Analyzing the Tech revolution in San Francisco
Introduction
In recent years, the revolution of technology industries has taken the world by surprise. Some of the largest and most influential companies have originated from the Bay Area. As these companies see an exponential growth in business, they require a subsequent influx of able employees to maintain their standards or challenge existing technologies. Taking an outward look at the city statistics may not reveal the full picture. Even though the unemployment rate is just 4.8%, compared to 8.3% for California as a whole, the influx of so many young, rich tech workers has caused significant tensions in the city.
From the lack of affordable housing and support for San Francisco’s poorest communities to the number of increasing evictions there are numerous issues at hand. These issues can significantly pile up to develop into a socially harmful situation that needs to be dealt with and overcome.
The questions I ask with my analysis is how does this exponential growth influence cities which have a large concentration of tech companies? How does such a big economic transformation, as brought in Silicon Valley, affect society? With my final project, I decided to explore the changes that San Francisco has seen since the Tech Revolution. I inspect change in household income, the change in median rent and affordability, and lastly, I inspect if there have been any evictions near high concentration of tech bus stops.
Data Collection and Methodology
In order to answer the questions I had about the changes San Francisco has seen, I initially gathered data from the SF open data portal. However, I felt that the data available was limited in order for me to get an understanding get the change in living coniditons in districts within SF. However, I was able to fetch the data on evictions in SF from the open data portal with ease.
I proceeded to use an API to fetch the data from American American Community Survey (ACS), which is an ongoing statistical survey by the U.S. Census Bureau.
To get data about household income, I had to refer to the ACS data for 2010 and 2015. Initially I used the census reporter API to fetch the latest data from ACS, however I was able to directly download the ACS data from Social Explorer. The ACS 5-year estimates are published for all geographic areas, including census tracts, and block groups. I opted to use data for each census tract in the San Francisco county.
The tables in ACS for each year represent a survey topic conducted. As an illustration, the area I wanted information was represented in the household income table which is represented as the code B19001 in the ACS data. To get a better understanding and perform some preliminary analysis, I merged different tables based on the census tract. Pandas was helpful in quickly cleaning the data and merging with other datasets. Further, I used a shape file from the US Census Bureau that mapped each census tract to a geoid. This way, I was able to upload my dataset and merge it with the shape file in CartoDB to quickly map my data on a map.
Also initially, I experimented to the Zillow API to understand the change in prices of property over the years, however, the ACS data was sufficient in providing the change in median rent across SF.
Tech Companies Taking Over San Francisco
Increased attention has been paid to tech migration into downtown San Francisco, with companies trading office parks for converted industrial warehouses and Class A office buildings near existing transit, housing, and entertainment centers.
In fact, more than 80 percent of the city’s new office demand over the past two years was driven by technology companies, according to Colin Yasukochi, director of research and analysis for northern California at CBRE, as reported by Bloomberg Business.
This map shows the investment of tech company for office spaces in SF. The size of the bubbles represent the site of the real estate space that the company has invested. Some of the largest offices are those that belong to Uber, Salesforce.
The challenge to the tech sector’s continued growth in San Francisco, and in the Bay Area more broadly, rests on the ability of the private sector to work dynamically with the public sector to anticipate the reverberant, if positive, impacts of tech-sector growth on the systems and neighborhoods on which it relies. With ever-pressing issues of affordability, Silicon Valley will also need to apply innovation to providing housing choices necessary for a robust and diverse workforce.
Rise in Household Income
The maps above show the median household income spread across SF. The household income represented here is adjusted according to inflation according to ACS.
In 2010, the range of median income varies from around $43,000 to a high of around $155,000. The income is especially high in areas of South Market, Portrero Hill, and parts of Mission. These areas along with central San Francisco have households that are earning more than $100,000 as income in 2010.
While, it’s not surprising to notice that the total median household income has increased in 2015, some areas seem to have a higher concentration of high median household income while the other areas are past identical to 2010. It is crucial to point out that while the minimum media income remains identical to 2010, the maximum median income has increased to around $176,000. Areas of Inner Sunset in SF which were earning close to $150,000 in 2010 are now in the $175,000 income range. The same is also reflected in Russian Hill and the Marina. There is definitely a noticeable trend of an increase in median income since 2010. It is also worthwhile to consider that the household income data from ACS is adjusted according to inflation.
Below, I perform statistical analysis on the household income in SF for 2010 and 2015. The household income is divided into 16 buckets. This provides a better understanding of the distribution of income across SF. Both the graphs show that majority of the households in SF earn more than $50,000. However, it is interesting to see the difference between household income distribution for higher buckets in 2010 and 2015.
