Abstract
Predictions of apartments prices in New York City (NYC) have always been of interest to new homeowners, investors, Wall Street funds managers, and inhabitants of the city. In recent years, average prices have risen to the highest ever recorded rebounding after the 2008 economic recession. Although prices are trending up, not all apartments are. Different regions of the city have appreciated differently over time; knowing where to buy or sell is essential for all stakeholders. In this project, we propose a predictive analytics framework that analyzes new alternative data sources to extract predictive features of the NYC real estate market. Our experiments indicated that restaurant health inspection data and crime statistics can help predict apartments prices in NYC. The framework we introduce in this work uses an artificial recurrent neural network with Long Short-Term Memory (LSTM) units and incorporates the two latter predictive features to predict future prices of apartments. Empirical results show that feeding predictive features from (1) restaurant inspections data and (2) crime statistics to a neural network with LSTM units results in smaller errors than the traditional Autoregressive Integrated Moving Average (ARIMA) model, which is normally used for this type of regression. Predictive analytics based on non-linear models with features from alternative data sources can capture hidden relationships that linear models are not able not discover. The framework presented in this study has the potential to serve as a supplement to the traditional forecasting tools of real estate markets.
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Acknowledgments
We would like thank Jing Wang, who contributed with helpful discussions to the initial analysis of this work. Also, the NYU High Performance Computing team, especially Shenglong Wang, who was always available to help with technical issues in the computer cluster.
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Moraes, R.M., Bari, A., Zhu, J. (2019). Restaurant Health Inspections and Crime Statistics Predict the Real Estate Market in New York City. In: Nicosia, G., Pardalos, P., Umeton, R., Giuffrida, G., Sciacca, V. (eds) Machine Learning, Optimization, and Data Science. LOD 2019. Lecture Notes in Computer Science(), vol 11943. Springer, Cham. https://doi.org/10.1007/978-3-030-37599-7_45
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