regional analysis of impediments to fair housing (rai) part 1

SEVEN50 REGIONAL ANALYSIS OF IMPEDIMENTS TO FAIR HOUSING DRAFT |1 Carras Community Investment, Inc.

CARRAS COMMUNITY INVESTMENT, INC.

Part 1:

Seven/50 Regional Analysis of Impediments to Fair Housing (RAI) OCTOBER 2012 DRAFT

In partial fulfillment of the requirements of Fair Housing Planning for the Seven|50 Southeast Florida Prosperity Plan—HUD Sustainable Communities Regional Planning Initiative

PART 1, SECTIONS:

1 Introduction: Analyzing Impediments to Fair Housing 2 SEFLA Region Background Data 3 Fair Housing Profile 4 Lending Profile 5 Identified Impediments

SEVEN50 REGIONAL ANALYSIS OF IMPEDIMENTS TO FAIR HOUSING DRAFT |2


Executive Summary This report contains a Regional Analysis of Impediments to fair housing (RAI) which identifies, explains, and analyzes the fair housing milieu in Southeast Florida (SEFLA). It is produced in partial fulfillment of HUD defined Fair Housing Equity Assessment (FHEA) requirements for the Seven|50 regional plan. This plan is being produced by a consortium led by the South Florida and Treasure Coast Regional Planning Councils as a grantee of HUD’s Sustainable Communities Regional Planning Initiative. The counties included in this analysis, listed in order from north to south, are 1) Indian River, 2) Martin, 3) St. Lucie, 4) Palm Beach, 5) Broward, 6) Miami-Dade, and 7) Monroe.

Summary of Key Findings: 1. SEFLA Region Background Data

The region has very high levels of cost-burdened households especially for renters. 60% of renting households, regardless of income, pay more than 30% of their monthly income on housing costs while 46% of household that own pay the same proportion.

Our analysis identifies single female householders with children as especially in need. As a region, one third of all single-female households with children are below poverty level.

efficient public transportation options are lacking throughout the entire region—the development of which are especially essential near areas that are primarily minority and renter-occupied when employment opportunities are not located near these areas

2. Fair Housing Profile Assisted housing units are highly concentrated: 94% of all units within properties that offer assisted housing are assisted. The vast majority of assisted housing units are reserved for renters in the 55-60% AMI. Only 627 of the nearly 85,000 assisted

housing units in SEFLA are reserved for those in the lowest income bracket, and therefore those in the greatest need of affordable housing opportunities. 512 of these are in Broward County compared to only 40 in Miami-Dade (which has the highest poverty rate in the region at 17%); none are located in Indian River, St. Lucie and Martin Counties.

Disability is, over the 5-year period and for each county, the largest alleged discriminating factor in fair housing complaints (43%). This is followed by race (17%), national origin (13%), and familial status (11%).

3. Lending Profile There are disparities in loan origination rates and subprime lending rates across the seven South Florida counties. Though

these disparities are seen across racial groups, the data suggests no racial group at a significant disparity or disadvantage across all seven counties.

4. Top 10 Identified Impediments 1: Lack of Knowledge, Awareness of, or Education on Fair Housing Protections 2: Fair and Equal Lending Disparities 3: Housing/Lending Discrimination on the Basis of Race, Color, National Origin, Religion, Sex, Familial Status and Disability 4: Shortage of Affordable Housing Opportunities 5: Violations of Federal, State and Local Housing Laws



6: Housing Market Segregation 7: Predatory Lending 8: Limited Funding to Meet Need for Affordable Housing 9: Zoning/Land Use 10: Improvement of the Housing Discrimination Complaint Process



1. Analyzing Impediments to Fair Housing The right to fair housing choice is among America’s most basic civil rights as defined by federal, state and local laws and as such it is structurally integral to the Sustainable Communities Regional Planning Grant program. Specifically, HUD defines impediments to fair housing choice as:

• Any actions, omissions, or decisions taken because of race, color, religion, sex, disability, familial status, or national origin which restrict housing choices or the availability of housing choices

• Any actions, omissions, or decisions which have the effect of restricting housing choices or the availability of housing choices on the basis of race, color, religion, sex, disability, familial status, or national origin.

All households, regardless of arbitrary factors such as familial status and race are guaranteed equal access to housing opportunities yet disparities in the receipt of these opportunities based in such grounds are still commonplace throughout regions. The seven-county Southeast Florida Region (SEFLA) is no different. Sustainable Communities Regional Grantees are therefore required to complete a Fair Housing and Equity Assessment (FHEA) and recommended to produce a Regional Analysis of Impediments to fair housing (Regional AI) in hopes of addressing these disparities. The broad purpose of performing a Regional AI is to increase housing choice through assembling fair housing information and identifying problems. More specifically focusing on furthering fair housing on a regional scale allows grantees to:

1. Overcome spatial segregation making assisted housing accessible to all in all areas of the metro region, which overcomes jurisdictional and artificial program delivery barriers.

Figure 1: SEFLA Region



2. The ability to integrate assisted housing waiting lists into one regional waiting list & the integration of racial or ethnic groups into areas where they have low impact (areas where they compose less than 30% of population).

3. To make public housing a path to social mobility rather than housing of last resort by modernizing them to be appealing to both current residents and suburban residents.

4. To secure the cooperation of other important actors whose impact upon fair housing is substantial (jobs, schools, transportation agencies, social service agencies, Government not for Profits, Government Agencies).

5. To break down the statistical racial disparity between HUD’s public housing program and its Section 8 existing housing program by encouraging more non-minorities by promoting desegregation within assisted and insured programs by establishing a one stop metropolitan wide housing assistance, marketing, information and referral center.

6. To discourage discrimination in all programs by encouraging all persons regardless of color, national origin, sex, disability or familial status to consider all housing options.

This report contains the SEFLA Regional AI. The counties included in this analysis, listed in order from north to south, are 1) Indian River, 2) Martin, 3) St. Lucie, 4) Palm Beach, 5) Broward, 6) Miami-Dade, and 7) Monroe. While the study area is relatively large and heterogeneous, there are definite benefits to conducting an AI at this scale as many fair housing issues, especially those most intractable, are best addressed at a regional level. By analyzing fair housing on this level, the jurisdiction seeks to overcome spatial separation and segregation by eliminating housing delivery barriers, integrating waiting lists between jurisdictions, and broadening the housing choices available to all eligible participants throughout SEFLA. As outlined in the U.S. Department of Housing and Urban Development, Office of Fair Housing and Equal Opportunity, Fair Housing Planning Guide, recipients of HUD’s housing and community development grants must certify that they will affirmatively further fair housing (AFFH). These provisions are found within the mandate of Section 808(e)(5) of the Fair Housing Act. The extent of the AFFH obligation has never been defined statutorily. However, HUD defines it as requiring a grantee to:

1. Conduct an analysis to identify impediments to fair housing choice within the jurisdiction 2. Take appropriate actions to overcome the effects of any impediments identified through the analysis 3. Maintain records reflecting the analysis and actions taken in this regard. Grantee jurisdiction providing opportunities for inclusive

patterns of housing occupancy regardless of race, color religion, sex familial status, disability and national origin. This report fulfills the first of three AFFH requirements by reviewing impediments to fair housing choice in the public and private sector.



Enti ty Engaged to Conduct the 2012, Seven-50 SEFLA Regional AI

Carras Community Investment Inc. (CCI, Inc.) CCI, Inc. is a nationally recognized leader in implementing asset-based strategies for housing and community development. Our professional expertise in affordable housing, fair lending, and economic development includes advisory services in market analysis, strategic planning, and financing. Over the past thirty years, CCI, Inc. has provided planning services to over 200 clients across the country, resulting in over $10 billion of investment in underserved communities.



Methodology and Data Sources Regional Profile - Methodology

This section includes background data on the jurisdiction to serve as bases for identifying and contextualizing impediments. Variable are chosen that relate to the degree of segregation and restricted housing by race and ethnicity, and families with children in particular (see table1). Variables and data sources are also chosen to parallel as best as possible a dataset compiled by HUD’s office of Policy Development & Research (PD&R). This dataset was intended to provide program participants with data to support this analysis and additional fair housing research. Regrettably however, the data package provided for SEFLA was incomplete (only data for the three most southern counties was provided -Broward, Miami-Dade and Monroe) and therefore inappropriate to use for the full regional profile. However, given that a large proportion of the population, especially minority residents, is located within Broward and Miami-Dade counties this information is still relevant to the discussion. Therefore, this data is summarized in the Appendix under the HUD PD&R Data Package section. Descriptions of their variables and methodology are cited directly from the FHEA 2012 Data Documentation guide provided to all entitlement regions through HUD’s regional planning grant program. Data within this section is collected at both the census tract and county level. Given the large area analyzed in this report most data is summarized at a county level. Data displayed within maps, however, utilizes census tract level data to show, in detail, the spatial relationship and patterns of variables within counties and throughout the region as a whole. Per the 2010 Census, a total of 1,333 populated census tracts exist across the seven counties of Southeast Florida: 29 in Indian River County, 43 in St. Lucie County, 34 in Martin County, 331 in Palm Beach County, 360 in Broward County, 508 in Miami-Dade County, and 28 in Monroe County. When reading the report maps, it is important to consider that lower population densities and distributions are likelier to occur in geographically larger areas such as those in the Western portions of most counties and the more northern counties. Most data comes from the American Community Survey 2006-2010 estimates. Where census 2010 data is available however it is used in place of ACS data to allay concerns about sampling error. Table 2 lists the specific data source for variables within the Regional Profile section.



Table 1: Data Sources for the Regional Background Profile Section Section Variable Data Source

Demographics Population totals and density

2010 Census SF1 Age

Segregation Race and Ethnicity 2010 Census SF1

Housing Tenancy

ACS 2006-2010 Cost Burdon

Poverty Family Poverty Rate

ACS 2006-2010 Poverty By Race & Ethnicity

Labor and Commuting

Unemployment

ACS 2006-2010 Labor force Participation Rate

Commute Time

Commute Mode

Table 2: Populated Census Tracts by County County Number of Populated Census Tracts

Indian River 29

St. Lucie 43

Martin 34

Palm Beach 331

Broward 360

Miami-Dade 508

Monroe 28

TOTAL 1,333



Fair Housing Profile - Methodology The process creating a Fair Housing Profile included review national and local studies/reports, and data gathered from local agencies and organizations. Major data sources include:

• All most-recent existing AIs produced by local jurisdictions within SEFLA

County INDIAN RIVER

COUNTY ST. LUCIE COUNTY

MARTIN COUNTY

PALM BEACH COUNTY

BROWARD COUNTY MIAMI-DADE

COUNTY MONROE COUNTY

Entitlement Jurisdiction

• None

• City Of Port Saint Lucie

• City Of Fort Pierce

• None

• Palm Beach

County • City Of Boca

Raton • City Of Boynton

Beach • City Of Deerfield

Beach • City Of Delray

Beach • City Of West

Palm Beach • Town Of

Wellington • Town Of Jupiter

• Broward County • City Of Coconut

Creek • City Of Coral

Springs • City Of Hollywood • City Of Fort

Lauderdale • City Of Lauderhill • City Of Miramar • City Of Margate • City Of Pembroke

Pines • City Of Plantation • City Of Pompano

Beach • City Of Sunrise • City Of Tamarac • City Of Westin • Town Of Davie

• Miami-Dade

County • City Of

Miami • City Of

Miami Beach • City Of

Miami Gardens

• City Of North Miami

• City Of Homestead

• City Of Hialeah

• None

• Florida Housing Data Clearing House (Existing Assisted Housing Inventory) • Data from various state and local Fair Housing Agencies

o Florida Commission on Human Relations o Palm Beach County Office of Equal Opportunity o Broward County Office of Equal Opportunity | Civil Rights Division o Miami-Dade Office of Human Rights and Fair Employment Practices



Lending Profile- Methodology Lending data for this section was retrieved from the University of Florida Housing Data Clearinghouse. Racial lending data and overall 2010 lending data by county and for the State of Florida were analyzed to identify disparities and trends in lending patterns by race, ethnicity and by county. The data used for the county lending analysis is original research, due to the fact that the most recent lending data in the existing county-level Analysis of Impediments to Fair Housing documents available (Miami-Dade & Broward County) are from the years 2008 & 2009. The data used in this analysis is more recent, from the year 2010.

Identified Impediments to Fair Housing- Methodology This section of the South Florida Regional Analysis of Impediments to Fair Housing Choice examines the ten most prevalent impediments to fair housing choice in the region. This examination was done through the examination of available analysis of impediments (AIs) to fair housing choice documents for entitled communities within the study-recognized seven-county south Florida region, both at the municipal and at the county level. Three countywide and twenty-eight municipal-level AIs were consulted in this analysis. Two counties in the region, Martin County & Monroe County, lack entitled communities and information on the impediments to fair housing within these counties are not included within this analysis, though, since the issues pointed out in the other counties and municipalities in the region are largely similar, it is likely that the non-entitled counties experience impediments similar to those discussed in this analysis.

SEVEN50 REGIONAL ANALYSIS OF IMPEDIMENTS TO FAIR HOUSING DRAFT |11 Carras Community Investment, Inc.


2. SEFLA Regional Background Data: CONTEXTUALIZING THE FAIR HOUSING DISCUSSION This section discusses data primarily at the county and regional level, although maps are displayed using census tract level data to more accurately represent spatial patterns. Municipalities and landmark areas are referenced occasionally to contextualize and orient the discussion but are not the primary units of analysis.

Demographics

Figure 2: SEFLA, Total Population by Rank, 2010

• Total Population: 138,028 • Population Density: 274.5 people/mi2

• Dominant Age Group: 50-54

Indian River County



St. Lucie County



Martin County

• Total Population: 1,320,134 • Population Density: 670.2 people/mi2

• Dominant Age Group: 45 to 49

Palm Beach County

• Total Population: 1,748,066 • Population Density: 1,444.9 people/mi2


Broward County

• Total Population: 2,496,435 • Population Density: 1.315.5 people/mi2


Miami-Dade County



Monroe County

73,090

138,028

146,318

277,789

1,320,134

1,748,066

2,496,435

Monroe County

Indian River County

Martin County

St. Lucie County

Palm Beach County

Broward County

Miami-Dade County

SEFLA, Total Population by Rank, 2010



The three most populous counties are, in decreasing order, Miami-Dade, Broward, and Palm Beach. These three counties are similarly the densest in terms of population per square mile. Given Broward County’s smaller land area however, population density is actually higher within Broward than Miami-Dade. Map 1, to the left, shows that the region’s population is also concentrated in the eastern portion of the counties, which is a reflection of both historic settlement patterns as well as the Everglade swamp areas to the west. In all, the region has a total population of over 6 million and a population density of around 800 people per square mile. That is equivalent to the 5th largest metropolitan region in the United States.

Map 1: Population Density, Seven-50 SE Florida Region, 2010

• Total Population: 6,199,860 • Population Density: 807.4 people per mi2

• Dominant Age Group: 45-49 SEFLA



Figure 3: SEFLA, Population Density by Rank, 2010 Race & Ethnicity Three primary categories of race and ethnicity are discussed within this analysis (White, African American, and Hispanic/Latino). These three categories account for the large majority of all persons living in SEFLA. Persons of other ancestries or of two or more races are not analyzed in this report because of their less significant representation within the study area. It is important to note, people of Hispanic origin may be any race. This should be kept in mind when comparing race and ethnicity, which is done throughout the report. Someone of Black or White race may also be categorized as Hispanic due to Census methodology. Within this section, the spatial and segregation patterns of residence by race and ethnicity are the focus. Beyond segregation patterns, race & ethnicity are analyzed in conjunction with other variables throughout the remainder of the Regional Profile; in analyzing equitable access to fair housing, understanding the relationship of race and ethnicity to other germane variables is fundamentally important.

