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Computer Vision and Real Estate: Do Looks Matter and Do Incentives Determine Looks

Author

Listed:
  • Edward L. Glaeser
  • Michael Scott Kincaid
  • Nikhil Naik

Abstract

How much does the appearance of a house, or its neighbors, impact its price? Do events that impact the incentives facing homeowners, like foreclosure, impact the maintenance and appearance of a home? Using computer vision techniques, we find that a one standard deviation improvement in the appearance of a home in Boston is associated with a .16 log point increase in the home’s value, or about $68,000 at the sample mean. The additional predictive power created by images is small relative to location and basic home variables, but external images do outperform variables collected by in-person home assessors. A home’s value increases by .4 log points, when its neighbor’s visually predicted value increases by one log point, and more visible neighbors have a larger price impact than less visible neighbors. Homes that went through foreclosure during the 2008-09 financial crisis experienced a .04 log point decline in their appearance-related value, relative to comparable homes, suggesting that foreclosures reduced the incentives to maintain the housing stock. We do not find more depreciation of appearance in rental properties, or more upgrading of appearance by owners before resale.

Suggested Citation

  • 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.
    • Handle: RePEc:nbr:nberwo:25174
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    References listed on IDEAS

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    1. John Y. Campbell & Stefano Giglio & Parag Pathak, 2011. "Forced Sales and House Prices," American Economic Review, American Economic Association, vol. 101(5), pages 2108-2131, August.
    2. 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.
    3. Hough, Douglas E. & Kratz, Charles G., 1983. "Can "good" architecture meet the market test?," Journal of Urban Economics, Elsevier, vol. 14(1), pages 40-54, July.
    4. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    5. Asabere, Paul K & Hachey, George & Grubaugh, Steven, 1989. "Architecture, Historic Zoning, and the Value of Homes," The Journal of Real Estate Finance and Economics, Springer, vol. 2(3), pages 181-195, September.
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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

    Citations

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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. Ka Shing Cheung & Chung Yim Yiu, 2022. "The economics of architectural aesthetics: Identifying price effect of urban ambiences by different house cohorts," Environment and Planning B, , vol. 49(6), pages 1741-1756, July.
    3. William N Goetzmann & Christophe Spaenjers & Stijn Van Nieuwerburgh, 2021. "Real and Private-Value Assets [Gendered prices]," The Review of Financial Studies, Society for Financial Studies, vol. 34(8), pages 3497-3526.
    4. Sumit Agarwal & Maggie R. Hu & Adrian D. Lee, 2022. "Street Name Fluency and Housing Prices," The Journal of Real Estate Finance and Economics, Springer, vol. 65(2), pages 181-229, August.
    5. Mathieu Aubry & Roman Kräussl & Gustavo Manso & Christophe Spaenjers, 2023. "Biased Auctioneers," Journal of Finance, American Finance Association, vol. 78(2), pages 795-833, April.
    6. Skripkiūnas Tomas & Navickas Valentinas, 2023. "Architectural Factors Influencing a Housing Market Value: A Theoretical Framework," Real Estate Management and Valuation, Sciendo, vol. 31(1), pages 25-35, March.
    7. 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.
    8. 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.
    9. Stuart Gabriel & Matteo Iacoviello & Chandler Lutz, 2021. "A Crisis of Missed Opportunities? Foreclosure Costs and Mortgage Modification During the Great Recession [Synthetic control methods for comparative case studies: Estimating the effect of California," The Review of Financial Studies, Society for Financial Studies, vol. 34(2), pages 864-906.
    10. Aubry, Mathieu & Kräussl, Roman & Manso, Gustavo & Spaenjers, Christophe, 2019. "Machine learning, human experts, and the valuation of real assets," CFS Working Paper Series 635, Center for Financial Studies (CFS).
    11. Kirill Solovev & Nicolas Prollochs, 2021. "Integrating Floor Plans into Hedonic Models for Rent Price Appraisal," Papers 2102.08162, arXiv.org.

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    More about this item

    JEL classification:

    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • R30 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Real Estate Markets, Spatial Production Analysis, and Firm Location - - - General
    • Z11 - Other Special Topics - - Cultural Economics - - - Economics of the Arts and Literature

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