COVID-19 changed people’s lifestyles all over the world. This event will focus on analyzing resident housing property sales data in New York City from 2019 to 2023, before, in, and after COVID periods. By examining trends in sale prices, property characteristics, and neighborhood differences, this analysis aims to uncover key insights into the residential real estate market. Furthermore, machine learning techniques will be applied to predict property values and classify neighborhoods based on various factors such as location and building type. An end-to-end data pipeline process will be demonstrated in this talk (data collection, wrangling, visualization, feature engineering, machine learning modeling) via the python notebook.
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