Data Study on Real Estate Sales in New York City

Posted on May 7, 2018
The skills the author demoed here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.
The most recent version of this app is accessible online here, while its source code may be found here.

Introduction

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.

Data

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:

Data Study on Real Estate Sales in New York City

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

Data Findings

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*.

App Instructions

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 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