1

Redistribution Effect 

of Introducing 2010 Census 

and 2005-2009 ACS Data

Into the CDBG Formula

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REDISTRIBUTION EFFECT OF

INTRODUCING 2010 CENSUS

AND 2005–2009 ACS DATA

INTO THE CDBG FORMULA

Prepared for

U.S. Department of Housing and Urban Development

Office of Policy Development and Research

Prepared by

Paul Joice

Ben J. Winter

Heidi Johnson

December 2011
TABLE OF CONTENTS i

Executive Summary ��������������������������������������������������������������������������������������������������������������������� iii

Chapter 1–Introduction �����������������������������������������������������������������������������������������������������������������1

Chapter 2–Redistributive Effects of New Data on Community Development Block Grant Entitlement 

Communities��������������������������������������������������������������������������������������������������������������������������������6

Chapter 3–Redistributive Effects of New Data on Community Development Block Grant Nonentitlement 

Communities������������������������������������������������������������������������������������������������������������������������������24

Chapter 4–Confirming Key Trends������������������������������������������������������������������������������������������������29

Conclusion ��������������������������������������������������������������������������������������������������������������������������������33

Acknowledgements ��������������������������������������������������������������������������������������������������������������������34

References���������������������������������������������������������������������������������������������������������������������������������35

Additional Reading ��������������������������������������������������������������������������������������������������������������������36

Appendix 1–HUD Administrative Regions (map) ����������������������������������������������������������������������������37

Appendix 2–All FY 2011 Entitlement Jurisdictions ��������������������������������������������������������������������������38
ii EXECUTIVE SUMMARY

The Community Development Block Grant (CDBG) program is one of the longest running programs of 

the U.S. Department of Housing and Urban Development’s (HUD) Office of Community Planning and 

Development (CPD). CDBG is a grant to states and local governments that is distributed by a need-based 

formula. Fiscal year (FY) 2012 marks the first year that the CDBG allocation formula will rely on the 

Census Bureau’s new annual data source—the American Community Survey (ACS)—and the 2010 Census 

population counts. This report introduces the ACS to CDBG stakeholders and provides detailed information 

on how using the 2010 Census and ACS data shifts funding amounts. This analysis compares actual FY 

2011 allocations with alternate allocations using FY 2011 appropriations, the FY 2011 grantee universe, 

and the new data that will be used in FY 2012. Allocations presented should not be interpreted as actual 

FY 2012 grant amounts.

The CDBG statute specifies that funding be allocated based on the most recent data compiled by the United 

States Bureau of the Census demonstrating population, poverty, overcrowded housing, pre-1940 housing, 

and growth lag. Starting in FY 2012, population and growth lag will be computed using the 1960 and 

2010 Censuses; in FY 2011, these variables were derived from the 2009 Intercensal Population Estimates 

and the 1960 Census. Poverty, overcrowding, and pre-1940 housing will be calculated with the 2005–

2009 ACS 5-year estimates in FY 2012, replacing data from the 2000 Census, which was used in the FY 

2011 allocations. The ACS estimates of overcrowding and pre-1940 housing are particularly noteworthy.

At a national level, the estimated number of overcrowded housing units in metropolitan areas in the 

2005–2009 ACS is 46.4 percent lower than in the 2000 Census. Because the CDBG formula is allocated 

based on a grantee’s share of each variable, any entitlement community that experiences a decrease in 

overcrowding of less than 46.4 percent (or an increase) receives additional CDBG funding allocated by 

that variable.

1 Even more puzzling, the estimated number of pre-1940 housing units in all metropolitan 

areas, as of 2005–2009, is 7.7 percent higher than the estimate in the 2000 Census. Any entitlement 

community that experiences an increase of less than 7.7 percent in pre-1940 housing (or a decrease) loses 

CDBG funding allocated by that variable.

This report observes several interesting trends by jurisdiction type, region, and size. Among entitlement 

communities:

w Principal cities and urban counties receive more funds (0.2 and 1.2 percent, respectively), while 

satellite cities lose 3.1 percent. These changes are driven largely by increasing shares of poverty in 

urban counties, decreasing shares of overcrowding in satellite cities, and increasing shares of pre-1940 

housing in principal cities.

w The Rocky Mountain, Great Plains, and Midwest regions have the largest increase in average grant 

amount, at 9.7, 6.9, and 5.2 percent, respectively.

w Entitlement grantees in Puerto Rico see their grants go down by an average of 22.7 percent due to 

decreasing shares of all formula variables, but particularly due to decreasing shares of poverty and 

overcrowding.

1 This report uses the terms “increase” and “decrease” to refer to differences between the 2000 Census and 2005-2009 ACS. For reasons 

discussed throughout the report, the differences between the Census and ACS estimates reflect differences in the methods used by the two surveys 

in addition to differences in conditions between the two time periods. While “increase” and “decrease” may overstate the temporal aspect of the 

difference, we use the language for simplicity.
w The largest grantees (cities with a population of 1 million or more) experience a slight decrease in iii

average funding (-0.9 percent). The smallest grantees (cities with a population of 50,000 to 99,999 

and under 50,000) experience larger funding decreases of 1.2 and 3.4 percent, respectively.

w Grantees with a population of 200,000 to 999,999 see their average grant go up 1.5 percent but, 

due to population growth, their per capita grant remains constant.

Table ES-1. Distribution of Funds to Entitlement Jurisdictions, by Type, Region, and Size

FY 2011 Allocation ($) New Data Allocation ($) Percent Change (%)

Entitlement 

Jurisdictions 

(n)

 Average 

Grant 

(thousands)

Per Capita 

Grant2

Average 

Grant 

(thousands)

Per Capita 

Grant

Average 

Grant

Per Capita 

Grant

Jurisdiction Type 

Principal city 637 2,390 15.2 2,395 15.2 0.2 0.0

Satellite city 347 830 11.2 805 10.8 – 3.0 – 3.6

Urban county 182 2,725 6.6 2,755 6.6 1.1 0.0

Region

New England 77 1,490 19.8 1,510 20.0 1.3 1.0

New York/

New Jersey 105 3,375 15.0 3,355 15.0 – 0.6 0.0

Mid-Atlantic 102 2,580 13.2 2,450 12.4 – 5.0 – 6.1

Southeast 189 1,370 8.0 1,365 8.0 – 0.4 0.0

Midwest 205 2,035 13.6 2,140 14.4 5.2 5.9

Southwest 120 1,855 9.4 1,920 9.8 3.5 4.3

Great Plains 39 1,740 11.2 1,860 12.0 6.9 7.1

Rocky Mountain 46 980 6.8 1,075 7.6 9.7 11.8

Pacific/Hawaii 204 2,165 10.4 2,100 10.2 – 3.0 – 1.9

Northwest/

Alaska 52 1,265 8.0 1,295 8.2 2.4 2.5

Puerto Rico 27 2,075 23.6 1,605 18.2 – 22.7 – 22.9

Community Size

1 million or more 16 32,800 15.4 32,500 15.2 – 0.9 – 1.3

200,000–

999,999 260 3,775 9.8 3,830 9.8 1.5 0.0

100,000–

199,999 184 1,540 11.0 1,545 11.0 0.3 0.0

50,000–

99,999 435 825 11.8 815 11.6 – 1.2 – 1.7

49,999 or fewer 271 580 17.2 560 16.6 – 3.4 – 3.5

All Entitlements 1,166 1,980 11.4 1,980 11.4 0.0 0.0

2 While the FY 2011 CDBG allocation amount uses 2009 population estimates and the new data allocation uses 2010 Census population 

counts, per capita amounts in this table are calculated using 2010 Census population counts in the denominator.
iv Several of the changes observed with the introduction of new data are noteworthy—in particular the 

increase in pre-1940 housing units, dramatic drop in overcrowded housing units, and changes in 

population between the 2009 population estimates and 2010 Census population counts. However, 

these trends are confirmed by other research and have a variety of plausible explanations. Among these 

explanations are differences in nonsampling error between the different surveys, residency rules, and 

unique trends in migration and vacancy rates in the years following the 2000 Census. Most significant 

among these are the differences in nonsampling error—specifically, the ACS data collection methods are 

more likely to result in more accurate estimates of the number of rooms in a housing unit and the age of the 

structure than the methods employed by the decennial census. The decennial census relied more heavily 

on mail-in survey responses, whereas ACS interviewers have better opportunities to explain the survey 

questions and to verify responses.

The ACS 5-year estimates will be updated every year, and HUD intends to use the annual updates for 

the CDBG allocation formula. These annual data will provide up-to-date estimates of conditions in CDBG 

communities across the country, and have the added benefit of stability from year to year.
1

CHAPTER

1INTRODUCTION

Purpose

The law implementing the Community Development Block Grant (CDBG) program calls for using “the most 

recent data compiled by the United States Bureau of the Census” for allocating the CDBG funds (42 U.S.C.

ch. 69, sec. 5302 (b)). When CDBG was created in 1974, the most recent data was the 1970 Census.

Since then, the most significant updates followed the 1980, 1990, and 2000 Censuses. But in the years 

following the 2000 Census, the Census Bureau began a major transition to an annual survey known as 

the American Community Survey (ACS). The ACS generally asks the same questions as the old long form 

of the census and will now be the Census Bureau’s most comprehensive, nationally available source of 

information on demographic, social, economic, and housing characteristics.

Fiscal year (FY) 2012 marks the first year that new ACS data will be used in the CDBG formula. The 

purpose of this report is to introduce the ACS to CDBG stakeholders who may not be familiar with this 

new source of data, and to provide detailed information on how using the 2010 Census and the ACS 

data leads to shifts in CDBG funding. This chapter provides background on both the CDBG program and 

the ACS. Chapter 2 discusses how the new data affect allocations for entitlement grantees, focusing on 

redistribution across regions and types of grantees. Chapter 3 presents similar analysis for nonentitlement 

areas. Chapter 4 verifies key trends we observe and probes further to identify possible explanations.

Attached to this report are two appendixes; appendix 1 is a map showing the U.S. Department of Housing 

and Urban Development (HUD) Administrative Regions plus Puerto Rico, and the table in appendix 2 

illustrates how the new data affect the allocation of each FY 2011 CDBG grantee.

History of the CDBG Program and Formula

Title I of the Housing and Community Development Act of 1974 specifically terminated several grant 

programs: Urban Renewal, Model Cities, open space land and beautification grants, neighborhood 

facilities grants, basic water and sewer facilities grants, and public facility loans. These programs were 

competitive grants, meaning that HUD reviewed applications from local authorities and chose the projects 

that appeared to have the greatest merit and need. They were also categorical grants, meaning that 

different programs existed for different needs.

As part of the Nixon Administration’s New Federalism, federal policymakers merged these categorical 

grants to create CDBG. CDBG was intended to simplify the federal role in community development and 

to provide more decision-making authority to local officials, who were believed to be more able to assess 

the specific community development needs in their jurisdiction. The formula-based design enables HUD to 

quickly and easily allocate funds to local officials, while still targeting funds based on objectively measured 

needs. It also enables long-term planning by local governments by establishing a relatively stable annual 

funding stream.

The statutory objective of CDBG is the development of viable urban communities, by providing decent 

housing and a suitable living environment and expanding economic opportunities, principally for people 

with low and moderate incomes. This objective served as the driving force in designing the original 

needs formula (Bunce, 1976), which allocated funds using a weighted combination of three variables: 

population, poverty, and overcrowded housing.

3

3 HUD defines overcrowded as having more than one person per room
2 Congress required that the Secretary of HUD submit a report by March 31, 1977, containing 

recommendations for modifying the original parameters of the CBDG program, including the formula 

design. The HUD study found that the existing formula was highly responsive to the poverty dimension but 

unresponsive to the nonpoverty dimensions of community development need. A separate study conducted 

by the Brookings Institution also found that the major flaw of the 1974 formula was its unresponsiveness to 

the severe physical, social, and fiscal problems of older, deteriorating metropolitan cities (Bunce, 1976).

As a result of this evidence, HUD argued that two additional variables were needed—number of housing 

units built before 1940 and population growth lag—to guarantee funding to cities experiencing long-term 

physical decline (Bunce and Goldberg, 1979). This work led to the creation of the dual-formula system that 

HUD continues to use.

The other significant development in the CDBG program occurred in 1981 with the creation of the State 

CDBG Program. Before 1981, HUD reserved 20 percent of CBDG funds to be used in nonmetropolitan 

areas that were not receiving direct CDBG grants. HUD administered a competition for these funds.

Beginning in FY 1982, HUD offered states the opportunity to administer this program. In doing so, 

the formula was modified so that the total state nonentitlement areas, including both metropolitan and 

nonmetropolitan areas, would receive a 30 percent share of the CDBG allocation, with the remaining 70 

percent allocated exclusively to entitlement communities (Bunce, Neal, and Gardner, 1983). Since 1981, 

the process and formula for CDBG allocations have remained essentially the same.

4

Basic Formula Operation

There are three types of CDBG grantees: metropolitan cities, urban counties, and states.

5 Metropolitan 

cities and urban counties are collectively known as entitlement communities, while states may also be 

referred to as nonentitlement areas. The process for allocating funds begins with identifying entitlement 

communities.

Entitlement communities, which must meet criteria established in section 102 of the Housing and 

Community Development Act, are broadly divided into metropolitan cities and urban counties. A 

metropolitan city qualifies by being the principal city of a metropolitan area or by being a satellite city 

within a metropolitan area that has a population of 50,000 or more.

6 Urban counties are counties in 

metropolitan areas that have a population of 200,000 or more after excluding entitlement cities. All areas 

that are not part of a metropolitan city or urban county are nonentitlement areas; states receive funding 

based on their nonentitlement areas from the 30 percent set-aside established in 1981 for the State CDBG 

Program.

Because of the 70/30 funding split and slightly different treatment of metropolitan cities, urban counties, 

and states, the CDBG “formula” is actually five formulas—not one. Here we present a brief overview of 

how these formulas work; for a full description, see chapter 2 of Richardson and Meehan (2003).

Formula A—the original CDBG formula created in 1975—has three variables: population, people in 

poverty, and overcrowded units. Formula B—the formula added in 1979—uses population growth lag 

since 1960, people in poverty, and pre-1940 housing units. These variables are each divided by a total to 

4 Several changes have been made to the CDBG statute related to the qualification criteria for certain types of jurisdictions, as well as provisions 

meant to prevent abrupt funding decreases as a result of annexation or consolidation of city and county governments. The formula, variables, and 

variable weights have not changed. For more information see Richardson and Meehan’s (2003) appendix C.

5 The Hawaii nonentitlement CDBG program is administered by the three nonentitlement counties in the state, but funds are allocated to the 

program the same way as to other state nonentitlement programs. This report groups the Hawaii program with other state grantees.

6 Principal city, a designation of the Office of Management and Budget (OMB), refers to a core city of a metropolitan or micropolitan statistical 

area. This term has replaced the central city designation previously used by OMB and HUD. Because cities in micropolitan statistical areas do not 

qualify as CDBG entitlements, they are the same thing for these purposes. Satellite city is not a formal HUD or OMB designation and simply refers 

to any metropolitan city that is not a central city. See GAO (2004) for more information.
determine each grantee’s “share” of that characteristic.

7 Those shares are then weighted and 3

multiplied by the available funding. Table 1-1 shows the actual FY 2011 figures that went into the grant 

calculation for Phoenix, Arizona, a Formula A grantee. The first two rows show population, poverty, and 

overcrowding statistics for Phoenix and for all metropolitan areas. The third row is Phoenix’s share of each.

Each variable is then multiplied by its weight and the available funding ($2.3 billion in FY 2011) to show 

how much it contributed to Phoenix’s overall FY 2011 grant of $17,676,581.

Table 1-1. Sample FY 2011 Formula A Grant Calculation (Phoenix, Arizona)

Population Poverty Overcrowding

Phoenix 1,593,659 205,320 58,109

All metropolitan areas 261,125,846 28,710,022 5,674,944

Phoenix share 0.61% 0.72% 1.02%

Variable weight 0.25 0.50 0.25

Phoenix share of grant 

money $3,081,105 $7,220,857 $5,169,432

Formula B for entitlement communities is slightly more complicated because of the denominator used to 

calculate a grantee’s share of each variable. Poverty and pre-1940 housing are calculated as shares of 

all metropolitan areas, whether the grantee is a metropolitan city or an urban county. Growth lag for a 

metropolitan city is calculated relative to the total for all metropolitan cities, but growth lag for an urban 

county is calculated relative to the total for all entitlement communities (metropolitan cities and urban 

counties). For an example of a Formula B allocation see Table 1-2, which presents the data that went into 

the FY 2011 allocation for Detroit, Michigan.

