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The effect of environment on housing prices: Evidence from the Google Street View

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  • Guan‐Yuan Wang

Abstract

As Google Street View visually depicts areas with disparate social characteristics, we use them to analyze the effects of environmentally locational factors on housing prices by constructing a convolutional neural network model. Instead of manual classification and judgment, the model decomposes views' pixels then assigns latent scores for street views. This score factor can improve the interpretability and the prediction accuracy of hedonic models and machine learning models. We empirically show this score is statistically significant and has stronger predictive power, suggesting that Google Street View provides visual cues regarding the dwelling's location and improve the regional and housing research.

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  • Guan‐Yuan Wang, 2023. "The effect of environment on housing prices: Evidence from the Google Street View," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(2), pages 288-311, March.
    • Handle: RePEc:wly:jforec:v:42:y:2023:i:2:p:288-311
    DOI: 10.1002/for.2907
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    1. Edward L. Glaeser & Scott Duke Kominers & Michael Luca & Nikhil Naik, 2018. "Big Data And Big Cities: The Promises And Limitations Of Improved Measures Of Urban Life," Economic Inquiry, Western Economic Association International, vol. 56(1), pages 114-137, January.
    2. Nikhil Naik & Ramesh Raskar & César A. Hidalgo, 2016. "Cities Are Physical Too: Using Computer Vision to Measure the Quality and Impact of Urban Appearance," American Economic Review, American Economic Association, vol. 106(5), pages 128-132, May.
    3. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    4. Benson, Earl D & Hansen, Julia L. & Schwartz Jr., Arthur & Smersh, Greg T., 1998. "Pricing Residential Amenities: The Value of a View," The Journal of Real Estate Finance and Economics, Springer, vol. 16(1), pages 55-73, January.
    5. Thies Lindenthal, 2020. "Beauty in the Eye of the Home‐Owner: Aesthetic Zoning and Residential Property Values," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 48(2), pages 530-555, June.
    6. Bourassa, Steven C. & Hoesli, Martin & Peng, Vincent S., 2003. "Do housing submarkets really matter?," Journal of Housing Economics, Elsevier, vol. 12(1), pages 12-28, March.
    7. 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).
    8. 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.
    9. Rosen, Sherwin, 1974. "Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition," Journal of Political Economy, University of Chicago Press, vol. 82(1), pages 34-55, Jan.-Feb..
    10. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    11. Song, Yan & Knaap, Gerrit-Jan, 2003. "New urbanism and housing values: a disaggregate assessment," Journal of Urban Economics, Elsevier, vol. 54(2), pages 218-238, September.
    12. Tyrvainen, Liisa & Miettinen, Antti, 2000. "Property Prices and Urban Forest Amenities," Journal of Environmental Economics and Management, Elsevier, vol. 39(2), pages 205-223, March.
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