2017 Manhattan Rolling Sales Data Visualization with Microsoft Power BI

In this post, we explore data from New York City’s Department of Finance.  This data set includes the rolling sales of all New York City properties sold in the past 12 month period for all tax classes. I conducted this analysis in 2017, so the data in the link above may have been updated. This data included neighborhood information, building type, square footage, year built as well as the final sale price, among other information. 

In this exercise, we were asked to visually demonstrate Manhattan’s sold properties according to these metrics:

  1. How many 1 to 3 family homes were sold in each neighborhood?
  2. For any property sold for over $1,000,000, when did the building boom associated with it occur?
  3. What is the average square footage sold to each tax class?
  4. Geographically speaking, how close are businesses to large cooperatives and apartment buildings?

Answer: Here is the visual to answer all those questions:
Below are more detailed answers to each question!

Power BI Visualization of Manhattan Rolling Sales Data

Before I explain the output, Here is some clarification form the New York City’s department of finance regarding tax classes from the data. 

Every property in the city is assigned to one of four tax classes (Classes 1, 2, 3, and 4), based on the use of the property.

Class 1: Includes most residential property of up to three units (such as one-, two-, and three-family homes and small stores or offices with one or two attached apartments), vacant land that is zoned for residential use, and most condominiums that are not more than three stories.

Class 2: Includes all other property that is primarily residential, such as
cooperatives and condominiums.

Class 3: Includes property with equipment owned by a gas, telephone or electric company.

Class 4: Includes all other properties not included in class 1,2, and 3, such as offices, factories, warehouses, garage buildings, etc.

New York City Department of Finance Glossary of Terms for Property Sales Files.

1. How many 1 to 3 family homes were sold in each neighborhood?

We can see in the visualization below that the vast majority of 1-3 family homes are in the more affluent neighborhoods of New York City. Case in point, the Upper West Side of Manhattan had sold approximately 670 properties in 2017 all of which were 1-3 family homes. 

Total of 1-3 family homes sold per neighborhood in Manhattan

2. For any property sold for over $1,000,000, when did the building boom associated with it occur?

In the visualization below, we can see that a great number of properties sold at a value exceeding $1,000,000 were built at the turn of the 20th and the 21st centuries. This could be related to the advances in architecture or the technological innovations that ushered in the new centuries. It would be fascinating to speak to a subject matter expert to attempt to discern the reasons behind such valuations, nevertheless, this is what the data tells us! 

The number of homes sold with a value of over $1M and the years they were built.

3. What is the average square footage sold to each tax class?

The visualization below shows some rather predictable information regarding the average gross square footage of almost every tax class. Class 1, residential homes of 1-3 families have on average the lowest gross square footage in Manhattan, contrary to class 4 which includes offices, factories and warehouses. Interestingly enough, class 2 properties which include primarily residential properties such as apartment buildings constitute approximately 38% of the total properties sold in Manhattan. This information can be very useful to any real estate agent, buyer or seller of properties in NYC. It is worth noting that class 3 (properties with equipment for gas, electric or telephone companies) either did not experience any sales or had exchanged ownership without any transfer of monies, I would be very curious to examine this over time and to learn when those properties sold last. 


4. Geographically speaking, how close are businesses to large cooperatives and apartment buildings?

In the visualization below, we can see that the split between class 4 properties and class 2 properties is almost even in all but three areas which have been marked solely for residential neighborhoods (see the read dots). This makes intuitive sense, any business in an urban setting would prefer to have access to a larger number of customers, many of whom could be people residing in apartment buildings. 


This was an incredible exercise that showed the power of visualization in distilling large amounts of data to very accessible and relevant information. This exercise has also encouraged me to continue exploring visualizations and hopefully even get better at them. 

The information above is from the Graduate Certificate in Business Analytics: Descriptive Analytics course at Penn State University.