Team Names: Demolition Group Siman Ning, Chin-Wei Chen, Saba Fatima
URB 591 Pilot Project Report
Building Demolition and Socio-Economic Change In the past decade, Seattle has been growing fast with huge changes in the downtown area. Many old structures were torn down and new buildings are being built. The wave of construction brings up concern for gentrification and displacement.
Gentrification is defined as “a process in which a poor area (as of a city) experiences an influx of middle-class or wealthy people who renovate and rebuild homes and businesses and which often results in an increase in property values and the displacement of earlier, usually poorer residents” (Merriam-Webster).
Building demolition could be a good indicator of displacement. We are interested in knowing whether neighborhoods with lower socioeconomic status are experiencing more to building demolition, and what other factors could contribute to the building demolition.
1. Research question(s) and hypothesis Research Question:Is building demolition associated with changing racial, ethnic, and socio-economic composition of gentrifying tracts? Hypothesis: We expect to see high demolition activity in tracts with low Socio-Economic Index
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Team Names: Demolition Group Siman Ning, Chin-Wei Chen, Saba Fatima
2. Pilot design (1) Selection of Seattle tracts as sample group for pilot test study (2) Tracking and mapping the socio-economic variations in the tracts between 2010-2018 in three-year time frames. The socio-economic index is calculated as a function of the median income level of the tracts, the white ratio, and the adult education levels in the tracts (3) Next, observations were made on the frequency of occurrence of demolition activity in the sample Seattle tracts (4) The relationship between socioeconomic status changes and demolition was tested on a few multiple regression models. Multiple linear regression models were built to test the relationship between socioeconomic status and building demolition (5) Synthesizing key results from pilot study and outlining limitations and constraints of the study
3. Database/sample selected The analysis for this study is conducted at the city and urban tract levels. Seattle was selected as our study area for this pilot project. The analysis of this project is carried out at the tract level. There are three data sources for our project. Firstly, we use the US Census to access several socio-economic data we need. In the US Census data, we use the median value of annual household income to represent income in each census 2
Team Names: Demolition Group Siman Ning, Chin-Wei Chen, Saba Fatima
tract. Aslo, the ratio which adults’ age are 25 or older and earned a bachelor's degree or higher will sum up the numbers. Then, it will categorize the annual population, including White, Asian, Black or African American, Hispanic or Latino (of any race), two or more races, and other races. Computing the ratio of the white population represents the race variable. Other than socioeconomic status, we also gather the changes of total population in different periods to control for population density change in each tract. The second data source is the Seattle GIS Portal database. We calculate the sum of demolished buildings in each tract of the calendar year to represent building demolition in our research. Then we further gathered the sum of demolished buildings into three periods (2010-2012, 2013-2015, 2016- 2018). It should be noted that we only count for residential building demolition permits, including single family and multi-family. Lastly, we gathered the median unit price of residential property in each of the census tract from the King County Assessor. This is done by first combining the sales data
and the parcel data from King County Assessor. To account for
inflation we also introduce the Case-Shiller Index to transform all sales value into 2020 dollars. Table 1 summarizes the data sources and sample measurement. Table 1. The summary of data source & measurement Data Source US Census
Sample selected & Measurement ●
Household Income: the median value of household annual income of each census tract
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Team Names: Demolition Group Siman Ning, Chin-Wei Chen, Saba Fatima
● ● Seattle GIS Portal
King County Assessor
Education level: the ratio of adults (25 or older) and their education level Race: the ratio of non-white population
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Building demolition: calculate the sum of demolished buildings in each tract over a certain period of time.
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Property value: select the sales value of the census tract. Parcel: get the area of each parcel to calculate unit price.
