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Successfully predicting gentrification could have many social and commercialapplications; however, real estate sales are difficult to predict because theybelong to a chaotic system comprised of intrinsic and extrinsiccharacteristics, perceived value, and market speculation. Using New York Cityreal estate as our subject, we combine modern techniques of data science andmachine learning with traditional spatial analysis to create robust real estateprediction models for both classification and regression tasks. We compareseveral cutting edge machine learning algorithms across spatial, semi-spatialand non-spatial feature engineering techniques, and we empirically show thatspatially-conscious machine learning models outperform non-spatial models whenmarried with advanced prediction techniques such as feed-forward artificialneural networks and gradient boosting machine models.

The Spatially-Conscious Machine Learning Model