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.
Street views, satellite imageries and remote sensing data have been integrated into a wide spectrum of topics in the social sciences. Computer vision methods not only help analysts and policymakers make better decisions and produce more effective solutions but they also enable models to achieve more precise predictions and greater interpretability. In this paper, we review the growing literature applying such methods to economic issues and the social sciences, in which social scientists employ deep learning approaches to utilise image data to retrieve additional information. Typically, image data produce better results than traditional approaches and can provide detailed results and helpful insights to improve society and people’s well-being.
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