Table 1-2. Sample FY 2011 Formula B Grant Calculation (Detroit, Michigan)

Growth Lag Poverty Pre-1940 Housing

Detroit 1,599,276 243,153 112,022

Total 32,391,457 28,710,022 13,370,481

Detroit share 4.94% 0.85% 0.84%

Variable weight 0.20 0.30 0.50

Detroit share of grant money $19,940,863 $5,862,153 $9,665,317

The formula for the nonentitled areas of states generally operates like the entitlement formula, with two 

exceptions: Formula B uses population instead of growth lag, and the denominator for all the variables is 

the sum for all nonentitled areas instead of the sum for nonmetropolitan areas.

Allocations are calculated from both Formula A and Formula B for every grantee, and the grantee receives 

whichever amount is greater. This method causes the sum of all allocations to exceed the available 

appropriations, so a pro rata reduction is applied to all grantees. In FY 2011 the pro rata reduction was 

12.475 percent for entitlements and 17.704 percent for nonentitlements. The allocation amounts in Tables 

1-1 and 1-2 have the pro rata reduction factored in.

Data Sources for Formulas Before the 2010 Census

Since the establishment of the CDBG program, the decennial census has been the primary source of 

the data in the CDBG formula. Before 2010, it was the only nationwide source of data for poverty, 

7 The denominator varies by formula type, variable, and grantee type.
4 overcrowding, and pre-1940 housing that was available for all communities eligible for CDBG. In the 

years following the release of the decennial census data, the Census Bureau’s Population Estimates Program 

creates population estimates based on administrative records for births, deaths, and migration. These 

estimates are provided to HUD for all units of general local government, and HUD uses them to update the 

population variable (Formula A) and the growth lag variable (Formula B) for every grantee. The Census 

Bureau also identifies new incorporations and reports major boundary changes (usually due to annexation); 

in these cases, HUD has reassigned the data that corresponds to the area being annexed or incorporated.

Aside from annual population estimate updates and adjustments due to new incorporations or annexations, 

HUD has not historically been able to update the formula data between decennial censuses.

American Community Survey

Amid the information revolution of the 1980s and 1990s, researchers and federal policymakers began 

to call for regular data to fill the void between each decennial census. In 1985, Congress authorized a 

mid-decade census, but funds were not appropriated. In 1994, the Census Bureau began developing 

a continuous measurement survey that might provide current and consistent nationwide data on social, 

demographic, and economic conditions. The Census Bureau tested the American Community Survey (ACS) 

throughout the late 1990s, and, in 2000, the agency carried out a large-scale demonstration called the 

Census 2000 Supplementary Survey (C2SS). The C2SS was similar to the ACS but was coordinated with 

the 2000 Census and carried out in 1,239 counties. After extensive study of the data quality produced 

in C2SS and ACS test sites, the Census Bureau concluded that continuous measurement was feasible and 

could be done reliably and cost effectively. The ACS was implemented nationwide in 2005.

In many ways, the ACS is very similar to the long form of the decennial census. Households that receive 

the ACS are legally obligated to respond, just as they were when they received the short form or long form 

of the 2000 Census. Every question in the ACS must be justified to the Office of Management and Budget 

(OMB), and nearly all questions are necessary to carry out a federal law or implementing regulation. In the 

first year the survey was conducted, the content of the ACS survey instrument was nearly identical to the 

2000 Census long form, and changes from year to year are kept to a minimum. However, the methodology 

of the ACS differs from the decennial census in some important ways, four of which this report highlights: 

sample size, period estimates, margins of error, and residence rules.

The first and most critical difference between the ACS and the decennial census long form is the sample 

size. The long form of the 2000 Census was mailed to one in six households (approximately 18 million), 

all within several months of April 1, 2000. Administering a survey to such a large sample on a continuous 

basis was determined to be infeasible, so the ACS sample includes only 3 million households per year.

8 

ACS surveys go out by mail each month to 250,000 independently sampled households. Each monthly 

sample receives 2 months of followup (first by telephone and then in person), so at all times one sample 

is in the mail survey phase, one sample is in the telephone followup phase, and one sample is in the 

in-person followup phase. This reliance on a smaller, and continuous, sample leads to the second key 

difference between the decennial census and the ACS, which is the use of period estimates. The decennial 

census has always been a count of the population as of April 1st of the year in question. The ACS is 

administered year-round, so the results must be interpreted as representative for the entire year, rather 

than a point in time. In addition, the fact that the annual sample for the ACS is about one-sixth the size 

of the sample for the 2000 Census long form has led the Census Bureau to create multiyear estimates. By 

combining multiple years of survey responses, the Census Bureau can effectively increase the sample size 

and continue to release estimates for small geographic areas. The resulting data release program includes 

“1-year” estimates for places with a population of more than 65,000, “3-year” estimates for places with 

8 When the ACS began, 3 million was 2.5 percent of all housing units, but the sample size did not increase with the number of U.S. households.

In FY 2011, the Census Bureau budget included funds to expand the sample size to around 3.5 million.
a population of more than 20,000, and “5-year” estimates for all geographies down to the census tract.

9 5

Because of the small geographic units necessary to build CDBG grantee boundaries (particularly urban 

counties), HUD will use 5-year ACS estimates for the CDBG formula. The data discussed in this report are 

based on surveys from 2005 to 2009 and represent conditions averaged over that period.

Even in the 5-year estimates, the full sample size is only 12.5 percent of households—considerably less 

than the 16.7 percent of households that received the long form of the 2000 Census. This small sample 

size leads to the third important difference between the decennial census and the ACS: precision and 

accuracy of data. Data quality problems can stem from either sampling error or nonsampling error.

Sampling error represents the possibility that the households that were surveyed are not representative of 

all households, simply due to the random nature of a survey sample. Sampling error can be measured 

and expressed by margins of error and similar figures. Due to its smaller sample size, the ACS has a 

higher sampling error—in other words, it is less precise—than the decennial census long form survey.

Nonsampling error includes any other mistakes in the administration of a survey, such as data entry errors 

or misunderstandings (such as cases in which survey recipients do not understand a question). Any such 

issues might lead the survey results to be biased—in other words, consistently inaccurate. The ACS is 

continually administered by a professional survey staff and has well-developed protocols for addressing 

errors in nonresponse, measurement, and data processing. As a result, the ACS minimizes nonsampling 

error. Although nonsampling error is very difficult to explicitly measure, it is likely that nonsampling error 

in the ACS is actually lower than it was for the 2000 Census long form survey, which relied heavily on 

temporary workers (Love et al., 2004; Salvo et al., 2007).

The final notable difference between the ACS and the long form of the decennial census is the “residence 

rule,” which determines who should respond to a survey. For the decennial census, households receiving a 

long form survey were required to respond if the sampled housing unit was their “usual place of residence” 

as of April 1st of the year in question. A household that lived in Arizona from September to May but 

spent its summers in a vacation home in Maine would not have responded to a census form mailed to the 

Maine home. The ACS uses “current residence,” which is defined as a house where you have lived, or 

plan to live, for at least 2 months.

10 If the household in the previous example received an ACS survey at 

its Maine residence in June, they would be considered a current resident of that housing unit and would 

respond to the survey. This rule can substantially affect the population being surveyed in places with a 

large percentage of seasonal residents (Love et al., 2004). For instance, if Arizona households that summer 

in Maine are consistently high-income households, the ACS would indicate higher household incomes in 

Maine and lower household incomes in Arizona when compared with the 2000 Census (independent of 

any actual change in income).

These four methodological issues are among the many reasons that the Census Bureau cautions users 

against comparing ACS estimates with estimates from previous decennial censuses. For reasons discussed 

throughout this report, the differences between the Census and ACS estimates reflect differences in the 

methods used by the two surveys in addition to differences in conditions between the two time periods. Of 

course, this report directly compares estimates from the two surveys to measure the shifts in funding caused 

by differences between them. For the sake of simplicity, this report uses the terms increase and decrease to 

describe differences between the 2000 Census estimates and the 2005-2009 estimates; readers should be 

aware that increases and decreases may be the result of differences between the two surveys more than 

actual changes in conditions.

9 Full list of geographies in the 5-year data: http://www.census.gov/acs/www/data_documentation/geography/.

10 Note: The 2010 Census continues to use the “usual place of residence” rule.
6

CHAPTER

2REDISTRIBUTIVE EFFECTS OF NEW DATA ON

COMMUNITY DEVELOPMENT BLOCK GRANT 

ENTITLEMENT COMMUNITIES

For the fiscal year (FY) 2012 formula allocation, the U.S. Department of Housing and Urban Development 

(HUD) will introduce 2010 Census population data as well as estimates of poverty, overcrowding, and 

pre-1940 housing from the 2005–2009 American Community Survey (ACS) data. This chapter addresses 

how introducing these new data affects the distribution of Community Development Block Grant (CDBG) 

funding across entitlement jurisdictions. It focuses on how funds are redistributed by region, jurisdiction 

size, jurisdiction type (principal city, satellite city, or urban county), and allocation formula. For purposes 

of comparison, this study keeps constant the overall CDBG appropriation amount and the CDBG grantee 

universe from FY 2011. All other variables (population, poverty, overcrowding, growth lag, and pre-1940 

housing) are updated. Actual funding amounts for jurisdictions in FY 2012 will depend on congressional 

appropriations and the number of new entitlement jurisdictions that qualify for CDBG funding.

A. Introduction of New Data in the CDBG Formula and the Effect on Allocations to Entitlement Jurisdictions

Table 2-1 shows the sources of data for the FY 2011 and FY 2012 CDBG allocations. Since FY 2003, 

HUD has used the intercensal population estimates and the 1960 Census for the population and growth lag 

variables. Also since FY 2003, HUD has used the 2000 Census long form data to calculate the poverty, 

overcrowding, and pre-1940 housing variables.

Because new population counts are now available with the 2010 Census, HUD will use those data for 

allocations in FY 2012. As described in the previous chapter, the long form data collected in previous 

decennial censuses have been replaced with ACS. Starting in FY 2012, HUD will use 5-year ACS estimates 

to maintain the most recent data possible in the CDBG formula.

Table 2-1. Comparison of Formula Variables and Data Sources From FY 2011 and FY 2012 Allocations

Factors FY 2011 Allocation FY 2012 Allocation

Formula 

A Factors

Population 2009 Population Estimates 2010 Census 

Poverty 2000 Census 2005–2009 ACS

Overcrowding 2000 Census 2005–2009 ACS

Formula B 

Factors

Growth lag 2009 Population Estimates and 

1960 Census 2010 Census and 1960 Census

Poverty 2000 Census 2005–2009 ACS

Pre-1940 housing 2000 Census 2005–2009 ACS

Table 2-2 gives a general picture of how the CDBG formula data have changed since the 2009 Population 

Estimates and the 2000 Census. Because all CDBG entitlement jurisdictions lie within metropolitan areas, 

these tables focus only on the aggregate of metropolitan areas. To show how the change of variables 

may affect the distribution of funds for grantees, Table 2-2 disaggregates the metropolitan geography by 

entitlement cities (which consist of principal city and satellite city entitlement communities) and the rest of 

the outlying areas in metropolitan areas, otherwise known as the “balance” (which consist of urban county 

entitlements and other nonentitlement portions of metropolitan areas). The “Balance of Metropolitan Areas” 

can be thought of as suburban or rural areas within metropolitan areas, and the cities can be thought of as 

the urban portions of metropolitan areas.
Table 2-2. Change in Formula Variables in Metropolitan Areas 7

Entitlement Cities Balance of Metropolitan 

Areas

Total Metropolitan Areas

Population

2009 Population Estimates

2010 Census

Percent Change

126,330,750

125,843,466

– 0.4%

134,795,096

136,008,672

0.9%

261,125,846

261,852,138

0.3%

People in Poverty

2000 Census

2005–2009 ACS

Percent Change

18,401,833

20,671,664

12.3%

10,308,189

12,724,840

23.4%

28,710,022

33,396,504

16.3%

Overcrowded Housing Units

2000 Census

2005–2009 ACS

Percent Change

3,861,310

2,002,160

– 48.1%

1,813,634

1,037,538

– 42.8%

5,674,944

3,039,698

– 46.4%

Pre-1940 Housing Units

2000 Census

2005–2009 ACS

Percent Change

8,338,128

9,320,169

11.8%

5,032,353

5,084,319

1.0%

13,370,481

14,404,488

7.7%

On average, the 2010 Census counts fewer people in cities and more people in the suburban portion of 

metropolitan areas than the 2009 Population Estimates Program. This statistic does not necessarily mean 

that in one year, cities lost population while their suburban counterparts grew. It could also mean that the 

2010 Census figures are actually more accurate in measuring population than are intercensal estimates.

Even so, future years of CDBG funding between decennial census years must use intercensal population 

estimates in an attempt to capture the most recent changes in entitlement growth and contraction.

As shown in Table 2-3, this change in population measurement tends to favor urban county entitlement 

jurisdictions under Formula A, as their shares of total metropolitan population increase while cities’ shares 

are flat. Conversely, the change in population tends to favor principal cities under Formula B, as population 

loss contributes to greater growth lag.

The 5-year ACS estimates show an overall increase in the number of people living under the poverty line 

in metropolitan areas since the 2000 Decennial Census. The percentage increase of people in poverty 

is almost twice as great in the suburban portions of metropolitan areas as in cities. Table 2-3 shows how 

this difference tends to favor urban counties more than cities in both Formula A and B entitlements. For 

Formula A grantees, urban counties benefit from a 2.7 percent increase in the amount of funding allocated 

by poverty while satellite cities experience a 1.1 percent decrease; principal cities see only a modest 0.1 

percent increase.

The ACS estimates drastically lower amounts of overcrowded units than the 2000 Census estimated. It 

is not entirely clear why this phenomenon is occurring (see chapter 4 for a discussion of this change).

On average the ACS estimates a greater decline of overcrowded units in cities than it does in suburban 

portions of metropolitan areas. As a result, urban county entitlements in Formula A gain a modest 0.1 

percent in the amount of funding allocated by the overcrowding variable while principal and satellite cities 

experience a 0.2 and 1.1 percent decline in funding, respectively, because their metropolitan share of 

overcrowded units decreases.

The ACS also estimates drastically higher counts of housing units built before 1940 than the 2000 Census 

estimated. The difference is especially prevalent in cities, where the ACS estimates of pre-1940 housing are 

a remarkable 11.8 percent higher than estimates from the 2000 Census; in comparison, suburban portions 
8 of metropolitan areas show only a 1 percent increase. In particular, the ACS shows an 

additional 252,000 pre-1940 units in New York City alone, 107,000 additional units in Chicago, 

60,000 in Los Angeles, 30,000 in Detroit, and about 20,000 extra in Cleveland, St. Louis, and 

Milwaukee.

11 Together, these additional units have a substantial effect on the distribution of CDBG funding.

Table 2-3 shows that principal cities using Formula B experience a 1.3 percent increase in funding 

allocated by this variable, while satellite cities and urban counties lose 0.3 and 1.4 percent, respectively.

Table 2-3. Average Change of Entitlement Grant Amounts by Entitlement Type

Grant Amount (000,000’s) Percent Change by Variable

Formula A Formula B

Jurisdiction TypeNo. ofJurisdictionsFY 2011 Total Allocation AmountNew Data Total Allocation

Amount

Total % Change % Change Due To Switching 

Formulas

PopulationPovertyOvercrowdingGrowth LagPovertyPre-1940 Housing

Principal city 637 1,523 1,526 0.2 – 0.4 0.0 0.1 – 0.2 0.2 – 0.9 1.3

Satellite city 347 288 279 – 3.1 0.2 0.0 – 1.1 – 1.1 – 0.8 0.0 – 0.3

Urban county 182 496 502 1.2 0 0.3 2.7 0.1 – 0.5 0.0 – 1.4

B. The Effect of Introducing 2010 Census and ACS Data on Individual Formula Grants

To illustrate how data from the 2010 Census and 2005–2009 ACS 5-year estimates affect the grants that 

entitlement jurisdictions receive, this report details these changes for three grantees: Phoenix, Arizona, a 

Formula A entitlement city; Chicago, Illinois, a Formula B entitlement city; and Arlington County, Virginia, 

an urban county that switches from Formula A to Formula B with the introduction of the new data.