74.3

269.2

274.5

485.7

670.2

807.4

1315.5

1444.9

Monroe County

Martin County

Indian River County

St. Lucie County

Palm Beach County

7-County SE Florida Region

Miami-Dade County

Broward County

SEFLA, Population Density by Rank, 2010



Indian River

Map 2: Indian River County Race/Ethnicity Dot Map

Figure 4: Indian River County, Population by Race/Ethnicity 2010

116,346

12,397

15,465

White

Black or African American

Hispanic or Latino:



St. Lucie County

Map 3: St. Lucie County Race/Ethnicity Dot Map

Figure 5: St. Lucie County, Population by Race/Ethnicity 2010

Martin County

199,336

53,036

45,995

White Black or African American Hispanic or Latino:



Map 4: Martin County Race/Ethnicity Dot Map

Figure 6: Martin County, Population by Race/Ethnicity 2010

Palm Beach County

Map 5: Palm Beach County Race/Ethnicity Dot Map

Figure 7: Palm beach County, Population by Race/Ethnicity 2010

199,336

53,036

45,995

White


Hispanic or Latino:

970,121

228,690

250,823 White


Hispanic or Latino:



Broward County

Map 6: Broward County Race/Ethnicity Dot Map

Figure 8: Broward County, Population by Race/Ethnicity 2010

Miami-Dade County

1,102,231 467,519

438,247 White


Hispanic or Latino:



Map 7: Miami-Dade County Race/Ethnicity Dot Map

Figure 9: Miami-Dade County, Population by Race/Ethnicity 2010

Monroe County

Map 8: Monroe County Race/Ethnicity Dot Map

1,841,887

472,976

1,623,859

White


Hispanic or Latino:

65,409

4,194

15,071 White


Hispanic or Latino:



Figure 10: Monroe County, Population by Race/Ethnicity 2010

SEFLA Region



Figure 11: Race/ethnicity for the SEFLA Region, 2010

Within the regions total population of 6.1 million, nearly 4.5 million are white accounting for two thirds of the population. 2.1 million African American persons and 2.4 Hispanics of all races reside within the SEFLA jurisdictional boundary. Monroe, Martin, and Indian River Counties have the largest percentages of white people in comparison to their total populations. Similarly within these counties, the Hispanic and Black populations are relatively dispersed. St. Lucie County has higher percentages of minority populations than the three previously mentioned counties. Additionally within St. Lucie there is a segregated concentration of African Americans to the south of the St. Lucie County International Airport. Palm Beach, Broward, and Monroe are more diverse in population yet they also have higher levels of segregation. Areas where African Americans are highly concentrated include north Miami-Dade County, central Broward County, and the Belle Glade and West Palm Beach areas in Palm Beach County. Central Broward County and north Miami-Dade County both have neighborhoods of historical African American affiliation including Sistrunk, Lauderdale Lakes and Lauderhill in Broward and Opa-locka and Miami Gardens in Miami-Dade.

71%

20%

39%

White

Black or African American Hispanic or Latino



Housing Housing occupancy by tenancy and monthly housing costs are the two major variables analyzed within this section. A

breakdown of these variables by race and ethnicity is also undertaken. Housing occupancy by tenure relates to neighborhood stability in that high rental turnover tends to lend itself to fluctuating neighborhood conditions which is generally viewed negatively. A housing unit is considered to be owner-occupied if the owner or co-owner lives in the unit, even if it is mortgaged or not fully paid for. All other occupied units are classified as "renter occupied," including units rented for cash rent and those occupied without payment of cash rent. Monthly housing costs as a percentage of household income provides information on the cost of monthly housing expenses for owners and renters. The information offers an excellent measure of housing affordability and excessive shelter costs. Households spending more than 30 percent of their monthly income on housing costs and considered cost-burdened and tend to be at risk of economic hardship over time.

The trends of housing occupancy by tenancy and housing costs burden on a county scale are similar throughout the SEFLA. Throughout the region, the vast majority of housing units are owner-occupied. This trend holds true for the white population of each county, but is reversed for both Hispanics and African American households in each county. Renter-occupied households as a whole are more likely to spend more than 30% of their monthly income on housing costs than those that own. When considering various incomes, within lower income brackets there are more renters than owners. Also within this income range, households that rent have higher probabilities of paying above 30% of their household income than those that own. Conversely within the higher income brackets there are more owner-occupied housing units and henceforth more owners are cost-burdened in these brackets. Proportionally, however renting households making less than $20,000 are the most cost-burdened group within each county in SEFLA

In the region as a whole, there are 1.5 million owner-occupied housing units in comparison to .75 million renter- occupied units. Spatially, in the urbanized areas of the counties, owner occupied housing is prominent inland in the more suburban areas. Conversely rental opportunities are more common in the eastern and denser portions of the region. The region has very high levels of cost-burdened households especially for renters. 60% of renting households, regardless of income, pay more than 30% of their monthly income on housing costs while 46% of household that own pay the same proportion. Moving into the future, more affordable housing options is a key issue for SEFLA.



Indian River

Figure 12: Indian River County, Cost Burdened households by tenure, 2010

Figure 13: Indian River Tenancy by Race/Ethnicity, 2010

Figure 14: Indian River County, Housing Occupancy by Tenure, 2010

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

Less than $20,000

$20,000 to $34,999

$35,000 to $49,999

$50,000 to $74,999

$75,000 or more

Households paying 30% or more of their income on monthly housing costs, 2010

Indian River County

Owner-occupied Renter-occupied

93%

5%

5%

81%

14%

14%

White


Hispanic or Latino origin

Renter-occupied housing units

44,186

13,374 Owner-occupied housing units




St. Lucie County

Figure 15: St. Lucie County, Cost Burdened households by tenure, 2010

Figure 16: St. Lucie County Tenancy by Race/Ethnicity, 2010

Figure 17: St. Lucie County, Housing Occupancy by Tenure, 2010

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

Less than $20,000

$20,000 to $34,999

$35,000 to $49,999

$50,000 to $74,999

$75,000 or more


St. Lucie County


84%

12%

9%

69%

24%

18%

White




Owner-occupied housing units

78,340





Martin County

Figure 18: Martin County, Cost Burdened households by tenure, 2010

Figure 19: Martin County, Tenancy by Race/Ethnicity, 2010

Figure 20: Martin County, Housing Occupancy by Tenure, 2010

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

Less than $20,000

$20,000 to $34,999

$35,000 to $49,999

$50,000 to $74,999

$75,000 or more


Martin County


96%

2%

4%

86%

7%

16%

White





47,063

12,140





Palm Beach County

Figure 21: Palm Beach County, Cost Burdened households by tenure, 2010

Figure 22: Palm Beach County, Tenancy by Race/Ethnicity, 2010

Figure 23: Palm Beach County, Housing Occupancy by Tenure, 2010

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

Less than $20,000

$20,000 to $34,999

$35,000 to $49,999

$50,000 to $74,999

$75,000 or more

Households paying 30% or more of their income on monthly housing costs, 2010 Palm Beach County


87%

9%

10%

67%

24%

21%

White

Black or African

American



384,995





Broward County

Figure 24: Broward County, Cost Burdened households by tenure, 2010

Figure 25: Broward County, Tenancy by Race/Ethnicity, 2010

Figure 26: Broward County, Housing Occupancy by Tenure, 2010

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

Less than $20,000

$20,000 to $34,999

$35,000 to $49,999

$50,000 to $74,999

$75,000 or more


Broward County


76%

17%

18%

59%

32%

23%

White





463,511

205,387





Miami-Dade County

Figure 27: Miami-Dade County, Cost Burdened households by tenure, 2010

Figure 28: Miami-Dade County, Tenancy by Race/Ethnicity, 2010

Figure 29: Miami-Dade County, Housing Occupancy by Tenure, 2010

0.0% 5.0%

10.0% 15.0% 20.0% 25.0% 30.0% 35.0%

Less than $20,000

$20,000 to $34,999

$35,000 to $49,999

$50,000 to $74,999

$75,000 or more

Households paying 30% or more of their income on monthly housing costs, 2010 Miami-Dade County


79%

14%

62%

71%

22%

66%

White





480,532

347,024





Monroe County

Figure 30: Monroe County, Cost Burdened households by tenure, 2010

0.0%

5.0%

10.0%

15.0%

20.0%

Less than $20,000

$20,000 to $34,999

$35,000 to $49,999

$50,000 to $74,999

$75,000 or more


Monroe County




Figure 31: Monroe County, Tenancy by Race/Ethnicity, 2010

Figure 32: Monroe County, Housing Occupancy by Tenure, 2010

SEFLA Region Table 3: Occupied Housing Units by Race/Ethnicity and Tenure, 2010- SEFLA Region

Occupied housing

units Owner-occupied housing

units Renter-occupied housing

units

Total 2,269,261 1,517,837 751,424

White 84% 87% 75%


12% 9% 19%

Hispanic or Latino origin 19% 17% 26%

95%

3%

12%

89%

8%

22%

White





19,210

10,581





Tenure of Occupied Housing Units:

Map 9: Occupied Housing Units by tenure,2010, SEFLA Region

Figure 33: SEFLA Region, Occupied Housing Units by tenure, 2010

1,517,837

751,424





Figure 34: Seven-50 SE Florida Region Occupied Housing Units by Tenure and County, 2010

46% 60%

34%

58% 43%

57%

35% 51%

42% 56%

48% 57% 50%

61% 46%

60% O

wn

er-

oc

cu

pie

d

Re

nte

r-o

cc

up

ied

Ow

ne

r-o

cc

up

ied

Re

nte

r-o

cc

up

ied

Ow

ne

r-o

cc

up

ied

Re

nte

r-o

cc

up

ied

Ow

ne

r-o

cc

up

ied

Re

nte

r-o

cc

up

ied

Ow

ne

r-o

cc

up

ied

Re

nte

r-o

cc

up

ied

Ow

ne

r-o

cc

up

ied

Re

nte

r-o

cc

up

ied

Ow

ne

r-o

cc

up

ied

Re

nte

r-o

cc

up

ied

Ow

ne

r-o

cc

up

ied

Re

nte

r-o

cc

up

ied

7-County SE Florida Region Average

Indian River County, Florida

St. Lucie County, Florida

Martin County, Florida

Palm Beach County, Florida

Broward County, Florida

Miami-Dade County, Florida

Monroe County, Florida

Tenure of Occupied Housing by County and for the SEFLA Region



Figure 35: Seve-50 SE Florida Region, Cost Burdened households by tenure, 2010

0.00%

5.00%

10.00%

15.00%

20.00%

Less than $20,000

$20,000 to $34,999

$35,000 to $49,999

$50,000 to $74,999

$75,000 or more


SEFLA Region

Owner-occupied housing units Renter-occupied housing units



Poverty This section summarizes both individual and family poverty rates within the 7 counties individually as well as for SEFLA as

a whole. Poverty indicators show the percentage of individuals or families that are below poverty threshold. The Census Bureau identifies poverty thresholds according to the composition of the household; different types of households (by age and size) have different poverty thresholds. Since poverty is defined at the family level and not the household level, the poverty status of the household is determined by the poverty status of the householder. “Population below poverty level” refers to the sum of people in families and the number of unrelated individuals identified as poor. We summarize individual poverty rates by race/ethnicity and age. The family poverty rate distinguishes between families and families with children under the age of 18, which is then further divided into various household types including married couples below the poverty level and single female householders below the poverty level.

For SEFLA poverty is a large concern. In total there are over 850,000 people below poverty level, 13% percent of the total population. 15% of all families with children under the age of 18 are below the poverty level and of the population under 18, one fifth are living below the poverty line. Miami-Dade has the highest proportion of individuals living in poverty at 17%. Furthermore, poverty is clearly linked to race and ethnicity throughout the region. While the predominant race throughout the region is white, proportionally there are about half as many white people in poverty in comparison to both African American and Hispanic populations. Finally, our analysis identifies single female householders with children as especially in need. As a region, one third of all single-female households with children are below poverty level. In St. Lucie County, the proportion reaches its highest at 37% and within other counties the lowest this percentage reaches is 27% in Broward.



Indian River

Figure 36: Indian River County, Individuals below poverty level, 2010

Figure 37: Indian River County, Family Poverty Rates, 2010

Figure 38: Indian River County, Poverty Rate by Race and Ethnicity, 2010

16,984

5,322

9,322

2,340

Population for whom poverty

status is determined

Under 18 years 18 to 64 years 65 years and over

9%

5%

27%

16%

9%

31%

% Families Below Poverty Level

% Married Couples below Poverty Level

% Female householders with no husband present below

Poverty Level

With related children under 18 years Families

7.50%

23.80%

18.20% White


Hispanic or Latino origin (of any race)



St. Lucie County

Figure 39: St. Lucie County, Individuals below poverty level, 2010

Figure 40: St. Lucie County, Family Poverty Rates, 2010

Figure 41: St. Lucie County, Poverty Rate by Race and Ethnicity, 2010

36,457

12,725

19,513

4,219




10%

6%

27%

17%

9%

37%




Poverty Level


7.70%

19.60%

19.00%

White





Martin County

Figure 42: Martin County, Individuals below poverty level, 2010

Figure 43: Martin County, Family Poverty Rates, 2010

Figure 44: Martin County, Poverty Rate by Race and Ethnicity, 2010

14,724

4,507

8,046

2,171




6%

3%

25%

13%

7%

34%




Poverty Level


4.80%

17.30%

30.90%

White





Palm Beach County

Figure 45: Palm Beach County, Individuals below poverty level, 2010

156,759

48,840

86,743

21,176






Figure 46: Palm Beach County, Family Poverty Rates, 2010

Figure 47: Palm Beach County, Poverty Rate by Race and Ethnicity, 2010

9%

5%

24%

15%

7%

31%




Poverty Level


6.00%

19.80%

16.60% White





Broward County

Figure 48: Broward County, Individuals below poverty level, 2010

Figure 49: Broward County, Family Poverty Rates, 2010

Figure 50: Broward County, Poverty Rate by Race and Ethnicity, 2010

210,964

63,164

118,477

29,323




9%

5%

21%

13%

6%

27%




Poverty Level


6.20%

16.20%

9.40% White





Miami-Dade County

Figure 51: Miami-Dade County, Individuals below poverty level, 2010

Figure 52: Miami-Dade County, Family Poverty Rates, 2010

Figure 53: Miami-Dade County, Poverty Rate by Race and Ethnicity, 2010

410,093

118,792

217,985

73,316




14%

9%

26%

18%

10%

35%




Poverty Level


11.90%

22.10%

13.90% White





Monroe County

Figure 54: Monroe County, Individuals below poverty level, 2010

Figure 55: Monroe County, Family Poverty Rates, 2010

Figure 56: Monroe County, Poverty Rate by Race and Ethnicity, 2010

7,776

1,275

5,327

1,174




7%

4%

25%

10%

3%

30%




Poverty Level


6.70%

12.40%

12.60% White





SEFLA Region

Figure 57: SEFLA Region, Individuals below poverty level, 2010

Figure 58: SEFLA Region, Family Poverty Rates, 2010

Figure 59: SEFLA Region, Poverty Rate by Race and Ethnicity, 2010

853,757

254,625

465,413

133,719




9%

5%

25%

15%

7%

32%




Poverty Level


7.26%

18.74%

17.23% White





Figure 60: SEFLA Region, Percent Population below poverty level by census

tract, 2010

Figure 61: SEFLA Region, Percent Population below poverty level by county, 2010

13% 14%

10% 12% 12%

17%

11%



Labor Force and Commuting This section examines the labor force participation rate as well as basic commuting trends by county and for the

region. The labor force participation rate is a representation of the proportion of the area’s population in the labor force. This includes both employed and unemployed (job-seeking) civilians 16 years and over as well as members of the U.S. Armed Forces (people on active duty with the United States Army, Air Force, Navy, Marine Corps, or Coast Guard). Conversely, those not in the labor force includes all people 16 years old and over who are not accounted for by the labor force participation rate. These are typically students, retired workers, homemakers, institutionalized people and people doing incidental unpaid family work. Of the 7 counties, Broward has the highest labor force participation rate at 68%. Both Martin and Indian River have the lowest at 55%. The region on average has a labor force participation of 60% compared to 65% in the United State.

Commuting trends may seem out of place in a fair housing assessment but commuting patterns are directly reflective

of the relationship between housing and jobs. To understand commuting trends in this section we summarize mean travel time to work by mode of travel, mode of travel by tenure, and mode of travel by race/ethnicity. Mean travel time to work is measured in minutes and represents the average travel time that workers usually took to get from home to work. The travel time also accounts for time spent waiting for public transportation, as well as for picking up carpool passengers. Mode of travel refers to the method of transportation used to get from home to work. High travel times may indicate an imbalance between jobs and housing. Similarly the relationship between tenure and transportation mode can inform the type of transportation options that should be readily available to certain demographics.



We find in the region that individuals utilizing public transportation options have higher average travel times to work. And in all counties more renters use public transit than homeowners. The same is true of minorities in comparison to white persons. Overall there is a staggering difference between those that drive alone (78% in the 7-county area) and those that use public transportation only (4%). This all indicates that efficient public transportation options are lacking throughout the entire region—the development of which are especially essential near areas that are primarily minority and renter-occupied when employment opportunities are not located near these areas.



