Abstract
The rapid growth of U.S. Sunbelt cities has intensified urban development pressures. Low-income housing demolitions are a result of such pressures as they are “low hanging fruit” for urban renewal, which can be further intensified by housing quality perceptions. By combining deep learning on Street View images (STV) with machine learning, we provide a model that accurately predicts demolition orders and allows assessing the heterogeneity of these predictions depending on the evaluator’s perceptions. Based on fast-growing San Antonio (TX) data, our results show that automated models can be useful to assess human perception biases of code enforcers.
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