Phoenix is a Formula A entitlement city. Formula A grantees are generally growing communities with 

poverty and overcrowding. As with many other entitlement cities, however, Phoenix’s population as 

measured by the 2010 Census is lower than the 2009 population estimate. Because a jurisdiction’s need 

is assessed relative to other jurisdictions for CDBG funding purposes, population loss does not guarantee 

a loss of funding, even for a Formula A grantee. Funding allocations based on the population variable are 

reduced only if the jurisdiction’s share of population across all metropolitan areas is reduced. Metropolitan 

areas as a whole experience a small increase in population from the 2009 population estimates to the 

2010 Census. Therefore, Phoenix’s reduction in its share of population is slightly greater than its reduction 

in population itself.

Based on the ACS 5-year estimates there are an additional 71,464 people in poverty in Phoenix compared 

with the 2000 Census. Other metropolitan areas do not have such a large increase in people in poverty, 

so Phoenix’s share of this variable also increases. The poverty column in Table 2-4 represents how 

Phoenix’s allocation would change if no change existed in population or overcrowding with the 

introduction of new data.

11 These figures are rounded to the nearest thousand.
The last variable that determines Phoenix’s allocation as a Formula A grantee is overcrowding. As 9

discussed earlier in this report, the ACS estimates drastically lower amounts of overcrowded units across 

the country compared with the 2000 Census. Phoenix’s level of overcrowding drops steeply but not as 

much as in other metropolitan areas, as the total drop for all metropolitan areas exceeds 46 percent. A 

Formula A entitlement grantee who experiences either an increase in overcrowding or a loss less than 46 

percent will gain in its allocation share of this variable. Phoenix therefore sees a small gain in funding from 

this variable, despite its large reduction in the estimated number of overcrowded units. On balance, the 

reductions in Phoenix’s grant amount due to a greater population loss relative to other metropolitan areas is 

offset by its gains due to poverty and overcrowding.

Table 2-4. Effect of 2010 Census and 2005–2009 ACS Data on Formula A Grantee (Phoenix, Arizona)

Population Poverty Overcrowding Total 

Data

FY 2011 (n) 1,593,659 205,320 58,109

2010 Census and 

2005–2009 ACS 

data (n)

1,445,632 276,784 33,552

Change (%) – 9.3 34.8 – 42.3

Share

FY 2011 (%) 0.61 0.72 1.02

2010 Census and 

2005–2009 ACS 

data (%)

0.55 0.83 1.10

Change (%) – 10 16 8

Grant

FY 2011 ($000s) 3,081 7,221 5,169 15,471

2010 Census and 

2005–2009 ACS 

data ($000s)

2,787 8,345 5,549 16,681

Change (%) – 10 16 7 7.8

Chicago is an older city whose CDBG grant is allocated by Formula B. Formula B includes population 

growth lag, which is the difference between an entitlement city’s actual growth since 1960 and the 

average growth since 1960 for all entitlement cities. From the 2009 population estimates to the 2010 

Census, Chicago’s population decreases by 155,670; the increase in Chicago’s growth lag indicates that 

its rate of growth is less than that of all entitlement cities.

The number of people in poverty in Chicago increases between the 2000 Census and the 2005–2009 

ACS by 3.5 percent. Instead of gaining funds, however, Chicago’s allocation is reduced by the poverty 

variable because its share of poverty across all metropolitan areas declines. Here again, it is clear how 

an entitlement’s standing relative to other areas on each variable is an essential component of the formula 

mechanics. This loss, however, is offset by Chicago’s gain in funding from pre-1940 housing units. While 

central cities gained pre-1940 housing units nationally, the units gained in Chicago gave that city a greater 

share of the metropolitan total than it had held previously. Note that the increase in Chicago’s grant amount 

due to pre-1940 housing is not as large as the increase in its share of the pre-1940 housing variable; this 

is due to the pro rata reduction that brings grant amounts in line with actual allocations.
10 Table 2-5. Effect of 2010 Census and 2005–2009 ACS Data on a Formula B Grantee (Chicago, Illinois)

Growth Lag Poverty Pre-1940 Housing Total

Data

FY 2011 (n) 2,484,926 556,791 438,095

2010 Census and 

2005–2009 ACS 

data (n)

2,599,394 576,344 545,476

Change (%) 4.6 3.5 24.5

Share

FY 2011 (%) 7.67 1.94 3.28

2010 Census and 

2005–2009 ACS 

data (%)

7.98 1.73 3.79

Change (%) 4 – 11 16

Grant

FY 2011 ($000s) 30,984 11,749 33,084 75,816

2010 Census and 

2005–2009 ACS 

data ($000s)

32,207 10,425 38,128 80,761

Change (%) 4 – 11 15 6.6

The characteristics of jurisdictions can change over time, and the formula that provides them with the most 

funding can also change. Arlington County is one such grantee, which as an urban county had previously 

been allocated CDBG funds using Formula A. With the introduction of new data, however, the formula 

yielding the larger allocation switched to Formula B. People in poverty is a component of both Formula 

A and Formula B; however, it is assigned a lower weight in Formula B. This is advantageous to Arlington 

County, where poverty was reduced, in contrast to other metropolitan areas where poverty increased as a 

whole. The reduction in allocation share that Arlington County experiences on this variable therefore has 

less effect on its overall grant as a Formula B entitlement.

Population is also a component of both formulas, although Formula B focuses on growth lag since the 1960 

Census. Population loss generally benefits a jurisdiction’s allotment more as a Formula B grantee, while 

gains in population are more beneficial among Formula A grantees. The biggest change for Arlington 

County, however, is in its overcrowded housing units. Overcrowding decreases by a whopping 70.6 

percent when using ACS data, which far exceeds the drop in overcrowding in other metropolitan areas.

Overcrowding had contributed the most to Arlington County’s allocation in FY 2011, and such a drastic 

reduction in its share of overcrowded units likely pushes it into Formula B. Despite Arlington County’s 

reduction in share on two of the Formula B variables (poverty, and pre-1940 housing units), its reduction in 

share on Formula A variables is more severe.
Table 2-6. Effect of 2010 Census and 2005–2009 ACS Data on a Grantee Switching From Formula A 11

to Formula B (Arlington County, Virginia)

PopulationGrowth LagPovertyOvercrowdingPre-1940 HousingTotal

Data

FY 2011 (n) 229,440 31,467 14,803 7,259 10,358

2010 Census and 2005–2009 ACS data (n) 219,959 38,934 14,772 2,131 9,451

Change (%) – 4.1 23.7 – 0.2 – 70.6 – 8.8

Share

FY 2011 (%) 0.09 0.09 0.05 0.13 0.08

2010 Census and 2005–2009 ACS data (%) 0.08 0.11 0.04 0.07 0.07

Change (%) – 4 24 – 14 – 45 – 15

Grant

FY 2011 ($000s) 444 0 521 646 0 1,610

2010 Census and 2005–2009 ACS data 

($000s) 0 442 267 0 661 1,370

Change (%) – 15

C. The Effect of Introducing 2010 Census and ACS Data on Entitlement Jurisdictions

When appropriation levels are held constant, formulas are “zero sum.” That is, if one jurisdiction increases 

its funding from a formula change, funding for one or more other jurisdictions will decrease. This section 

focuses on how the addition of population from the 2010 Census and poverty, overcrowding, and pre1940 housing from the 2005–2009 ACS 5-year estimates affect the redistribution of funds among CDBG 

entitlement jurisdictions.

Change in Average Distribution of Grants to Entitlement Jurisdictions

Table 2-7 gives an overview of the changes in average and per capita grant amounts by Jurisdiction Type, 

HUD Administrative Region, and Community Size due to the introduction of new data. In FY 2011, HUD 

allocated funds to principal cities at a rate of $15.20 per capita, satellite cities at a rate of $11.20 per 

capita and urban counties at a rate of $6.60 per capita. By introducing new census and ACS variables, 

funding per capita decreases by about 3.6 percent in satellite cities, but remains constant in principal cities 

and urban county entitlement jurisdictions.

By introducing the new variables, communities in Puerto Rico, the Mid-Atlantic, and the Pacific/Hawaii 

regions experience the highest percentage declines in per capita funding, at 22.9, 5.9, and 1.9 percent, 

respectively. Jurisdictions in the Rocky Mountain, Great Plains, and Midwest regions experience the 

greatest increases in per capita funding, with 11.8, 7.1, and 5.9 percent, respectively.

The smallest communities (under 50,000 inhabitants) stand to lose the most funding per capita— about 

60 cents per person, some 3.5 percent less than what was allocated in FY 2011. Communities between 

200,000 and 1 million inhabitants see the largest increase in average grant amounts, but funding per 

capita remains constant at $9.80 per person (lower than both larger and smaller communities). For the 

nation’s largest cities and communities (more than 1 million inhabitants), the average grant amount declines 

by 0.9 percent, and funding per capita decreases by 1.3 percent, or 20 cents per person.
12 Table 2-7. Distribution of Funds to Entitlement Jurisdictions

FY 2011 Allocation ($) New Data Allocation ($) Percent Change (%)

Entitlements (n)

Average 

Grant 

(thousands)

Per Capita 

Grant12

Average 

Grant 

(thousands)

Per Capita 

Grant

Average 

Grant13

Per Capita 

Grant

Jurisdiction Type

Principal city 637 2,390 15.2 2,395 15.2 0.2 0.0

Satellite city 347 830 11.2 805 10.8 – 3.0 – 3.6

Urban county 182 2,725 6.6 2,755 6.6 1.1 0.0

Region

New England 77 1,490 19.8 1,510 20.0 1.3 1.0

New York/

New Jersey

105 3,375 15.0 3,355 15.0 – 0.6 0.0

Mid–Atlantic 102 2,580 13.2 2,450 12.4 – 5.0 – 6.1

Southeast 189 1,370 8.0 1,365 8.0 – 0.4 0.0

Midwest 205 2,035 13.6 2,140 14.4 5.2 5.9

Southwest 120 1,855 9.4 1,920 9.8 3.5 4.3

Great Plains 39 1,740 11.2 1,860 12.0 6.9 7.1

Rocky Mountain 46 980 6.8 1,075 7.6 9.7 11.8

Pacific/Hawaii 204 2,165 10.4 2,100 10.2 – 3.0 – 1.9

Northwest/

Alaska

52 1,265 8.0 1,295 8.2 2.4 2.5

Puerto Rico 27 2,075 23.6 1,605 18.2 – 22.7 – 22.9

Community Size 

1 million or more 16 32,800 15.4 32,500 15.2 – 0.9 – 1.3

200,000–

999,999

260 3,775 9.8 3,830 9.8 1.5 0.0

100,000–

199,999

184 1,540 11.0 1,545 11.0 0.3 0.0

50,000–

99,999

435 825 11.8 815 11.6 – 1.2 – 1.7

49,999 or fewer 271 580 17.2 560 16.6 – 3.4 – 3.5

All Entitlements 1,166 1,980 11.4 1,980 11.4 0.0 0.0

Effect Caused by Changes in Each Variable

As described in earlier sections of this paper, a grantee’s share of funding is mostly derived from its share 

of each variable’s national metropolitan total. If a particular variable increases for a grantee more than the 

aggregate rate of change at the metropolitan level, the CDBG formula will increase funding to that grantee 

for that particular variable. For variables that decrease at a national metropolitan level (like overcrowding 

with the introduction of the ACS) a grantee will receive an increase of CDBG funding if it experiences a 

growth (or lesser decline) than the metropolitan rate of change.

12 While the FY 2011 CDBG allocation amount uses 2009 population estimates and the New Data Allocation uses 2010 Census population 

counts, per capita amounts in this table are shown as allocation amount over population as counted in the 2010 Census.

13 This column shows the percent change in the average grant amount, while Table 2-3 shows the percent change in total grant amount.
Table 2-8 shows the distribution of rates of change for population, poverty, overcrowding, and pre-1940 13

housing for entitlement jurisdictions. The table shows the rate of change for population and poverty for all 

grantees, as both variables are used in both formulas. However, overcrowding is shown only for Formula A 

grantees and pre-1940 housing for Formula B grantees. To illustrate which grantees would lose funding due 

to the introduction of new variables, the highlighted cells indicate where the national metropolitan totals lie.

All grantees that fall below the highlighted cells experience an increase in CDBG funding allocated by that 

particular variable because their share of that variable increases. Conversely, grantees that fall above the 

highlighted cells have a decrease in funding. The national change in population is not indicated, as that 

variable is used to determine growth lag in Formula B as well as population in Formula A.

Table 2-8. Distribution of Grantees by Percent of Funding Gained or Lost for Population, Poverty, 

Overcrowding, and Pre-1940 Housing Variables14

Loss/Gain Population Poverty Overcrowding 

(Formula A)

Pre-1940 Housing 

(Formula B)

>20% loss 6 19 634 10

10–20% loss 29 68 43 34

5–10% loss 85 49 2 37

0–5% loss 381 65 7 75

0–5% gain 501 70 6 76

5–10% gain 128 101 5 63

10–20% gain 30 169 5 75

>20% gain 6 625 7 56

Total 1,166 1,166 709 426

The table shows that change in population among grantees has a somewhat normal distribution, where 

most grantees fall within a 5 percent positive to 5 percent negative change. The poverty, overcrowding, 

and pre-1940 housing variables are much more skewed. More than three-fourths of entitlement jurisdictions 

experienced a growth in poverty of more than 5 percent, and over half experienced a growth in poverty 

of more than 20 percent. Given that the overall growth in poverty in metropolitan areas was 16.3 percent, 

grantees whose poverty grew less than 16.3 percent ultimately will lose CDBG funding allocated by that 

variable.

Of Formula A grantees, 89 percent experience a decline in overcrowded units greater than 20 percent.

Equally as remarkable, 63 percent of Formula B grantees exhibit an apparent increase in pre-1940 

housing units since 2000. At a national level, overcrowded units decreased by 46.4 percent. Thus, any 

grantee that experienced a decline in overcrowding less than 46.4 percent receives additional CDBG 

funding allocated by that variable. The variable for pre-1940 housing units increased by 7.7 percent at 

a national level. Thus, any grantee whose rate of change for that variable is less than 7.7 percent loses 

CDBG funding allocated by pre-1940 housing.

14 Overcrowding and Pre-1940 Housing columns exclude grantees that switch formulas, because they either lose 100 percent or gain from a 

starting point of zero.
14 Effect by Region

The introduction of the 2010 Census and ACS 5-year estimates into the CDBG formula affects regions very 

differently across the country. Even more diverse is the experience of jurisdictions within regions. Tables 

2-9, 2-10, and 2-11 shed light on these changes by demonstrating the shifting shares of entitlement funds 

across and within regions as well as the average change of grants due to particular variables.

Table 2-9 illustrates the average percent change of funding due to the shifting shares of each variable by 

HUD Administrative Region.

15 Nationally, jurisdictions in Formula A gain the most funding by increasing 

shares of poverty, while Formula B grantees lose the most funding by declining shares of poverty. Formula B 

grantees gain the most funding by their increasing shares of pre-1940 housing. Overall, five HUD regions 

lose funding and six gain funding. A total of $39.7 million, or 1.7 percent of the total appropriation, shifts 

from the five losing regions to the six gaining regions.

