Successfully predicting gentrification could have many social and commercial applications; however, real estate sales are difficult to predict because they belong to a chaotic system comprised of intrinsic and extrinsic characteristics, perceived value, and market speculation. Using New York City real estate as our subject, we combine modern techniques of data science and machine learning with traditional spatial analysis to create robust real estate prediction models for both classification and regression tasks. We compare several cutting edge machine learning algorithms across spatial, semispatial, and nonspatial feature engineering techniques, and we empirically show that spatially conscious machine learning models outperform nonspatial models when married with advanced prediction techniques such as Random Forests, generalized linear models, gradient boosting machines, and artificial neural networks.

中文翻译：

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空间意识机器学习模型

成功预测高级化可能具有许多社会和商业应用；但是，房地产销售很难预测，因为它们属于由内在和外在特征，感知价值和市场投机行为组成的混沌系统。以纽约市房地产为主题，我们将数据科学和机器学习的现代技术与传统空间分析相结合，以创建用于分类和回归任务的强大房地产预测模型。我们比较了几种跨空间，半空间和非空间特征工程技术的前沿机器学习算法，并通过经验表明，与先进的预测技术（例如随机森林，广义线性模型，