naik, 2018. "Computer Vision and Real Estate: Do Looks Matter and Do Incentives Determine Looks," NBER Working Papers 25174, National Bureau of Economic Research, Inc.
    6. Kerry D. Vandell & Jonathan S. Lane, 1989. "The Economics of Architecture and Urban Design: Some Preliminary Findings," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 17(2), pages 235-260, June.
    7. Stephen A. Samaha & Wagner A. Kamakura, 2008. "Assessing the Market Value of Real Estate Property with a Geographically Weighted Stochastic Frontier Model," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 36(4), pages 717-751, December.
    8. David M. Drukker & Peter Egger & Ingmar R. Prucha, 2013. "On Two-Step Estimation of a Spatial Autoregressive Model with Autoregressive Disturbances and Endogenous Regressors," Econometric Reviews, Taylor & Francis Journals, vol. 32(5-6), pages 686-733, August.
    9. 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.
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    Blog mentions

    As found by, 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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    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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    Machine learning; Hedonic valuation;


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