Data Visualization on Residential Property Investment

Posted on Oct 22, 2018
The skills we demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.


The purpose of this Shiny web app is to better advise clients in residential property investments in New York City by visualizing the data on sales and rental market trends in major areas.

Check out the app HERE, and find the code HERE.


Sales and rental data are compiled from OneBlockOver by Streeteasy. The dataset used in this web app is combined using:

- Borough
- Neighborhood
- Date
- Condo Listing Median Price
- Condo Sale Median Price
- Rental Studio Median Price
- Rental One Bedroom Median Price
-Β Median Price of Rental Two Bedroom

- Rental Three+ Bedroom Median Price

Boroughs in consideration are Manhattan, Brooklyn, Queens.
The date of the data point ranges from Jan 2014 to Aug 2018, aggregated monthly.

Boundary data of the neighborhoods are acquired from

How to use this web app:

Start by clicking on the "Visualization" tab on the side menu to go to the visualization page. Beware that it takes a little time to load so don't be startled by the error messages. VoilΓ !

Data Visualization on Residential Property Investment

First, choose a borough and neighborhood from the right side of the page.

Data Visualization on Residential Property Investment

Then, you will see that the neighborhood you selected will be highlighted on the map at the top of the page.

Data Visualization on Residential Property Investment

On the bottom left, there will be a brief summary of the market trend of property sales in your chosen neighborhood, whether it has been increasing or decreasing. And it will provide a rough estimate of the percentage change in property value and value prediction for 2019 and 2023 (5 years). You can also opt to see an interactive chart of Listing and Sale price over the years.

On the bottom right, there is a chart showing the rental prices for different properties types, whether it is a Studio, One Bedroom, or even Three+ Bedroom.

You will notice a slider and selector below the location selectors, it is used to set a budget and a room count so that the tool will calculate a rough estimate of your mortgage payment, given 20% down payment,Β  30-year term, and an annual rate of 5.04%.

Future Works:

  • There is some backend issue with the interface that will require an overhaul, which I will tackle first.
  • Still making improvements on the map feature. The idea is to show nearest neighborhood info on the map for comparison.
  • Dataset is not really complete for neighborhoods that brokers do not post listings on Streeteasy. Need more detailed and comprehensive data.
  • Need more features to consider such as crime rate, school ranking, etc. for different neighborhoods.Β 

About Author

Related Articles

Leave a Comment

No comments found.

View Posts by Categories

Our Recent Popular Posts

View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI