Introduction + Topic of Research

New York City has historically been a populous city due to its history with immigration while also being a major global financial center, it is undoubtedly the most populous city in the United States. With a steady increase in population, this naturally raises the question of housing in New York City, and how the increasing population paired with the current economic state of the US could change the housing market.

As a migrant student, soon-to-be working individual in the city, and a noteworthy member of Generation Z, I’ve been in countless conversation with my peers and colleagues on the affordability of living in this city (Yes, many Gen Z individuals talk about buying homes and inflation too). Not surprisingly, many people I’ve talked to think that $1 Million is the least you could pay for a nice apartment, citing that it probably isn’t too far away from a convenient locations such as central work areas such as FiDi or Midtown. This being a fact or not, remains unknown to me as of this moment, but I think it is interesting that many individuals around my age believe that $1 million is not a guarantee for owning a nice housing unit in the city.

This report is intended to be a preliminary look at how the value of individual housing units have changed over the years. It also acts as a gateway into a larger scale investigation on the affordability and current living situation and trends of people living in the city.

Methodology + Tools

The tools I’ve selected for this research project were Microsoft Excel, Social Explorer and Data Wrapper. The goal is to arrive at a base-line idea of how

Step 1:

In order to form a conclusive-enough preliminary look into the housing situation of the city, I’ve decided to start by utilizing data from Social Explorer, which is a platform that provides demographic data using interactive maps. This allows for a more visual understanding of what I want to parse and present

Following my chosen topic, I’ve decided to compare the number of housing units that are $1 million and above according to different years.

Step 2

After I’ve done selecting the right category and years I wanted to see, the data is then exported to a .csv file and imported into Microsoft Excel, where the data could the filtered and simplified.

The exported datasets from Social Explorer contain many additional columns of information, some of which are useful but some are not. Even though the map I looked at on Social Explorer only explicitly mention “Owner-Occupied” housing units, the exported dataset included the number of “Owner-Occupied” and “Renter-Occupied” units, it definitely allowed for an extra layer of understanding for my topic. Some of the columns I’ve decided to leave out included geo location identifiers and additional demographic data that did not necessarily build onto the desired point what I’m trying to learn.

Step 3

Finally, to conclusive produce visualizations that could be I’ve utilized Datawrapper, which is able to convert my previously edited csv files into digestible and interactive charts.

Using Datawrapper, I was able to manually edit and customize my charts, by choosing the style, color, adding grids, in addition to inserting additional annotated information. Right below in the results section, I will include the Datawrapper charts I’ve made which leads to a (not-so-surprising) conclusion.

Interpretation of the Map and Visualizations

Looking at the interactive map paints one part of the picture. The map when hovered over allows us to see the percentage of housing units that are valued over $1 million within the given year.

To see the difference in units reaching more than $1 million in a more streamlined way, I’ve decided to check the number of occupied housing units for both owner and renter-occupied housing units over a span of 3 years, from 2018 to 2020. The “Grouped Bars” style is used as it distinguishes the difference between “owner” and “renter” between each category. Since the topics “owner” and “renter” does not necessarily have any cultural or significant correlations with colors, I’ve chosen green and blue to represent them, as the hue of those two colors are just enough to distinguish the two categories.

From the three datasets I have visualized, we could see the increase or decrease in either the amount of housing units valued more than $1 million, or the amount of people either owning or renting. Scrutinizing each borough brings even more clarity on those trends.

The Bronx and Brooklyn for example both have an increase of owners and renters from 2018-2020. This could indicate that while more people could afford housing units that are valued more than $1 million, there are still many people who could not afford to own but to rent housing units that are expensive. Perhaps this trend points towards efforts of gentrification allowing for more people who can afford these units to move in.

Queens on the other hand shows trends of an increase in owners but a decrease in renters. This could mean that more people who live in Queens have the ability to own and less are renting.

Manhattan and Staten Island surprisingly share the same trends over the years. A continuous decrease in ownership; a brief increase in renters from 2018-2019 then a decrease in 2020. During 2019 and 2020, there is a general decrease in both ownership and renter-ship. This might be the result of people moving out of these boroughs. The pandemic is also a reason that could be appoint as the culprit however, we have seen an increase in ownership in Queens; and an increase of ownership and renter-ship for both the Bronx and Brooklyn.

Conclusion + Reflections

While a conclusive hypothesis cannot be formed on the current housing trends and living situations of New York City, especially only with the resources above, from this research I’ve found several supporting points that surround some of the common chatter among my peers.

There are effects of gentrifications in the Bronx and Brooklyn at play, not only did both boroughs have more owners of housing units valued over $1 million, there are more renters of those buildings as well. Queens stays true being the borough for families and couples, not to mention it already is home to the highest population of foreign-born individuals. The trends of more ownership and less renter-ship in Queens could only support this. Staten Island has always been a mystery for a migrant person such as me, and it looks like the population is moving out, or more housing units there has decreased in value over time. As for Manhattan, it is hard to believe that housing units are decreasing in value, but it is possible to hypothesize that the value of houses are going up even more or that more Manhattan residents are leaving the borough for other borough such as the Bronx, Brooklyn or Queens.

This research project is lacking in many ways, but it shows exactly how much a small piece of data can do and inform. This was a great starting point for the topic I am researching in. The hardest part of this project is learning what works and what doesn’t when it comes to datasets, the amount of filters that has to be set up, and the additional information that might not be vital to the point of what the author wants to show. Moving forward, I believe more comparative information such as demographics and income could complement this dataset and help me and whoever is interested in uncovering the trends of living in New York City.