The Spatially-Conscious Machine Learning Model
This addresses the problem of real estate prediction for social and commercial applications, but appears incremental as it combines existing spatial and machine learning methods.
The paper tackled predicting real estate sales in New York City, a chaotic system, by combining spatial analysis with machine learning, and found that spatially-conscious models outperformed non-spatial ones when paired with techniques like neural networks and gradient boosting.
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, semi-spatial and non-spatial feature engineering techniques, and we empirically show that spatially-conscious machine learning models outperform non-spatial models when married with advanced prediction techniques such as feed-forward artificial neural networks and gradient boosting machine models.