Poor and
Minority Impacts
from
Hurricane Ike
Shannon Van Zandt, Ph.D.,
AICP
Research supported by a grant from the Nationa...
Objectives and outline
• Introduce group to ―living laboratory‖
research from 2008’s Hurricane Ike on
Galveston Island (TX...
Geography of Opportunity
• Sprawl, concentrated poverty,
and segregation have shaped
metropolitan areas in ways
that exace...
Inequalities may be due to:
• Discrimination in lending and real estate industries
• A lack of, and a poor distribution of...
Housing inequalities determine the
spatial pattern of Social
Vulnerability (SV)
Levels of Social Vulnerability Analysis
Base Social Vulnerability Indicators (percentages) 2
nd
Order 3
rd
Order
1. Single...
Example: SV indices overlaid with
Cat 1&2 surge zones
coastalatlas.tamug.edu
Hurricane Ike
• Hurricane Ike (Galveston, TX 2008)
provided an opportunity to validate
SV mapping technique and examine
im...
Data and methods
• Multiple data sources used:
– Primary data:
• Longitudinal panel survey of 1500 single family structure...
In the urban core of Galveston, many
lower quality homes are only elevated a
foot or less off the ground, if at all.
Here,...
In contrast, a West End vacation home
sits well above the surge level, a block
off the gulf coast, these high-quality
home...
PREDICTED
Using the Social
Vulnerability
Indicators from the
Coastal Community
Planning Atlas
OBSERVED
From Primary Data
C...
PREDICTED
Using the Social
Vulnerability
Indicators from the
Coastal Community
Planning Atlas
OBSERVED
From Primary Data
C...
PREDICTED
Using the Social
Vulnerability
Indicators from the
Coastal Community
Planning Atlas
OBSERVED
From Primary Data
C...
Higher levels of damage seen to
minority neighborhoods—even
after accounting for the age of
the housing and the proximity ...
FINDING: Lower-value homes
recovered more slowly
$0
$50,000
$100,000
$150,000
$200,000
$250,000
2008_09 2009_04 2009_09 20...
FINDING: Long-term displacement
of African-Americans Galveston
46%
25%
25%
51
%
39
%
1%
Bolivar
35
%
19
%
42
%
Mainland
Hi...
Summary
• Disparate impacts to SV populations and
their housing generate the potential for
redevelopment and population
ch...
THE NEXT
GENERATION
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Shannon Van Zandt, Texas A & M University – “Poor and Minority Impacts from Hurricane Ike”

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  • Regional development patterns favor some communities and undermine others. Sprawl, concentrated poverty, and segregation have shaped metropolitan areas in ways that exacerbate existing economic and social inequalities (Squires and Kubrin, 2005). Over the past century, these forces have consistently and progressively favored suburban areas as households with means have left the city, leaving those without means in urban areas and decimating the flow of resources needed to maintain the quality of life there. Orfield (1997) claims that the forces of suburbanization have created a “push-pull” of regional polarization. Central cities and inner suburbs are left as the least able to resist, and the “residual category for those without choices” (Rusk, 1993 as quoted in Orfield 1997, p. 74). The resulting polarization creates both pockets of problems and pockets of privilege—concentrated areas that either host opportunity or are mostly devoid of it. Pockets of poverty are associated with low-performing schools, social isolation, crime, unemployment, and low levels of educational attainment. In contrast, privileged areas are associated with high-quality schools, low crime, proximity to services such as health care and banking, and healthier physical and social conditions (for a review, see Squires and Kubrin, 2005). This spatial distribution of need and resources forms what is sometimes called the “geography of opportunity.” The geography of opportunity is based on two main premises: first, that where one lives is critical for taking advantage of available opportunities; and second, that households have unequal abilities to live in places with good opportunities (Abrams, 1955; Galster and Killen, 1995; Briggs, 2005).
  • To illustrate these issues, I’m going to share with you results from some of my research in Texas metros, including Dallas, Austin, and Galveston.These studies have examined the impact of inequitably distributed housing opportunities on outcomes, including access to high quality schools and educational outcomes for school children, as well as exposure to crime and impacts from natural disasters.