Indian River

Figure 62: Indian River County, Labor Force, 2010

Figure 63: Indian River County, Mean Travel Time to Work by Travel Mode, 2010

54.50%

45.50%

In labor force Not in labor force

0

10

20

30

40

50

Total Drive Alone Carpool Public transportation

Mean travel time to work (minutes)



Figure 64: Indian River County, Travel Mode by housing tenure, 2010

Figure 65: Indian River County, Travel Mode by race/ethnicity, 2010

0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00%



0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00%

100.00%

White





St. Lucie County

Figure 66: St. Lucie County, Labor Force, 2010

Figure 67: St. Lucie County, Mean Travel Time to Work by Travel Mode, 2010

58.40%

41.60%

In labor force Not in labor force 0

10

20

30

40

50

60

70





Figure 68: St. Lucie County, Travel Mode by housing tenure, 2010

Figure 69: St. Lucie County, Travel Mode by race/ethnicity, 2010

Martin County

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%



0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00%

White





Figure 70: Martin County, Labor Force, 2010

Figure 71: Martin County, Mean Travel Time to Work by Travel Mode, 2010

Figure 72: Martin County, Travel Mode by housing tenure, 2010

Figure 73: Martin County, Travel Mode by race/ethnicity, 2010

54.70%

45.30%


20

21

22

23

24

25

26

27



0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00%



0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00%

100.00%

White





Palm Beach County

Figure 74: Palm Beach County, Labor Force, 2010

Figure 75: Palm Beach County, Mean Travel Time to Work by Travel Mode, 2010

Figure 77: Palm Beach County, Travel Mode by race/ethnicity, 2010

60.50%

39.50%


0

10

20

30

40

50



0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00%

White





Figure 76: Palm Beach County, Travel Mode by housing tenure, 2010

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%





Broward County

Figure 78: Broward County, Labor Force, 2010

Figure 79: Broward County, Mean Travel Time to Work by Travel Mode, 2010

67.40%

32.60%


0

10

20

30

40

50

60





Figure 80: Broward County, Travel Mode by housing tenure, 2010

Figure 81: Broward County, Travel Mode by race/ethnicity, 2010

Miami-Dade County

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%



0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00%

White





Figure 82: Miami-Dade County, Labor Force, 2010

Figure 83: Miami-Dade County, Mean Travel Time to Work by Travel Mode, 2010

Figure 84: Miami-Dade County, Travel Mode by housing tenure, 2010

Figure 85: Miami-Dade County, Travel Mode by race/ethnicity, 2010

62.80%

37.20%


0

10

20

30

40

50



0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%



0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00%

White





Monroe County

Figure 86: Monroe County, Labor Force, 2010

Figure 87: Monroe County, Mean Travel Time to Work by Travel Mode, 2010

65.00%

35.00%


0

5

10

15

20

25





Figure 88: Monroe County, Travel Mode by housing tenure, 2010

Figure 89: Monroe County, Travel Mode by race/ethnicity, 2010

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%



0.00% 10.00% 20.00% 30.00% 40.00% 50.00% 60.00% 70.00% 80.00% 90.00%

100.00%

White





SEFLA Region

Figure 90: SEFLA Region, Labor Force by County, 2010

Figure 91: SEFLA Region, Mean Travel Time to Work by Travel Mode, 2010

Figure 92: SEFLA Region, Travel Mode by housing tenure, 2010

Figure 93: SEFLA Region, Travel Mode by race/ethnicity, 2010

0% 20% 40% 60% 80% 100%

Indian River County Martin County

St. Lucie County Palm Beach County

Broward County Miami-Dade County

Monroe County 7-County SE Florida Region


0

10

20

30

40

50



0%

10%

20%

30%

40%

50%

60%

70%

80%




0%

10%

20%

30%

40%

50%

60%

70%

80%

90%


White





Figure 94: SEFLA Region, County, Travel Mode by County

0% 10% 20% 30% 40% 50% 60% 70% 80%

Drive Alone

Carpool

Public Transportation

Travel Mode By County, 2010

Indian River County, Florida St. Lucie County, Florida Martin County, Florida Palm Beach County, Florida

Broward County, Florida Miami-Dade County, Florida Monroe County, Florida 7-County SE Florida Region



3. Fair Housing Legal Evaluation Fair Housing Laws and Programs

Housing development and the affordability of what is produced is highly influenced both by public and private forces such as real estate markets, profits, zoning, land use, impact fees, and concurrency requirements. The existence of a regulatory framework is necessary to promote and protect fair housing opportunities. This section summarizes existing fair housing laws and programs at the federal, state, and local level.

Federal Fair Housing Act & U.S. HUD, Fair Housing and Equal Opportunity The 1968 Fair Housing Act prohibits discrimination during the sale and rental of housing so that all people in the United

States have an increased opportunity to maintain stable and healthy lives for themselves and their families. As amended by the Fair Housing Amendments Act of 1988, it is unlawful to discriminate on the basis of race, color, sex, religion, national origin, disability and familial status. The U.S. Department of Housing and Urban Development (HUD), and on occasion the U.S. Department of Justice, is responsible for investigating and enforcing violations of the Fair Housing Act. HUD also provides Fair Housing Assistance Program (FHAP) funds annually and on a noncompetitive basis to State and local agencies that enforce fair housing laws substantially equivalent to the Fair Housing Act.

Florida Fair Housing The Florida Fair Housing Act1, which closely reiterates the Federal Fair Housing Act, was passed by the Florida

Legislature in 1983, and amended in 1989. It declares it illegal to discriminate in the sale, rental, advertising, financing, or brokerage of housing. It goes on further to ensure the accessibility of all new multifamily developments built within Florida.

The preceding Regional Profile section highlights that affordable housing options, which are integral to fair housing

opportunity, are a problem for SEFLA. The issue of affordable housing has been long debated in Florida and there are a number of programs to fund this initiative. In Florida, Section 163.3177 Fla. Stat. (2000) and Rule 9J-5 of the Florida Administration Code require that each community, county and the State of Florida adopt a housing element in their Comprehensive Plan which must contain standards, plans, and principles to create and preserve safe and healthy affordable housing options. Of these laws the William E. Sadowski Affordable Housing Act, passed in 1992 is notably important. The act established one of the largest dedicated trust funds for affordable housing. Revenues are generated through a documentary stamp tax, a real estate transfer fee levied when registering a deed or mortgage into public records. The Sadowski Act increased the existing documentary stamp tax by 10 cents per $100 (from 60 cents to 70 cents)

1 State of Florida, Civil Rights Statutes, Title XLIX, Chapter 760.2



for the purposes of funding housing programs in the State. Three years later, the bill reallocated an additional 10 cents to the housing trust. The funds are uniquely tied to the real estate market so that revenues increase as housing prices (and the subsidy required to house Florida’s workforce) increase.

The Sadowski Act not only funded existing state housing programs such as the Predevelopment Loan Program (PLP),

the State Apartment Incentive Loan (SAIL), and the Homeownership Assistance Program (HAP) but also established several new initiatives, including the Low-Income Emergency Home Repair Program, the HOME Investment Partnerships Program, the HOPE Program, the Florida Affordable Housing Guarantee Program, the Affordable Housing Catalyst Program for Technical Assistance and Training, and the State Housing Initiatives Partnership Program (SHIP). Programs ranged from rental housing, homeownership, special needs, and more recently, disaster relief and workforce housing.

The Florida Housing Finance Corporation (Florida Housing) was designated as the administrator for all of the programs,

directing 30 percent of the revenue into a statewide trust and the other 70 percent into a local housing trust. The local funds are distributed to all 67 counties and 48 entitlement cities in the State through the SHIP program. The SHIP program ensures both accountability and flexibility.

• Accountability: In order to receive funding, each local government must adopt a SHIP Plan in accordance with state guidelines and local comprehensive plans.

• Flexibility: Local jurisdictions are able to direct money towards the specific needs of their community. Locally adopted strategies set-aside funding for extremely low to moderate-income households and support a variety of services, including new construction, rehabilitation, down payment assistance, homebuyer education, and foreclosure prevention.

When the Florida House and Senate entered the 2008 session, they were confronted with a major budget shortfall. The current fiscal year faced over $1 billion deficit, estimates for 2009 were upwards of $3 billion. Legislators were forced to cut programs, limit spending, and search for other sources of revenue. Consequently, the Florida Legislature transferred $250 million from dedicated affordable housing trust funds to general revenue. They also implemented a $243 million cap on existing and future funding for affordable housing. Funds generated through the Sadowski Act above that amount will be automatically deposited into general revenue. It is estimated that as a result of the cap, an additional $185 million in housing funds will be lost over the next two years (FY07-08 and FY 08-09).

Although the budget cuts took place in the 2007-2008 fiscal year, they were originally authorized three years earlier

(prior to the budget crisis) under Governor Jeb Bush. The cap was approved after attempts to completely remove the housing trust fund failed in the 2003 and 2004 Legislative Sessions. The Governor decided to “maintain trust fund spending



at levels consistent with historical expectations while using windfall revenues to fund other priorities.” Housing advocates argued that this view was misleading. They claimed that while revenues into the housing trust funds had grown substantially since their creation, funding was purposely tied to documentary stamp tax revenues so that subsidies for affordable housing would rise as the cost of land, construction and housing increased.

The loss of these revenues has drastically cut local housing programs. Housing supporters continue to advocate to the

State to “scrap the cap” and reauthorize the Sadowski Act, which is set to sunset. In the meantime, there is a strong need for a dedicated source of funding for affordable housing at the Regional, County and City level. Both a linkage fee program and inclusionary housing program have been considered, but never created.

Fair Housing Assistance Agencies within SEFLA

Table 4: SEFLA Fair Housing Assistance Agencies

County Name Agency Type

N/A- State Level Federal Commission on Human Rights State Agency; Government

Palm Beach

Legal Aid Society of Palm Beach County Private

Palm Beach County Office of Equal Opportunity

Government

Broward Broward County Office of Equal Opportunity | Civil Right Division

Government

Miami

Housing Opportunities for Project Excellence, H.O.P.E Inc.

Private

Miami-Dade Office of Human Rights and Fair Employment Practices (OHRFEP)

Government



Exist ing Ass is ted Housing Inventory 2

Table 5: Total Properties and Units, Assisted Housing

Number of Properties

Total Units

Assisted Units

% Assisted

Indian River 26 2,858 2,856 99.9%

St. Lucie 18 2,623 2,480 95%

Martin 17 1,221 1,219 100%

Palm Beach 93 13,802 12,652 92%

Broward 121 21,116 17,129 81%

Miami-Dade 334 42,346 40,514 96%

Monroe 17 861 838 97%

7-County SE Florida Region 626 84,827 77,688 94.3%

Figure 95: Assisted Housing Units By County

Population Rank Assisted Units Rank

Indian River 6 4 St. Lucie 4 5 Martin 5 6 Palm Beach 3 3 Broward 2 2 Miami-Dade 1 1

Monroe 7 7 Table 6: Assisted Housing Units and Population, Ranks by County

Within SEFLA there are currently 626 properties providing assisted housing units for a total of almost 78-thousand units. These assisted units are highly concentrated: 94% of all units within properties that offer assisted units are assisted. Miami-Dade, Broward, Palm Beach, and Monroe are consistent in their ranking of assisted units in comparison to their rank in population. However, Indian River ranks 6th in population yet has more assisted units than St. Lucie and Martin counties which both have higher population totals.

2 All data within the Existing Assisted Housing Inventory is drawn from the Florida Housing Data Clearinghouse

2856 2480 1219 12652 17129

40514

838

77688



Table 7 Income Restrictions, Florida Housing Properties

Number of Units Designated for Renters with Income:

<=35% AMI 40-50% AMI 55-60% AMI 65-80% AMI >80% AMI

Indian River 100 423 1790 8 0

St. Lucie 179 209 1853 0 0

Martin 60 50 770 1 0

Palm Beach 344 825 7708 324 62

Broward 460 652 9870 212 512

Miami-Dade 1537 3505 21771 136 40

Monroe 73 57 555 121 13

SEFLA 2753 5721 44317 802 627

Various assisted housing units are reserved for renters in certain income brackets. The vast majority of these are for renters in the 55-60% AMI. Regionally, there are 44 thousand units designated for this income bracket. For those in the lowest income bracket, and therefore those in the greatest need of housing opportunities designated specifically for



Figure 96: Number of Units Designated for Renters with Income at

them, only 627 units are designated for these renters across the 7-county SEFLA region. 512 of these are in Broward County compared to 40 in Miami-Dade where 17% of the population is below poverty level and zero in Indian River, St. Lucie and Martin Counties.

Table 8 Target Population, Assisted Housing

Family Elderly Farmworker Fisher Homeless Persons with Disabilities

Indian River 2294 685 426 - - 16

St. Lucie 2090 286 104 - - -

Martin 1055 140 117 - - 24

Palm Beach 10170 3628 916 - 93 46

Broward 14383 4202 173 - - 71

Miami-Dade 28835 11981 1582 - 815 318

Monroe 777 28 14 92 - 19

Figure 97: Target Population, Assisted Housing, SEFLA

0 5000 10000 15000 20000

Indian River

St. Lucie

Martin

Palm Beach

Broward

Miami-Dade

Monroe

<=35% AMI 40-50% AMI 55-60% AMI 65-80% AMI >80% AMI



SEFLA 59604 20950 3332 92 908 494

Figure 98: Target Population, Assisted Housing, By County

The target population for the vast majority of assisted housing is families followed by elderly peoples, farmworkers, homeless, persons with disabilities, and finally fishers (this is only a target population in Monroe County). All 7 counties have assisted housing targeting families, elderly and farmworkers. St. Lucie county is alone in its lack of any assisted housing targeting persons with disabilities. Finally homeless people are the target populations for assisted housing in only Palm Beach and Miami-Dade.

Family

Elderly

Farmworker

Fisher

Homeless

Persons with Disabilities

0 5000 10000 15000 20000 25000 30000

Indian River

St. Lucie

Martin

Palm Beach

Broward

Miami-Dade

Monroe

Persons with Disabilities

Homeless

Fisher

Farmworker

Elderly

Family



Figure 99: Race and Ethnicity of Tenants in Assisted Housing Units as of 2008

Table 9 Race and Ethnicity of Tenants in Assisted Housing Units as of 2008

% Minority % Black % Hispanic

Indian River 8% 6% 0%

St. Lucie 81% 72% 9%

Martin 10% 2% 6%

Palm Beach 58% 37% 21%

Broward 68% 43% 24%

Miami-Dade 91% 21% 70%

Monroe 61% 9% 52%

SEFLA 54% 27% 26%

54% of the region’s assisted housing units have minority tenants; 27% are Black and 26% Hispanic. Miami-Dade has the highest percentage of minority tenants at 91% followed by St. Lucie County at 81% (72% or which are Black). People of Hispanic origin occupy notably none of Indian River’s assisted housing units.

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

100%

% Minority % Black % Hispanic



Fair Housing Discr iminat ion Complaints : 2007-2012 This section details fair housing complaints reported to various authorized Fair Housing Agencies (FHAP) throughout the

region. The year that cases are closed in is the year that those complaints are accounted for. Data was collected from various sources: 1) already completed AIs for Palm Beach, Broward, and Miami-Dade Counties, 2) data compiled by HUD for all seven counties for years 2010-2012 specifically for the purposes of this report, 3) public records requests from the Florida Commission on Human Rights as well as the Palm Beach and Miami-Dade County Offices of Equal Opportunity.

It should be noted, that the total number of bases/issues in the housing discrimination complaint tables are not necessarily equal to the total number of individuals that filed complaints because oftentimes, the complaints alleged multiple bases and issues. Similarly, there may be overlap of reports between tables so trends therefore summary of the proportions of the basis for complaints are examined as opposed to total numbers. Furthermore, national studies conducted by HUD provide evidence of a major underreporting of housing discriminations3. Therefore, the following trends can be used as a tool to begin understanding housing discrimination within SEFLA but should not be interpreted to represent the problem of housing discrimination exhaustively.