●
4. Methods/Applications This project proposes research method and steps in the following section: (1) Develop a SES index to reflect the socioeconomic status of each census tract.The SES index in our analysis is a summary index to reflect the average socioeconomic status in three basic aspects - income level, education level and race. This is a method similar to the HDI index used in the United Nations Development Program(Satterthwaite, 2016). The three aspects of socioeconomic status are weighted equally. The SES index can be written in a formula as the following:
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SES index = (𝐼𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛 × 𝐼𝑖𝑛𝑐𝑜𝑚𝑒 × 𝐼𝑟𝑎𝑐𝑒 ) Where
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Team Names: Demolition Group Siman Ning, Chin-Wei Chen, Saba Fatima
𝐼𝐸𝑑𝑢𝑐𝑎𝑡𝑖𝑜𝑛 = Ratio of adults (25 or older) who earn bachelor's degree or higher, 𝐼𝑖𝑛𝑐𝑜𝑚𝑒 = Percentile rank of the median tract household income in the region 𝐼𝑟𝑎𝑐𝑒= Ratio of white population The SES index should be between 0-1. If the index is close to 0, it indicates a very low socioeconomic status, and if the index is close to 1, it indicates a higher socioeconomic status. We will use the 5 year estimates of ACS survey data from 2012, 2015 and 2018 to generate the SES index. The change of SES index between different periods are calculated by following equation: SES_index_change = log (SES_indexi / SES_indexj)
(2) Use GIS to geocode building demolition permits and aggregate count by tract. 95.6% of the building demolition permits have a coordination, which would help us locate the demolition buildings. With the tract shapefile, we can aggregate the count of building demolition permits every 3 years. This means we will collect counts of demolition from 2010-2012, 2013-2015 and 2016-2018 by census tract.
(3) Aggregate the sales of property by census tract. King County Assessor provides the transaction data of property sales. We can link the sales data with the parcel data, and find the median sales price in each census tract
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Team Names: Demolition Group Siman Ning, Chin-Wei Chen, Saba Fatima
during 2010-2012, 2013-2015 and 2016-2018. Before using the sales data, we want to do some processing: (a) Find out the unit price of property. Considering the sales price is related to both area of land and unit price, we remove the impact of land area by dividing the sales price by area of parcel. (b) Adjust for inflation to the S&P/Case-Shiller WA-Seattle Home Price Index for longitudinal comparison. (c) Find the median unit price of property in each tract. Then use the log transform of the unit price for easier comparison.
(4) Run a multi regression model. We test the regression model between SES index and building demolition count, SES index and building demolition count change, and the individual aspects of SES (race, income and education) with demolition count and count change.
5. Preliminary results ● Descriptive Statistics and GIS Analysis (1)
Non-White Populatiaon Picture 1 shows the ratio of non-white population disttbution in each tract between 2010 and 2018. The highest ratio of non-white population is concentrated in the south-eastern, and southern tracts of Seattle making them more diverse in terms of ethnicity. The tracts to the east of the University of Washington and Northern Seattle display a noticeable non-white diversity index next. Between 2010 and 2018, the non-white
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Team Names: Demolition Group Siman Ning, Chin-Wei Chen, Saba Fatima
diversity ratio slightly decreased in south-central Seattle, and sligtly increased in southern seattle tracts.
Picture 1. Non-White Populatiaon Ratio GIS distribution (2010 vs 2018) (2)
Median Household Income
Picture 2 shows the ratio of median household income distribution in each tract between 2010 and 2018. The lowest median household income is observed in the South-Eastern Seattle tracts, followed by the north and north-eastern tracts. The median household income is not mapped on a ratio-based system and therefore are not directly comparable.
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Team Names: Demolition Group Siman Ning, Chin-Wei Chen, Saba Fatima
Picture 2. Median Household Income GIS distribution(2010 vs 2018) (3)
Education
In terms of education level, Picture 3 shows the ratio of non-college Degree for Adults over age 25 GIS distribution in each tract between 2010 and 2018. In 2010, the highest ratio of Non-College Degree for adults over the age of 25 was seen in the south-easter and southern Seattle tracts, as well as the northern and some north-western Seattle tracts. In 2018, while most of the tracts retain the same relative ratio, an increase in the education levels of adults in some of the northern and south-central tracts is observed.