  • The racial distribution of public school children suggests that large numbers of African-Americans (likely public housing residents) are likely experiencing long-term displacement. 
  • Shannon Van Zandt, Texas A & M University – “Poor and Minority Impacts from Hurricane Ike”

    1. 1. Poor and Minority Impacts from Hurricane Ike Shannon Van Zandt, Ph.D., AICP Research supported by a grant from the National Science Foundation (#0928926) entitled Developing A Living Laboratory for Examining Community Recovery and Resilience After Disaster and from a series of grants funded by NOAA, the TGLO and the CCC. The authors and not the NSF, NOAA, TGLO, or the CCC are responsible for the any findings and opinions expressed in this presentation or the paper upon which it is based. The full paper can be found in Housing Policy Debate, 22:1, 29-55
    2. 2. Objectives and outline • Introduce group to ―living laboratory‖ research from 2008’s Hurricane Ike on Galveston Island (TX) – My focus on social vulnerability factors, particularly as they relate to the spatial distribution of housing • Highlight related findings
    3. 3. Geography of Opportunity • Sprawl, concentrated poverty, and segregation have shaped metropolitan areas in ways that exacerbate existing economic and social inequalities • The geography of opportunity is based on two main premises: – where one lives is critical for taking advantage of available opportunities; – households have unequal abilities to live in places with good opportunities
    4. 4. Inequalities may be due to: • Discrimination in lending and real estate industries • A lack of, and a poor distribution of housing opportunities  Housing market segmentation  Uneven regional growth  Clustering of low-income housing Consequences include:  Poorer access to opportunity  Greater exposure to hazards
    5. 5. Housing inequalities determine the spatial pattern of Social Vulnerability (SV)
    6. 6. Levels of Social Vulnerability Analysis Base Social Vulnerability Indicators (percentages) 2 nd Order 3 rd Order 1. Single parent households with children/Total Households Child care Needs Socially Vulnerable Hotspot 2. Population 5 or below/Total Population 3. Population 65 or above/Total Population Elder Care Needs4. Population 65 or above & below poverty/Pop. 65 or above 5. Workers using public transportation/Civilian pop. 16+ and employed Transportation needs6. Occupied housing units without a vehicle/Occupied housing units (HUs) 7. Occupied Housing units/Total housing units Temporary Shelter and housing recovery needs 8. Persons in renter occupied housing units/Total occupied housing units 9. Non-white population/Total population 10. Population in group quarters/Total population 11. Housing units built 20 years ago/Total housing Units 12. Mobile Homes/Total housing units 13. Persons in poverty/Total population 14. Occupied housing units without a telephone/Total occupied HU Civic Capacity needs 15. Population above 25 with less than high school/Total pop above 25 16. Population 16+ in labor force and unemployed/Pop in Labor force 16+ 17. Population above 5 that speak English not well or not at all/Pop > 5 Source: Van Zandt, S., W.G. Peacock, *D. Henry, H. Grover, W. Highfield, and S. Brody. 2012. Mapping Social Vulnerability to Enhance Housing and Neighborhood Resilience. Housing Policy Debate 22(1): 29-55.
    7. 7. Example: SV indices overlaid with Cat 1&2 surge zones coastalatlas.tamug.edu
    8. 8. Hurricane Ike • Hurricane Ike (Galveston, TX 2008) provided an opportunity to validate SV mapping technique and examine impacts for socially vulnerable groups • Select study objectives – Did the spatial distribution of vulnerable populations mitigate or exacerbate damage and loss to property? – Do social vulnerability factors facilitate or impede decision-making with regard to dislocation and early repair/rebuilding decisions? – How do pre-existing physical and social development patterns alter the long-term recovery trajectories for socially vulnerable households and housing in physically and socially vulnerable neighborhoods?