3 “Do We Know More Now? Trends in Public Knowledge, Support, and Use of Fair Housing Laws,” 2006



Indian River County Indian River County, Florida

Table 10 Housing Discrimination Complaints- 2010-8/31/2012 Reported to HUD/FHAP

2010 2011 2012 Total %

RACE 0 0 0 0 0%

COLOR 0 0 0 0 0%

NATIONAL ORIGIN 0 0 2 2 12%

RELIGION 0 0 0 0 0%

SEX 1 0 1 2 12%

FAMILIAL STATUS 0 0 1 1 6%

DISABILITY 2 2 5 9 53%

AGE 0 0 0 0 0%

OTHER 0 1 2 3 18%

Total 3 3 11 17

Indian River County, Florida Table 11 Housing Discrimination Complaints-

2007-2012 Florida Commission on Human Relations

2007 2008 2009 2010 2011 2012 Total %

RACE 0 1 0 0 0 0 1 5%

COLOR 0 0 0 0 0 0 0 0%

NATIONAL ORIGIN 0 0 8 0 0 0 8 42%

RELIGION 0 0 0 0 0 0 0 0%

SEX 0 0 0 0 0 0 0 0%

FAMILIAL STATUS 0 1 0 0 0 0 1 5%

DISABILITY 1 3 1 1 0 2 8 42%

AGE 0 0 0 0 0 0 0 0%

OTHER 0 1 0 0 0 0 1 5%

Total 1 6 9 1 0 2 19



Figure 100: Average Summary Percentage of Housing Complaints for all Cited Agencies, Indian River County, 2007-2012

Figure 101: Summary of Housing Discrimination Complaints, By Year, Indian River County, 2007-2012

Table 12: Bases for Discrimination Complaints, Rank and Proportion, Indian River County 2007-2012

Basis % Rank

DISABILITY 48% 1

NATIONAL ORIGIN 27% 2

OTHER 11% 3

SEX 6% 4

FAMILIAL STATUS 6% 5

RACE 3% 6

RELIGION 0% 7

COLOR 0% 8

AGE 0% 9

There are relatively few fair housing complaints in Indian River. Lower population levels as well as a lack of local fair housing agencies may account this for. 48% of complaints registered over the 5 years are based on disability discrimination, and 27% are related to national origin. There are no fair housing complaints in alleged discrimination of religion, color, or age. 2012 showed a general spike in the proportion of complaints overall.

3%

27%

6%

6%

48%

11%

RACE

COLOR

NATIONAL ORIGIN

RELIGION

SEX

FAMILIAL STATUS

DISABILITY

AGE

OTHER 0%

5%

10%

15%

20%

25%

30%

35%

40%

2007 2008 2009 2010 2011 2012



St. Lucie County St. Lucie County, Florida

Table 13 Housing Discrimination Complaints- 2010-8/31/2012 By HUD or FHAP

2010 2011 2012 Total %

RACE 5 0 0 5 25%

COLOR 0 0 0 0 0%


RELIGION 1 0 0 1 5%

SEX 2 0 0 2 10%



AGE 0 0 0 0 0%

OTHER 0 0 0 0 0%

Total 16 3 1 20

St. Lucie County, Florida

Table 14: Housing Discrimination Complaints- 2007-2012 Florida Commission on Human Relations

2007 2008 2009 2010 2011 2012 Total %

RACE 2 4 2 1 0 0 9 24% COLOR 0 0 0 0 0 0 0 0% NATIONAL ORIGIN 0 0 0 0 0 0 0 0% RELIGION 0 0 0 0 0 0 0 0% SEX 0 0 2 1 0 0 3 8% FAMILIAL STATUS 0 4 1 2 1 0 8 21% DISABILITY 2 9 3 1 1 1 17 45% AGE 0 0 0 0 0 0 0 0% OTHER 0 1 0 0 0 0 1 3%



Total 4 18 8 5 2 1 38



Figure 102: Average Summary Percentage of Housing Complaints for all Cited Agencies, St. Lucie County, 2007-2012

Figure 103: Summary of Housing Discrimination Complaints, By Year, St. Lucie County, 2007-2012

Table 15: Bases for Discrimination Complaints, Rank and Proportion, St. Lucie County 2007-2012

Basis % Rank

DISABILITY 35% 1

RACE 24% 2


SEX 9% 4


RELIGION 3% 6

OTHER 1% 7

Since 2010 the number of complaints has been declining in St. Lucie but over the five-year period the number of reported cases has varied substantially. 35% of complaints from 2007-2012 are based in disability discrimination, 24% for race and 23% for familial status. There are no registered fair housing complaints for either age or color.

24%

5%

3%

9%

23%

35%

1%

RACE

COLOR

NATIONAL ORIGIN

RELIGION

SEX

FAMILIAL STATUS

DISABILITY

AGE

OTHER

0%

5%

10%

15%

20%

25%

30%

35%

40%

2007 2008 2009 2010 2011 2012



COLOR 0% 8

AGE 0% 9

Martin County Martin County, Florida

Table 16: Housing Discrimination Complaints- 2010-8/31/2012 By HUD or FHAP

2010 2011 2012 Total %

RACE 1 1 0 2 13% COLOR 0 0 0 0 0% NATIONAL ORIGIN 0 1 0 1 6% RELIGION 0 0 0 0 0% SEX 0 1 0 1 6% FAMILIAL STATUS 0 0 0 0 0% DISABILITY 4 2 4 10 63% AGE 0 0 0 0 0% OTHER 1 1 0 2 13% Total 6 6 4 16

Martin County, Florida


2007 2008 2009 2010 2011 2012 Total %

RACE 0 1 0 1 1 0 3 9% COLOR 0 0 0 0 0 0 0 0% NATIONAL ORIGIN 1 0 1 1 1 0 4 12% RELIGION 0 0 1 2 0 0 3 9% SEX 0 2 0 0 3 0 5 15% FAMILIAL STATUS 0 2 1 0 0 0 3 9% DISABILITY 1 2 0 7 2 2 14 42% AGE 0 0 0 0 0 0 0 0%



OTHER 0 0 0 0 1 0 1 3% Total 2 7 3 11 8 2 33



Figure 104 Average Summary Percentage of Housing Complaints for all Cited Agencies, Martin County, 2007-2012

Figure 105 Summary of Housing Discrimination Complaints, By Year, Martin County, 2007-2012

Table 18: Bases for Discrimination Complaints, Rank and Proportion, Martin County 2007-2012

Basis % Rank

DISABILITY 52% 1

RACE 11% 2

SEX 11% 3


OTHER 8% 5

RELIGION 5% 6


COLOR 0% 8

AGE 0% 9

In Martin county between 2007 and 2012 over half of the registered fair housing complaints are against disability discrimination. All other categories (besides color, for which there are no complaints) range from 5-11%. There was a rise in the proportion of complaints in 2010.

11%

9%

5%

11%

5%

52%

8% RACE

COLOR

NATIONAL ORIGIN

RELIGION

SEX

FAMILIAL STATUS

DISABILITY

AGE

OTHER

0%

5%

10%

15%

20%

25%

30%

35%

40%

2007 2008 2009 2010 2011 2012



Palm Beach County



2007 2008 2009 2010 2011 2012 Total %

RACE 0 0 0 1 1 0 2 12% COLOR 0 0 0 0 0 0 0 0% NATIONAL ORIGIN 0 0 0 1 0 0 1 6% RELIGION 0 0 0 0 0 0 0 0% SEX 0 1 0 1 0 1 3 18% FAMILIAL STATUS 1 1 0 0 1 0 3 18% DISABILITY 0 4 0 0 3 0 7 41% AGE 0 0 0 0 0 0 0 0% OTHER 0 0 0 0 1 0 1 6%


Table 19: Housing Discrimination Complaints- 2010-8/31/2012 By HUD/FHAP

2010 2011 2012 Total %

RACE 26 16 10 52 18% COLOR 2 3 1 6 2% NATIONAL ORIGIN 18 10 6 34 12% RELIGION 3 3 2 8 3% SEX 10 7 2 19 7% FAMILIAL STATUS 18 9 4 31 11% DISABILITY 54 42 32 128 44% AGE 0 0 0 0 0% OTHER 3 9 2 14 5% Total 134 99 59 292



Total 1 6 0 3 6 1 17


Table 21: Housing Discrimination Complaints- FY 2007-2012 Legal Aid Society Of Palm Beach County, Inc.

2007 2008 2009 2010 2011 2012 Total %

RACE 11 16 15 9 11 --- 62 17% COLOR 0 0 0 0 0 --- 0 0% NATIONAL ORIGIN 12 20 17 9 16 --- 74 20% RELIGION 1 1 0 0 0 --- 2 1% SEX 6 3 1 1 1 --- 12 3% FAMILIAL STATUS 5 3 14 8 6 --- 36 10% DISABILITY 31 25 15 33 38 --- 142 39% AGE 2 4 8 10 0 --- 24 7% OTHER 4 2 2 2 0 --- 10 3% Total 72 74 72 72 72 --- 362


Table 22: Housing discrimination Complaints- FY 2007-2012 Palm Beach County Office of Equal Opportunity

2007 2008 2009 2010 2011 2012 Total %

RACE 25 13 13 20 16 14 101 19% COLOR 0 0 0 0 0 0 0 0% NATIONAL ORIGIN 12 6 6 15 7 7 53 10% RELIGION 1 1 3 2 1 2 10 2% SEX 4 0 4 6 4 5 23 4% FAMILIAL STATUS 15 9 24 30 1 9 88 16%



DISABILITY 33 37 43 51 44 33 241 45% AGE 0 2 2 0 1 1 6 1% OTHER 6 2 1 2 2 3 16 3% Total 96 70 96 126 76 74 538



Figure 106: Average Summary Percentage of Housing Complaints for all Cited Agencies, Palm Beach County, 2007-2012

Figure 107: Summary of Housing Discrimination Complaints, By Year, Palm Beach County, 2007-2012

Table 23: Bases for Discrimination Complaints, Rank and Proportion, Palm Beach County 2007-2012

Basis % Rank

DISABILITY 42% 1

RACE 16% 2



SEX 8% 5

OTHER 4% 6

AGE 2% 7

RELIGION 1% 8

Palm Beach County had a spike in 2010 for fair housing complaints, and overall has larger number of complaints than its three northern counterparts. This makes sense given the larger population however as well as the fact that there are two local FHAPs, the Palm Beach County Office of Equal Opportunity and the Legal Aid Society of Palm Beach County. 42% of complaints are based on discrimination against disability, 16% against race, 14% against familial status, and 12% against national origin.

16%

1%

12%

1% 8%

14%

42%

2%

4% RACE

COLOR

NATIONAL ORIGIN

RELIGION

SEX

FAMILIAL STATUS

DISABILITY

AGE

OTHER

0%

5%

10%

15%

20%

25%

30%

2007 2008 2009 2010 2011 2012



COLOR 1% 9



Broward County Broward County, Florida

Table 24: Housing discrimination Complaints- 2010-8/31/2012 By HUD/FHAP

2010 2011 2012 Total %

RACE 27 32 13 72 20%

COLOR 0 4 0 4 1%


RELIGION 4 2 2 8 2%

SEX 8 6 5 19 5%


DISABILITY 74 40 36 150 41%

AGE 0 0 0 0 0%

OTHER 6 2 8 16 4%

Total 159 127 80 366


Table 25: Housing discrimination Complaints- 2007-2012 Florida Commission on Human Relations

2007 2008 2009 2010 2011 2012 Total %

RACE 0 4 5 4 3 0 16 21%

COLOR 0 0 1 0 0 0 1 1%


RELIGION 0 2 2 1 0 0 5 7%

SEX 0 2 1 1 1 0 5 7%


DISABILITY 2 4 6 9 3 1 25 33%

AGE 0 0 0 0 0 0 0 0%



OTHER 0 0 0 0 0 0 0 0%

Total 4 19 17 19 12 4 75


Table 26: Housing discrimination Complaints – FY 2007-2012 HOPE FAIR HOUSING CENTER

2007 2008 2009 2010 2011 2012 Total %

RACE 16 14 17 8 ---- ---- 55 36%

COLOR 0 0 0 0 ---- ---- 0 0%

NATIONAL ORIGIN 2 3 1 0 ---- ---- 6 4%

RELIGION 0 0 0 1 ---- ---- 1 1%

SEX 0 2 1 0 ---- ---- 3 2%

FAMILIAL STATUS 0 11 1 2 ---- ---- 14 9%

DISABILITY 24 14 19 10 ---- ---- 67 44%

AGE 0 4 2 0 ---- ---- 6 4%

OTHER 0 0 2 0 ---- ---- 2 1%

Total 42 48 43 21 ---- --- 154


Table 27: Housing discrimination Complaints- FY 2007-2012 Broward County Office of Equal Opportunity

2007 2008 2009 2010 2011 2012 Total %

RACE ---- 18 15 25 ---- ---- 58 19%

COLOR ---- 0 0 0 ---- ---- 0 0%

NATIONAL ORIGIN ---- 16 14 18 ---- ---- 48 15%

RELIGION ---- 2 1 2 ---- ---- 5 2%

SEX ---- 2 6 5 ---- ---- 13 4%

FAMILIAL STATUS ---- 6 8 10 ---- ---- 24 8%

DISABILITY ---- 54 47 56 ---- ---- 157 50%



AGE ---- 1 0 1 ---- ---- 2 1%

OTHER ---- 1 2 1 ---- ---- 4 1%

Total ---- 100 93 118 ---- ---- 311



Figure 108: Average Summary Percentage of Housing Complaints for all Cited Agencies, Broward County, 2007-2012

Figure 109: Summary of Housing Discrimination Complaints, By Year, Broward County, 2007-2012

Table 28: Bases for Discrimination Complaints, Rank and Proportion, Broward County 2007-2012

Basis % Rank

DISABILITY 42% 1

RACE 24% 2



SEX 4% 5

RELIGION 3% 6

OTHER 2% 7

AGE 1% 8

COLOR 1% 9

Fair Housing Discrimination complaints within Broward County between 2007 & 2012 are quite similar to Palm Beach. Notably, complaints against race are more prevalent however which might be reflective of the larger proportion of minorities in Broward. Also similar to other counties in the region is the higher proportion of complaints closed in 2010. Broward County Office of Equal Opportunity did not provide data for 2007, 2011, and 2012. Likewise we were unable to obtain data for 2011 and 2012 for complaints registered with HOPE in Broward County. Data in tables 26 & 27 therefore is reflective of the county’s 2010 AI.

24%

1%

13%

3% 4%

10%

42%

1% 2%

RACE

COLOR

NATIONAL ORIGIN

RELIGION

SEX

FAMILIAL STATUS

DISABILITY

AGE

OTHER

0%

5%

10%

15%

20%

25%

30%

35%

40%

2007 2008 2009 2010 2011 2012



Miami-Dade County Miami-Dade County, Florida


2010 2011 2012 Total %

RACE 19 14 8 41 17%

COLOR 0 3 0 3 1%


RELIGION 5 3 3 11 5%

SEX 8 2 10 20 8%


DISABILITY 39 26 24 89 36%

AGE 0 0 0 0 0%

OTHER 4 4 6 14 6%

Total 103 65 76 244


Table 30: Housing discrimination Complaints- 2007-2012 Florida Commission on Human Relations

2007 2008 2009 2010 2011 2012 Total %

RACE 9 16 17 17 16 5 80 18%

COLOR 0 1 1 0 3 1 6 1%

NATIONAL ORIGIN 7 11 15 10 8 12 63 14%

RELIGION 0 3 8 4 4 2 21 5%

SEX 4 5 9 7 2 8 35 8%


DISABILITY 20 42 39 32 29 23 185 41%

AGE 0 0 0 0 0 0 0 0%



OTHER 4 6 6 5 2 3 26 6%

Total 50 93 104 81 67 61 456


Table 31: Housing discrimination Complaints – FY 2007-2012 HOPE FAIR HOUSING CENTER

2007 2008 2009 2010 2011 2012 Total %

RACE 33 9 12 16 7 --- 77 36%

COLOR 0 1 0 0 0 --- 1 0%

NATIONAL ORIGIN 8 3 0 3 3 --- 17 8%

RELIGION 0 0 0 0 0 --- 0 0%

SEX 1 0 0 0 1 --- 2 1%

FAMILIAL STATUS 3 3 5 2 3 --- 16 7%

DISABILITY 27 7 22 19 7 --- 82 38%

AGE 0 0 2 2 0 --- 4 2%

OTHER 1 3 2 3 6 --- 15 7%

Total 73 26 43 45 27 --- 214


Table 32: Housing discrimination Complaints- FY 2007-2012 Miami-Dade County Office of Human Rights and Fair Employment Practices

2007 2008 2009 2010 2011 2012 Total %

RACE 10 9 14 4 4 8 49 16%

COLOR 2 0 0 1 0 0 3 1%

NATIONAL ORIGIN 12 8 8 4 18 8 58 19%

RELIGION 5 0 1 0 1 0 7 2%

SEX 1 2 1 0 3 4 11 4%




DISABILITY 14 13 18 7 3 8 63 20%

AGE 3 1 3 0 2 3 12 4%

OTHER 25 11 24 1 21 12 94 30%

Total 73 46 71 17 58 48 313



Figure 110: Average Summary Percentage of Housing Complaints for all Cited Agencies, Miami-Dade County, 2007-2012

Figure 111: Summary of Housing Discrimination Complaints, By Year, Miami-Dade County, 2007-2012

Table 33: Bases for Discrimination Complaints, Rank and Proportion, Miami-Dade County 2007-2012

Basis % Rank

DISABILITY 34% 1

RACE 21% 2


OTHER 12% 4


SEX 5% 6

RELIGION 3% 7

AGE 1% 8

COLOR 1% 9

In Miami-Dade county, the range of fair housing complaints is more spread out between bases than for counties with smaller populations. 34% can be attributed to disability, 21% to race, 15% to national origin, and 12% to “other” complaints (mostly for retaliation). Over the five years, there is more stability in the proportion of complaints overall even though there is still a spike in 2010. As with Broward County, HOPE acts as a FHAP. We did not receive updated information for 2012 from this organization however so data in table 31 is cited from Miami-Dade Counties 2011 AI.

