Millennials & Demographics of NYC and SF Neighborhoods
Note: Rithu was a coop student at Compass from Waterloo University in the Summer of ’17. This is one of the several projects she worked on.
As a real estate company with the mission to help everyone find their place in the world, we want to understand people’s behavioral patterns with respect to where they live. There are many questions to answer — where do certain demographics live? Do they move as they get older? Do people with similar lifestyles live around the same area? We spent some time trying to look at different factors that might affect where people live, focusing on millennials, since they are a key demographic group that are changing a lot of well-established patterns.
Analysis Focus
We chose to investigate where millennials live as our first step in this exploration. Once we mapped that out, we examined other factors that may influence their choice of residence. We settled on looking into commute times and salary since they both influence where one wants to live and where one can live. We hoped to find out if there are any overarching trends that explain whether neighborhoods can be defined by their demographics and characteristics. We focused on two main locations for this project, New York City and the San Francisco Bay Area. We chose them because Compass serves both of these regions and they are known to attract large millennial populations. In this post, we focus mainly on New York City and discuss some of the interesting findings from the Bay Area.
Data Sources
All data used for this project is taken from the U.S. Census, with the main dataset being the American Community Survey and its estimates. All maps are broken up by ZIP Codes and the Census data is broken up by ZIP Code Tabulation Areas (ZCTAs). Although the Census takes place every 10 years, for some of the years in-between (more regularly from 2010 onward), the U.S. Census Bureau produces estimates based on smaller sample surveys. Since they are estimates, for some years, data is not available for all ZIP Codes. To create the choropleth maps, we used QGIS.
Millennial Migration
We did two different analyses with millennial data. For the first, we mapped out the concentration of people between the ages of 20–34 for seven years across ZIP Codes so we could see where people in this age group live and how it’s changed. There is no solid definition of when the Millennial generation starts and ends but generally, the range is from mid 1980s to late 1990s. There are some years where those in our desired age group do not fall in the millennial range. For example, if you were 20–34 years old in 2000 or 27–34 years old in 2010, you are not a millennial. However, since we are looking at people of the same age, we thought their residential preferences might be comparable even if they are not technically of the same generation.
New York City Analysis
Above is a map of the New York City boroughs, which can be used as a reference. In what follows, we present the findings along with the maps that visualize the data. It may be helpful to zoom in or open the image in a new tab to see an enlarged map.
From 2000 to 2010, we see a move along the East River in Queens and Brooklyn. Moving into 2015, we see increased concentration in Greenpoint, Williamsburg, and Brooklyn Heights, which appears to align with housing trends. There is also some shift out of the ZIP Codes along the east side of Queens, e.g. the Flushing area.
Following a Cohort
Where do people move as they get older? Do they really move out of the city to the ‘burbs? For our second analysis, we followed an age cohort over the years. The cohort is as follows:
- 2000: 20–34 year olds
- 2010: 30–44 year olds
- 2015: 35–49 year olds
(We could not find data for 2005 at the level of granularity we wanted.)
New York City Surrounding Areas
If people do move out of the city as they get older, we figured it might be more beneficial to look at New York City and its surrounding areas.
As our cohort gets older, we notice Manhattan getting lighter and the suburbs getting darker, which suggests movement out of the city. We don’t necessarily see any particular cluster in the suburbs increase in concentration but that is to be expected; the suburbs are spread out and it’s unlikely that everyone moves to the same area.
Commute Time
Now that we have an idea of where people are living and moving, it is time to look at factors that may have an influence over those decisions. In large metropolitan areas, commute time can be a very important deciding factor for those looking to move. As such, we looked at average commute times and the most popular method of commute for each ZIP Code.
New York City area
As one might have expected, commute times are relatively low in much of Manhattan and slightly longer in the surrounding area. Although it appears that there isn’t much of a significant change in commute times over the years, we can see that they decreased in north Brooklyn and increased in the extremities of the map. This could indicate that people are moving further out — perhaps due to housing costs?
The most common mode of commute (see below) is also what one might expect — public transit is more popular in the urban areas that overlap with the MTA lines. One exception of note is the Murray Hill/Midtown East area of Manhattan, where walking is the most common method of commute. Beyond the MTA coverage area, driving wins out.
Salary Data
Another factor that plays an important role in choosing where to live, is salary. It helps one decide the affordability of a home. There may also be implicit expenses or lifestyle changes that come with the neighborhood and income will help determine the affordability of those as well. To look at salary data at an individual level, we are looking at median salaries of workers. (Mainly for 2015, there were more ZIP Codes that did not have data, those are shown in peach.)
Extended NYC area
In Manhattan, over the years, we see an increase in income for neighborhoods such as Chelsea, Gramercy, and Murray Hill. In Brooklyn and Queens, change is less apparent.
The Bay Area Highlights
The Bay Area was the second region we analyzed. Comparing the results from New York City to those from the Bay Area, there were some interesting similarities and striking differences. Here are some of the highlights:
- When looking at the concentration of 20–34 year olds, we noticed that there was an overall decrease from 2000 to 2015. The tech industry is obviously predominant in the Valley but with more startups housing their headquarters in other major cities, millennials in tech could be moving to those cities instead.
- The result of following a cohort was analogous to New York City; the movement across ZIP Codes showed that people spread out as they got older.
- The mode of commute was where we saw a striking difference between New York City and the Bay Area. While Manhattanites mostly favor public transit, driving is more common in the Bay Area.
- Finally, when looking at median salary, there seems to be a slight overall increase. However, if we compare the salary distribution to the Millennial Migration map for the Bay Area, we see that there are lower concentrations of millennials in some of the ZIP Code clusters with higher median salaries. It is often assumed that millennials are landing high-paying jobs (especially in the tech industry) and either there are other causes for the relationship we see here, or that assumption is not entirely true.
Final Thoughts
We can see that neighborhoods are dynamic, complex, and act as non-homogeneous systems. While we noticed some interesting patterns that may correspond to speculated changes in the housing market and changes in lifestyle, perhaps these patterns point more to demographic trends being extractable and potentially predictable, rather than the neighborhood trends. We didn’t observe any overarching trends that relate the demographics and characteristics of a neighborhood as we hoped.
Disclaimer: The information contained herein does not purport to be complete nor, necessarily, accurate; it is presented for research and educational purposes only and shall not be relied upon for any other purposes, including without limitation, in the sale, rental or financing of any real estate property. Compass disclaims all liability for representations and warranties, express and implied, contained in or for omissions from such information.