    9. 9. Data and methods • Multiple data sources used: – Primary data: • Longitudinal panel survey of 1500 single family structures • Longitudinal panel survey of approximately 550 households – Secondary data sources • Galveston permit data • County appraisal district (CAD) parcel data • Analyses include: – Correlation analysis of impacts and actions taken by socially vulnerable groups – Spatial analysis relating development patterns to damage – Longitudinal analysis of housing recovery – Long-term displacement
    10. 10. In the urban core of Galveston, many lower quality homes are only elevated a foot or less off the ground, if at all. Here, a poorly-constructed home has slid off its foundation, and the other structural systems have also collapsed. FINDING: Inequitable development patterns affected damage received
    11. 11. In contrast, a West End vacation home sits well above the surge level, a block off the gulf coast, these high-quality homes received only wind damage, which as seen here, was quite minimal.
    12. 12. PREDICTED Using the Social Vulnerability Indicators from the Coastal Community Planning Atlas OBSERVED From Primary Data Collected After Hurricane Ike Transportation-dependent populations Evacuated later r=-0.249* Source: Van Zandt, S., W.G. Peacock, *D. Henry, H. Grover, W. Highfield, and S. Brody. 2012. Mapping Social Vulnerability to Enhance Housing and Neighborhood Resilience. Housing Policy FINDING:
    13. 13. PREDICTED Using the Social Vulnerability Indicators from the Coastal Community Planning Atlas OBSERVED From Primary Data Collected After Hurricane Ike Households with high recovery needs r=-0.235* Had higher levels of overall damage Source: Van Zandt, S., W.G. Peacock, *D. Henry, H. Grover, W. Highfield, and S. Brody. 2012. Mapping Social Vulnerability to Enhance Housing and Neighborhood Resilience. Housing Policy FINDING:
    14. 14. PREDICTED Using the Social Vulnerability Indicators from the Coastal Community Planning Atlas OBSERVED From Primary Data Collected After Hurricane Ike Households with high social vulnerability Applied less to FEMA and SBA for aid r=-0.289* Source: Van Zandt, S., W.G. Peacock, *D. Henry, H. Grover, W. Highfield, and S. Brody. 2012. Mapping Social Vulnerability to Enhance Housing and Neighborhood Resilience. Housing Policy FINDING:
    15. 15. Higher levels of damage seen to minority neighborhoods—even after accounting for the age of the housing and the proximity of the housing unit to water and the seawall. Source: Highfield, W., W.G. Peacock, and S. Van Zandt. 2013. Determinants of Damage to Single-Family Housing from Hurricane-induced Surge and Flooding: Why Hazard Exposure, Structural Vulnerability, AND Social Vulnerability Matter in Mitigation Planning. Conditional accept at the Journal of Planning Education & Research. FINDING: Minority neighborhoods received greater degrees of damage
    16. 16. FINDING: Lower-value homes recovered more slowly $0 $50,000 $100,000 $150,000 $200,000 $250,000 2008_09 2009_04 2009_09 2010_09 • The average property value pre-storm was $152,155, and dropped 20.1% due to Ike damage. • Average property values regained 95.5% of the pre- storm value within two years. • Lower value homes experienced greater damage, lost a greater proportion of their value, and have only recovered 82% of their pre-storm value. 5% 37% 39% 19% Distribution of Damage No Damage Minor Moderate Severe HouseValue Single-Family Housing Appraisal date Source: Van Zandt, S. T. Chang, and W.G. Peacock. 2011. Residential Rebuilding After Disaster: Findings from Galveston, TX. Association of College Schools of Planning, Salt Lake City, UT, October 14,
    17. 17. FINDING: Long-term displacement of African-Americans Galveston 46% 25% 25% 51 % 39 % 1% Bolivar 35 % 19 % 42 % Mainland Hispanic White African-American Distribution of Students enrolled in GISD, January 2010 Van Zandt, S. , W.G. Peacock, D. Henry, and S. Willems. Demographic Impacts of Natural Disasters. Urban Affairs Association Annual Meeting, Pittsburgh, PA, April 21, 2012.
    18. 18. Summary • Disparate impacts to SV populations and their housing generate the potential for redevelopment and population change, including: – Loss of affordable housing stock – Exacerbation of pre-existing inequities • Highlights need for: – Targeting of resources – Capacity-building within SV populations – Pre-event planning for equitable recovery
    19. 19. THE NEXT GENERATION

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