Gentrification Displacement in Seattle

Gentrification has been a reoccurring theme in the next transition of “growth” in the United States leading to changes in the social and economic fabric within certain areas as there is a rising popularity towards living in the urban areas that were once less desirable. In this study, we will be looking at the effects of gentrification on various factors ranging from race/ethnicity, educational attainment, poverty rates, and employment/industry of each census tract. Apart from the social impacts of gentrification, we’ll be taking a look at the tangible changes of who is actually being displaced and see the immense growth in the price of property/rent. As such, gentrification is a highly contested and complex issue that raises crucial questions relating to the urban environment, social justice, and equity. To combat these rising and future issues, we examined and determine vulnerable areas to try prevent the process of gentrification at its root rather than pruning the growing branches. In other words, we need to stop the gentrification phenomenon before it intrude the neighborhoods of Seattle.

The term gentrification can be described as an influx of businesses and individuals with generally higher educational and income levels settling in traditionally poorer neighborhoods, resulting in a displacement of the population who was living there. Gentrification had happened before dating back to the late 19th century, which has dramatically reshaped cities like “Seattle, San Francisco, and Boston” (Osman, 2016). For instance, baby boomers purchase and renovate cheap housing in old cities, causing rent and property values to increase. Millennials have the opportunity to attend college, therefore able to make a great amount of money out of college. In what follows, we will define the term gentrification as the changing of characteristics within poor urban working-class communities influenced by wealthier people moving in and converting into more of an “upscale” community, typically displacing inhabitants that have been there for a while and Seattle is no exception. Following Seattle OPCD's existing research, we wish to update the body of literatures with most recent data either statistically or geographically. That being said, our research question is: How does gentrification affect the displacement of minorities in Seattle?

Below is a reference map for the city of Seattle.

Seattle Office of Planning & Community Development (OPCD) conducted research on Seattle's growth strategy which dives deep and produces an in-depth analysis of the displacement issue. In the research, they introduce the steps to compute the displacement risk index which illustrates areas with high and low chances of experiencing displacement. In our research, we wish to update the areas reflecting the recent changes in the Seattle landscapes. We can do that by examining neighborhood characteristics, thus incorporating the spatial and temporal pattern to determine vulnerable areas which ultimately reduces the spread of displacement.

Following Seattle OPCD's list of displacement factors, they considered 14 risk factors. While some data sources are easy to access, but some other sources are private which typically requires special permission. For example, private datasets such as median rent are from realtor organizations which computes the "ratio of rent per net rentable square foot by tract to the Seattle average for rent per net retable square root" (Seattle OPCD). We tried reaching out the several organizations via email, but did not receive any responses. Due to the limitation of accessing private datasets, we were able to consider 6 out of the 14 factors. However, we were able to use the six factors to model an updated version and an compressed old version of Seattle OPCD's displacement risk index to compare and contrast the changes across two time periods. Specifically, these factors and its sources are as follows.

• People of color refers to the percentage of the population that is a race other than non-Hispanic White. The data source is the 2010 and 2019 DP05: ACS Demographic and Housing Estimates dataset from the U.S. Census Bureau database. The dataset consist of the population statistics for the various demographic characteristics.
• Education attainment refers to the percentage of the population 25 years or older who lack a Bachelor's degree. The data source is the 2008–2012 and 2015–2019 S1501: Education Attainment dataset from the U.S. Census Bureau database. The dataset contains the population's education background data.
• Housing tenancy refers to the percentage of households that are renters. The data source is the 2010 and 2019 DP04: Selected Housing Characteristics dataset from the U.S. Census Bureau database. The dataset consist of housing characteristics such as number of housing units and resident types.
• Housing cost-burdened households refer to the percentage of households with income below 80% of area median income (AMI) that are cost burdened (paying > 30% of income on housing). The data source is from Consolidated Housing Affordability Strategy (CHAS) between the two time periods 2009–2013 and 2015–2019. The dataset displays the conditions and characteristics of housing units and households across the United States.
• Severely housing cost-burdened households pays > 50% of income on housing. The data source is the same as above: CHAS.
• Household income refers to the percentage of the population whose income is below 200% of poverty level. The data source is the 2008–2011 and 2015–2019 S1701: Poverty Status in the Past 12 Months dataset from the U.S. Census Bureau database. The dataset consist of demographic characteristic for the population currently experiencing poverty.
• Proximity to transit refers to the location near a current and future light rail stations. The data source is the from the Open Transit Data (OTD) portal, which contains the spatial data for current and future-planned link stations.

Most of our datasets were sourced from the census and ACS, thus data wrangling is necessary. During the data cleaning and formatting process, we used R programming language to filter out the unwanted columns and perform computations for each displacement risk index factors. Once the datasets are ready, we first joined them to the Seattle census tract shapefile, then leverage standardized the percentages into Z scores. Lastly, we compute and reclassify the composite risk scores into different vulnerable levels. The main goal here is to replicate the Seattle OPCD's existing research with more recent data. Through the replication, we can compare and contrast differences in relationships results between our updated findings and the city of Seattle’s past findings.