21%

1%

15%

3% 5%

7%

34%

1% 12% RACE

COLOR

NATIONAL ORIGIN

RELIGION

SEX

FAMILIAL STATUS

DISABILITY

AGE

OTHER

0%

5%

10%

15%

20%

25%

2007 2008 2009 2010 2011 2012

SEVEN50 REGIONAL ANALYSIS OF IMPEDIMENTS TO FAIR HOUSING DRAFT


Monroe County Monroe County, Florida


2010 2011 2012 Total %

RACE 2 1 0 3 19%

COLOR 0 0 0 0 0%


RELIGION 0 1 0 1 6%

SEX 0 0 1 1 6%



AGE 0 0 0 0 0%

OTHER 1 0 0 1 6%

Total 8 4 4 16

Monroe County, Florida


2007 2008 2009 2010 2011 2012 Total %

RACE 0 1 1 1 1 0 4 17%

COLOR 0 0 0 0 0 0 0 0%


RELIGION 0 0 0 0 0 0 0 0%

SEX 0 0 0 0 0 0 0 0%


DISABILITY 4 8 0 1 2 1 16 67%

AGE 0 0 0 0 0 0 0 0%



OTHER 0 0 0 0 0 0 0 0%

Total 4 9 1 3 5 2 24



Figure 112: Average Summary Percentage of Housing Complaints for all Cited Agencies, Monroe County, 2007-2012

Figure 113: Summary of Housing Discrimination Complaints, By Year, Monroe County, 2007-2012

Table 36: Bases for Discrimination Complaints, Rank and Proportion, Monroe County 2007-2012

Basis % Rank

DISABILITY 46% 1

RACE 18% 2



RELIGION 3% 5

SEX 3% 6

OTHER 3% 7

COLOR 0% 8

AGE 0% 9

Monroe has very few fair housing complaints. Of those registered 46% claim discrimination based on disability, 18% on race, and 14% on both national origin and familial status. Over the time period there have been large variances in the proportion of complaints year by year with the largest amount in 2010.

18%

14%

3%

3% 14%

46%

3%

RACE

COLOR

NATIONAL ORIGIN

RELIGION

SEX

FAMILIAL STATUS

DISABILITY

AGE

OTHER

0%

5%

10%

15%

20%

25%

30%

2007 2008 2009 2010 2011 2012



SEFLA Figure 114: Average Summary Percentage of Housing Complaints for all Cited

Agencies, SEFLA, 2007-2012

Figure 115: Summary of Housing Discrimination Complaints, SEFLA

Table 37: Bases for Discrimination Complaints, Rank and Proportion, SEFLA 2007-2012

Basis % Rank

DISABILITY 43% 1

RACE 17% 2



SEX 7% 5

OTHER 6% 6

RELIGION 2% 7

AGE 1% 8

COLOR 0.3% 9

Housing complaints based on discrimination against disability ranks highest in SEFLA by more than twice as much as racially based complaints which rank second. Disability is, over the 5-year period and for each county, the largest alleged discriminating factor in fair housing complaints (43%). For the region race (17%), national origin (13%), and familial status (11%) follow in ranking which has been consistent over time except for in 2012 when race dipped slightly below national origin (See Figure 117). There is variation of rank across individual counties (see figures 116). Within all counties except Indian River, there was a spike in reported fair housing complaints in 2010.

17%

0.3%

14%

2% 7%

11%

43%

1%

6% RACE

COLOR

NATIONAL ORIGIN

RELIGION

SEX

FAMILIAL STATUS

DISABILITY

AGE

OTHER 0%

5%

10%

15%

20%

25%

30%

2007 2008 2009 2010 2011 2012



Figure 116: Top 4 Ranked Bases for Complaints by County, 2007-2012 Average

0%

10%

20%

30%

40%

50%

60%

DISABILITY RACE NATIONAL ORIGIN FAMILIAL STATUS



Figure 117: Top 4 Ranked Bases for Complaints SEFLA 2007-2012

0

50

100

150

200

250

300

350

400

450

2007 2008 2009 2010 2011 2012

DISABILITY RACE NATIONAL ORIGIN FAMILIAL STATUS



4. Lending Profile 4 .1 Potent ia l Discr iminatory Lending Pract ices

Housing/Lending Discrimination on the Basis of Color, National Origin, Religion, Sex, Familial Status and Disability, (Protected class discrimination) Discrimination in housing acquisition and financing on the basis of color, national origin, religion, sex, familial status or disability is illegal under the Fair Housing Act, also known as Title VIII of the U.S. Civil Rights Act. Under the act, mortgage lenders cannot refuse a loan to one of the classes of people protected under the act, also referred to as protected classes, nor can they set different conditions to a loan based on the above factors (Palm Bay FL, 2009; U.S. Department of Housing and Urban Development, N.D.). Discrimination under Title VIII can occur in a variety of ways in addition to those already mentioned. Discrimination under Title VIII may occur when a financier or lender refuses to provide information to a member of a protected class based on their membership in that class.

Subprime Lending, Redlining & Predatory Lending Subprime lending is defined as higher than average rate loans given to persons who are of higher credit risk due to less than satisfactory credit. The higher rate regularly referred as “higher cost” reflects the increased risk of lending to a loan applicant with less than satisfactory credit. There are other indicators of subprime loans besides the loan rate being higher than average, like the potential for the loan to reset to much higher rates in the future (Coconut Creek AI, 2011). Subprime lending is more prevalent in minority-majority neighborhoods than in non-minority majority neighborhoods, suggesting an overrepresentation of minority recipients of mortgages that are subprime in nature (Department of Housing and Urban Development, N.D.). Because of the apparent concentration of subprime loans in minority neighborhoods and among minority loan applicants, it has been argued that subprime lenders target minority communities through reverse redlining.

Redlining was a practice that enabled minorities and their neighborhoods to be systematically excluded from acquiring home loans. This limited the housing choices for minorities, preventing them from improving and purchasing housing in their “red-lined” communities (Broward County, AI, 2011; Encyclopedia of Chicago, 2005). This practice led to deteriorating housing stock in minority-majority communities and increased rental tenancy in those areas.

Predatory lending has no officially recognized federal definition. The federal government does, however, associate certain practices with the act of predatory lending. These acts are seen as indicators of predatory lending and they include: high pressure and misleading sales practices, abusive and aggressive collection practices, balloon payments (an oversized payment due at the end of a loan), steering borrowers to higher cost mortgages when they qualify for lower cost ones and the failure to report credit information that allows borrowers to get the best rates on loans based on their complete credit history, among others (U.S. Senate Committee on Banking, Housing and Urban Affairs, 2000; www.Investopedia.com, 2012).



4.2 Home Mortgage Disc losure Act Data Analys is (HMDA) 4 The following analysis examines the equity in lending trends within SEFLA. The South Florida region held over six million people in 2010, with 89.6 percent of them living in Miami-Dade, Broward and Palm Beach County. Because of the concentration of South Floridians that live in these three counties, a few statistics on the lending in the leading cities of these counties is warranted.

The City of Miami is located in Miami-Dade County, the most populous county in the seven-county South Florida region, and serves as its county seat. In Miami, American Indians/Alaskan Natives have the highest percentage of home loan denials among all racial groups, at 36.62%. African Americans have the second highest denial rate at 33% and Hispanics and Whites have moderate loan denial rates of 22% and 17% respectively. Asian/Pacific Islanders have the lowest home loan denials rates, at 15.15%. When looking at loan rates across income levels, home loan denial rates are highest among low income individuals and lowest among upper income individuals (City of Miami AI, 2007).

The City of Fort Lauderdale is located in Broward County, Florida, the second-most populous county in the seven-county South Florida region with a population of 1,780,172 people in 2010(U.S. Census Bureau, 2010). In Fort Lauderdale during the year 2008, African Americans earning less than 50% of the area’s median income (AMI) had the highest percentage of loan denials under FHA/VA loans and Hispanics/Latinos earning less than 50% of the area’s median income (AMI) had the highest percentage of loan denials under conventional loans, edging out the less than 50% AMI African American denial percentage by one percentage point. Rates of loan denials under both loan types generally dropped as the income class increased, except for a few instances, most notably, African Americans conventional loan applicants above 120+% AMI had a higher percentage of loan denials than their 1000-119% AMI counterparts at with rates of 41% to 35% respectively. The most prevalent reason for loan denials in the city for all races was debt (Fort Lauderdale AI, 2010).

The City of West Palm Beach is located in Palm Beach County, Florida, the third-most populous county in the seven-county South Florida region with a population of 1,320,134 people in 2010 (U.S. Census Bureau, 2010). In West Palm Beach in 2009, American Indian/Alaska Natives had the lowest percentage of approved and accepted loan applications, at 33.33%. Whites had the highest percentage of approved and accepted loan applications at 62.16%, almost twice that of American Indian/Alaska Natives. The range of approved and accepted applications across AMI income classes with available income fell within a percentage point 16 range, between a low of 46.50% for applicants earning less than 50% of the metropolitan statistical area (MSA) median income and a high of 62.83% for applicants earning 120% or more of the MSA median income (City of West Palm Beach, 2011).

4 Data source throughout HMDA Analysis:: 2010 Florida Housing Data Clearinghouse HMDA Home Mortgage Lending Data.



Loan Dispositions in SEFLA Of the seven counties in the South Florida region, Martin County has the highest overall loan origination rate, at 68.88% and Miami-Dade County has the highest overall loan denial rate, at 25.64%. Only two counties in the region, Martin and Indian River Counties, have loan origination rates that are above the state’s loan origination average of 61.59%. Three South Florida Counties, Indian River County, Martin County and St. Lucie County, have loan denial rates below the state average of 19.61%. Four of the seven South Florida Counties have loan denial rates that are higher than the state rate of 19.01%.

Overall Loan Dispositions for the Seven County Southeast Florida Region & The State of Florida

County Originated/Approved Denied Other Total

Broward 12954 (55.87%)

5277 (22.76%)

4957 (21.38%)

23188

Indian River County

1133 (67.76%)

261 (15.61%)

278 (16.05%)

1672

Martin County

1193 (68.88%)

261 (15.07%)

278 (16.05%)

1732

Miami-Dade County

10807 (51.41%)

5390 (25.64%)

4824 (22.95%)

21,021

Monroe County

707 (59.97%)

242 (20.53%)

230 (19.51%)

1179

Palm Beach County

9765 (60.21%)

3241 (19.98%)

3213 (19.81%)

16219

St. Lucie County

2571 (61.35%)

802 (19.14%)

818 (19.52%)

4191

Florida 144931 (61.59%)

46139 (19.61%)

44259 (18.81%)

235329



Loan Application Denial Reasons for the Seven County Southeast Florida Region By County County Debt-to-

Income Ratio Employment History

Credit History Collateral Insufficient Cash

Unverifiable Information

Credit Application Incomplete

Mortgage Insurance Denied

Other Reason Not Available

Total Denied Applications

Broward County

1363 (25.83%)

85 (1.61%) 516 (9.78%) 1234 (23.38%) 138 (2.61%) 208 (3.94%) 415 (7.86%) 6 (0.11%) 516 (9.78%) 796 (15.08%) 5277

Indian River County 76 (29.12%) 10 (3.83%) 23 (8.81%) 65 (24.90%) 10 (3.83%) 8 (3.07%) 35 (13.41%) 1 (0.38%) 15 (5.74%) 18 (6.90%) 261

Martin County 66 (25%) 4 (1.56%) 34 (12.89%) 69 (26.14%) 6 (2.27%) 15 (5.68%) 22 (8.34%) 1 (0.39%) 23 (8.71%) 24 (9.09%) 264

Miami-Dade County

1275 (23.65%)

61 (1.13%) 463 (8.59%) 1353 (25.10%) 116 (2.15%) 221 (4.10%) 438 (8.12%) 6 (0.11%) 511 (9.48%) 946 (17.55%) 5390

Monroe County 72 (29.75%) 3 (1.24%) 15 (6.20%) 63 (26.03%) 7 (2.89%) 13 (5.37%) 20 (8.26%) 0 (0%) 26 (10.74%) 23 (9.50%) 242

Palm Beach County

836 (25.79%)

50 (1.54%) 306 (9.44%) 689 (21.26%) 78 (2.41%) 136 (4.20%) 357 (11.02%) 6 (0.19%) 293 (9.04%) 490 (15.12%) 3241

St. Lucie County 224

(27.93%) 16 (2 %)

107 (13.34%)

152 (18.95%) 25 (3.12%) 41 (5.11%) 61 (7.60%) 4 (0.50%) 83 (10.35%) 89 (10.35%) 802

The most prevalent reason for loan application denials among the counties in South Florida in is the loan applicant’s debt to income ratio, with the largest percentage of 2010 county-wide loan denials in five of the regions’ counties (Broward County, Indian River County, Monroe County, Palm Beach County & St. Lucie County) attributed to that reason. The second leading reason for loan denials in South Florida was collateral, with the largest percentage of countywide loan denials in two of the regions’ counties, Miami-Dade County and Martin County, attributed to that reason.



Disposition of Loans by Race Broward County

Applicant Race Loan Originated Loan Denied Other Total

American Indian or Alaska Native

40 (41.24%) 25 (25.77%) 32 (32.98%) 97

Asian 448 (55.24%) 171 (21.09%) 192 (23.67%) 811

Black or African American 2166 (49.83%) 1259 (28.96%) 922 (21.21%) 4347

Native Hawaiian or Other Pacific Islander

40 (42.55%) 34 (36.17%) 20 (21.28%) 94

White 9036 (59.48%) 3095 (20.37%) 3060 (20.14) 15191

Information not provided by applicant in mail, Internet, or telephone application

1165 (45.53%) 684 (26.72%) 710 (27.75%) 2559

Not applicable 59 (66.29%) 9 (10.11%) 21 (23.60%) 89

In Broward County, Native Hawaiian or Other Pacific Islander loan applicants had the highest loan denial rates in 2010. White loan applicants had the highest loan origination rates in the county, outpacing second place Asians by 4.24 percentage points.

Indian River County



4 (80%) 0 (0%) 1 (20%) 5

Asian 11 (68.75%) 0 (0 %) 5 (31.25%) 16



0 0 0 0

White 982 (68.38%) 230 (16.02%) 224 (15.60%) 1436


94 (58.75%) 24 (15%) 42 (26.25%) 160

Not applicable 1 (100%)

0 (0%) 0 (0%) 1

In Indian River County, White loan applicants had the highest loan denial rates in 2010. American Indian or Alaska Native loan applicants had the highest loan origination rates in the county, outpacing second place African American loan applicants by 4.07 percentage points.



Martin County Applicant Race Loan Originated Loan Denied Other Total


2 (50%) 1 (25%) 1 (25%) 4

Asian 13 (72.22%) 2 (11.11%) 3 (16.67%) 18

Black or African American 10 (50%) 6 (30%) 4 (20%) 20


0 (0%) 0 (0%) 1 (100%) 1

White 1062 (67.17%) 222 (14.04%) 297 (18.79%) 1581


101 (58.05%) 32 (18.39%) 41 (23.56%) 174

Not applicable 5 (83.33%) 1 (16.67%) 0 (0%) 6

In Martin County, African American loan applicants had the highest loan denial rates in 2010. Asian loan applicants had the highest loan origination rates in the county, outpacing second place White loan applicants by 5.05 percentage points.