Table 2-9. Shifting Shares of CDBG Entitlement Funding by Region

Entitlement Communities Share of Entitlement Funds (%)

Region (n) (%) FY 2011 New Data % Change of Funding

New England 77 6.6 5.0 5.0 1.3

New York/New Jersey 105 9.0 15.4 15.3 – 0.6

Mid-Atlantic 102 8.7 11.4 10.8 – 4.9

Southeast 189 16.2 11.2 11.2 – 0.2

Midwest 205 17.6 18.1 19.0 5.3

Southwest 120 10.3 9.6 10.0 3.5

Great Plains 39 3.3 2.9 3.1 6.8

Rocky Mountain 46 3.9 2.0 2.1 9.5

Pacific/Hawaii 204 17.5 19.2 18.6 – 3.1

Northwest/Alaska 52 4.5 2.8 2.9 2.2

Puerto Rico 27 2.3 2.4 1.9 – 22.6

Total 1,166 100.0 100.0 100.0 0.0

15 The regions used here are the 10 HUD administrative regions plus Puerto Rico. See appendix 1 for a map.
Table 2-10. Average Change of Entitlement Grant Amounts by Jurisdiction 15

Grant Amount (000,000’s) Formula A Formula B

RegionNo. of JurisdictionsFY 2011 Total Allocation AmountNew DataTotal Allocation AmountTotal % ChangeDue to Switching FormulasDue to PopulationDue to PovertyDue to OvercrowdingDue to Growth LagDue to PovertyDue to Pre-940 Housing

New 

England 77 115 116 1.3 0.0 0.00 0.00 0.00 – 1.10 – 0.70 3.20

New 

York/

New 

Jersey

105 354 352 – 0.6 0.1 0.00 – 0.10 0.10 – 0.40 – 2.80 2.60

MidAtlantic 102 263 250 – 4.9 – 0.1 0.10 – 0.10 – 0.40 – 0.80 – 0.90 – 2.80

Southeast 189 259 258 – 0.2 – 1.3 0.10 3.90 – 3.30 0.60 – 0.20 0.20

Midwest 205 417 439 5.3 0.1 0.10 1.80 0.20 0.50 0.60 2.00

Southwest 120 222 230 3.5 0.0 0.10 4.20 0.60 0.00 – 0.70 – 0.70

Great 

Plains 39 68 73 6.8 0.4 0.10 2.30 0.40 0.70 0.80 2.10

Rocky 

Mountain 46 45 49 9.5 — – 0.10 9.10 1.70 – 0.30 0.40 – 1.30

Pacific/

Hawaii 204 442 428 –3.1 0.0 0.00 – 4.00 1.00 0.00 – 0.20 0.00

NW/

Alaska 52 66 67 2.2 0.0 0.10 3.60 0.20 – 0.60 0.10 – 1.20

Puerto 

Rico 27 56 43 – 22.6 – 4.2 – 0.50 – 10.00 – 7.90 – – – 

TOTAL 1,166 2,307 2,307 0.0 – 0.2 0.00 0.50 – 0.20 – 0.10 – 0.60 0.60

Funding Decreases—Puerto Rico, Mid-Atlantic, Pacific/Hawaii

The regions that experience the largest percentage loss of funding are Puerto Rico (-22.6 percent), the MidAtlantic (-4.9 percent), and the Pacific/Hawaii regions (-3.1 percent), with a net loss of more than $12.5 

million each. Within Puerto Rico, almost all entitlement jurisdictions (96.4 percent) stand to lose more than 

10 percent of their CDBG funding due to the introduction of new data. In the Mid-Atlantic region, more 

than one-half of grantees lose more than 5 percent of funding, while only 16.7 percent gain more than 5 

percent of funding. Funding declines are less severe in the Pacific region, where 41.1 percent of grantees 

lose more than 5 percent of funding, while one-fourth gain more than 5 percent.

Decreases in funding for Puerto Rico jurisdictions are driven by their large drop in shares of poverty and 

overcrowding, and to a lesser extent, their decline in population as measured from the 2009 population 

estimates to the 2010 Census. Richardson and Meehan (2003) found similar results from the introduction 

of 2000 Census data to the CDBG formula; 95 percent of jurisdictions in Puerto Rico experienced declines 

in CDBG funding in FY 2003. That same study found that the Great Plains and Midwest were other regions 

that experienced the largest declines due to the introduction of the 2000 Census data. However, those two 

regions are among the top gainers by introducing the 2010 Census and 5-year ACS data sources into the 

CDBG formula.
16

Funding Increases—Rocky Mountain, Great Plains, Midwest, and Southwest

The Rocky Mountain, Great Plains, Midwest, and Southwest regions experience the greatest percentage 

increases in funding, at 9.5, 6.8, 5.3, and 3.5 percent, respectively. The Midwest alone stands to receive 

an increase of $22 million, or 5.3 percent, about one-half of the funding from the net loss of the five 

regions that lose funding. Their increase in funding is largely due to their growing share of metropolitan 

poor for Formula A grantees, and a combination of Formula B grantees’ increasing shares of growth lag, 

poverty, and to a larger extent, pre-1940 housing. In fact, the ACS shows an additional 202,618 units 

than the 2000 Census in Chicago, Detroit, Cleveland, and Milwaukee, which accounts for 20 percent of 

the overall net increase of pre-1940 housing units in metropolitan areas.

In the Rocky Mountain region, almost three-fourths of grantees receive funding at least 5 percent greater 

than their FY 2011 allocation. This increase of funding is largely due to the region’s sharp increase in 

shares of the nation’s metropolitan poor.

About 60 percent of grantees in the Great Plains see similar increases in funding. This collective increase 

is largely due to increasing shares of poverty for both Formula A and B grantees and significant increases 

in Formula B grantees’ share of pre-1940 housing. To a lesser extent, increases in the Great Plains can be 

attributed to higher shares of overcrowding and growth lag.

In the Southwest, more than one-half of grantees see an increase of funding in excess of 5 percent. These 

increases are largely caused by sharp rises in their share of the metropolitan poor for Formula A grantees.

Stable—New England

Only one region exhibits somewhat “stable” changes in the amount of funding distributed by the new 

data sources. Of entitlement jurisdictions in New England, 48 percent have gains or losses of less than 5 

percent. In total, the region experiences a small net gain of funding, although 36.4 percent of grantees lose 

more than 5 percent and only 15.6 percent gain funding. The net gain is largely due to the region’s sharp 

increase of pre-1940 housing, as measured by the ACS. The ACS shows 37,131 additional pre-1940 

units in Boston, Hartford, and Providence, alone. Together, those three cities account for 4 percent of the 

national net increase in pre-1940 housing.

Mixed—Northwest/Alaska, Southeast, and New York/New Jersey

The Northwest/Alaska region experiences an overall increase in funding, largely due to their Formula A 

grantees’ increasing share of people in poverty. However, Formula B grantees in the region experience 

losses of funding due to their declining shares of pre-1940 housing and growth lag.

The Southeast is the most mixed region, where 28 percent of grantees lose more than 5 percent of 

funding and 44.5 percent gain more than 5 percent. The most significant source of funding declines in the 

Southeast is the region’s declining share of people in overcrowded units, but that is more than offset by 

sharp increases in the region’s share of metropolitan poor.

The New York/New Jersey region would experience a net decline in funding, if not for the extremely 

large increase in pre-1940 housing. In New York City alone, the ACS estimates 251,689 more pre-1940 

housing units, which accounts for almost one-fourth of the total national increase in pre-1940 housing units.

In fact, almost one-half of all grantees in the region lose more than 5 percent of funding, while only 14.3 

percent experience funding increases in excess of 5 percent.
Table 2-11. Percent of Jurisdictions by Region Gaining and Losing Funds Due to New Data 17

RegionEntitlement Communities (n) Loss >10%Loss 5–10%Loss 5–Gain 5%Gain 5–10%Gain >10%

New England 77 10.4 26.0 48.1 9.1 6.5

New York/New Jersey 105 18.1 28.6 39.0 5.7 8.6

Mid-Atlantic 102 21.6 32.4 29.4 6.9 9.8

Southeast 189 16.9 11.1 26.5 11.1 34.4

Midwest 205 5.4 13.7 37.1 15.1 28.8

Southwest 120 10.8 8.3 30.0 10.8 40.0

Great Plains 39 2.6 10.3 28.2 7.7 51.3

Rocky Mountain 46 4.3 2.2 19.6 8.7 65.2

Pacific/Hawaii 204 27.9 13.2 32.4 10.3 16.2

Northwest/Alaska 52 5.8 13.5 34.6 15.4 30.8

Puerto Rico 27 96.3 3.7 0.0 0.0 0.0

Total 1167 16.6 15.6 32.1 10.4 25.3

Effect by Jurisdiction Type

This section analyzes changes in CDBG funding by three different types of entitlement communities: 

principal cities, satellite cities, and urban counties. In FY 2011, CDBG funding was allocated to 1,166 

entitlement communities, which consist of 637 principal cities, 347 satellite cities, and 182 urban counties.

Table 2-12 shows that the introduction of new variables results in a loss of funding for 45 percent of 

principal cities and 55 percent of satellite cities, but only 37 percent of urban counties.

Table 2-12. Number of Entitlement Jurisdictions Gaining or Losing Funds by Jurisdiction Type

Loss/Gain Total (n) Principal Cities Satellite Cities Urban Counties

>20% loss 42 19 22 1

10–20% loss 152 76 61 15

5–10% loss 182 97 58 27

0–5% loss 170 94 51 25

0–5% gain 204 125 53 26

5–10% gain 121 74 27 20

10–20% gain 186 106 38 42

>20% gain 109 46 37 26

Total 1,166 637 347 182

The evidence of more urban county gainers and, to a lesser extent, principal cities, signals an overall 

shift of funding from satellite cities to urban counties. Table 2-13 indicates that urban counties experience 

an increased share of overall CDBG funding from 21.5 to 21.8 percent as a result of introducing the 

new data. As described earlier in this chapter, this is mainly due to the fact that suburban portions of 

metropolitan areas have gained larger metropolitan shares of people in poverty, overcrowded units, and 

population as a whole. Funding through Formula B shifts some dollars towards principal cities, mainly 

through the greater rate of change for their share of metropolitan housing units built before 1940. As Table 

2-10 indicates, principal cities’ overall share of funds slightly increases due to the change in data.
18 Table 2-13. Changing Share of Entitlement Funding by Jurisdiction Type

Entitlement Communities Share of Entitlement Funds (%)

Region (n) (%) FY 2011 New Data 

Principal city 637 54.6 66.0 66.1

Satellite city 347 29.8 12.5 12.1

Urban county 182 15.6 21.5 21.8

Total 1,166 100.0 100.0 100.0

Effect by Formula Type

As described in the previous chapter, the CDBG formula is a dual allocation system, in which grantees are 

assigned to the track on which they score the highest. As a result of introducing new data sources, 31 of 

1,166 entitlement jurisdictions switch formula tracks, with 10 going from Formula A to Formula B and 21 

going from Formula B to Formula A. Formula A targets communities that are growing in population and 

exhibit high poverty and overcrowding rates. Formula B targets slow-growth or contracting jurisdictions that 

have high poverty and an old, deteriorating housing stock. Like Tables 2-12 and 2-13, Tables 2-14 and 

2-15 show the distribution of jurisdictions gaining or losing funds by formula type and the changing shares 

of entitlement funding by formula. Although the aggregate share of funding for Formula B grantees remains 

relatively constant, this masks substantial variation. Of Formula B grantees, 63 percent lose funding, while 

only 37 percent of Formula A grantees lose funding. Of those that switched formulas, 58 percent lose 

funding, which translates into a net loss of funding in that cohort.

Table 2-14. Number of Entitlement Jurisdictions Gaining or Losing Funds by Formula Type

Loss/Gain Total (n) Formula A Formula B Switch Formulas

>20% loss 42 31 8 3

10–20% loss 152 99 48 5

5–10% loss 182 61 119 2

0–5% loss 170 69 93 8

0–5% gain 204 108 92 4

5–10% gain 121 83 34 4

10–20% gain 186 157 27 2

>20% gain 109 101 5 3

Total 1,166 709 426 31

Table 2-15. Changing Share of Entitlement Funding by Formula Type

Share of Entitlement Funds (%)

Formula Total (n) % FY 2011 New Data

A 709 60.8 46.2 46.5

B 426 36.5 51.8 51.8

Switch 31 2.7 1.9 1.7

Total 1,166 100.0 100.0 100.0
D. Big Cities, Big Gainers, Big Losers 19

Big Cities

The 25 cities with the largest populations in 2010 account for about 27 percent of the funds allocated by 

the CDBG entitlement formula. Even small changes in the formula can result in millions of dollars of change 

in allocations to these communities. Table 2-16 shows that, in aggregate, the largest cities experience a 

modest 0.5 percent decrease in funding due to the introduction of new variables; however, about one-half 

of these cities experienced changes in entitlement amounts in excess of 5 percent.

Table 2-16. Largest Entitlement Cities, Ranked by 2010 Population (Part 1)

Reason for Change (%)—

Formula A

Reason for Change (%)—

Formula B

City

FY 2011 Grant 

($000)

New Data Grant 

($000)

Change (%)

Population

Poverty

Overcrowding

Growth lag

Poverty

Pre-1940

Housing

New 

York, NY 

(B)

163,359 168,010 2.85 — — — 0.80 – 4.76 6.81

Los 

Angeles, 

CA (A)

64,578 59,056 – 8.55 –0.15 –10.33 1.93 — — —

Chicago, 

IL (B) 75,816 80,761 6.52 — — — 1.61 – 1.75 6.65

Houston, 

TX (A) 27,343 27,161 – 0.66 – 1.16 1.95 – 1.45 — — —

Philadelphia, 

PA (B) 46,187 43,091 – 6.70 — — — – 0.30 – 1.32 – 5.08

Phoenix, 

AZ (A) 15,471 16,681 7.82 – 1.90 7.26 2.46 — — —

San 

Antonio, 

TX (A)

13,226 12,959 – 2.02 – 0.73 2.99 – 4.28 — — —

San 

Diego, 

CA (A)

13,603 11,999 – 11.79 – 0.04 – 7.97 – 3.79 — — —

Dallas, TX 

(A) 15,882 16,139 1.62 – 1.28 5.68 – 2.78 — — —

Honolulu, 

HI (A) 8,786 8,442 – 3.91 0.95 – 6.86 2.00 — — —

San Jose, 

CA (A) 9,151 8,790 – 3.95 – 0.45 0.83 – 4.33 — — —

Indianapolis, 

IN (B)

8,670 9,580 10.49 — — — – 2.97 5.97 7.49
20

Reason for Change (%)—

Formula A

Reason for Change (%)—

Formula B

City

FY 2011 Grant 

($000)

New Data Grant 

($000)

Change (%)

Population

Poverty

Overcrowding

Growth lag

Poverty

Pre-1940

Housing

San 

Francisco, 

CA (B)

18,584 18,181 – 2.17 — — — – 0.02 – 0.98 – 1.17

Austin, TX 

(A) 6,878 7,503 9.09 0.05 8.89 0.15 — — —

Columbus, 

OH (A) 5,845 7,061 20.81 0.51 13.64 6.65 — — —

Fort 

Worth, TX 

(A)

6,153 6,536 6.22 0.36 7.87 – 2.01 — — —

Louisville, 

KY 

(Switch)

10,777 7,482 – 30.57 – 8.70 – 3.39 – 2.43 – 20.99 – 3.39 4.94

Charlotte, 

NC (A) 4,358 4,951 13.61 1.11 12.30 0.20 — — —

Detroit, 

MI (B) 33,531 37,533 11.94 — — — 6.19 0.84 4.90

El Paso, 

TX (A) 7,676 7,780 1.36 0.68 2.46 – 1.78 — — —

Memphis, 

TN (A) 7,320 7,387 0.92 – 0.83 2.89 – 1.14 — — —

NashvilleDavidson, 

TN (A)

4,508 4,778 6.00 – 0.46 7.14 – 0.68 — — —

Baltimore, 

MD (B) 21,039 20,066 – 4.63 — — — 0.04 – 3.74 – 0.93

Boston, 

MA (B) 17,497 17,882 2.20 — — — 1.20 – 1.51 2.52

Seattle, 

WA (B) 10,729 10,400 – 3.07 — — — 0.01 – 0.73 – 2.35

Table 2-16. Largest Entitlement Cities, Ranked by 2010 Population (Part 2)
In total, 9 out of the 25 most populous cities experience an increase greater than 5 percent of their FY 21

2011 grant amount due to the introduction of the new data. Columbus, Ohio, a Formula A grantee, 

experienced the largest percentage increase of 20.81 percent (an additional $1.216 million). This change 

is largely due to that city’s increasing shares of poverty and overcrowding, and to a lesser extent, a modest 

increase in its share of metropolitan population.

Only four of the largest cities experienced a decline in funding greater than 5 percent. The most severe, 

Louisville, is due to a technical change in its formula allocation. 42 U.S.C. 5306(b)(6)(A) stipulates that 

for certain city-county consolidated governments, HUD may allocate CDBG funding to each portion of 

the city as if the consolidation never occurred. In the past, HUD received special tabulations of census 

data to enable the allocation of funding to the old Louisville city jurisdiction under Formula B, and the 

county balance under Formula A. That method allowed the old Louisville city jurisdiction to be allocated 

funding using the growth lag factor and the balance of the county to take advantage of the variables that 

tend to favor growing jurisdictions. In April 2011, staff in the Census Bureau’s population estimates office 

informed HUD staff they did not have a method for producing population estimates for the old Louisville 

city boundaries after 2010, so that data would no longer be available. Barring further developments with 

the Census Bureau, HUD plans to treat the Louisville-Jefferson County consolidated government as all other 

entitlement jurisdictions. The effect on Louisville funding causes a decrease of funding by 30.57 percent, 

largely due to the growth lag variable’s sharp decline, which the central portion of the city received under 

the FY 2011 allocation.

The introduction of new variables causes San Diego, Los Angeles, and Philadelphia to lose 11.8, 8.6, 

and 6.7 percent, respectively, of their total allocations. This loss is mostly driven by San Diego and Los 

Angeles’s shrinking share of the country’s metropolitan poor. Philadelphia’s funding shrank largely because 

of its declining shares of pre-1940 housing counts.

