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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.

Suggested Citation

  • 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. Xiangwen Xue & Qi Zhang & Xinyu Cai & Vadim V. Ponkratov, 2023. "Multi-Criteria Decision Analysis for Evaluating the Effectiveness of Alternative Energy Sources in China," Sustainability, MDPI, vol. 15(10), pages 1-14, May.
    2. Sihyun An & Yena Song & Hanwool Jang & Kwangwon Ahn, 2025. "Toward transparent and accurate housing price appraisal: Hedonic price models versus machine learning algorithms," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-29, December.
    3. Pilarczyk Aleksandra & Kondak Anna & Grzelka Kornelia & Wiejak-Roy Grażyna & Bieda Agnieszka, 2025. "Devil in the Details – Visual Perception of the Landscape Features by Potential Residential Buyers," Real Estate Management and Valuation, Sciendo, vol. 33(1), pages 85-103.

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