Building the Real Estate Map for First time Homebuyers in Queens

Lab Reports, Maps, Visualization


Buying a home can be nerve-racking, especially if you’re a first-time home buyer. Some existing property search tools like Zillow and Trulia only tells people the listing prices at the moment, so getting to know what was the homes’ value in the neighborhood can be really useful for first time home buyers.

More than many industries, Real Estate relies heavily on location-based data. This map used Carto with 5 data sets to map out the real estate sales price from March 2018 to February 2019 in prospect areas in queens which are popular among first-time homebuyers. For this data visualization, with the data collected from the NYC Department of finance,  I mainly focused on 9 popular areas in Queens: Astoria, Bayside, College Point, Douglaston, Elmhurst, Fresh Meadow, Little Neck and Ridgewood, and Whitestone.


The initial investigation into this visualization looked into Building A Real Estate Investment Strategy With Location Intelligence (Fig. 1.1).  In that visualization, they visualised three factors: local public and alternative transit option(Fig. 1.2), taxi pickup/dropoff hotspots(Fig. 1.3), demographic data(Fig. 1.4) to provide a full view of an area, incorporating many of the key insights and data points that are most helpful when assessing investment. The idea of laying out different factors to identify a real estate investment opportunity early and ensuring the investment decisions inspired me on the visualization of building the real estate map for first-time homebuyers in queens.

(Fig. 1.1)
(Fig. 1.2)
(Fig. 1.3)
(Fig. 1.4)


Subway Lines | NYC Open Data : city subway lines.

Subway Stations | NYC Open Data : city Subway Stations

NYC Zoning Map | NYC Open Data : a data set consists of 6 classes of zoning features: zoning districts, special purpose districts, special purpose district subdistricts, limited height districts, commercial overlay districts, and zoning map amendments.

NYC Department of finance Rolling Sales Data : The Department of Finance’s Rolling Sales files lists properties that sold in the last twelve-month period in New York City for all tax classes.

Open Refine : a standalone open-source desktop application for data cleanup and transformation to other formats

Google sheets: a spreadsheet software used to explore data and translate addresses to geo data.

Carto: a Software as a Service cloud computing platform that provides GIS and web mapping tools for display in a web browser. CARTO Turns Your Location Data Into Business Outcomes.


Starting with an investigation into the original data set of Queens Rolling Sales File.  All Sales From March 2018 – February 2019. I narrowed down to 9 popular areas among first-time homebuyers in queens and cleaned the rows that don’t have a sales price. In order to make a fair comparison, I only chose one building class: One family house. The original data set only has the address for all the previous sales, so I used google sheets add-on feature “geocell” to translate all the address to geodata. After the data was cleaned and ready for analysis, it was imported into Carto.

Results and interpretation

(Fig. 2.1)

World-leading real estate companies know that location is pivotal in getting investment and pricing decisions spot on – identifying opportunities ahead of the competition. On top of that, not only locations, when it comes to buying a house; price, location, and house size are three top things buyers tend to consider.

In this Map, all the real estate sales in those nine areas From March 2018 – February 2019 are shown on the map with the value different by the shade of the color(Fig. 2.1). The lot size is related to the size of the shape, which is visually easily distinguished without clicking. For example, if buyers want to find a big land with a lower price, they can easily find a bigger dot with a lighter color. Whether the property is close to the subway station or train station is also a key fact for buyers to consider, so I added another layer showing the subway and LIRR lines as well as stations.

The side widgets show the building class(only showing one family house in this report), and the average price for a one family house of each area.  So the buyers can get a general idea of the price for all the areas and easily see the price difference, as well as filtering each area. Four major information is shown as a pop-up of each property: Neighbor, Address, Sales Price, and Sales Date. The zoning map of New York is added as the background of the map as well as giving more definition to the design.


Because the data I found didn’t have longitude and latitude, I used google sheets add-on feature “geocell” to translate the address to geodata, however, it only allows translating a certain amount of rows.  If given more time, I want to cover the sales price in more areas. Making a map to help make decisions for investment was my original plan, so adding rental income data will clearly show where the potential investment areas are. If given more time, I want to cover more areas and the average rental price for each building class.