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Team Names: Demolition Group Siman Ning, Chin-Wei Chen, Saba Fatima
Picture 3. Ratio of Non-College Degree for Adults over age 25 GIS distribution (2010 vs 2018) (4)
Socioeconomic Change Picture 4 shows Socioeconomic Change distribution in each tract among three time periods defined in this research. The highest ratio of socioeconomic change in 2010-2012 is in the tracts of Downtown and Central Area. In 2013-2015, we could see the tracts in Downtown and Lake City have the highest ratio of socioeconomic change.The tracts in Rainier Valley and Rainier Beach, Roosevelt, and West Seattle have the higher ratio of socioeconomic change than other areas in the third time period.
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Team Names: Demolition Group Siman Ning, Chin-Wei Chen, Saba Fatima
Picture 4. Socioeconomic Change GIS distribution (2010-2012/2013-2015/2016-2018)
(5)
Demolition
Picture 5 presents the total demolition count in Seattle. It is apparent that the number in 2017 ranks the highest between 2005-2020. When we see the total demolition counts in our three temporal research periods, the third period has over 2000 demolition cases and has the most demolition cases. However, demolition cases in 2010-2012 have the lowest cases among three temporal periods. Then, Picture 6 gives us further research and demonstrates demolition GIS distribution in each tract of three time periods. The highest demolition cases in 2010-2012 is located at the tracts in the University of Washington Area. Then, in 2013-2015, we could see the tracts in Crown Hill have the highest number of demolition cases. The tracts in Crown Hill and Downtown have the highest number of demolition cases in 2016-2018.
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Team Names: Demolition Group Siman Ning, Chin-Wei Chen, Saba Fatima
Picture 5. Total demolition count 2005-2020
Picture 6. Total demolition count 2005-2020
Regression Analysis
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Team Names: Demolition Group Siman Ning, Chin-Wei Chen, Saba Fatima
Table 2. Model 1 and 2 - SES index and demolition count/ count change The regression results in Table 2 suggests that the count of residential demolition permits has a positive impact on the socioeconomic status index change, after controlling for other variables such as unit land price and population density change. However, the relationship was not significant in all three periods. As for the change of the demolition permits, we also didn’t find it significant to socioeconomic status index change during all three periods and the signs were not consistent. 12
Team Names: Demolition Group Siman Ning, Chin-Wei Chen, Saba Fatima
We also find that increasing the population density has a significant, positive impact on socioeconomic status index change during the 2010-2012 period, with other factors being equal. During 2013-2015, however, population density still remains positive but not significant to the socioeconomic status change. In the year 2016-2018, the increase of population density has a negative but not significant impact on socioeconomic status index change. In terms of the median unit sales price, we find that it is significant and positive in 2013-2015, positive but not significant in 2010-2012, and negative but not significant in 2016-2018. Considering that a composite index might hide the real relationship between demolition count and socioeconomic status, we test the three aspects of income, education and race as individual dependent variables. The test of count change was not shown because count change didn’t have a significant relationship with all three aspects.
Table 3. Modeling income, race, and education to building demolition count 13
Team Names: Demolition Group Siman Ning, Chin-Wei Chen, Saba Fatima
In table 3, we find that demolition count has a significant and positive impact on income change through all three periods. Increasing unit land value has a positive impact on the tract income level in 2010-2012 and 2013-2015. Unit land value has an expected sign in 2016- 2018, but the relationship turns insignificant. Increasing population does impact the income level, but the changing signs suggest there might be other confounding variables not included in this model. In terms of education change, demolition count has a both significant negative impact on education level in 2010 - 2012 and 2013- 2015. The sign changes during period 2 and changes back to negative in 2016-2018. Tracts seeing increasing land value will see an increase in population education level in 2010- 2012 and 2013- 2015 , but not in 20162018. Tracts seeing increasing population will see decrease in education levels in 2010 -2012 and 2013- 2015, but not in 2016-2018. As for race change in each tract, demolition count has a positive but not significant impact on the ratio of white population in 2010- 2012 and 2016-2018. Strangely, increasing land price has a negative significant impact on white ratio in 2016-2018. Increasing population has a negative impact on white ratio in 2010 - 2012 and 20132015, but a positive impact in 2016-2018.