Miami-Dade County Applicant Race Loan Originated Loan Denied Other Total


32 (36.78%) 22 (25.29%) 33 (37.93%) 87

Asian 199 (50.90%) 99 (25.32%) 93 (23.80%) 391



23 (32.85%) 38 (54.29%) 9 (12.86%) 70

White 8546 (52.85%) 4064 (25.13%) 3561 (22.02%) 16171


840 (41.54%) 567 (28.04%) 615 (30.42%) 2022

Not applicable 226 (77.13%) 26 (8.87%) 41 (14%)

293

In Miami-Dade County, Native Hawaiian or Other Pacific Islander loan applicants had the highest loan denial rates in 2010. Whites had the highest loan origination rates in the county, outpacing second place Asian loan applicants by 1.95 percentage points.



Monroe County



2 (50%) 1 (25%) 1 (25%) 4

Asian 6 (85.71%) 1 (14.29%) 0 (0%) 7



1 (100%) 0 (0%) 0 (0%) 1

White 627 (61.35%) 218 (21.33%) 177 (17.32%) 1022


51 (43.97%) 20 (17.24%) 45 (38.79%) 116

Not applicable 15 (83.33%) 0 (0%)

3 (16.67%) 18

In Monroe County, American Indian or Alaska Native loan applicants had the highest loan denial rates in 2010, though this may be attributed more to the low numbers of loan applicants from this racial category in the county than to any true outcome disparity. Native Hawaiian or Pacific Islander loan applicants had the highest loan origination rates in the county, outpacing second place Asians by 14.29 percentage points. It is important to note that, like the racial category in the county with the highest loan denial rates, the difference between the Native Hawaiian or Other Pacific Islander loan origination rate and that of all other rates may not indicate a disparity in that racial category favor due to its small applicant pool (1 applicant).

Palm Beach County Applicant Race Loan Originated Loan Denied Other Total


18 (40.90%) 13 (29.55%) 13 (29.55%) 44

Asian 284 (57.84%) 94 (19.14%) 113 (23.01%) 491



27 (52.94%) 12 (23.53%) 12 (23.53%) 51

White 7815 (62.72%) 2308 (18.52%) 2338 (18.76%) 12461


752 (49.25%) 362 (23.71%) 413 (27.05%) 1527

In Palm Beach County, American Indian or Alaskan Native loan applicants had the highest loan denial rates in 2010. White loan applicants had the highest loan origination rates in the county, outpacing second place Asians by 4.88 percentage points.



Not applicable 51 (71.83%) 8 (11.27%) 12 (16.90%) 71



St. Lucie County



6 (42.86%) 4 (28.57%) 4 (28.57%) 14

Asian 44 (61.97%) 17 (23.94%) 10 (14.08%) 71

Black or African American 295 (59%) 116 (23.2%) 89 (17.8%)

500


9 (64.29%) 3 (21.43%) 2 (14.29%) 14

White 2006 (62.94%) 553 (17.35%) 628 (19.71%) 3187


203 (51.39%) 108 (27.34%) 84 (21.27%) 395


1 (10%)

1 (10%)

10

In St. Lucie County, American Indian or Alaska Native loan applicants had the highest loan denial rates in 2010. Native Hawaiian or Other Pacific Islander loan applicants had the highest loan origination rates in the county, outpacing second place Whites by 1.35 percentage points.

Florida



455 (46.19%) 244 (24.77%) 286 (29.04%) 985

Asian 3733 (59.20%) 1282 (20.33%) 1291 (20.47%) 6306



427 (53.58%) 225 (28.23%) 145 (18.19%) 797

White 114938 (63.66%) 33104 (18.34%) 32496 (18%) 180538


13239 (53.28%) 5579 (22.45%) 6032 (24.27%) 24850

Not applicable 790 (78.84%) 84 (8.38%) 128 (12.77%) 1002

Statewide, Native Hawaiian or Other Pacific Islander loan applicants had the highest loan denial rates in 2010. Whites had the highest loan origination rates in the state, outpacing second place Asians by 4.46 percentage points.



Loan Dispositions by Hispanic/Non-Hispanic Ethnicity

Broward County

Applicant Ethnicity

Loan Originated

Application Denied

Other Total

Hispanic or Latino

3014 (55.08%)

1353 (24.73%)

1105 (20.19%)

5472

Not Hispanic or Latino

8820 (57.60%)

3303 (21.57%)

3190 (20.83%)

15313


1059 (45.80%)

612 (26.47%)

641 (27.72%)

2312

Not applicable 61 (85.92%)

9 (9.90%)

21 (23.10%)

91

Hispanic or Latino loan applicants in Broward County received loan denials at a higher rate than non-Hispanic loan applicants, with the Hispanic or Latino loan denial rate outpacing the non-Hispanic loan denial rate by 3.16 percentage points in 2010. Hispanics or Latino loan applicants in Broward received loan originations at a lower rate than non-Hispanics, with the Hispanic loan origination rate falling 2.52 percentage points behind the non-Hispanic loan origination rate in 2010.

Indian River County

Applicant Ethnicity

Loan Originated

Application Denied

Other Total

Hispanic or Latino

73 (73%)

14 (14%)

13 (13%)

100


964 (68.42%)

224 (15.90%)

221 (15.68%)

1409


95 (58.64%)

23 (14.20%)

44 (27.16%)

162


0 (0%)

0 (0%)

1

Hispanic or Latino loan applicants in Indian River County received loan denials at a lower rate than non-Hispanic loan applicants, with the non-Hispanic loan denial rate outpacing the Hispanic loan denial rate by 1.9 percentage points in 2010. Hispanic of Latino loan applicants in Indian River County received loan originations at a higher rate than non-Hispanic loan applicants, with the Hispanic loan origination rate outpacing the non-Hispanic loan origination rate by 4.58 percentage points in 2010.



Martin County Applicant Ethnicity

Loan Originated

Application Denied

Other Total

Hispanic or Latino

45 (61.64%)

19 (26.02%)

9 (12.34%)

73


1042 (67.37%)

213 (13.77%)

292 (18.88%)

1547


101 (56.74%)

31 (17.42%)

46 (25.84%)

178


1 (16.67%)

0 (0%)

6

Hispanic or Latino loan applicants in Martin County received loan denials at a higher rate than non-Hispanics, with the Hispanic or Latino loan denial rate outpacing the non-Hispanic loan denial rate by 12.25 percentage points in 2010. Hispanics in Martin County received loan originations at a lower rate than non-Hispanics, with the Hispanic loan origination rate falling 5.73 percentage points behind the non-Hispanic loan origination rate in 2010.

Miami-Dade County

Applicant Ethnicity

Loan Originated

Application Denied

Other Total

Hispanic or Latino

6559 (51.79%)

3339 (26.37%)

2766 (21.84%)

12664


3338 (52.09%)

1571 (24.52%)

1498 (23.38%)

6407


683 (41.29%)

453 (27.39%)

518 (31.32%)

1654


27 (9.12%)

42 (14.19%)

296

Hispanic or Latino loan applicants in Miami-Dade County received loan denials at a higher rate than non-Hispanics, with the Hispanic loan denial rate outpacing the non-Hispanic loan denial rate by 1.85 percentage points in 2010. Hispanic or Latino loan applicants in Miami-Dade County received loan originations at a lower rate than non-Hispanics, with the Hispanic loan origination rate falling 0.3 percentage points behind the non-Hispanic loan origination rate in 2010.



Monroe County

Applicant Ethnicity Loan Originated

Application Denied

Other Total

Hispanic or Latino 84 (59.57%)

34 (24.11%)

23 (16.31%)

141


554 (61.01%)

191 (21.03%)

163 (17.95%)

908


54 (48.21%)

17 (15.18%)

41 (36.60%)

112


0 (0%)

3 (16.67%)

18

Hispanic or Latino loan applicants in Monroe County received loan denials at a higher rate than non-Hispanics, with the Hispanic loan denial rate outpacing the non-Hispanic loan denial rate by 3.08 percentage points in 2010. Hispanic or Latino loan applicants in Monroe County received loan originations at a lower rate than non-Hispanics, with the Hispanic loan origination rate falling 1.44 percentage points behind the non-Hispanic loan origination rate in 2010.

Palm Beach County Applicant Ethnicity

Loan Originated

Application Denied

Other Total


578 (25.58%)

414 (18.32%)

2260


7692 (62.22%)

2300 (18.60%)

2371 (19.18%)

12363


754 (49.54%)

355 (23.32%)

413 (27.14%)

1522


8 (1.35%)

15 (20.27%)

74

Hispanic or Latino loan applicants in Palm Beach County received loan denials at a higher rate than non-Hispanics, with the Hispanic loan denial rate outpacing the non-Hispanic loan denial rate by 6.98 percentage points in 2010. Hispanic or Latino loan applicants in Palm Beach County received loan originations at a lower rate than non-Hispanics, with the Hispanic loan origination rate falling 6.12 percentage points behind the non-Hispanic loan origination rate in 2010.



St. Lucie County


Application Denied

Other Total


118 (21.42%)

114 (20.69%)

551


2046 (63.07%)

580 (17.88%)

618 (19.05%)

3244


198 (51.30%)

103 (26.68%)

85 (22.02%)

386


1 (10%)

1 (10%)

10

Hispanic or Latino loan applicants in St. Lucie County received loan denials at a higher rate than non-Hispanic loan applicants, with the Hispanic loan denial rate outpacing the non-Hispanic loan denial rate by 3.54 percentage points in 2010. Hispanics in St. Lucie County received loan originations at a lower rate than non-Hispanics, with the Hispanic loan origination rate falling 5.18 percentage points behind the non-Hispanic loan origination rate in 2010.

Florida


Application Denied

Other Total


9727 (24.80%)

7694 (19.61%)

39227


109580 (64.06%)

30903 (18.07%)

30565 (17.90%)

171048


12749 (55.03%)

5423 (22.56%)

5868 (24.41%)

24040


86 (8.56%)

132 (13.15%)

1004

Hispanic or Latino loan applicants statewide received loan denials at a higher rate than non-Hispanics, with the Hispanic loan denial rate outpacing the non-Hispanic loan denial rate by 6.73 percentage points in 2010. Hispanics in statewide received loan originations at a lower rate than non-Hispanics, with the Hispanic loan origination rate falling 8.47 percentage points behind the non-Hispanic loan origination rate in 2010.



Loan Denial Disparities by Race and Ethnicity Loan Application Denial Disparities Across the Seven County South Florida

Region By Race

Applicant Race

Broward County

Disparity

Indian River

County Disparity

Martin County

Disparity

Miami-Dade

County Disparity

Monroe County

Disparity

Palm Beach County

Disparity

St. Lucie County

Disparity


1.27 N/V 1.78 1.01 1.17 1.59 1.64

Asian 1.04 N/V 0.79 1.01 0.67 1.03 1.38


1.42 0.8 2.14 1.15 0.85 1.52 1.34


1.78 N/V N/V 2.16 N/V 1.27 1.24

White N/A N/A N/A N/A N/A N/A N/A

Loan Application Denial Disparities Across the Seven County South Florida Region By Hispanic/Non-Hispanic Ethnicity

Applicant Ethnicity

Broward County

Disparity

Indian River

County Disparity

Martin County

Disparity

Miami-Dade

County Disparity

Monroe County

Disparity

Palm Beach County

Disparity

St. Lucie County

Disparity

Hispanic 1.15 0.88 1.89 1.08 1.15 1.38 1.2

Across the seven-county South Florida Region, the applicant race with the largest loan application denial disparity from that of whites are Native Hawaiian or Other Pacific Islander loan applicants in Miami-Dade County, who are 2.16 times more likely than a white applicant in that county to receive a home loan denial. African-Americans in Martin County had the second largest disparity, being 2.14 times more likely to receive a loan denial than a white applicant. The applicant race with the lowest application denial disparity from that of whites are Asian loan applicants in Monroe County, who are .67 times more likely to receive a home loan than whites in Monroe County.

Across the seven-county South Florida region, the county the highest disparity between Hispanic or Latino and non-Hispanic loan applicants in 2010 was in Martin County, Florida. In Martin County, Hispanics were 1.89 times more likely to receive a loan denial in 2010.



Non-Hispanic

N/A N/A N/A N/A N/A N/A N/A



Subprime Lending Across the seven-county South Florida region, only Miami-Dade County had a subprime/high cost loan origination rate higher than the state average in 2010. The county with the lowest rate of subprime/high cost loans is Monroe County at 1.54% of all its loans in 2010.

Subprime/High Cost Loans in the Seven County Southeast Florida Region Compared to the State of Florida

County High Cost Non-High Cost or Unknown

Total

Broward County 334 (2.91%)

11148 (97.09%)

11482

Indian River County 19 (2.25%)

826 (97.75%)

845

Martin County 25 (2.73%)

891 (97.27%)

916

Miami-Dade County 410 (4.58%)

8551 (95.42%)

8961

Monroe County 5 (1.54%)

320 (98.46%)

325

Palm Beach County 201 (2.48%)

7896 (97.52%)

8097

St. Lucie County 51 (2.55%)

1950 (97.45%)

2001

Florida 4177 (3.50%)

115111 (96.50%)

119288



Subprime Lending by Race Broward County

Race High-Cost Non-High Cost or Unknown

Total


0 37 (100%)

37

Asian 5 (1.25%)

395 (98.75%)

400

Black or African American 94 (4.56%)

1967 (95.43%)

2061


1 (2.85%)

34 (97.14%)

35

White 221 (2.78%)

7726 (97.23%)

7947


13 (1.30%)

988 (98.70%)

1001


1 (100%)

1

Indian River Race High-Cost Non-High Cost

or Unknown Total


0 (0%)

3 (100%)

3

Asian 0 (0%)

8 (100%)

8


37 (94.87%)

39

White 15 (2.05%)

715 (97.95%)

730


2 (3.07%)

63 (96.92%)

65

African-American loan applicants had the highest rate of subprime/high cost loans in Broward County in the year 2010 at 4.56% of all its racial category loan originations. Asian loan applicants had the lowest rate of subprime/high cost loans in the county at 1.25% of all its racial category loan originations.

African-American loan applicants had the highest rate of subprime/high cost loans in Indian River County in the year 2010 at 5.12% of all its racial category loan originations. Asian loan applicants had the lowest rate of subprime/high cost loans in the county at 1.25% of all its racial category loan originations.



Miami-Dade County Race High-Cost Non-High Cost

or Unknown Total


4 (14.81%)

23 (85.19%)

27

Asian 6 (3.77%)

153 (96.23%)

159


876 (96.58)

907


0 (0%)

18 (100%)

18

White 342 (4.79%)

6799 (95.21%)

7141


27 (3.87%)

671 (96.13%)

698


11 (100%)

11

Monroe County Race High-Cost Non-High Cost

or Unknown Total


0 (0%)

1 (100%)

1

Asian 0 (0%)

3 (100%)

3

Black or African American 0 (0%)

4 (100%)

4


0 (0%)

1 (100%)

1

White 5 (1.69%)

291 (98.31%)

296


0 (0%)

20 (100%)

20

White loan applicants had the highest rate of subprime/high cost loans in Monroe County in the year 2010 at 1.69% of all its racial category loan originations. For all other racial categories listed, none of their loan originations were high cost in 2010.

American Indian or Alaskan Native loan applicants had the highest rate of subprime/high cost loans in Miami-Dade County in the year 2010 at 14% of all its racial category loan originations. Native Hawaiian or Other Pacific Islander loan applicants had the lowest rates of subprime/high cost loans in the county at 0% of all its racial category loan originations.



Palm Beach County Race High-Cost Non-High Cost

or Unknown Total


1 (7.14%)

13 (92.86%)

14

Asian 2 (.8%)

225 (99.12%)

227


739 (96.34%)

767


0 (0%)

24 (100%)

24

White 163 (2.57%)

6313 (97.48%)

6476


7 (1.19%)

582 (98.81%)

589

St. Lucie County

Race High-Cost Non-High Cost or Unknown

Total


0 (0%)

6 (100%)

6

Asian 0 (0%)

33 (100%)

33


242 (96.8%)

250


0 (0%)

8 (100%)

8

White 39 (2.50%)

1519 (97.50%)

1558

American Indian or Alaskan Native loan applicants had the highest rate of subprime/high cost loans in Palm Beach County in the year 2010 at 7.14% of all its racial category loan originations. Native Hawaiian or Other Pacific Islander loan applicants had the lowest rates of subprime/high cost loans in the county at 0% of all its racial category loan originations.

African American loan applicants had the highest rate of subprime/high cost loans in St. Lucie County in the year 2010 at 3.2% of all its racial category loan originations. Native Hawaiian or Other Pacific Islander loan applicants had the lowest rates of subprime/high cost loans in the county at 0% of all its racial category loan originations.




