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Valuing walkability: New evidence from computer vision methods

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  • Yencha, Christopher

Abstract

Walkability describes the efficiency and pleasure of walking in an area and is an aspect of urban design that has received much attention. Frequently used measures of walkability largely ignore the quality of nearby pedestrian pathing, such as sidewalks, in quantifying walkability. This paper expands upon the literature's understanding of walkability by supplementing current measures of walkability with data gathered from street-level images using computer vision techniques. Using hedonic methods and a sample of almost 60,000 house transactions in Ohio, I find that nearby establishments are an amenity capitalized into home prices only when there also exists access to adequate pedestrian pathing, and that walkability measures derived from computer vision methods contain information not found in other commonly used measures of walkability. With cities increasingly pushing towards creating more walkable neighborhoods, this paper provides evidence that walkable space is indeed valued by residents.

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  • Yencha, Christopher, 2019. "Valuing walkability: New evidence from computer vision methods," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 689-709.
    • Handle: RePEc:eee:transa:v:130:y:2019:i:c:p:689-709
    DOI: 10.1016/j.tra.2019.09.053
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    Cited by:

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    2. Djihed Berkouk & Tallal Abdel Karim Bouzir & Luigi Maffei & Massimiliano Masullo, 2020. "Examining the Associations between Oases Soundscape Components and Walking Speed: Correlation or Causation?," Sustainability, MDPI, vol. 12(11), pages 1-16, June.
    3. Yencha, Christopher, 2023. "Spatial heterogeneity and non-fungible token sales: Evidence from Decentraland LAND sales," Finance Research Letters, Elsevier, vol. 58(PA).
    4. Sander van Cranenburgh & Francisco Garrido-Valenzuela, 2023. "Computer vision-enriched discrete choice models, with an application to residential location choice," Papers 2308.08276, arXiv.org.
    5. Yibang Zhang & Yukun Zou & Zhenjun Zhu & Xiucheng Guo & Xin Feng, 2022. "Evaluating Pedestrian Environment Using DeepLab Models Based on Street Walkability in Small and Medium-Sized Cities: Case Study in Gaoping, China," Sustainability, MDPI, vol. 14(22), pages 1-23, November.
    6. Bondemark, Anders, 2023. "Walk this way how do individuals with different preferences for active travel modes respond to walkability?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 175(C).
    7. Bradley Bereitschaft, 2023. "The changing ethno-racial profile of ‘very walkable’ urban neighbourhoods in the US (2010–2020): Are minorities under-represented?," Urban Studies, Urban Studies Journal Limited, vol. 60(4), pages 638-654, March.
    8. Kun Yuan & Hirokazu Abe & Noriko Otsuka & Kensuke Yasufuku & Akira Takahashi, 2023. "A Comprehensive Evaluation of Walkability in Historical Cities: The Case of Xi’an and Kyoto," Sustainability, MDPI, vol. 15(6), pages 1-23, March.
    9. Otsuka, Noriko & Wittowsky, Dirk & Damerau, Marlene & Gerten, Christian, 2021. "Walkability assessment for urban areas around railway stations along the Rhine-Alpine Corridor," Journal of Transport Geography, Elsevier, vol. 93(C).

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    More about this item

    Keywords

    Walkability; Land use; Residential real estate; Hedonic regression; Computer vision;
    All these keywords.

    JEL classification:

    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access
    • R21 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - Housing Demand
    • R40 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - General

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