While 2010 had a similar number of households earning between $75,000 and $100,000, and that of greater than $200,000, in 2015, one can clearly notice the uneven distribution of income in higher brackets. There are more than 50,000 households earning an income of $200,000 or more in 2015. It can also be observed that the number of households in lower buckets have reduced since 2010. With the influx of people working at tech companies in SF, it seems that high-income households in SF are growing rapidly.
Rent Affordability
It is generally recommended that one does not spend more than 25% of their income on rent. In fact, Real estate economists say that whenever people pay more than 30 percent of income on housing, the local economy suffers because households have less money to spend on other stuff, like food and consumer goods.
The Bay Area has seen some of the highest annual rent increases in the country. At the same time, San Francisco has clearly seen an increase in household income (as analyzed above). How does this translate to affordability of rent? I decided to map the percentage of income that each household was spending on rent.
In 2010, the map shows that significant areas in SF are paying more than 30% on just rent. While there seems to be a concentration of low rent affordability in downtown San Francisco, key districts of central San Francisco and the Bayview district seem to have rent affordability under control. By comparing the map of the recent growth in Tech offices with this map, I noticed that areas around the up and coming tech offices had low rent affordability in 2010.
Within a span of 3 years, San Francisco saw quite a significant change in affordability of rent. While districts within downtown Berkeley were still not very affordable, areas around Mission, South of Market, and Financial district are some examples where households were able to spend less than 35% income on rent. These are areas that are heavily surrounded by Tech companies.
Analyzing Evictions
There have been numerous protests in San Francisco against the influx of ‘tech’ people since the average rent across the city has been increasing. Not only has this led to an increase in rents, but it’s also simultaneously increased the number of No Fault evictions. What are No Fault evictions?
According to Coalition for Economic Survival(http://cesinaction.org.dnnmax.com/AnnouncementsActions/60DayEvictionNoticesNowRequiredinCalifornia.aspx), a “no-fault” eviction is an eviction that results in the termination of a tenant’s lease not because the tenant breached the lease’s terms, but rather because the lease term has expired, and the landlord doesn’t want to renew it. In rent-regulated buildings the landlord is free to terminate the tenancy when a lease expires, as long as the owner is not discriminating for unlawful reasons such as the tenant’s race, religion, ethnicity, gender, sexual orientation, or national origin.
But are there high evictions in places where household income is high or is it spread across the city. The below scatter plot displays the relationship between the household income for a census tract in SF and the number of evictions in that census tract in 2015.
The plot shows that evictions were particularly high in areas that had a high average median income. Evictions reaching numbers of 60 in areas that had an average median income around $140,000. For low number of evictions, the distribution is spread across the wide range of household incomes. But its definitely concentrated in higher household income blocks for higher number of evictions.
In the scatter plot above, the rate of change in household income from 2010 to 2015 is plotted against the eviction rate in the same time range. The distribution is quite similar to the distribution we saw in figure. Eviction rates are higher in areas where SF has seen a higher increase in median income.
I was especially curious about the protests that took place for the use of shuttle buses by Google and other tech companies to ferry employees from their homes in San Francisco to corporate campuses in Silicon Valley and in San Francisco as well. It was understood that protesters viewed the buses as symbols of gentrification and displacement in a city where the rapid growth of the tech sector had driven up housing prices as analyzed above. I was also curious to see if there is possibly an increase in the number of evictions located around these tech bus stops as research had pointed out. The data comes with one obvious caveat: Evictions, and bus stops are highly likely to occur in clusters simply because no one places bus stops where people don’t live. Nonetheless, from the map, one can observe the high concentration of evictions near bus stops while less concentration in other areas. Since the eviction rate is calculated based on the population of each district, it provides an accurate measure of evictions per number of households in the area.
Conclusion
Overall, this was a very interesting research topic for me as it helped me understand the dynamic change that San Francisco has seen over the last 5 years. While Tech companies have been growing and expanding their businesses in and around SF, it has definitely brought in an influx of people to SF. Even taking the inflation into account, the media household income has dramatically increased from the median income in 2010. Moreover, there is an uneven distribution of income as seen in the first part of my statistical analysis.
While median household income has increased, rent affordability across the city has more or less remained the same. This shows that rent prices are also increasing proportianately to the income. The statistical analysis of the eviction rate and household income showed that districts that had a high household income were likely to have higher number of no-fault evictions. This explains the protests against the growing businesses of Tech companies by local SF residents. Finally, the high number of evictions near the tech bus stops gives an idea of the possible affects of the influence of Silicon Valley in San Francisco.
As possible next steps, I am going to refer to the ACS for any updates on the survey data so that I can evaluate other metrics of rent, income, employement and educations.