Big Gainers

Table 2-17 shows the entitlement jurisdictions that experienced the largest percentage growth in formula 

allocation due to the introduction of the new data. The average change between these top 10 gainers 

is 53.53 percent, totaling an aggregate increase of only $2.53 million. Most of the changes can be 

attributed to an increase in poverty and overcrowding shares. Increases due to the share of population 

are pronounced only in Elk Grove, California, Johns Creek, Georgia, and Surprise City, Arizona, where 

populations increased 13.11, 24.17, and 23.4 percent, respectively, from the 2009 population estimates 

to the 2010 Census population counts. Notably, all these grantees receive allocations through Formula A, 

and most are suburban jurisdictions that grew substantially from 2000 to 2010.
22 Table 2-17. Biggest Percentage Gainers of CDBG Entitlement Funds

Reason for Change (%)—Formula A

City FY 2011 

Grant ($000)

New Data 

Grant ($000)

Change (%) Population Poverty Overcrowding

Elk Grove, CA 

(A) 455 763 67.59 7.35 35.59 24.65

Springdale, 

AR (A) 473 774 63.76 0.46 27.22 36.09

Nampa, ID (A) 491 784 59.57 0.04 46.21 13.32

Johns Creek 

City, GA (A) 162 254 56.83 17.56 30.33 8.93

Missouri City, 

TX (A) 271 413 52.23 – 5.47 46.43 11.27

Horry County, 

SC (A) 1,403 2,081 48.35 1.17 21.31 25.87

McKinney City, 

TX (A)

499 738 47.79 1.19 33.07 13.53

Surprise City, 

AZ (A)

342 503 47.12 12.40 27.18 7.54

Avondale City, 

AZ (A)

461 677 46.72 – 3.85 33.97 16.61

Bellevue, NE 

(A)

224 325 45.37 – 0.90 22.40 23.88

Big Losers

The average loss for the 10 grantees with the largest percentage declines in funding is 32.3 percent, with 

an aggregate loss of $12.145 million. These grantees’ absolute value of aggregate change is much higher 

than the top gainers’ change because these grantees are generally larger in size. The grantee with the 

largest percentage decline in funding is Hialeah, FL, which loses 40.94 percent of their CDBG funding.

This decline in funding is mainly caused by a drastic 85.35 percent decline in overcrowded units, much 

higher than the national decline. Miami Beach and Miami-Dade County lose significant amounts of CDBG 

funding, mainly due to their decreasing shares of poverty and overcrowded units.

Hammonton, NJ is a small community of only 14,791. This city loses much of its funding because of a 

large decrease in pre-1940 housing. The 2010 Census also measures a 10 percent increase in population 

since the 2009 population estimates, which eliminates that grantee’s growth lag.
Table 2-18. Biggest Percentage Losers of CDBG Entitlement Funds 23

Reason for Change (%)—

Formula A

Reason for Change (%)—

Formula B

City

FY 2011 

Grant ($000)

New Data 

Grant ($000)

Change (%)

Population

Poverty

Overcrowding

Growth lag

Poverty

Pre-1940 

Housing

Hialeah, 

FL (A) 3,809 2,250 – 40.94 0.26 – 4.41 – 36.79 — — —

Hammonton, NJ (B) 147 89 – 39.05 — — — – 11.72 2.70 – 30.03

Miami 

Beach, FL 

(A)

1,572 1,020 – 35.14 – 0.07 – 17.65 – 17.43 — — —

Alexandria, 

VA (A)

1,143 760 – 33.45 – 1.76 – 8.18 – 23.51 — — —

Alhambra, 

CA (A) 1,240 859 – 30.74 – 0.34 – 8.67 – 21.72 — — —

Louisville, 

KY 

(Switch)

10,777 7,482 – 30.57 – 8.70 – 3.39 – 2.43 – 20.99 – 3.39 4.94

Canovanas 

Municipio, 

PR (A)

1,207 838 – 30.53 – 0.09 – 20.86 – 9.58 — — —

Guaynabo 

Municipio, 

PR (A)

1,727 1,229 – 28.82 – 0.61 – 13.09 – 15.12 — — —

MiamiDade 

County, FL 

(A)

16,285 11,896 – 26.95 0.12 – 6.10 – 20.97 — — —

Arecibo 

Municipio, 

PR (A)

2,472 1,809 – 26.81 – 0.52 – 13.09 – 13.20 — — —
24

CHAPTER

3REDISTRIBUTIVE EFFECTS OF NEW DATA ON

COMMUNITY DEVELOPMENT BLOCK GRANT 

NONENTITLEMENT COMMUNITIES

As noted earlier in chapter 1, the CDBG formula includes a 30 percent set-aside for the 50 states and 

Puerto Rico. These state grantees administer funds on behalf of communities not served by the entitlement 

program. Table 3-1 shows how the formula variables change with the introduction of new data from 

the 2010 Census and 2005–2009 ACS, comparing trends in entitlement communities with trends in 

nonentitled areas. The trends in nonentitlement areas are generally consistent with the findings presented in 

chapter 2, although the increase in pre-1940 housing is much less pronounced in nonentitlement areas.

Table 3-1. Change in Formula Variables in Entitlement and Nonentitlement Areas

Entitlement Communities Nonentitled Areas

Population

2009 Population Estimates

2010 Census

Percent Change

201,180,773

201,270,119

0.0%

108,932,489

110,340,632

1.3%

People in Poverty

2000 Census 

ACS 2005–2009

Percent Change)

23,471,950

27,014,044

15.1%

11,978,807

14,008,083

16.9%

Overcrowded Housing Units

2000 Census

ACS 2005–2009

Percent Change

5,019,582

2,630,534

– 47.6%

1,232,717

778,680

– 36.8%

Pre-1940 Housing Units

2000 Census

ACS 2005–2009

Percent Change

10,576,185

11,578,443

9.5%

6,825,438

6,882,096

0.8%

Tables 3-2 and 3-3 show how the nonentitlement allocation is broken down between the two formulas, 

the variables for actual FY 2011 allocations, and for the projected allocations with new data. Both 

Formula A and Formula B include population and poverty, but they are more heavily weighted under 

Formula A. Incorporating new data leads to a larger percentage of nonentitlement funds being allocated 

through Formula A. These tables also show how much funding is distributed by each variable, which is 

influenced by the weight assigned to it and by the distribution of values for the variable. In both FY 2011 

actual allocations and allocations using new data, overcrowding, poverty, and pre-1940 housing have 

the greatest influence on the allocation results. The per capita funding amount went down for Formula A 

grantees and is stable or up for Formula B grantees, which makes sense because the population is typically 

rising in Formula A jurisdictions and stable or falling in Formula B jurisdictions. Incorporating the new data 

also causes the number of dollars allocated for each overcrowded household to increase substantially, from 

$165 to $264.7.
Table 3-2. Significance of Formula Variables to Nonentitlement Allocations, FY 2011 25

FY 2011

Variable (weight) Grant ($000s) Per Capita ($)16 Dollars per unit

Formula A

Population (0.25) 104,120 1.8 1.8

Poverty (0.5) 258,148 4.6 34.0

Overcrowding (0.25) 149,329 2.6 165.0

Subtotal 511,596 9.0 NA

Formula B

Population (0.2) 79,455 1.5 1.5

Poverty (0.3) 89,237 1.7 20.4

Pre-1940 housing (0.5) 308,522 5.7 59.6

Subtotal 477,214 8.9 NA

Total 988,810 18.4 NA

Table 3-3. Significance of Formula Variables to Nonentitlement Allocations, New Data

New Data

Variable (weight) Grant ($000s) Per Capita ($) Dollars per unit

Formula A

Population (0.25) 116,853 1.9 1.9

Poverty (0.5) 277,466 4.4 29.4

Overcrowding (0.25) 149,514 2.4 264.7

Subtotal 543,833 8.7 NA

Formula B

Population (0.2) 71,429 1.5 1.5

Poverty (0.3) 80,887 1.7 17.7

Pre-1940 housing (0.5) 292,661 6.1 59.9

Subtotal 444,978 9.3 NA

Total 988,810 9.0 NA

Table 3-4 lists all 51 state CDBG programs, grouped by region, and demonstrates the effect of the new 

data on allocation amounts. As in chapter 2, this analysis uses the universe of actual FY 2011 grantees.

Any newly qualified entitlement grantees in FY 2012 will cause a decrease in funding for most other 

CDBG grantees. Although new entitlements can form out of urban counties or even entitlement cities, 

this more commonly affects state programs because they get credit only for nonentitled parts of the state 

(Richardson and Meehan, 2003). Because of the 30 percent set-aside for nonentitled areas, the addition 

of a new entitlement has some strange effects. The state in which the new entitlement is located would lose 

funding for its nonentitlement program. All other entitlement grantees would lose a small amount, because 

more grantees would be sharing the same pot of 70 percent of the appropriations. However, other state 

programs would gain because the nonentitlement total for each variable would decrease. By decreasing 

the denominator in all formula calculations all states would have an increased share, except the state where 

the new entitlement is located.

16 Per capita funding is calculated with 2010 Census population counts in both Tables 3-2 and 3-3. Dollars per unit in Table 3-2 uses 2009 

population estimates.
26 Table 3-4. State-by-State Effect of New Data on Allocations (Part 1)

States Formula 

Type

FY 2011 

Grant 

($000)

New Data 

Grant

Change (%) Population 

(%)

Poverty (%) Overcrowding 

(%)

Pre-1940 

Housing 

(%)

New England

CT B 12,319 12,495 1.4 0.2 0.4 — 0.9

MA B 30,463 31,113 2.1 – 0.1 – 0.4 — 2.6

ME B 11,497 11,868 3.2 0.0 0.0 — 3.2

NH B 8,394 8,682 3.4 – 0.1 1.3 — 2.3

RI B 4,753 5,142 8.2 0.0 – 1.6 — 9.8

VT B 6,743 6,966 3.3 0.0 0.4 — 2.9

New York/New Jersey

NJ B 6,279 6,369 1.4 0.0 – 0.2 — 1.7

NY B 44,032 45,004 2.2 0.1 – 0.5 — 2.6

Mid-Atlantic

DE A 1,873 2,012 7.4 0.9 6.2 0.2 —

MD B 7,340 7,463 1.7 0.3 – 0.5 — 1.9

PA B 42,284 42,103 – 0.4 0.2 – 0.8 — 0.2

VA Switch 17,861 18,477 3.4 6.6 21.2 20.8 – 45.2

WV B 15,384 14,775 – 4.0 0.3 – 5.9 — 1.6

Southeast

AL A 23,605 23,277 – 1.4 0.6 – 3.6 1.5 —

FL A 24,841 25,804 3.9 0.6 2.8 0.5 —

GA A 36,631 39,521 7.9 0.3 6.8 0.7 —

KY A 24,941 25,876 3.7 0.0 – 1.7 5.4 —

MS A 27,635 26,701 – 3.4 0.2 – 2.4 – 1.2 —

NC A 41,132 45,975 11.8 0.5 8.0 3.2 —

SC A 20,113 20,243 0.6 0.3 2.5 – 2.1 —

TN A 24,450 27,666 13.2 0.3 7.1 5.7 —

Midwest

IL B 29,385 29,509 0.4 0.2 1.8 — – 1.5

IN B 28,548 30,402 6.5 0.3 5.0 — 1.3

MI B 32,656 34,028 4.2 0.1 5.7 — – 1.6

MN B 18,513 18,769 1.4 0.2 1.6 — – 0.4

OH B 43,395 44,889 3.4 0.2 2.8 — 0.5

WI B 25,705 26,359 2.5 0.2 2.7 — – 0.4
Table 3-4. State-by-State Effect of New Data on Allocations (Part 2) 27

States Formula 

Type

FY 2011 

Grant 

($000)

New Data 

Grant

Change (%) Population 

(%)

Poverty (%) Overcrowding 

(%)

Pre-1940 

Housing 

(%)

Southwest

AR A 17,627 18,299 3.8 0.4 – 0.1 3.5 —

LA A 25,670 23,377 – 8.9 0.4 – 9.6 0.3 —

NM A 13,018 9,453 – 27.4 0.4 – 8.0 – 19.7 —

OK A 14,578 14,579 0.0 0.4 – 0.9 0.5 —

TX A 66,605 65,939 – 1.0 0.6 – 1.4 – 0.2 —

Great Plains

IA B 23,878 23,614 – 1.1 0.2 0.6 — – 1.9

KS B 15,291 15,268 – 0.2 0.2 – 0.1 — – 0.2

MO Switch 21,614 22,471 4.0 5.4 23.5 24.0 – 48.9

NE B 11,183 11,145 – 0.3 1.0 – 0.9 — – 0.4

Rocky Mountain

CO A 8,703 8,924 2.5 – 0.2 2.6 0.1 – 

MT B 6,260 6,165 – 1.5 0.5 – 3.2 — 1.2

ND B 4,429 4,057 – 8.4 0.5 – 5.4 — – 3.4

SD B 6,049 5,921 – 2.1 0.1 – 2.7 — 0.4

UT A 4,308 4,580 6.3 0.3 1.4 4.6 —

WY B 2,957 2,872 – 2.9 0.9 – 7.2 — 3.4

Pacific/Hawaii

AZ A 11,109 9,962 – 10.3 0.6 1.0 – 11.9 —

CA A 35,842 33,527 – 6.5 0.1 – 3.3 – 3.3 —

HI A 4,867 5,235 7.6 0.8 – 5.4 12.2 —

NV A 2,543 2,488 – 2.2 0.2 5.9 – 8.2 —

Northwest/Alaska

AK A 2,633 2,387 – 9.3 0.4 – 0.2 – 9.6 —

ID A 7,927 8,276 4.4 0.3 3.9 0.1 —

OR A 13,153 13,445 2.2 0.1 2.4 – 0.3 —

WA A 14,094 13,590 – 3.6 0.1 0.9 – 4.6 —

Puerto Rico

PR A 43,699 31,750 – 27.3 – 0.3 – 8.6 – 18.4 —

Two states are poised to switch formula types; Missouri and Virginia go from Formula B to Formula A and 

see their grants increase by 4 and 3.4 percent, respectively. The remaining 49 programs are split with 24 

using Formula B and 25 using Formula A.

Some regional trends are quickly apparent. All state grantees in the New England region are Formula B 

grantees, and all see their grants increase with the addition of new census and ACS data, primarily due 

to the increase in pre-1940 housing. Rhode Island has a particularly significant increase of 8.2 percent, 

the third largest percentage increase. A similar story can be seen in the Midwest, where all state grantees 

receive funds under Formula B and all grantees see their funding increase. However, driving the change in 

the Midwest is an increase in poverty rather than in pre-1940 housing.
28 The Southeast region has only Formula A grantees and almost all are poised to see their grants increase, 

including the two states with larger increases than Rhode Island—Tennessee (13.2 percent) and North 

Carolina (11.8 percent). The changes in individual variables are mixed in the southeast, but the poverty 

variable is primarily driving funding changes in the states poised to receive larger grants.

In the Southwest—where all five states are Formula A grantees—a consistent decrease in the share of 

poverty is bringing grants down. The only two state programs slated to gain funding are Arkansas and 

Oklahoma, which offset their decreasing shares of poverty with increasing shares of population and 

overcrowding.

Two state programs have particularly severe decreases in funding. New Mexico and Puerto Rico 

experience 27.4 and 27.3 percent declines in funding largely as a result of substantial relative decreases 

in poverty and overcrowding. Other states that experience significant declines in funding are varied in 

geography and characteristics, although four out of five (all except North Dakota) are Formula A grantees: 

Arizona (-10.3 percent), Alaska (-9.3 percent), Louisiana (-8.9 percent), North Dakota (-8.4 percent), and 

California (-6.5 percent).

As demonstrated in chapters 2 and 3, changes from the 2000 Census to the 2005–2009 ACS have 

significant implications for the CDBG formula allocations, particularly associated with certain variables.
29

CHAPTER

4CONFIRMING KEY TRENDS

Apparent declines in overcrowding and increases in pre-1940 housing are particularly substantial and 

surprising. In some cases we also observe large and unexpected changes from the 2009 population 

estimates used for the FY 2011 allocation to the 2010 Census population count that will be used for the 

FY 2012 allocation. This chapter investigates these key trends in an attempt to confirm their validity and to 

explore their possible causes.

