Andy Fraser
Housing
Inequality in NE Portland.
A Case Study in Housing Inequality: The
King Neighborhood of North East Portland, OR. 1990-2000.
Introduction
Oregon within a historical frame has often been qualified as one of the most racist state outside of the
Jim Crow south. Trends in state-wide, institutionalized discrimination extend
from the deportation of freed-slaves and the annual taxation of ethnic
minorities (approximately five dollars a year) in the late 19th
century (Steinberg, 2005); the apartheid-like policies imposed on Native
Americans; the internment of Japanese Americans; to laws prohibiting
interracial marriage and “minority” homeownership—both of which weren’t amended
until 1952 and 1972 respectively (Steinberg, 2005).
Discriminatory
housing and bank policies are some of the most destructive and pervasive
aspects of Oregon’s racist past. For example, Black residents working within
the Portland-Metro area were prohibited from renting or owning homes outside of
the segregationist housing projects of Vanport and Albina. Similar to Hurricane
Katrina, the flooding of Vanport in 1948—equally caused by the failure of
levees—disproportionately affected Black residents, leaving thousands homeless.
The only option for these former Vanport residents—due to the Oregon’s
institutionalized racism—was to move to Albina and other
segregationist housing developments in North East Portland (or to leave Oregon).
Redlining,
the process by which banks and mortgage companies actively neglected and
refused services to Black and lower income residents wasn’t officially made
illegal until 1972 under the “Home Mortgage Disclosure Act” and “the Community Reinvestment Act”, but it
unofficially persisted well into the 1990s.
Today,
North East Portland is still considered one of the only “predominantly Black”
regions of Portland—and Oregon more generally. The disproportionate number of
Black residents living in NE Portland is a direct vestige of segregationist and
discriminatory housing policies. Similarly the insidious homeownership gap
within Oregon’s Black community is a consequence of redlining and laws
forbidding Black homeownership in the Portland-Metro area. In other words,
redlining had two devastating effects that are visible today: it actively
denied Black residents the freedom to purchase and own homes (thereby forcing
them to pay typically white “landlords”) and it continuously prevented Black
citizens from investing into their own communities whilst establishing equity
and savings.
Due to
the homeownership gap, Black residents of Portland are disproportionately
vulnerable to the effects of increased rental costs typically associated with
efforts of urban renewal, “beautification” and gentrification. Using GIS, this
research project aims to begin visualizing trends in housing inequality and the
historical landscapes of racism and discrimination within North East Portland.
As an
introduction however, the frame of this study is extremely small: it focuses exclusively
on the King Neighborhood of NE Portland, a region historically at the center of
the city of Albina—the only region Black citizens could reliably find housing
after the flooding of Vanport (Albina was annexed/appropriated by the City of
Portland in 1981).
The
King Neighborhood is partitioned within this study in terms of block groups and
these groups are related to census data for “gross rent as a percentage of
income” and “race/ethnicity” respective of block groups for 1990 and 2000.
Given these factors, the hypothesis of this project is this: Black residents of the King Neighborhood are
disproportionately vulnerable to the effects of increased rent—between 1990 and
2000 the percentage of Black residents living within this neighborhood will
decrease in response to increased rent costs as a percentage of income.
Methods
Accuracy: The
census data provided by the US Census Bureau has a 90% confidence interval/margin
of error. Regarding the 1990 “rent as a percentage of income” data, human error
may have been introduced—I had to add all the rent-payers who paid 30% or more
for housing across 10,000 dollar increments of income and create a percentage
of this summation based on the total. I additionally added fields into all the
data sets and used the “field calculator” in order to quickly create sums and
percentages—this could be the source of additional human error.
