# The Spatially-Conscious Machine Learning Model

## TL;DR

This paper develops spatially-aware machine learning models to predict NYC real estate sales, demonstrating that incorporating spatial features improves accuracy over traditional non-spatial models.

## Contribution

It introduces a novel integration of spatial analysis with advanced machine learning techniques for real estate prediction, outperforming non-spatial approaches.

## Key findings

- Spatially-conscious models outperform non-spatial models.
- Advanced models like neural networks and gradient boosting benefit from spatial features.
- Empirical evidence shows improved prediction accuracy with spatial integration.

## Abstract

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.