As our capstone project in NYU Stern's MSBA program, we built a model that would identify real estate investment opportunities in New York City. For our model, we used traditional data like US cencus data, but also fast-moving, more granular data to identify such areas. Among the faster moving, less traditional data sources were location of trees in the city, number of restaurant reviews and ratings, taxicab trip start and end points. You can find more information on the home page of our shiny app.
The output of our model was turned into action through a Shiny app that lets the user do the following:
- Browse a map of New York City and identify areas of interest. Using the slider, you can show top n neighborhoods in terms of gentrification and investment potential. If you click on them, additional data about those neighborhoods will be shown alongside current listings on zillow.com
- Compare two individual locations against one another. Just type in the addresses of both locations (must be in NYC) and click the "Compare" button. Two maps will be shown side-by-side with additional indicators about the neighborhoods of each location.
RStudio Cloud Project: https://rstudio.cloud/spaces/12117/join?access_code=ZeyeNeU3a%2F1N4Nu3tQf25Xyf7cujhM01KxYwFNI2
GitHub Repo: https://github.com/phillyo/intelligentsia
If you would like to run the app locally, please make sure to follow the instructions in the readme.md file.