1990 “Rent as a Percentage of Income”
|
US Census Bureau:
americanfactfinder.gov
|
X
|
X
|
90% confidence interval
|
Data Set provides gross rent as a
percentage of income for Multnomah County in 1990.
|
2000 “Rent as a Percentage of Income”
|
US Census Bureau:
americanfactfinder2.gov
|
X
|
X
|
90% confidence interval
|
Data Set provides gross rent as a
percentage of income for Multnomah County in 2000.
|
1990 “Race”
|
US Census Bureau:
americanfactfinder.gov
|
X
|
X
|
90% confidence interval
|
Data provides the racial composition of
selected census tracts and block groups
|
2000 “Race”
|
US Census Bureau:
Americanfactfinder2.gov
|
X
|
X
|
90% confidence interval
|
Data provides the racial composition of
selected census tracts and block groups
|
1990 Block Group Shape File
|
US Census Bureau:
Americanfactfinder
|
NAD_1983_HARN
_Stateplane_oregon
_North_FIPS_3601
|
GCS_North_
American_
1983_HARN
|
90% confidence interval.
|
Provides block groups for Multnomah
county.
|
2000 Block Group Shape File
|
US Census Bureau
Americanfactfinder2
|
NAD_1983_HARN
_Stateplane_oregon
_North_FIPS_3601
|
GCS_North_
American_
1983_HARN
|
90% confidence interval.
|
Provides block groups for Multnomah
county.
|
2005 Oregon County Outline Map
|
Oregon Geospatial Database
|
NAD_1984_HARN
_Stateplane_oregon
|
GCS_WGS_
1984
|
95% confidence interval
|
Provides outline shape file for the
state of Oregon.
|
Analysis Methods and
Geoprocessing: from
americanfactfinder.gov and americanfactfinder2.gov the Multnomah County census
data for “rent as a percentage of income” and “race” for the years of 1990 and
2000 were downloaded. Census block-group shape files for 1990 and 2000 were
similarly downloaded from rlismetro.gov (Portland-Metro data discovery site).
Because the data sets are from the “American Community Survey”, the field
titles therefore differ from decennial census shape files. A unique field was
subsequently created within the data-sets called “FIPS”, which included block
group identifiers that were similar in all the data-sets and shape-files (the
only thing that differed was the field title). FIPS was then used as the field
to join the block group shape files with the census data. The census tracts
33.01, 33.02, 34.01, 34.02 (which include the 12 block groups) were then
selected using the “select by attribute” function within the attribute table of
the joined shape file and a new layer was created using the “create layer from
selection” feature. This process was similar in all four data-sets.
Within
each data set’s attribute table a new series of fields were created in order to
create numeric values from the “string” values the census uses. In the case of “race”,
all counts of Black residents were summed using the field calculator. Another
field was then created and again, the field calculator used to determine
percent of residents within King Neighborhood who are Black. This was repeated
for both 2000 and 1990 data.
For rent,
unique fields were again created to summate the percent of residents within
King Neighborhood who pay 30% or more of their income towards rent. The 30% or
more qualification is the accepted percentage for measuring inhibitive
rent/housing burdens—this value prevents the establishment of savings and
preventative health measures. Additional unique fields were then created to
calculate percentages.
The 1990
rent data-set required a preliminary step. The rent as a percentage of income
was broken down by 10,000 dollar increments (for example the number of
residents paying 30 percent or more towards rent who make between 20,000 and
30,0000 dollars)—so it therefore had to be added across income and percentage
increments as well.
The
reclassification operation was then used to create common or similar class
breaks between respective shape file/data-sets. The annotation of block group
labels was used to reposition labels if they interfered with other symbology
(such as the graduated symbols of “Rent vs. Race”). The state context map
utilized the “clipping” operation. Part of the “Portland-metro block group”
shapefile extends into Vancouver, WA. This portion was clipped in order to
avoid confusion.
Results
Between 1990 and 2000 the
percentage of Black residents living within the King Neighborhood block groups
dropped dramatically. Similarly between 1990 and 2000 the percentage of income
paid towards rent dropped precipitously—or in other words the burden of rent in
relation to income dropped as well.
This map indicates that the King Neighborhood saw a sharp
decrease in the number of Black residents between 1990 and 2000. In 1990 many
of the block groups associated with this neighborhood had a majority Black
population. By 2000 nearly all the block groups constitute a minority
population of Black residents. The final map in this results section will
illustrate the “percent change” in percent of black residents in conjunction
with the percent change in rent. It is important to note that the
classification breaks don’t match perfectly in this map due to a very large
swing in the range of percentages between these two data sources.