Drop in Overcrowding

The most dramatic finding of this report is the nationwide decrease in levels of overcrowding and its 

significant effect on CDBG allocations. Ong and Ong (2009) note similar results in California between 

the 2000 Decennial Census and the 2005–2007 ACS 3-year estimates. Overcrowding decreased for 

households of all sizes and units of all sizes, although the greatest drop in California is for units with 

three or fewer rooms. A Census Bureau report comparing the 2000 Census with ACS 3-year estimates 

for a sample of counties and tracts also finds differences in overcrowding, and concludes that they may 

be driven by differences in number of rooms reported, as household size is quite similar between the two 

surveys.

Table 4-1 presents figures from the 2000 Census and the 2005–2009 ACS on the components of 

overcrowding, which HUD defines as greater than one person per room. Between the 2000 Census and 

the 2005–2009 ACS, overcrowding rates drop from 5.7 to 3.0 percent. This change constitutes a drop 

of 47.7 percent, and is nearly identical for both owner-occupied and renter-occupied units. It appears that 

both elements of overcrowding, household size and rooms per unit, contribute to the negative change in 

overcrowding between the two surveys. There is a modest increase in the percentage of small households 

(with one or two people), and a decrease in the percentage of households of all other sizes. The most 

substantial decrease is among the largest households: the percentage of households with seven or more 

people is 18.4 percent lower in the 2005–2009 ACS than in the 2000 Census. The median number of 

rooms per unit increases only slightly, from 5.3 to 5.4. However, a much smaller portion of households 

reported having only one or two rooms in the 2005–2009 ACS, and the percentage of units with four or 

more rooms increased. The largest changes are in units with one room (from 2.2 to 1.4 percent of all units), 

two rooms (from 4.8 to 2.8 percent of all units), and with nine or more rooms (from 7.7 to 9.2 percent 

of all units). According to the American Housing Survey (AHS), overcrowding in the period from 2000 to 

2009 was generally around 2.3 percent, suggesting that the ACS estimate is more accurate than the 2000 

Census estimate.

17

17 Table 2-3 of the AHS reports a rate of overcrowding of 2.3 percent in 2001, 2.4 percent in 2003, 2.4 percent in 2005, 2.5 percent in 

2007, and 2.2 percent in 2009.
30 Table 4-1. Change in Overcrowding, 2000 Census Compared With 2005–2009 ACS

2000 Census

(%)

2005–2009 ACS

(%)

% Change

Overcrowded units 5.7 3.0 – 47.7

Owner occupied 3.1 1.6 – 47.4

Renter occupied 11.0 5.8 – 47.0

Household size (mean) 2.59 2.60 0.4

1 person 25.8 27.3 5.9

2 people 32.6 33.4 2.3

3 people 16.5 15.9 – 3.7

4 people 14.2 13.6 – 4.0

5 people 6.7 6.2 – 7.5

6 people 2.5 2.2 – 11.0

7 or more people 1.6 1.3 – 18.4

Unit size (median rooms) 5.3 5.4 1.9

1 room 2.2 1.4 – 36.8

2 rooms 4.8 2.8 – 42.3

3 rooms 9.8 9.1 – 7.9

4 rooms 16.0 17.0 6.6

5 rooms 20.9 21.2 1.7

6 rooms 18.5 18.7 1.3

7 rooms 12.1 12.4 2.5

8 rooms 8.1 8.3 2.5

9 or more rooms 7.7 9.2 18.9

Increase in Pre-1940 Housing

The increase in pre-1940 housing units is surprising because if these data were true population parameters, 

such an increase would hardly be possible. Pre-1940 structures can be removed from the housing stock 

through demolition, but can be added in only a few circumstances. If a pre-1940 housing structure is 

renovated and additional units are added (such as splitting a four-bedroom apartment into two, twobedroom apartments), the number of pre-1940 housing units would increase. Also, because the census 

and ACS do not survey nonresidential buildings, converting an old industrial or commercial building to 

residential use could increase the number of pre-1940 housing units. These scenarios may explain part 

of the apparent increase in pre-1940 housing, but it is likely that the number of pre-1940 units removed 

from the housing stock each year substantially exceeds the number of pre-1940 units added to the housing 

stock. This is confirmed by the Components of Inventory Change (CINCH) reports that HUD issues using 

data from the AHS. According to the authors’ analysis of CINCH reports, between 2001 and 2007, a 

total of 726,000 pre-1940 housing units were added to the national housing stock while 1,507,000 were 

removed from the housing stock. The net change of -781,000 suggests that the pre-1940 housing stock did 

not actually increase from the 2000 Census to the 2005–2009 ACS.

A more likely cause derives from the fact that both the 2000 Census and the 2005–2009 ACS are 

surveys, with varying degrees of error (both sampling and nonsampling). A Census Bureau report in 2004 

compared 2000 Census figures with 2005–2007 ACS figures and found the ACS consistently reported 

higher levels of pre-1940 housing (Diffendal et al., 2004). Similarly, the C2SS ascribes a larger portion 

of the housing stock to pre-1940 construction than the decennial census did. The difference is not as large 

(1.3 percentage points) as seen in the change from 2000 Census data to the 2005–2009 ACS estimates; 
however, wide variation existed across counties. The Census Bureau found significant differences in 9 of 31

the 18 counties studied, and the differences ranged from a Census 2000 estimate that was 0.2 percentage 

points higher in Broward, Florida, to 11.6 percentage points lower in the Bronx, New York (Love et al., 

2004). Salvo et al. (2007) compare ACS estimates in the Bronx with administrative data sources and find 

that the administrative data show even higher levels of pre-1940s housing than that captured by the ACS, 

but that the ACS estimates are much closer than the 2000 Census estimates.

Possible Causes for Discrepancies Between Census and ACS Data

As discussed in chapter 1, the year-round administration of the ACS combined with the “current residence” 

rule, means that the population responding to the ACS and the population that responded to the 2000 

Census are not exactly the same. This could broadly affect places with large concentrations of vacation 

homes or students (Love et al., 2004). Communities and states where many people live only a few months 

of the year see their characteristics become more reflective of the part-time residents (and their housing 

units), while the communities and states where those people come from (their “usual residence”) see their 

characteristics become less reflective of the part-time residents. The different levels of publicity for each 

survey may also influence who responds. The decennial census is more publicized and may elicit a better 

response rate than the ACS, particularly from hard-to-reach populations.

Perhaps the most likely cause of the apparent changes in overcrowding and pre-1940 housing is the 

level of nonsampling error in the two surveys. The Census Bureau believes that historically there has been 

confusion about how to correctly respond to the question of how many rooms are in a housing unit, based 

on discrepancies between the number of bedrooms and the total number of rooms reported in a housing 

unit (Woodward et al., 2007). Questions that confuse or mislead respondents are more problematic for 

the census than the ACS, because the census relies more on mail-in responses. The ACS extensively uses 

telephone and in-person interviewers who are able to explain to respondents what does and does not 

count as a “room.” Similarly, it is possible that households in old multifamily buildings do not know exactly 

when their building was built. Evidence from the C2SS suggests that interviewers visiting vacant units 

found higher levels of pre-1940 housing and more rooms per unit than would be expected based on 2010 

Census data (Love et al, 2004).

18 This would lead the ACS to indicate higher (and more accurate) levels of 

pre-1940 housing units than the census indicates.

The Census Bureau cautions data users from comparing estimates from the decennial census and the ACS.

This guidance is particularly emphasized when questions on a subject change. After an ACS Content Test 

in 2006 indicated widespread underreporting of rooms the Census Bureau changed the rooms question to 

improve accuracy. As a result, the Census Bureau recommends comparing the ACS and census measures of 

overcrowding “with caution” (U.S. Census Bureau, 2010).

Population Changes

In several places, the 2010 Census yields population counts substantially different from the 2009 

population estimates—usually suggesting population declines from 2009 to 2010. Fulton County, Georgia, 

had a population of 298,408 according to the Census Bureau’s 2009 population estimates, but only 

271,536 according to the 2010 Census. In Puerto Rico, all 27 entitlement communities and the state 

program are poised to lose funding from introducing the new data into the CDBG formula, partly because 

the 2009 population estimates are higher than the 2010 Census count for every Puerto Rico grantee.

Everywhere except Puerto Rico, the annual population estimates are constructed in part using data from 

the National Center for Health Statistics (NCHS) on the number of births. According to the Census Bureau, 

NCHS data generally indicate a larger population of young children than what is counted by the decennial 

18 See chapter 1 for an explanation of the Census 2000 Supplementary Survey (C2SS).
32 Census. This creates discrepancies between the annual estimates and the Census that are compounded 

annually to create the largest difference at the end of a decade. Despite this common issue, the national 

population estimate and most state estimates for 2010 aligned closely with the 2010 Census. The 2010 

population estimates use the same methodology as all annual estimates, and so differences between this 

estimate and the census provide an indication of the reliability of prior-year estimates. Nationally, the 

population estimate predicted 232,406 more people than the census counted in 2010, or 0.075 percent 

more. This represents a significant improvement over the previous decade’s annual population estimates, 

which undercounted the population on April 1, 2000, by more than 6.8 million (Cohn, 2011). The Pew 

Research Center conducted an analysis of state population estimates using similar (but not identical) 

methodology as the Census Bureau to predict 2010 estimates. They found that the estimates differed from 

the census by more than 2 percent for only six states, not including Puerto Rico, and the largest difference 

was found in Arizona, where the population estimate was 4.6 percent higher than the census count (Cohn, 

2011).

The Census Bureau estimates Puerto Rico’s population using data on births and deaths from the Puerto Rico 

Planning Board, instead of the NCHS. Migration is also estimated differently; the Census Bureau estimates 

total migration in the previous decade as the difference in projected population for 2000 and actual 

population for 2000, then derives an annual rate, which is applied to each year between 2000 and 2010.

This method assumes a constant rate of migration each decade (1990 to 2000 and 2000 to 2010), which 

may not be accurate. An increase in emigration after 2000 would cause annual population estimates to be 

inflated relative to the true population.

Funding to entitlement grantees is based on smaller area population estimates, which incorporate data 

on housing units to allocate population. For the 2009 population estimates, housing unit counts had to be 

estimated using vacancy rates and household size from the 2000 Census. If occupancy rates or household 

size changed differently within a county from the Census 2000, the population estimates of those 

jurisdictions may have been distorted. Some speculation exists that changes in vacancy rates since the 

2000 Census are one possible cause for the drop in population in urban areas at the 2010 Census (Exner, 

2011). Vacancy rates increase across the country between the 2000 and 2010 Census; among CDBG 

grantees, the average increase is 2.8 percentage points. As shown in Table 4-2, vacancy increases more 

in entitlement cities than in other types of jurisdictions. Jurisdictions that experience a greater increase in 

vacancy rates, especially compared with their county as a whole, may have inflated population estimates 

for 2009.

19

Table 4-2. Average Increase in Vacancy Rate Among CDBG Grantees

Grantee Type Increase in Vacancy Rate 

(percentage points)

Principal cities 3.0

Satellite cities 2.9

Urban counties 2.5

Nonentitlements 2.0

Total 2.8

19 Vacancy rates are calculated from the ACS 2005–2009 5-year average. Grantees from Wyoming are not included.
CONCLUSION 33

This report demonstrates the effects of incorporating updated data from the Census Bureau into the 

Community Development Block Grant formula. These data partially represent changes that have occurred 

since the 2000 Census in communities across the country. The study observes small population growth, 

considerable growth in poverty, and the extension of poverty from principal cities to the suburbs (satellite 

cities and urban counties) and beyond (nonentitlement areas). In addition to presenting those very real 

changes, this report also highlights the challenge of making the transition from the decennial census to 

the American Community Survey as the basis of nationwide formula allocations. Sharp breaks in several 

variables are at least partially attributable to changes between the surveys. Many grantees will have their 

funding cut as a result, while others will get an unexpected increase.

The ACS methodology has been rigorously tested and justified. The benefits that the ACS offers—

particularly its low nonsampling error and annual updates—are substantial, and the ACS is rightfully the

authoritative and comprehensive data source from the Census Bureau. The allocation changes described in 

this report are substantial for some grantees, but FY 2012 may be the last time such drastic changes occur 

as the result of new data.

20 By using annual updates of the ACS 5-year estimates, HUD expects future 

allocations to be stable and accurately reflect conditions in CDBG communities across the country.

20 In FY 2013 HUD will use ACS 2006-2010 estimates, which will be weighted to match population totals from the 2010 Census. This 

reweighting may cause slight funding shifts.
34 ACKNOWLEDGEMENTS

The authors thank Abubakari Zuberi in the U.S. Department of Housing and Urban Development (HUD) 

Office of Community Planning and Development (CPD) for assembling the data for this report—a 

monumental task that should not go unnoticed. The authors also thank Dwight Jefferson, Barry Steffen, 

Peter Kahn, Kurt Usowski, and Kevin Neary of HUD’s Office of Policy Development and Research (PD&R) 

and Steve Johnson of CPD for their thoughtful comments and suggestions. Finally, the authors thank Todd 

Richardson from PD&R and Bob Meehan, formerly of CPD, because this study borrowed heavily from their 

2003 report that studied the effects of introducing 2000 Census data into the CDBG formula.

Any errors and omissions that remain in the report are those of the authors, not of those who have 

contributed, nor of the Department.

The contents of this report are the views of the authors and do not necessarily reflect the views or policies of 

the U.S. Department of Housing and Urban Development or the U.S. Government.
REFERENCES 35

Bunce, Harold L. 1976. An Evaluation of the Community Development Block Grant Formula. Washington, 

DC: U.S. Department of Housing and Urban Development.

Bunce, Harold L., and Robert L. Goldberg. 1979. City Need and Community Development Funding.

Washington, DC: U.S. Department of Housing and Urban Development.

Bunce, Harold L., Sue G. Neal, and John L. Gardner. 1983. Effects of the 1980 Census on Community 

Development Funding. Washington, DC: U.S. Department of Housing and Urban Development.

Diffendal, Gregg J., Rita Jo Petroni, and Andre L. Williams. 2004. Meeting 21st Century Demographic 

Data Needs—Implementing the American Community Survey Report 8: Comparison of the American 

Community Survey Three-Year Averages and the Census Sample for a Sample of Counties and Tracts.

Washington, DC: U.S. Census Bureau.

Exner, Rich. 2011. Census Bureau’s 2009 Population Estimate for Cleveland Was Way Off Mark: 

Behind the Numbers. Available at http://www.cleveland.com/datacentral/index.ssf/2011/03/census_

bureaus_2009_population.html. (Accessed July 2011).

Government Accountability Office (GAO). 2004. Metropolitan Statistical Areas: New Standards and Their 

Impact on Selected Federal Programs. Washington, DC: Government Accountability Office.

Love, Susan P., Joan K. Broadwater, Deborah H. Griffin, Theresa F. Leslie, and David A. Raglin. 2004.

Meeting 21st Century Demographic Data Needs—Implementing the American Community Survey Report 

10: Comparing Selected Physical and Financial Characteristics of Housing With the Census 2000.

Washington, DC: U.S. Census Bureau.

Ong, Paul, and Jonathan Ong. 2009. Dramatic Decline in Household Overcrowding? An Analysis of 

California and Los Angeles. Los Angeles: UCLA School of Public Affairs.

Richardson, Todd, and Robert Meehan. 2003. Redistribution Effect of Introducing Census 2000 Data Into 

the CDBG Formula. Washington, DC: U.S. Department of Housing and Urban Development.

Salvo, Joseph J., Arun Peter Lobo, Adam L. Willett, and Joel A. Alvarez. 2007. An Evaluation of the 

Quality and Utility of ACS Five Year Estimates for Bronx Census Tracts and Neighborhoods. New York: 

New York City Department of City Planning.

U.S. Census Bureau. 2010. Comparing 2009 American Community Survey Data. Available at http://

www.census.gov/acs/www/guidance_for_data_users/comparing_2009/. (Accessed July 2011).

Woodward, Jeanne, Ellen Wilson, and John Chesnut. 2007. Evaluation Report Covering Rooms and 

Bedrooms. Washington, DC: U.S. Census Bureau.

U.S. Census Bureau. 2009. A Compass for Understanding and Using American Community Survey Data: 

What Researchers Need To Know. Washington, DC: U.S. Census Bureau.
36 ADDITIONAL READING

———. 2009. Design and Methodology: American Community Survey. Washington, DC: U.S. Census 

Bureau.

———. 2009. Methodology for the United States Resident Population Estimates by Age, Sex, Race, and 

Hispanic Origin (Vintage 2009): April 1, 2000 to July 1, 2009. Washington, DC: U.S. Census Bureau.

———. 2010. Methodology for the Puerto Rico Commonwealth and Municipio Population Estimates by 

Age and Sex (Vintage 2009): April 1, 2000 to July 1, 2009. Washington, DC: U.S. Census Bureau.