6. Finding summary Tracts with increasing demolition activity do show later increases in income level of population. This is likely because the demolition activity is followed by new construction projects, which will attract people to move in. As newer homes are usually more expensive compared to older homes with similar size and location, those with higher
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Team Names: Demolition Group Siman Ning, Chin-Wei Chen, Saba Fatima
income are more likely to get them. Tracts with increasing land value does show later increases in income level of population, although the relationship is not always significant. We suspect that land speculation would make people with higher income to invest in homes that they think would have increasing property value. But when the value gets too high, people will also ponder whether the investment is worth it. The changing signs of population density on income may be due to other confounding variables. It is likely that the population migrated in is not homogeneous, and we might want to consider other omitted variables such as employment and home ownership. Combining our knowledge that the technology industry boom in the past decade at Seattle, we suspect that employment is a very important variable that should be included in the study. In general, gentrification is a complex process. The gentrification trend in Seattle is mainly through income increase. With that being said, using one index is not the best way to measure gentrification. It could hide and reduce the weight of the major aspect in the index.
7. What we have learned (1) We give income, race and education levels equal weight for gentrification in our index. However, gentrification can be complex, and it is possible that the index actually skewed the gentrification levels, if income is the most important indicator of gentrification. Additionally, this study only
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Team Names: Demolition Group Siman Ning, Chin-Wei Chen, Saba Fatima
checks for what happens in Seattle. In other cities, gentrification may happen in a different way. (2) Within the socio-economic index, we simplify racial issues to whitenon-white, but it is possible that races other than white may have unequal situations, and this research does not look into that heterogeneity. (3) For using the sales data and assessed value, neither is perfect. The assessed value lacks real world meaning, yet the sales data is not land-specific. In our specific research, structures get demolished because the developers value the land more and want to take advantage of the land. We hope in the future there is a better measurement of unit land price. (4) We didn’t account for spatial autocorrelation due to knowledge and time limitation. However, it is important to realize that tracts could influence each other. Also, we try to check the changes in different times and view them independently, but in reality, what happened in the past might impact the future, thus the approach we used is not very ideal. (5) There are other confounding variables we didn’t include in the model. Employment could be important because of the boom of technology workers in Seattle. We should check the change of population in terms of renters and home-owners, as their location choice on home can be very different.
Bibliography
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Team Names: Demolition Group Siman Ning, Chin-Wei Chen, Saba Fatima
Fogel, R., 2020. Gentrification and changing foodscapes in Seattle, Urban@UW. [Online] Available at: https://depts.washington.edu/urbanuw/news/gentrification-and-changing-foodscapesin-seattle/ [Accessed 05 November 2020]. Jason Richardson, B. M. J. F., 2019. NCRC. [Online] Available at: https://ncrc.org/gentrification/ [Accessed 5 November 2020]. Jeanette Covington, R. B. T., 1989. Gentrification and crime: Robbery and Larceny Changes in Appreciating Baltimore Neighborhoods During the 1970s. Urban Affairs Quarterly, Volume 25, pp. 142-172. Williams, K. N., 2015. ncsociology. [Online] Available at: http://www.ncsociology.org/sociationtoday/v132/gentrification.html [Accessed 05 November 2020]. Satterthwaite D, 2016. Missing the Millennium Development Goal targets for water and sanitation in urban areas. Environ Urban 28:0956247816628435.
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