This map illustrates the
percentage of residents who pay 30% or more of their income towards rent. For
instance, “red” indicates a range of 60-80% of residents pay 30% or more of
their annual income towards rent. The “30% or more”, isn’t an arbitrary
quantity as the methods section also indicated. These values are used to
understand housing burdens—excessive housing costs equate to poor
savings-habits and other variables associated with economic wellbeing. When
looking at both the 1990 and 2000 values it is interesting to note the decrease
in rent burdens over time (few people seem to be paying inhibitive rent prices
in relation to income).
This map differs from the
other maps in that it indicates the percent
change in rent as a percentage of income and the percent change in the percent of Black residents living in King.
Here the “red” indicates the greatest absolute
value percent change (it actually is a negative value, meaning that rent
burden decreased from 1990 to 2000). And similar with the graduated blue
symbols: the largest circles represent the greatest percent change which
happens to be a negative value (meaning that the percentage of black residents
living in King decreased between 1990 and 2000). It is interesting to note what
appears to be a correlation: blocks with the greatest percent change in rent
similarly have the greatest percent change in the percent of black residents
living there (and vice versa).
Discussion
There
are a couple of interpretations that can explain these results. Because this
study was only interested with residents who paid 30% or more of income towards
rent, the movement of Black residents away from this neighborhood seems to
simultaneously be marked by the introduction of residents who pay less towards
rent as a percentage of income. Irrespective of racial demography, this trend
towards decreased housing burden is consistent with notions of gentrification,
where lower income residents are supplanted by wealthier ones. It is important
to note however, that gentrification is an extremely complicated phenomenon. So
while these findings are conducive to themes of gentrification, they
nonetheless fall short of defining it. Similarly it is important to note that
gentrification exclusively speaks of class and wealth—it does not inherently
address or incorporate race. Nonetheless gentrification can disproportionately
affect certain racial groups due to housing inequalities and histories of
discrimination—as seems to be the case with this study.
These
results don’t prove or disprove the hypothesis. The data sets don’t show an
increase in rent burdens which—according to the hypothesis—causes this movement
of Black residents away from the neighborhood. This study is therefore missing
the driver for this change in
demographics. While Black residents certainly did leave between 1990 and 2000,
it is still uncertain whether rent burden inspired this shift. It’s possible
that the 1980s were marked by steep rent increases (or income decreases) and
therefore the study is too narrow in terms of a historical perspective; or
maybe the study is simply too course and it needs yearly census data to
resolutely identify the conflict between rent and racial composition of
neighborhoods; or it could be that the two variables simple don’t correlate,
but this tentatively doesn’t seem likely given the initial correlation indicated
in the “percent change in rent vs. percent change in race” map.
Furthermore,
because this study only looks at twelve block groups, it’s possible that the
“conflict” between Black residents and rent prices (as the hypothesis predicts)
happened outside the borders of the King neighborhood. So while rent prices
might not have increased in King, perhaps they did in an adjoining region which
caused a decrease in percent of black residents in that respective area, thereby
causing cultural reverberations and more qualitative motives for this shift in
racial composition within King.
Lastly
it is possible that Black residents are leaving the King neighborhood, not out
of duress or due to the homeownership gap, but instead for other reasons. It
could be equally plausible that black residents are becoming more affluent and
are in turn buying or renting homes in other regions. While broadly this
doesn’t coincide with other research addressing issues of housing inequality in
Portland, it nonetheless could be true for this particular segment of North
East Portland. Ultimately, as one might expect with such a narrow study, this
research provides many more questions than it does answers. It is nonetheless
very interesting to see the initial hints of a correlation within the King
Neighborhood between the change in percent of rent paid in terms of income and
the change in percent of black residents. This study most certainly should be
extended to include many more regions within NE Portland and it should focus on
annual survey data in order to have a more detailed perspective on rent prices.
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Gina, 2000. Quantitative and Spatial Analysis Techniques for Analyzing Gentrification
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Steinberg . 2008. Retired Supreme
Court chief justice's long fight to destroy racial discrimination in Oregon's
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