Mapping Real Estate Sales in New York City

Posted on May 7, 2018

The most recent version of this app is accessible online here, while its source code may be found here.

This app is a visualization tool for examining real estate sales in New York City, drawing on data published by the City of New York, available here. The data, which cover the years 2003-2015, include a range of variables, including price, size, category of building, location, and date of sale.

The app plots sales from this data on a map to facilitate exploring the connections, in particular, between time, location and price. The following screenshot displays a number of its features:

A screenshot displaying some of the features of this app (click for a larger image)

As can be seen, the main panel of the app consists of a street map of New York City (sourced from the Open Street Map project), with dots plotted on it in varying sizes. Each of these dots represents a single property sale, with blue dots corresponding to residential properties and red ones to non-residential properties. (Note that "property" here is not synonymous with "building": many property sales are of single condominium units in large apartment buildings.)

The relative size of these dots is set by the second drop-down menu on the left-hand sidebar, labeled Weight by. By default, they are set to a constant size (relative to each other), which is a good format to give an overview of the overall volume and concentrations of sales. Alternatively, however, their sizes can be set to vary in proportion with some property of the sale. In the illustration above, for instance, they are scaled by price, with larger dots corresponding to greater prices. They can be scaled instead by square footage, or by price per square foot*.

Clicking on a particular dot brings up a pop-up bubble displaying salient data points about the sale it represents: address, date, price, and square footage. (Note that all of these data points are displayed when one clicks on a dot, regardless of whether they are reflected in the dots' scaling.) Clicking again will banish this bubble.

The map may be zoomed in or out via the + and - buttons in its top left corner. The area shown can be changed by clicking the map and dragging it as desired. The adjustable 'slider' beneath the map displays the range dates for which sales are plotted. By default, this range is set to thirty days, beginning with the date of the first sale in the loaded dataset, but it can be adjusted by moving the sliding buttons to the endpoints of the date range one is interested in. Alternatively, one may click on the slider to the right or left of the selected range, which keeps the range at the existing size, while 'jumping' it to the point clicked. Finally, clicking the play button at the bottom right of the slider causes the selected range to automatically progress from left to right until it has reached the end of the dates available. While this is progressing, the button may be clicked again to pause it.

*All references to "square footage" here denote gross square footage, meaning total floor space of a property.

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 airbnb Alex Baransky alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus API Application artist aws 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 Bundles California Cancer Research capstone Career Career Day citibike clustering Coding Course Demo Course Report D3.js data Data Analyst data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization Deep Learning Demo Day Discount dplyr employer networking feature engineering Finance Financial Data Science Flask gbm Get Hired ggplot2 googleVis Hadoop higgs boson Hiring hiring partner events Hiring Partners Industry Experts Instructor Blog Instructor Interview Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter lasso regression Lead Data Scienctist Lead Data Scientist leaflet linear regression Logistic Regression machine learning Maps matplotlib Medical Research Meet the team meetup Networking neural network Neural networks New Courses nlp NYC NYC Data Science nyc data science academy NYC Open Data NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time Portfolio Development prediction Prework Programming 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 Selenium sentiment analysis Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau team TensorFlow Testimonial tf-idf Top Data Science Bootcamp twitter visualization web scraping Weekend Course What to expect word cloud word2vec XGBoost yelp