———. 2010. Methodology for the Subcounty Total Resident Population Estimates (Vintage 2009): April 1, 

2000 to July 1, 2009. Washington, DC: U.S. Census Bureau.

———. 2002. National Intercensal Population Estimates Methodology. Washington, DC: U.S. Census 

Bureau. Available at http://www.census.gov/popest/archives/methodology/intercensal_nat_meth.pdf.
APPENDIX 1: HUD ADMINISTRATIVE REGIONS 37

Northwest/Alaska

Rocky Mountain

Pacific/Hawaii

Northwest/Alaska Pacific/Hawaii

New

England

Puerto Rico

Southwest Southeast

Great Plains

Midwest MidAtlantic

New York/

New Jersey
38 APPENDIX 2: ALL FY 2011 ENTITLEMENT JURISDICTIONS

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

AK ANCHORAGE

FY 2011 Grant 1,708 553 497 657 A

New Data Grant 1,920 563 710 647 A

Change (%) 12.4 1.6 42.6 -1.6

AL ANNISTON

FY 2011 Grant 599 115 337 147 B

New Data Grant 579 100 336 144 B

Change (%) -3.2 -12.6 -0.4 -2.2

AL AUBURN

FY 2011 Grant 662 112 28 523 A

New Data Grant 554 103 32 419 A

Change (%) -16.4 -8.0 12.8 -19.8

AL BESSEMER

FY 2011 Grant 637 168 317 151 B

New Data Grant 557 126 324 107 B

Change (%) -12.4 -25.2 2.2 -29.2

AL BIRMINGHAM

FY 2011 Grant 6,022 1,231 3,544 1,247 B

New Data Grant 6,235 1,061 3,693 1,481 B

Change (%) 3.6 -13.8 4.2 18.8

AL DECATUR

FY 2011 Grant 461 109 72 280 A

New Data Grant 465 107 56 302 A

Change (%) 0.8 -1.6 -23.0 7.8

AL DOTHAN

FY 2011 Grant 488 131 47 310 A

New Data Grant 494 126 49 319 A

Change (%) 1.2 -3.2 3.6 2.6

AL FLORENCE

FY 2011 Grant 376 151 118 107 B

New Data Grant 360 76 22 262 A

Change (%) -4.2 73.8

AL GADSDEN

FY 2011 Grant 1,053 182 631 240 B

New Data Grant 955 153 617 186 B

Change (%) -9.2 -16.0 -2.2 -22.6

AL HOOVER

FY 2011 Grant 262 141 48 73 A

New Data Grant 303 157 39 107 A

Change (%) 15.8 11.6 -18.0 46.0

AL HUNTSVILLE

FY 2011 Grant 1,179 347 144 687 A

New Data Grant 1,209 347 139 723 A

Change (%) 2.6 0.0 -3.6 5.2

AL JEFFERSON 

COUNTY

FY 2011 Grant 1,851 688 195 967 A

New Data Grant 1,839 697 243 899 A

Change (%) -0.6 1.2 24.8 -7.2
39

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

AL MOBILE

FY 2011 Grant 2,608 866 1,189 553 B

New Data Grant 2,416 732 1,183 501 B

Change (%) -7.4 -15.4 -0.4 -9.4

AL MOBILE COUNTY

FY 2011 Grant 1,792 411 277 1,103 A

New Data Grant 1,817 418 340 1,060 A

Change (%) 1.4 1.6 22.6 -4.0

AL MONTGOMERY

FY 2011 Grant 1,883 391 294 1,198 A

New Data Grant 1,739 397 250 1,092 A

Change (%) -7.6 1.6 -15.0 -8.8

AL OPELIKA

FY 2011 Grant 236 54 38 144 A

New Data Grant 254 51 27 176 A

Change (%) 7.6 -6.4 -29.2 22.6

AL TUSCALOOSA

FY 2011 Grant 851 180 88 583 A

New Data Grant 959 174 63 721 A

Change (%) 12.6 -3.2 -27.8 23.6

AR BENTONVILLE

FY 2011 Grant 174 71 33 70 A

New Data Grant 194 68 22 104 A

Change (%) 11.4 -4.4 -33.4 48.6

AR CONWAY

FY 2011 Grant 387 115 44 228 A

New Data Grant 470 114 46 310 A

Change (%) 21.4 -1.2 4.2 36.4

AR FAYETTEVILLE

FY 2011 Grant 594 149 76 368 A

New Data Grant 606 142 71 393 A

Change (%) 2.0 -4.8 -7.4 6.8

AR FORT SMITH

FY 2011 Grant 751 165 149 436 A

New Data Grant 866 166 210 490 A

Change (%) 15.4 0.4 41.2 12.2

AR HOT SPRINGS

FY 2011 Grant 359 138 36 184 B

New Data Grant 375 140 88 147 B

Change (%) 4.4 1.4 141.0 -20.2

AR JACKSONVILLE

FY 2011 Grant 247 61 43 143 A

New Data Grant 237 55 38 145 A

Change (%) -3.6 -10.6 -11.6 1.6

AR JONESBORO

FY 2011 Grant 506 128 52 326 A

New Data Grant 587 130 106 351 A

Change (%) 16.0 1.4 105.0 7.6

AR LITTLE ROCK

FY 2011 Grant 1,506 371 233 901 A

New Data Grant 1,499 373 231 895 A

Change (%) -0.4 0.6 -1.2 -0.8
40

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

AR NORTH LITTLE 

ROCK

FY 2011 Grant 687 204 349 134 B

New Data Grant 651 234 312 104 B

Change (%) -5.2 15.0 -10.6 -22.4

AR PINE BLUFF

FY 2011 Grant 659 273 261 124 B

New Data Grant 606 241 268 97 B

Change (%) -8.0 -11.6 2.4 -22.0

AR ROGERS

FY 2011 Grant 388 114 102 172 A

New Data Grant 449 108 118 224 A

Change (%) 15.8 -5.4 15.6 30.2

AR SPRINGDALE

FY 2011 Grant 473 132 140 200 A

New Data Grant 774 135 311 329 A

Change (%) 63.8 1.6 121.6 64.4

AR TEXARKANA

FY 2011 Grant 287 58 29 200 A

New Data Grant 257 58 28 171 A

Change (%) -10.4 -1.2 -1.2 -14.2

AR WEST MEMPHIS

FY 2011 Grant 378 53 55 270 A

New Data Grant 388 51 63 275 A

Change (%) 2.8 -3.8 14.8 1.6

AZ AVONDALE CITY

FY 2011 Grant 461 165 124 173 A

New Data Grant 677 147 201 329 A

Change (%) 46.8 -10.8 61.8 90.8

AZ CHANDLER

FY 2011 Grant 1,270 482 378 409 A

New Data Grant 1,381 455 383 543 A

Change (%) 8.8 -5.6 1.2 32.6

AZ FLAGSTAFF

FY 2011 Grant 559 117 134 308 A

New Data Grant 597 127 169 301 A

Change (%) 6.8 8.4 26.4 -2.4

AZ GILBERT

FY 2011 Grant 655 429 100 125 A

New Data Grant 839 402 134 303 A

Change (%) 28.2 -6.4 33.8 142.2

AZ GLENDALE

FY 2011 Grant 2,011 490 618 903 A

New Data Grant 2,336 437 766 1,132 A

Change (%) 16.2 -10.8 24.0 25.4

AZ MARICOPA 

COUNTY

FY 2011 Grant 2,059 826 425 808 A

New Data Grant 2,537 941 505 1,092 A

Change (%) 23.2 13.8 18.8 35.2

AZ MESA

FY 2011 Grant 3,105 903 970 1,232 A

New Data Grant 3,561 846 1,111 1,603 A

Change (%) 14.6 -6.2 14.6 30.2
41

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

AZ PEORIA CITY

FY 2011 Grant 633 316 120 198 A

New Data Grant 748 297 162 289 A

Change (%) 18.0 -5.8 35.0 46.0

AZ PHOENIX

FY 2011 Grant 15,471 3,081 5,169 7,221 A

New Data Grant 16,681 2,787 5,549 8,345 A

Change (%) 7.8 -9.6 7.4 15.6

AZ PIMA COUNTY

FY 2011 Grant 2,412 898 497 1,016 A

New Data Grant 2,715 865 595 1,255 A

Change (%) 12.6 -3.8 19.6 23.6

AZ PRESCOTT

FY 2011 Grant 265 83 33 150 A

New Data Grant 250 77 26 147 A

Change (%) -5.8 -7.0 -20.0 -2.0

AZ SCOTTSDALE

FY 2011 Grant 1,053 460 184 410 A

New Data Grant 1,056 419 168 469 A

Change (%) 0.4 -8.8 -8.4 14.6

AZ SURPRISE CITY

FY 2011 Grant 342 184 64 95 A

New Data Grant 503 227 89 188 A

Change (%) 47.2 23.0 40.6 98.4

AZ TEMPE

FY 2011 Grant 1,463 345 348 770 A

New Data Grant 1,500 312 308 880 A

Change (%) 2.4 -9.6 -11.4 14.2

AZ TUCSON

FY 2011 Grant 5,598 1,052 1,503 3,043 A

New Data Grant 5,723 1,003 1,536 3,184 A

Change (%) 2.2 -4.6 2.2 4.6

AZ YUMA

FY 2011 Grant 846 176 287 384 A

New Data Grant 950 179 306 465 A

Change (%) 12.2 1.8 6.8 21.2

CA ALAMEDA

FY 2011 Grant 1,231 124 309 798 B

New Data Grant 1,142 118 265 758 B

Change (%) -7.2 -4.8 -14.0 -5.0

CA ALAMEDA 

COUNTY

FY 2011 Grant 1,758 500 723 536 A

New Data Grant 1,677 519 556 602 A

Change (%) -4.6 3.8 -23.0 12.4

CA ALHAMBRA

FY 2011 Grant 1,240 164 652 424 A

New Data Grant 859 160 382 316 A

Change (%) -30.8 -2.6 -41.4 -25.4

CA ANAHEIM

FY 2011 Grant 4,494 653 2,235 1,607 A

New Data Grant 4,575 648 2,642 1,284 A

Change (%) 1.8 -0.8 18.2 -20.0
42

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

CA ANTIOCH

FY 2011 Grant 653 196 188 270 A

New Data Grant 739 197 170 372 A

Change (%) 13.2 0.8 -9.4 37.6

CA APPLE VALLEY

FY 2011 Grant 575 136 113 327 A

New Data Grant 652 133 121 398 A

Change (%) 13.4 -1.6 7.2 21.6

CA BAKERSFIELD

FY 2011 Grant 3,034 627 867 1,540 A

New Data Grant 3,372 670 1,091 1,611 A

Change (%) 11.2 6.8 25.8 4.6

CA BALDWIN PARK

FY 2011 Grant 1,325 149 700 476 A

New Data Grant 1,171 145 642 384 A

Change (%) -11.6 -2.4 -8.2 -19.4

CA BELLFLOWER

FY 2011 Grant 1,067 141 526 400 A

New Data Grant 890 148 465 277 A

Change (%) -16.6 4.8 -11.6 -30.8

CA BERKELEY

FY 2011 Grant 2,941 411 803 1,726 B

New Data Grant 2,598 306 661 1,631 B

Change (%) -11.6 -25.6 -17.6 -5.4

CA BUENA PARK

FY 2011 Grant 912 154 450 308 A

New Data Grant 801 155 422 223 A

Change (%) -12.2 0.6 -6.2 -27.4

CA BURBANK

FY 2011 Grant 1,056 221 404 431 B

New Data Grant 1,012 152 386 474 B

Change (%) -4.2 -31.2 -4.6 9.8

CA CAMARILLO

FY 2011 Grant 323 124 93 106 A

New Data Grant 309 126 93 91 A

Change (%) -4.2 1.6 -0.4 -14.2

CA CARLSBAD

FY 2011 Grant 461 190 110 161 A

New Data Grant 455 203 105 146 A

Change (%) -1.4 6.8 -4.6 -9.0

CA CARSON

FY 2011 Grant 985 178 518 289 A

New Data Grant 787 177 385 225 A

Change (%) -20.2 -0.8 -25.6 -22.2

CA CERRITOS

FY 2011 Grant 343 99 154 90 A

New Data Grant 266 95 85 86 A

Change (%) -22.4 -4.4 -45.0 -3.8

CA CHICO

FY 2011 Grant 873 164 134 575 A

New Data Grant 862 166 130 566 A

Change (%) -1.4 1.2 -3.2 -1.6
43

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

CA CHINO

FY 2011 Grant 565 160 230 175 A

New Data Grant 490 150 221 119 A

Change (%) -13.2 -6.2 -3.6 -32.2

CA CHINO HILLS

FY 2011 Grant 380 143 117 120 A

New Data Grant 339 144 103 92 A

Change (%) -10.8 1.0 -12.0 -24.0

CA CHULA VISTA

FY 2011 Grant 1,814 433 736 646 A

New Data Grant 1,841 470 758 613 A

Change (%) 1.4 8.8 3.0 -5.2

CA CITRUS HEIGHTS

FY 2011 Grant 578 164 171 244 A

New Data Grant 598 161 166 272 A

Change (%) 3.4 -1.8 -2.6 11.2

CA CLOVIS CITY

FY 2011 Grant 583 180 150 253 A

New Data Grant 595 184 140 271 A

Change (%) 2.0 2.2 -6.6 7.0

CA COMPTON

FY 2011 Grant 1,859 182 771 906 A

New Data Grant 1,778 186 930 662 A

Change (%) -4.4 2.4 20.6 -27.0

CA CONCORD

FY 2011 Grant 921 236 363 322 A

New Data Grant 942 235 366 340 A

Change (%) 2.2 -0.4 1.0 5.8

CA CONTRA COSTA 

COUNTY

FY 2011 Grant 3,119 1,132 931 1,055 A

New Data Grant 3,093 1,144 833 1,116 A

Change (%) -0.8 1.0 -10.6 5.8

CA CORONA

FY 2011 Grant 1,082 292 430 360 A

New Data Grant 1,202 294 539 370 A

Change (%) 11.0 0.6 25.2 2.6

CA COSTA MESA

FY 2011 Grant 1,228 213 542 472 A

New Data Grant 1,177 212 557 409 A

Change (%) -4.2 -0.6 2.6 -13.6

CA CUPERTINO CITY

FY 2011 Grant 353 105 160 88 A

New Data Grant 348 112 168 68 A

Change (%) -1.4 7.2 5.2 -23.2

CA DALY CITY

FY 2011 Grant 1,120 198 667 256 A

New Data Grant 1,011 195 630 186 A

Change (%) -9.6 -1.2 -5.4 -27.4

CA DAVIS

FY 2011 Grant 729 122 111 496 A

New Data Grant 670 127 86 458 A

Change (%) -8.0 4.0 -22.6 -7.6
44

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

CA DELANO CITY

FY 2011 Grant 747 102 308 336 A

New Data Grant 652 102 235 314 A

Change (%) -12.8 0.2 -23.8 -6.6

CA DOWNEY

FY 2011 Grant 1,337 207 717 412 A

New Data Grant 1,169 215 621 332 A

Change (%) -12.6 4.0 -13.4 -19.4

CA EL CAJON

FY 2011 Grant 1,129 183 402 544 A

New Data Grant 1,121 192 415 514 A

Change (%) -0.6 5.0 3.2 -5.4

CA EL CENTRO

FY 2011 Grant 608 80 232 296 A

New Data Grant 530 82 197 251 A

Change (%) -12.8 3.0 -15.4 -15.0

CA EL MONTE

FY 2011 Grant 2,435 235 1,147 1,053 A

New Data Grant 2,207 219 1,237 751 A

Change (%) -9.4 -6.8 7.8 -28.6

CA ELK GROVE

FY 2011 Grant 455 262 87 106 A

New Data Grant 763 295 199 268 A

Change (%) 67.6 12.8 128.8 152.2

CA ENCINITAS

FY 2011 Grant 364 119 96 148 A

New Data Grant 310 115 76 119 A

Change (%) -14.6 -3.6 -20.8 -19.6

CA ESCONDIDO

FY 2011 Grant 1,568 271 672 625 A

New Data Grant 1,547 277 700 569 A

Change (%) -1.4 2.4 4.2 -8.8

CA FAIRFIELD

FY 2011 Grant 738 200 239 299 A

New Data Grant 738 203 235 300 A

Change (%) 0.0 1.4 -1.6 0.4

CA FONTANA

FY 2011 Grant 1,909 363 801 744 A