As previously mentioned, we were able to cover 6 of the 14 factors. Leveraging these six factors, we will perform summary measures across two time periods for each other and ultimately the displacement risk index map.

Our second map compares 2010 and 2019 percentages of people of color within different Seattle neighborhoods. Comparing 2010 to 2019, we see a slight concentration change of people of color in 2019. Mainly, 2012 to 2019 shows an increase in high population concentration around South-East areas of Seattle spreading towards adjacent South-West areas. These South-West areas show a general increase in the concentration of people of color from 28%-44% in 2010 to 44%-59% in 2019 indicating how gentrification has demographically affected the composure of Seattle.

Our third map examines education attainment in Seattle neighborhoods for 2012 and 2019. The map measures the population percentage of people without a college bachelor's degree or higher; the percentage of people without a 4-year college degree. As we compare the two maps, 2019 shows an increased concentration of people without bachelor's degrees or higher in areas along the South End of Seattle in 2019 (55%-84%) showing us how gentrification affects those with low educational attainment at a higher rate.

Our fourth map measures housing tenancy in Seattle between 2010 and 2019. The map examines the total percentage of renter-occupied housing in Seattle. When comparing 2019 to 2012, we see an increased concentration of renter-occupied housing in areas highlighted by the 2012 map. Notable places include areas of: Central, North-Central, and Central-East; areas of Seattle. Overall, in 2019 we see an increased concentration of renter-occupied housing in Seattle in already highly renter-condensed areas showing how gentrification causes the rich to get richer but the poor also become poorer.

Our fifth map measures the percentage of Seattle households who spend more than 30% of their income on housing costs in 2013 and 2019. It is important to note here that comparing the two different maps at a glance can give a false impression that there is a notable decrease in Housing cost-burdened households from 2013 to 2019. Both the 2013 and 2019 maps measure relative concentrations within their own year, so what each year considers high concentration or low concentration is also relative. To avoid this misconception, we examine the differences between the legends of 2013 and 2019, which in doing so, paints an entirely different picture.

When keeping in mind the differences in legend concentration levels between 2012 and 2019, we see a general increase in housing cost-burdened households across Seattle. The most notable areas of housing cost-burdened households are areas of low concentration labeled by 2012, but labeled as high concentration in 2019. These areas are mainly located in Central-Seattle with concentration levels around 0-21% in 2012, but around 63-84% in 2019.

Our sixth map measures severely housing cost-burdened Seattle households in 2013 and 2019. These are households that spend more than 50% of their total income on housing costs. After considering concentration level differences for 2013 and 2019 legends, we see that there is an overall general decrease in severely housing cost-burdened households. Notable decreases are located in areas: South, and Central; of Seattle. These are areas that are considered high concentrations in 2013 (27%-34%), but are considered low concentrations in 2019 (0-4%).

Our seventh map measures the Seattle percentage population whose income is below 200% of the poverty level in 2012 and 2019. After considering differences in concentration levels between 2012 and 2019, we examine an overall increase in poverty concentration in areas located near the South and Central-South of Seattle. These areas show a concentration increase of 21%- 34% increasing to 32%-47% being another stipulation towards the widening gap opened further with gentrification.

According to our findings on our maps, the updated areas of high displacement risk are located in South-East areas of Seattle. Our displacement map also follows an interesting spatial correlation, where areas of high to moderate displacement risk follow along paths relating to Seattle light rail stations. Hence, our team speculates that locations for the Seattle light rail have a significant influence on the displacement risk. The light rail allows greater accessibility to community resources such as jobs, groceries, airport, schools, etc.. Easy accesses attracts more businesses and workers move in, thus spiking up the rent and living cost in a short amount of time. These minorities, who all of a sudden can’t afford their raised rents and cost of living are forced to leave the area which leads to more minorities being pushed away in order to find cheaper living.

The analysis of our displacement risk index shows a significant negative correlation between gentrification and at-risk communities as we see more light rails being built indicating that certain communities may experience a decline in living quality if they were to stay there. Using a combination of demographic, economic, and housing data we see communities at risk can be those with either high renter occupancy, low income, high-housing cost burden, non-white, and/or low educational attainment. The negative correlation between gentrification and the well-being of certain communities suggests that there is a need for proactive policy interventions that can help mitigate the negative effects of gentrification on vulnerable communities by looking at them through the census tract level. Policies that prioritize affordable housing, equitable access to education and employment opportunities, and community development can help ensure that communities at risk are not left behind during the process of gentrification. As with any analysis, it is important to note that there are limitations as the analysis was done using publicly available data that may not capture all aspects of the risk index in Seattle. In conclusion, gentrification has had significant negative impacts on the well-being of communities at risk of being displaced. The findings of this study suggest that policy interventions are needed to address the negative effects of gentrification and ensure that at-risk communities are not marginalized or displaced in the process.

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