4 (2.74%)

142 (97.26%)

146

Subprime Lending by Hispanic/Non Hispanic Ethnicity

Broward County Ethnicity High-Cost Non-High

Cost or Unknown

Total


2599 (96.19%)

2702

Not Hispanic or Latino 219 (2.79%)

7638 (97.21%)

7857


12 (1.30%)

908 (98.70%)

920

Not Applicable 0 (0%)

3 (100%)

3

Indian River County Ethnicity High-Cost Non-High

Cost or Unknown

Total


63 (98.45%)

64


700 (97.77%)

716


2 (3.07%)

63 (96.92%)

65

In 2010, Hispanic or Latino loan applicants had a higher rate of high cost loan originations than non-Hispanic loan applicants in Broward County. The Hispanic loan applicant high cost loan origination rate in 2010 was 1.02 percentage points higher than the non-Hispanic loan applicant high cost loan origination rate.

In 2010, Hispanic or Latino loan applicants had a lower rate of high cost loan originations than non-Hispanic or Latino loan applicants in Indian River County. The Hispanic applicant high cost loan origination rate in 2010 was 0.73 percentage points lower than the non-Hispanic or Latino loan applicant high cost loan origination rate.



Martin County Ethnicity High-Cost Non-High

Cost or Unknown

Total

Hispanic or Latino 0 (0%)

33 (100%)

33


785 (97.03%)

809


1 (1.35%)

73 (98.65%)

74

Miami-Dade County Ethnicity High-Cost Non-High

Cost or Unknown

Total


5273 (94.79%)

5563


2720 (96.32%)

2824

Information not provided by applicant in mail, Internet, or telephone

16 (2.85%)

546 (97.15%)

562

In 2010, Hispanic or Latino loan applicants had a lower rate of high cost loan originations than non-Hispanic or Latino loan applicants in Martin County. The Hispanic or Latino loan applicant high cost loan origination rate in 2010 was 2.97 percentage points lower than the non-Hispanic or Latino loan applicant high cost loan origination rate.

In 2010, Hispanic or Latino loan applicants had a higher rate of high cost loan originations than non-Hispanic or Latino loan applicants in Miami-Dade County. The Hispanic applicant high cost loan origination rate in 2010 was 1.53 percentage points higher than the non-Hispanic or Latino loan applicant high cost loan origination rate.



application


12 (100%)

12

Monroe County Ethnicity High-Cost Non-High

Cost or Unknown

Total


38 (97.44%)

39


260 (98.48%)

264


0 (0%)

22 (100%)

22

Palm Beach County Ethnicity High-Cost Non-High

Cost or Unknown

Total


1120 (95.40%)

1174


6196 (97.85%)

6332


11 (1.86%)

580 (98.14%)

591

In 2010, Hispanic or Latino loan applicants had a higher rate of high cost loan originations than non-Hispanic or Latino loan applicants in Monroe County. The Hispanic or Latino applicant high cost loan origination rate in 2010 was 1.04 percentage points higher than the non-Hispanic or Latino loan applicant high cost loan origination rate.

In 2010, Hispanic or Latino loan applicants had a lower rate of high cost loan originations than non-Hispanic or Latino loan applicants in St. Lucie County. The Hispanic applicant high cost loan origination rate in 2010 was 0.69 percentage points lower than the non-Hispanic or Latino loan applicant high cost loan origination rate.

In 2010, Hispanic or Latino loan applicants had a higher rate of high cost loan originations than non-Hispanic or Latino loan applicants in Palm Beach County. The Hispanic or Latino loan applicant high cost loan origination rate in 2010 was 2.45 percentage points higher than the non-Hispanic or Latino loan applicant high cost loan origination rate.



St. Lucie County Ethnicity High-Cost Non-High Cost

or Unknown Total


254 (98.07%)

259


42 (2.62%)

1561 (97.38%)

1603


4 (2.83%)

135 (97.12%)

139

Subprime Lending Disparities by Race and Ethnicity High Cost Loan Disparity Across the Seven County South Florida Region By Race

Applicant Race Broward County Disparity

Indian River County Disparity

Martin County Disparity

Miami-Dade County Disparity

Monroe County Disparity

Palm Beach County Disparity

St. Lucie County Disparity


No High Cost Loans

No High Cost Loans

No High Cost Loans

3.09 No High Cost Loans


Asian 0.45 No High Cost Loans

3.73 ,78 No High Cost Loans



1.64 2.5 No High Cost Loans


1.42 1.28


1.03 Not Listed Not Listed No High Cost Loans

No High Cost Loans

No High Cost Loans

No High Cost Loans

White N/A N/A N/A N/A N/A N/A N/A

The applicant race with the greatest high cost loan rate disparity from that of Whites are Asian loan applicants in Martin County, who are 3.73 times more likely to receive a high-cost loan origination than their white counterparts. Asian loan applicants in Palm Beach County have the lowest high cost loan rate disparity from that of White loan applicants. They are .31 times as likely as their White loan applicant counterparts to receive a high-cost loan origination.



High Cost Loan Disparity Across the Seven County South Florida Region by Hispanic Ethnicity Applicant Ethnicity

Broward County

Disparity

Indian River

County Disparity

Martin County

Disparity

Miami-Dade

County Disparity

Monroe County

Disparity

Palm Beach County

Disparity

St. Lucie County

Disparity

Hispanic Ethnicity 1.37 0.69 No High Cost Loans

1.42 1.69 2.14 0.74

Non-Hispanic Ethnicity

N/A N/A N/A N/A N/A N/A N/A

The greatest high cost loan origination disparity between Hispanic or Latino loan applicants and non-Hispanic or Latino loan applicants is in Palm Beach County, where Hispanics are 2.14 times more likely to receive a high cost loan than their non-Hispanic counterparts.

4.3 Summary of the Data There are disparities in loan origination rates and subprime lending rates across the seven South Florida counties. Though these disparities are seen across racial groups, the data suggests no racial group at a significant disparity or disadvantage across all seven counties. The full range of racial and ethnic groups included in this analysis has either higher loan denial or subprime lending rates in the counties analyzed.

All but three of the analyzed South Florida counties have overall loan origination rates higher than the state of Florida rate, which means that the counties with the higher rates, Broward, Monroe, Miami-Dade, Palm Beach & St. Lucie, have above average loan origination rates for the state of Florida. Interestingly, only three of the region’s seven counties, Indian River, Martin and St. Lucie, have loan denial rates below the state-wide rate. So, the region generally has a higher percentage of its loan applications that are originated but also a higher percentage of its loan originations that are denied than the state of Florida in general.

The most prevalent reason for loan denials within the counties of the South Florida region was the applicant’s debt to income ratio, the reason accounting for largest percentage of loan denial in five of the region’s seven counties (Broward County, Indian River County, Monroe County, Palm Beach County, & St. Lucie County). The second most prevalent reason for loan denials in the region, which accounted for the majority of loan denials in two of the region’s counties (Miami-Dade County & Martin County), was the amount of collateral the loan applicant had.



As stated earlier, loan dispositions based on race and the racial groups with the highest adverse dispositions varied across the region’s seven counties. In three of the region’s counties, American Indian or Alaska Native loan applicants had the highest loan denial rates (Monroe County, Palm Beach County and St. Lucie County). In two of the region’s counties, Native Hawaiian or Other Pacific Islander loan applicants had the highest loan denial rates (Miami-Dade County and Broward County). White applicants had the highest loan denial rate in Indian River County and African Americans had the highest loan denial rate in Martin County.

Hispanic applicants received loan denials at a higher rate than that of non-Hispanic applicants in six out of the seven South Florida counties, with only Hispanic applicants in Indian River County having lower loan application rates than non-Hispanics.

When examining the disparity between white applicants and applicants of other races across the seven counties, it was discovered that the largest disparity in loan denial rates from that of whites in the region were that of Native Hawaiian or Other Pacific Islander applicants in Miami-Dade County. The least disparity was that of Asian applicants in Monroe County. When examining the disparity between Hispanics and non-Hispanics in a similar manner, the largest disparity in loan denial rates between Hispanics and that of non-Hispanics was in Martin County.

When examining subprime lending by race, no single race uniformly had the highest rates of subprime originations across all seven counties. African Americans had the highest rates of subprime loan originations in three of the region’s counties (Broward, Indian River and St. Lucie County). American Indian or Alaska Natives had the highest rates of subprime loan originations in two of the region’s counties (Palm Beach and Miami-Dade). Whites had the highest rates of subprime loan originations in one of the region’s counties (Indian River County).

Hispanics had higher rates of subprime loan origination in four of the region’s seven counties (Palm Beach County, Monroe County, Miami-Dade County and Broward County. In three of the region’s counties, Hispanics had lower rates of subprime loan origination (St. Lucie County, Martin County and Indian River County).

When examining the disparity in the rate of subprime loan originations between white applicants and that of other races, the greatest disparity was found among Asian applicants in Martin County. The greatest disparity in the rate of subprime originations between Hispanics and that of non-Hispanics was found in Palm Beach County.



5. Identified Impediments to fair housing 5 .1 Key Impediments Region-wide

0

5

10

15

20

Housing/Lending Discrimination on the Basis of Race,

Color, National Origin, Religion,

Sex, Familial status and Disability

(Protected Classes)

Lack of Knowledge,

Awarness of or Education on Fair

Housing Protections

Fair and Equal Lending Disparities

Housing Market Segregation

Shortage of Affordable Housing

Opportunities

Violations of Federal, State and Local Fair Housing

Laws

Limited Funding to Meet Need for

Affordable Housing

Predatory Lending Improvement of the Housing

Discrimination Complaint Process

Zoning/Land Use

15

19

15

9 10 9 7

9

4 6

Number of South Florida Jurisdictions Citing Issue as an Impeidment to Fair Housing Choice

Number of South Florida Jurisdictions Citing Issue as an Impeidment to Fair Housing Choice



Impediment #1: Lack of Knowledge, Awareness of, or Education on Fair Housing Protections This impediment was found to be the most prevalent one within the seven county South Florida region, with 19 of the region’s 31 examined AIs finding it to be an impediment to fair housing choice in their communities. The cities of Coconut Creek and Miramar discuss this issue in detail in their AIs, stating that many potential homeowners lack the understanding of the path to homeownership. The city stated that a majority of loan denials were due to avoidable issues, such as incomplete loan applications, unverifiable information and collateral, among others (City of Coconut Creek, 2011; City of Miramar, 2011).

Impediment #2: Fair and Equal Lending Disparities This impediment was discussed in 15 of the region’s 31 examined AIs, making it the second most prevalent impediment in the region. The prevalence of this issue in the south Florida region is underlined in the subprime data discussed earlier in this analysis. The City of Miami discusses the impediment in its AI, stating that African Americans and Hispanics tend to have higher loan application failure rates when HMDA lending data is analyzed by race and ethnicity (City of Miami, 2007).

Impediment #3: Housing/Lending Discrimination on the Basis of Race, Color, National Origin, Religion, Sex, Familial Status and Disability This impediment was discussed in 15 of the region’s 31 examined AIs, making this issue tied with fair and equal lending disparities as the second most prevalent issue throughout the region. The data gathered on the lending patterns of the seven observed south Florida counties supports this finding, with the data indicating regular disparities on loan approvals and denials by race and by Hispanic/non-Hispanic ethnicity. The City of Fort Lauderdale’s AI discusses the impediment, stating that it occurs when, on the basis of a person being part of a protected class, lenders deny or alter services or access to housing through actions such as denying property insurance to applicants, conducting property appraisals in a discriminatory manner or by setting different terms or privileges in the sale of a dwelling, among other actions (City of Fort Lauderdale, 2010).

Impediment #4: Shortage of Affordable Housing Opportunities This impediment was discussed in 10 of the region’s 31 examined AIs, making it the region’s fourth most prevalent impediment. The city of Delray Beach discusses its particular situation under this impediment in detail, stating that it currently has 600 families on its waiting list for its City of Delray Beach Housing Choice Vouchers. The waiting list, due to the high number of families on it, has been closed until the number of families on the waiting list decreases to 200. In addition, families in the city seeking housing assistance typically wait four to five years before that assistance is received (City of Delray Beach, 2009).

Impediment #5: Violations of Federal, State and Local Housing Laws This impediment was discussed in nine of the region’s 31 examined AIs, making it tied with predatory lending as and Housing Market Segregation as the fifth most prevalent impediment to fair housing choice in the region. The City of Hollywood, Florida discusses this impediment in its AI, stating that it will tackle the impediment through a public relations campaign to promote the knowledge of fair



housing laws and assistance programs through various forms of media and to provide information about fair housing and phone numbers for assistance on the city’s website (City of Hollywood, 2010).

Impediment #6: Housing Market Segregation This impediment was discussed in nine of the region’s 31 examined AIs, making it the fifth most prevalent impediment to fair housing choice in the region, tied with the violation of federal, state and local housing laws impediment and the predatory lending impediment. The City of North Miami Beach seeks to tackle this impediment head on, calling for activities that are geared toward creating city neighborhoods that are more open and inclusive though affirmative training technical assistance to developers in the city using federal funding dollars to develop and redevelop housing as well as provide such training to city staff, community advocates, housing providers and area financial institutions (North Miami, 2011).

Impediment #7: Predatory Lending This impediment was discussed in nine of the region’s 31 examined AIs, making it tied for fifth most prevalent impediment in the region, tied with the housing market segregation impediment and the shortage of affordable housing opportunities impediment. The impacts of predatory lending, which was discussed earlier in the report, are described in detail in the City of Delray Beach AI, which states that predatory lending, due to its high costs, abusive practices, and hard requirements, has the potential to strip borrowers of home equity, ruin their credit records and increases the odds of home foreclosure. The city states that some of its census tracts are experiencing high levels of abandonment due to pending foreclosures which may be attributed to predatory lending (City of Delray Beach, 2009).

Impediment #8: Limited Funding to Meet Need for Affordable Housing This impediment was discussed in seven of the region’s 31 examined AIs, making it the eighth most prevalent impediment in the region. The City of Miami discusses this impediment, stating that it stems to annual decreases of federal funding to support affordable housing creation efforts. The city plans on overcoming this impediment by reducing the amount of federal subsidies per household through the tightening of bedroom restrictions and to attempt to accommodate as many Housing Opportunities for Persons with Aids (HOPWA) residents as it can in its Long-term Rental Assistance Program (City of Miami, 2007).

Impediment #9: Zoning/Land Use This impediment was discussed in six of the region’s 31 examined AIs, making it the ninth most prevalent impediment in the region. The City of Miramar, Florida discusses the impediment in detail in their AI. The city admits that its existing zoning, land use and future land use regulation influence the number of housing options in their city and their quality. The city provides a way to work with the influence, stating that the more types of zoned housing districts that a jurisdiction has the more likely that it will be able to accommodate diverse housing types developable on varying lot sizes, lowering their cost and increasing their affordability. The city follows this approach, providing sixteen residential housing districts in their zoning code ranging from a rural district of one dwelling per many acres, to a



Residential-25 with 25 dwellings per acre. The city also moved to remove regulatory barriers to the construction of affordable housing based in its zoning code and created a dense Traditional Neighborhood District along its State Road 7 Corridor (City of Miramar, 2011).

Impediment #10: Improvement of the Housing Discrimination Complaint Process This impediment was discussed in four of the region’s 31 examined AIs, making it the tenth most prevalent impediment in the region. Pembroke Pines, Florida discusses some of the issues under this impediment, stating that the complaint process is hampered by organizational deficiencies and redundancy. Pembroke Pines elaborates on these points through illustrations, to include the fact that there are three entities (Broward County Civil Rights Division 4, U.S. HUD and HOPE, Inc.) who receive complaints of housing discrimination in the county and since housing complaints may be, and are at times, files in more than one of the entities, calculating a true number or volume of housing discrimination incidents in the city is difficult, due to the numbers being skewed by redundancies. Another situation that the city details is the fact that Broward county, when they receive discrimination complaints, are not required to list more than the name of the lender involved in the complaint and the basis of the complaint, but not the location of the complaint. Because of this, the assessment of location-based levels of housing discrimination is made more difficult (Broward County, 2011).



5.2 Key Impediments by County

Broward County

Key Impediments in Broward County Florida

Impediment Housing/Lending Discrimination on the Basis of Race, Color, National Origin, Religion, Sex, Familial status and Disability (Protected Classes)

Lack of Knowledge, Awareness of, or Education on, Fair Housing Protections



Shortage of Affordable Housing Opportunities

Violations of Federal, State and Local Fair Housing Laws

Limited Funding to Meet Need for Affordable Housing

Predatory Lending Improvement of the Housing Discrimination Complaint Process

Zoning/Land Use

Number of Jurisdictions in Broward County Citing Impediment In Their AI

10 10 8 5 4 5 3 5 3 4

The most cited impediments to fair housing choice in Broward County is the lack of knowledge, awareness of, or education on fair housing protections impediment and the housing/lending discrimination on the basis of being a member of a protected class impediment, with ten jurisdictions in the county citing each as impediments to fair housing choice.

