Authors: Mingkai Deng, Yvonne Zhou, Jerry Shi
Summary:
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We examined the effectiveness of NYU Furman Center's classification of the gentrifying stages of NYC neighborhoods using NYC housing and neighborhoods data provided by the Center.
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To enhance the original classification, we proposed an approach using the hierarchical clustering method, and selected the number of clusters using the gap statistic metric.
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We used information from NYC Open Data to construct leading predictors for gentrification based on our classification, controlled for variables commonly associated with gentrification.
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Based on the insights, we made our predictions for NYC's next hotspots for gentrification.
Outcome: Best Insight Award in Columbia DataFest 2018, as judged by professors from Columbia's IEOR Department and Center for Urban Real Estate, NYU's Schack Institute of Real Estate, and data scientists from CapitalOne.
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