Shannon Van Zandt, Texas A & M University – “Poor and Minority Impacts from Hurricane Ike”
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. 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. 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. 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
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.
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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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
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.