Indian River County

Key Impediments in Indian River County Florida









Zoning/Land Use


No Entitlement Communities










Indian River County has no entitlement communities, thus, none of its jurisdictions have AI documents and impediment to fair housing choice information available.



Martin County

Key Impediments in Martin County Florida









Zoning/Land Use












Martin County has no entitlement communities, thus, none of its jurisdictions have AI documents and impediment to fair housing choice information available.

Miami-Dade County

Key Impediments in Miami-Dade County Florida









Zoning/Land Use


2 7 6 3 3 4 4 1 1 0

The most cited impediment of fair housing choice in Miami-Dade County is a lack of knowledge, awareness of, or education on fair housing protections, with seven jurisdictions within the county citing the issue as an impediment to fair housing choice. The second most cited impediment in Miami-Dade County is fair and equal lending disparities, with six jurisdictions within the county citing it as an impediment to housing choice.



Monroe County Key Impediments in Monroe County Florida









Zoning/Land Use












Monroe County has no entitlement jurisdictions, thus, none of its jurisdictions have AI documents and impediment to fair housing choice information available.

Palm Beach County Key Impediments in Palm Beach County Florida









Zoning/Land Use


1 1 0 0 1 0 0 3 0 2

The most cited impediment of fair housing choice in Palm Beach County is predatory lending, with three jurisdictions within the county citing the issue as an impediment to fair housing choice. The second most cited impediment in Palm Beach County is zoning/land use, with two jurisdictions within the county citing it as an impediment to housing choice.



St. Lucie County Key Impediments in St. Lucie County Florida









Zoning/Land Use


2 1 1 1 2 0 0 0 0 0

The most cited impediments of fair housing choice in St. Lucie County is housing/lending discrimination on the basis of being a member of a protected class and a shortage of affordable housing opportunities, with two jurisdictions within the county citing each issue as an impediment to fair housing choice.



6. Appendix

HUD PD&R Data Package- F O R B R O W A R D , M I A M I - D A D E , & M O N R O E C O U N T I E S O N L Y

Analyzing Segregation A primary metric for identifying segregation is the dissimilarity index. A dissimilarity index represents a summary measure of the extent to which the distribution of any two groups (frequently racial or ethnic groups) differs across census tracts or block-groups. Another common approach to measuring segregation is the isolation index, which compares a group's share of the overall population in a jurisdiction to the average neighborhood share for members of that group.

South Florida Regional Planning Council (Broward, Miami-Dade, and Monroe Counties)

Race/Ethnic Segregation

Share of Population Dissimilarity Index Isolation Index

(2010)

Program Participant

Area (2000)

Program Participant

Area (2010)

Program Participant

Area (2000)

Program Participant

Area (2010)

Program Participant

Area (2000)

Program Participant

Area (2010)

(1) (2) (3) (4) (5) (6)

Non-White/White 63% 72% 0.56 0.54 0.14 0.09

Black-African American/White 19% 20% 0.72 0.68 0.38 0.34

Hispanic/White 40% 48% 0.58 0.56 0.23 0.19

Asian/White 2% 2% 0.36 0.37 0.01 0.02

Pacific-Islander/White 0% 0% 0.49 0.52 0.02 0.03

Native-American/White 0% 0% 0.65 0.70 0.00 0.00

Note: The values in column (1) and (2) are the share of racial/ethnic groups in the participant geography in years 2000 and 2010, respectively. Columns (3) and (4) are the dissimilarity index for years 2000 and 2010. The index compares the spatial distribution of the two groups identified in the left-hand column, summarizing neighborhood differences over a larger geography (program participant geography or metro). Higher values of dissimilarity imply higher residential segregation. Column (5) is the isolation index calculated over the program participant geography for the year 2000, column (6) is the same for the year 2010. The isolation index



compares average neighborhood minority share for a minority person to the average minority share in the larger geography (program participant geography or metro). Again, higher values imply higher levels of segregation. These indexes are calculated using block group 100% count data from the 2000 and 2010 Decennial Census SF1.

Racially/Ethnically-Concentrated Areas of Poverty To assist communities in identifying racially/ethnically-concentrated areas of poverty (RCAPs/ECAPs), HUD PD&R has developed a census tract based definition for RCAP/ECAPs. The definition involves a racial/ethnic concentration threshold and a poverty test. The racial/ethnic concentration threshold is straightforward: RCAP/ECAPs must have a non-white population of 50 percent or more. Census tracts with this extreme poverty that satisfy the racial/ethnic concentration threshold are deemed RCAPs/ECAPs

South Florida Regional Planning Council (Broward, Miami-Dade, and

Monroe Counties)

RCAP /ECAP - Race & Ethnicity Summary Program Participant Area

Count Share

(1) (2)

RCAP/ECAP Tracts 34 3.8%

In RCAP/ECAP Tracts:

Total Population: 127,253 3.0%

Non-White: 121,166 3.9%

Black/African-American 63,959 7.3%

Note: Column (1) is the number of RCAP/ECAP tracts, and the total of persons in those RCAP/ECAP tracts in the program participant area. Column (2) is the share of tracts designated as, and population groups living in, RCAP/ECAPs.



Disparity in Access to Neighborhood Opportunity - All Persons South Florida Regional Planning Council (Broward, Miami-Dade, and Monroe Counties)

Panel A - All Persons Disparities

All

Persons White

Persons

Black /African

American Persons

Hispanic or Latino Persons

Asian Persons

Native American

Persons

Pacific Isldr.

Persons

Black - White [(3)-(2)]

Hispanic - White [(4)-(2)]

Asian -

White [(5)-(2)]

Native Amer. - White

[(6)-(2)]

Pacific Isldr. - White

[(7)-(2)]

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)

Opportunity Dimensions:

Poverty Index 47 63 35 45 58 42 48 28 18 4 20 14

School Proficiency Index 52 65 37 53 64 49 49 28 11 1 16 16

Labor Market Engagement Index 51 66 33 50 63 44 51 33 16 3 22 16

Job Access Index 48 50 44 46 49 49 48 7 5 2 2 2

Transit Access Index 55 46 62 54 43 53 52 -17 -8 2 -7 -7

Health Hazards Exposure Index 39 47 32 37 44 40 42 15 10 3 7 5

Counts 4,293,858 816,041 781,303 1,700,000 78,769 7,067 1,150

Panel B: Persons in Poverty Disparities

All Poor Persons

Poor White

Persons

Poor Black

Persons

Poor Hispanic or Latino Persons

Poor Asian

Persons

Poor Native

American Persons

Poor Pacific

Isldr. Persons

Poor Black - White [(3)-(2)]

Poor Hispanic - White [(4)-(2)]

Poor Asian

- White [(5)-(2)]

Poor Native Amer. - White

[(6)-(2)]

Poor Pacific Isldr. - White

[(7)-(2)]

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)


Poverty Index 33 50 23 32 51 0 0 27 18 -1 0 0

School Proficiency Index 41 54 29 43 59 0 0 25 11 -4 0 0

Labor Market Engagement Index 37 52 23 40 54 0 0 29 13 -2 0 0

Job Access Index 48 54 45 47 53 0 0 9 7 1 0 0

Transit Access Index 63 56 69 63 57 0 0 -13 -7 -1 0 0

Health Hazards Exposure Index 35 43 31 35 39 0 0 12 7 4 0 0

Counts 627,257 107,032 205,043 308,797 10,931 841 28



Note: Columns (1)-(7) provided a weighted average neighborhood percentile ranking for each dimension (row) described in the left-hand column, weighted by corresponding population group in each column header in Panel A. The percentiles are expressed as 100 centile buckets. Higher percentile values always reflect more favorable average neighborhood characteristics irrespective of the dimension being an asset (proficient schools) or a stressor (poverty). Exposure weighted average are calculated of the program participant geography. Columns (8)-(12) are the differences across average neighborhood conditions between whites and the column group indicated in the header. Positive values imply that whites are in a differentially higher ranking neighborhood on average than the particular group for the given dimension. Negative values imply the reverse, which the given racial/ethnic group is in a differentially higher ranking neighborhood relative to whites along the given dimension. Panel B repeats the analysis in Panel A, but focuses on the average neighborhood of persons in poverty (income< federal poverty line) . Disparities may differ due to rounding. Data for the opportunity dimensions are described in detail in the data documentation. Data on the populations in Panel A is from the 2010 Decennial Census SF1. Data on impoverished population in Panel B comes from the American Community Survey (ACS) 2006-2010 five year estimates. Population groups smaller than 250 people (in census 2010) or 1,000 people for ACS-sourced data are coded as zero. The higher minimum population threshold for the ACS data is motivated by concerns about sampling error.

HUD has developed a two-stage process for analyzing disparities in access to neighborhood opportunity. The first stage involves quantifying the degree to which a neighborhood offers features commonly associated with opportunity. This stage uses metrics that rank each neighborhood along a set of key dimensions. In the second stage, HUD combines these dimension rankings with data on where people in particular subgroups live to develop a measure of that group's general access or exposure to each opportunity dimension. These summary measures can then be compared across subgroups to characterize disparities in access to opportunity. HUD considers opportunity a multi-dimensional notion. To focus the analysis, HUD developed methods to quantify a selected number of the important stressors and assets in every neighborhood. These dimensions were selected because existing research suggests they have a bearing on a range of individual outcomes. In particular, HUD has selected six dimensions upon which to focus:

• Neighborhood School Proficiency • Poverty • Labor Market Engagement • Job Accessibility • Health Hazards Exposure • Transit Access

Invariably, these dimensions do not capture everything that is important to the well being of individuals and families. In quantifying indicators of neighborhood opportunity, HUD is not making a definitive assessment of one's life chances based on geography. HUD is quantifying features of neighborhoods for the purpose of assessing whether significant disparities exist in the spatial access or exposure of particular groups to these quality of life factors.



While these important dimensions capture a number of key concepts identified by research as important to quality of life, the measures are not without limitations. PD&R constrained the scope of HUD-provided items to those that are closely linked to neighborhood geographies and could be measured consistently at small area levels across the country. For example, HUD's measure of school performance only reacts to elementary school proficiency. It does not capture academic achievement for higher-grades of schooling, which is important to a community's well being, but likely less geographically-tied to individual neighborhoods than elementary schools. Similarly, the health hazard exposure measure only captures outdoor toxins, missing in-door exposures. The national-availability restriction is a necessity given that all HUD program participants must complete an Assessment of Fair Housing. HUD realizes that there are other assets and stressors that are relevant for opportunity, such as neighborhood crime or housing unit lead and radon levels. However, these lack consistent neighborhood-level data across all program participant geographies. As a consequence, HUD encourages program participants to supplement the data it provides with robust locally available data on these other assets and stressors, so that the analysis is as all encompassing as possible.



Disparity in Access to Neighborhood Opportunity - All Children South Florida Regional Planning Council (Broward, Miami-Dade, and Monroe Counties)

Panel A - All Persons Disparities

All

Children White

Children

Black /African

American Children

Hispanic or Latino Children

Asian Children

Native American Children

Pacific Isldr.

Children

Black -

White [(3)-(2)]

Hispanic - White [(4)-(2)]

Asian -

White [(5)-(2)]

Native Amer. - White

[(6)-(2)]

Pacific Isldr. - White

[(7)-(2)]

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)


Poverty Index 54 57 30 45 70 45 0 27 12 -‐13 12 0 School Proficiency Index 51 53 33 47 63 48 0 19 5 -‐10 4 0 Labor Market Engagement Index 53 55 37 49 76 48 0 18 7 -‐20 7 0 Job Access Index 48 47 58 52 52 50 0 -‐11 -‐5 -‐6 -‐4 0 Transit Access Index 10 10 10 10 10 10 0 0 0 0 0 0 Health Hazards Exposure Index 52 55 31 43 51 48 0 24 11 4 7 0 Counts 153,056 126,662 11,988 6,887 2,184 476 144

Panel B: Persons in Poverty Disparities

All Poor Children

Poor White

Children

Poor Black

Children

Poor Hispanic or Latino Children

Poor Asian

Children

Poor Native

American Children

Poor Pacific

Isldr. Children

Poor Black

- White [(3)-(2)]

Poor Hispanic - White [(4)-(2)]

Poor Asian

- White [(5)-(2)]

Poor Native Amer. - White

[(6)-(2)]

Poor Pacific Isldr. - White

[(7)-(2)]

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12)


Poverty Index 31 36 13 34 0 0 0 23 3 0 0 0 School Proficiency Index 43 47 31 45 0 0 0 15 2 0 0 0 Labor Market Engagement Index 35 38 26 40 0 0 0 13 -‐2 0 0 0



Job Access Index 51 46 63 57 0 0 0 -‐17 -‐10 0 0 0 Transit Access Index 10 10 10 10 0 0 0 0 0 0 0 0 Health Hazards Exposure Index 44 52 25 34 0 0 0 26 18 0 0 0 Counts 27,055 18,007 6,090 2,804 92 8 0

Note: columns (1)-(7) provided a weighted average neighborhood percentile ranking for each dimension (row) described in the left-hand column, weighted by corresponding population group in each column header in Panel A. The percentiles are expressed as 100 centile buckets. Higher percentile values always reflect more favorable average neighborhood characteristics irrespective of the dimension being an asset (proficient schools) or a stressor (poverty). Exposure weighted average are calculated of the program participant geography. Columns (8)-(12) are the differences across average neighborhood conditions between whites and the column group indicated in the header. Positive values imply that whites are in a differentially higher-ranking neighborhood on average than the particular group for the given dimension. Negative values imply the reverse, which the given racial/ethnic group is in a differentially higher ranking neighborhood relative to whites along the given dimension. Panel B repeats the analysis in Panel A, but focuses on the average neighborhood of children in poverty (income< federal poverty line) . Disparities may differ due to rounding. Data for the opportunity dimensions are described in detail in the data documentation. Data on the populations in Panel A is from the 2010 Decennial Census SF1. Data on impoverished population in Panel B comes from the American Community Survey (ACS) 2006-2010 five year estimates. Population groups smaller than 250 people (in census 2010) or 1,000 people for ACS-sourced data are coded as zero. The higher minimum population threshold for the ACS data is motivated by concerns about sampling error.

Identified Impediments to Fair Housing- Matrix

County Place

Housing/Lending

Discrimination on the Basis of Race, Color, National Origin,

Religion, Sex, Familial status and Disability (Protected Classes)

Lack of Knowledge, Awareness of or Education

on Fair Housing

Protections

Fair and Equal Lending

Disparities

Housing Market

Segregation

Shortage of Affordable Housing

Opportunities


Limited Funding to

Meet Need for Affordable Housing

Predatory Lending

Improvement of the Housing Discrimination Complaint Process

Zoning/Land Use

Broward

Broward County, FL

X X X X X

Broward

Coconut Creek, FL

X X X X X

Broward Coral Springs

X X X X



, FL

Broward Davie, FL X X

Broward

Fort Lauderdale, FL

X X X X X

Broward Hollywood, FL X X X

Broward Lauderhill, FL X X X X

Broward Margate, FL X

Broward Miramar, FL X X X X X

Broward

Pembroke Pines, FL

X X X X X

Broward Plantation, FL

X X X X

Broward

Pompano Beach, FL

X X X X X X

Broward Sunrise, FL X X X

Broward Tamarac, FL X X X X X

Indian River County












Miami Dade

Miami-‐Dade County, FL

X X X

Miami Dade

Hialeah, FL X X X X

Miami Dade

Homestead, FL

X X X

Miami Dade

Miami, FL X X X X X X X

Miami Dade

Miami Beach, FL

X X X X X

Miami Dade

Miami Gardens, FL

X X X X X



Miami Dade

North Miami, FL

X X X X

Martin












Monroe












Palm Beach

Palm Beach County

X X

Palm Beach

Boca Raton, FL

X

Palm Beach

Boynton Beach, FL

X

Palm Beach

Delray Beach, FL

X X X

Palm Beach

Wellington, FL

Palm Beach

West Palm Beach, FL

X

St. Lucie

Port St. Lucie, FL

X X

St. Lucie

Fort Pierce, FL

X X X X X

regional analysis of impediments to fair housing (rai) part 1

Documents

fair housing profile

fair housing draft

fair housing raioctober

fair housing protections

housing choices

housing costs

fair housing regional

housing market segregation