New Data Grant 1,983 378 936 669 A

Change (%) 4.0 4.0 16.8 -10.0

CA FOUNTAIN 

VALLEY

FY 2011 Grant 316 108 126 83 A

New Data Grant 304 107 101 97 A

Change (%) -3.8 -1.2 -19.8 17.2

CA FREMONT

FY 2011 Grant 1,498 397 717 384 A

New Data Grant 1,277 413 545 319 A

Change (%) -14.8 3.8 -24.0 -17.0

CA FRESNO

FY 2011 Grant 6,891 928 2,105 3,858 A

New Data Grant 6,424 954 2,290 3,180 A

Change (%) -6.8 2.8 8.8 -17.6
45

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

CA FRESNO COUNTY

FY 2011 Grant 3,588 578 1,266 1,744 A

New Data Grant 3,576 575 1,453 1,549 A

Change (%) -0.4 -0.6 14.8 -11.2

CA FULLERTON

FY 2011 Grant 1,363 256 609 498 A

New Data Grant 1,426 261 756 410 A

Change (%) 4.6 1.6 24.2 -17.8

CA GARDEN GROVE

FY 2011 Grant 2,344 322 1,221 801 A

New Data Grant 2,174 329 1,201 643 A

Change (%) -7.2 2.4 -1.6 -19.8

CA GARDENA

FY 2011 Grant 827 113 400 315 A

New Data Grant 678 113 299 266 A

Change (%) -18.0 0.6 -25.2 -15.6

CA GILROY CITY

FY 2011 Grant 445 97 198 149 A

New Data Grant 374 94 165 115 A

Change (%) -16.0 -3.0 -17.0 -23.2

CA GLENDALE

FY 2011 Grant 2,949 381 1,516 1,052 A

New Data Grant 2,173 370 1,083 720 A

Change (%) -26.4 -2.8 -28.6 -31.6

CA GLENDORA CITY

FY 2011 Grant 301 96 105 100 A

New Data Grant 221 97 63 61 A

Change (%) -26.8 0.4 -40.0 -38.8

CA GOLETA

FY 2011 Grant 242 57 101 84 A

New Data Grant 216 58 83 75 A

Change (%) -10.8 0.8 -17.8 -10.4

CA HANFORD

FY 2011 Grant 487 97 142 248 A

New Data Grant 467 104 158 205 A

Change (%) -4.2 7.6 11.0 -17.4

CA HAWTHORNE

FY 2011 Grant 1,541 162 785 593 A

New Data Grant 1,317 163 724 430 A

Change (%) -14.6 0.2 -7.8 -27.6

CA HAYWARD

FY 2011 Grant 1,554 279 787 488 A

New Data Grant 1,389 278 633 478 A

Change (%) -10.6 -0.4 -19.6 -2.0

CA HEMET

FY 2011 Grant 635 139 167 330 A

New Data Grant 783 152 262 368 A

Change (%) 23.2 9.2 57.4 11.8

CA HESPERIA

FY 2011 Grant 666 167 191 308 A

New Data Grant 919 174 294 452 A

Change (%) 38.0 4.4 53.8 46.6
46

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

CA HUNTINGTON 

BEACH

FY 2011 Grant 1,234 374 423 438 A

New Data Grant 1,060 366 337 357 A

Change (%) -14.0 -2.0 -20.2 -18.6

CA HUNTINGTON 

PARK

FY 2011 Grant 1,438 117 780 541 A

New Data Grant 1,422 112 874 436 A

Change (%) -1.2 -4.4 12.0 -19.4

CA INDIO CITY

FY 2011 Grant 864 164 334 366 A

New Data Grant 1,018 147 452 419 A

Change (%) 17.8 -10.8 35.6 14.4

CA INGLEWOOD

FY 2011 Grant 2,010 217 914 879 A

New Data Grant 1,657 211 838 607 A

Change (%) -17.6 -2.6 -8.2 -31.0

CA IRVINE

FY 2011 Grant 1,210 405 369 435 A

New Data Grant 1,292 409 356 526 A

Change (%) 6.8 1.0 -3.4 21.0

CA KERN COUNTY

FY 2011 Grant 4,767 764 1,463 2,540 A

New Data Grant 5,062 777 1,844 2,440 A

Change (%) 6.2 1.8 26.2 -4.0

CA LA HABRA

FY 2011 Grant 733 115 350 268 A

New Data Grant 748 116 447 184 A

Change (%) 2.0 1.2 27.8 -31.2

CA LA MESA

FY 2011 Grant 398 107 112 178 A

New Data Grant 378 110 91 177 A

Change (%) -5.0 2.4 -19.2 -0.4

CA LAGUNA NIGUEL

FY 2011 Grant 303 125 90 88 A

New Data Grant 260 121 40 99 A

Change (%) -14.0 -2.8 -55.8 13.0

CA LAKE FOREST

FY 2011 Grant 432 147 170 116 A

New Data Grant 465 149 204 112 A

Change (%) 7.6 1.6 20.0 -3.4

CA LAKEWOOD

FY 2011 Grant 647 151 292 205 A

New Data Grant 495 154 230 111 A

Change (%) -23.4 2.2 -21.2 -45.6

CA LANCASTER

FY 2011 Grant 1,245 282 321 641 A

New Data Grant 1,553 302 426 825 A

Change (%) 24.8 7.2 32.6 28.6

CA LIVERMORE

FY 2011 Grant 414 157 121 137 A

New Data Grant 395 156 107 132 A

Change (%) -4.8 -0.4 -11.6 -3.8
47

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

CA LODI

FY 2011 Grant 676 119 227 330 A

New Data Grant 706 120 306 280 A

Change (%) 4.6 0.8 34.8 -15.0

CA LONG BEACH

FY 2011 Grant 7,806 894 3,273 3,638 A

New Data Grant 6,779 891 3,302 2,585 A

Change (%) -13.2 -0.4 0.8 -29.0

CA LOS ANGELES

FY 2011 Grant 64,578 7,408 28,998 28,172 A

New Data Grant 59,056 7,312 30,243 21,500 A

Change (%) -8.6 -1.2 4.2 -23.6

CA LOS ANGELES 

COUNTY

FY 2011 Grant 26,692 4,400 11,666 10,626 A

New Data Grant 23,554 4,375 11,193 7,985 A

Change (%) -11.8 -0.6 -4.0 -24.8

CA LYNWOOD

FY 2011 Grant 1,417 135 725 557 A

New Data Grant 1,260 135 734 391 A

Change (%) -11.0 -0.2 1.2 -29.8

CA MADERA

FY 2011 Grant 876 110 277 490 A

New Data Grant 964 118 442 404 A

Change (%) 10.0 8.0 59.6 -17.6

CA MARIN COUNTY

FY 2011 Grant 1,441 485 407 549 A

New Data Grant 1,307 487 358 463 A

Change (%) -9.2 0.4 -12.2 -15.6

CA MERCED

FY 2011 Grant 1,130 147 367 615 A

New Data Grant 1,065 152 323 590 A

Change (%) -5.6 3.2 -12.0 -4.0

CA MILPITAS CITY

FY 2011 Grant 533 131 297 105 A

New Data Grant 438 129 194 115 A

Change (%) -17.8 -2.0 -34.4 9.4

CA MISSION VIEJO

FY 2011 Grant 441 183 136 122 A

New Data Grant 442 180 148 114 A

Change (%) 0.2 -1.6 9.4 -7.0

CA MODESTO

FY 2011 Grant 2,109 392 684 1,033 A

New Data Grant 1,910 388 593 930 A

Change (%) -9.4 -1.0 -13.4 -10.0

CA MONTEBELLO

FY 2011 Grant 944 119 458 367 A

New Data Grant 804 120 412 271 A

Change (%) -14.8 1.0 -10.0 -26.2

CA MONTEREY

FY 2011 Grant 252 44 77 131 B

New Data Grant 215 46 73 95 B

Change (%) -15.0 4.0 -4.6 -27.4
48

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

CA MONTEREY PARK

FY 2011 Grant 874 119 427 327 A

New Data Grant 646 116 302 228 A

Change (%) -26.0 -2.4 -29.4 -30.4

CA MORENO VALLEY

FY 2011 Grant 1,619 371 540 708 A

New Data Grant 2,083 373 898 813 A

Change (%) 28.8 0.6 66.4 14.8

CA MOUNTAIN 

VIEW

FY 2011 Grant 619 140 313 167 A

New Data Grant 562 143 280 139 A

Change (%) -9.2 2.2 -10.4 -16.8

CA NAPA CITY

FY 2011 Grant 615 146 244 225 A

New Data Grant 728 148 338 242 A

Change (%) 18.4 1.8 38.2 7.4

CA NATIONAL CITY

FY 2011 Grant 986 109 483 395 A

New Data Grant 873 113 405 355 A

Change (%) -11.4 4.0 -16.0 -10.2

CA NEWPORT 

BEACH

FY 2011 Grant 324 158 56 110 A

New Data Grant 393 164 86 143 A

Change (%) 21.4 4.0 53.6 30.0

CA NORWALK

FY 2011 Grant 1,384 198 762 424 A

New Data Grant 1,201 203 684 313 A

Change (%) -13.2 2.6 -10.2 -26.2

CA OAKLAND

FY 2011 Grant 7,579 1,614 1,786 4,179 B

New Data Grant 8,141 1,247 1,951 4,943 B

Change (%) 7.4 -22.8 9.2 18.2

CA OCEANSIDE

FY 2011 Grant 1,640 334 655 650 A

New Data Grant 1,324 322 561 440 A

Change (%) -19.2 -3.6 -14.4 -32.2

CA ONTARIO

FY 2011 Grant 2,184 332 1,003 849 A

New Data Grant 1,941 316 997 628 A

Change (%) -11.0 -4.8 -0.6 -26.0

CA ORANGE

FY 2011 Grant 1,197 265 496 436 A

New Data Grant 1,276 263 673 341 A

Change (%) 6.6 -0.6 35.6 -22.0

CA ORANGE 

COUNTY

FY 2011 Grant 3,390 991 1,276 1,123 A

New Data Grant 3,371 972 1,324 1,075 A

Change (%) -0.6 -2.0 3.8 -4.2

CA OXNARD

FY 2011 Grant 2,472 363 1,213 897 A

New Data Grant 2,313 382 1,113 818 A

Change (%) -6.4 5.2 -8.2 -8.8
49

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

CA PALM DESERT

FY 2011 Grant 320 100 87 132 A

New Data Grant 317 93 88 137 A

Change (%) -0.6 -6.8 0.8 3.0

CA PALM SPRINGS

FY 2011 Grant 455 93 137 225 A

New Data Grant 349 86 105 158 A

Change (%) -23.4 -7.8 -23.2 -29.8

CA PALMDALE

FY 2011 Grant 1,357 278 436 643 A

New Data Grant 1,460 295 447 719 A

Change (%) 7.6 5.8 2.4 11.8

CA PALO ALTO

FY 2011 Grant 607 59 230 318 B

New Data Grant 482 59 168 254 B

Change (%) -20.6 0.4 -26.8 -20.0

CA PARADISE

FY 2011 Grant 199 51 35 113 A

New Data Grant 177 51 25 102 A

Change (%) -11.2 -1.2 -29.6 -10.2

CA PARAMOUNT 

CITY

FY 2011 Grant 1,085 106 557 422 A

New Data Grant 896 104 488 303 A

Change (%) -17.4 -2.0 -12.4 -28.0

CA PASADENA

FY 2011 Grant 2,056 441 390 1,225 B

New Data Grant 1,967 343 452 1,172 B

Change (%) -4.4 -22.2 15.8 -4.4

CA PERRIS CITY

FY 2011 Grant 583 109 219 256 A

New Data Grant 730 132 294 304 A

Change (%) 25.2 21.4 34.6 18.8

CA PETALUMA

FY 2011 Grant 311 107 91 114 A

New Data Grant 314 112 104 98 A

Change (%) 0.8 4.8 15.0 -14.0

CA PICO RIVERA

FY 2011 Grant 816 122 418 276 A

New Data Grant 725 121 427 176 A

Change (%) -11.2 -0.2 2.0 -36.0

CA PITTSBURG

FY 2011 Grant 590 126 235 229 A

New Data Grant 605 122 241 243 A

Change (%) 2.6 -3.2 2.2 6.0

CA PLEASANTON 

CITY

FY 2011 Grant 250 131 62 57 A

New Data Grant 269 136 67 67 A

Change (%) 8.0 3.8 8.0 17.4

CA POMONA

FY 2011 Grant 2,552 295 1,162 1,095 A

New Data Grant 2,278 287 1,282 709 A

Change (%) -10.8 -2.4 10.4 -35.2
50

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

CA PORTERVILLE

FY 2011 Grant 724 101 227 396 A

New Data Grant 732 104 260 368 A

Change (%) 1.2 3.6 14.4 -7.2

CA RANCHO 

CORDOVA CITY

FY 2011 Grant 627 122 208 297 A

New Data Grant 628 125 198 306 A

Change (%) 0.2 2.6 -4.8 2.8

CA RANCHO 

CUCAMONGA

FY 2011 Grant 919 332 272 315 A

New Data Grant 902 319 336 247 A

Change (%) -1.8 -4.0 23.6 -21.4

CA RANCHO SANTA 

MARGARITA

FY 2011 Grant 221 96 78 48 A

New Data Grant 223 92 83 48 A

Change (%) 0.6 -3.6 6.8 -1.0

CA REDDING

FY 2011 Grant 751 175 143 433 A

New Data Grant 788 173 194 421 A

Change (%) 5.0 -1.0 35.4 -2.8

CA REDLANDS

FY 2011 Grant 512 135 148 228 A

New Data Grant 451 133 115 204 A

Change (%) -11.8 -2.0 -22.8 -10.6

CA REDONDO 

BEACH

FY 2011 Grant 367 129 107 131 A

New Data Grant 299 129 71 99 A

Change (%) -18.6 -0.2 -34.0 -24.0

CA REDWOOD CITY

FY 2011 Grant 653 144 354 155 A

New Data Grant 759 148 382 229 A

Change (%) 16.2 2.8 7.8 47.4

CA RIALTO

FY 2011 Grant 1,196 191 450 555 A

New Data Grant 1,181 191 585 405 A

Change (%) -1.2 0.2 30.0 -27.2

CA RICHMOND

FY 2011 Grant 1,228 199 471 558 A

New Data Grant 1,145 200 488 458 A

Change (%) -6.8 0.2 3.6 -18.0

CA RIVERSIDE

FY 2011 Grant 3,036 576 1,086 1,374 A

New Data Grant 3,053 586 1,344 1,123 A

Change (%) 0.6 1.8 23.8 -18.2

CA RIVERSIDE 

COUNTY

FY 2011 Grant 8,045 2,127 2,453 3,465 A

New Data Grant 8,635 2,219 3,036 3,379 A

Change (%) 7.4 4.4 23.8 -2.4

CA ROCKLIN CITY

FY 2011 Grant 185 104 24 58 A

New Data Grant 203 110 28 65 A

Change (%) 9.6 6.0 15.2 13.2
51

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

CA ROSEMEAD

FY 2011 Grant 1,028 105 500 424 A

New Data Grant 765 104 449 212 A

Change (%) -25.6 -1.2 -10.2 -50.0

CA ROSEVILLE

FY 2011 Grant 473 224 112 138 A

New Data Grant 581 229 137 215 A

Change (%) 22.8 2.4 22.2 56.2

CA SACRAMENTO

FY 2011 Grant 5,228 902 1,521 2,804 A

New Data Grant 4,643 899 1,518 2,226 A

Change (%) -11.2 -0.4 -0.2 -20.6

CA SACRAMENTO 

COUNTY

FY 2011 Grant 5,247 1,259 1,469 2,519 A

New Data Grant 5,094 1,256 1,546 2,292 A

Change (%) -3.0 -0.4 5.2 -9.0

CA SALINAS

FY 2011 Grant 2,223 279 1,111 833 A

New Data Grant 2,106 290 1,065 751 A

Change (%) -5.2 4.0 -4.2 -9.8

CA SAN 

BERNARDINO

FY 2011 Grant 3,245 384 1,073 1,788 A

New Data Grant 3,152 405 1,222 1,525 A

Change (%) -2.8 5.6 14.0 -14.8

CA

SAN 

BERNARDINO 

COUNTY

FY 2011 Grant 6,414 1,202 2,069 3,144 A

New Data Grant 6,516 1,214 2,407 2,895 A

Change (%) 1.6 1.0 16.4 -8.0

CA SAN 

BUENAVENTURA

FY 2011 Grant 783 202 268 313 A

New Data Grant 827 205 294 328 A

Change (%) 5.6 1.6 9.6 4.6

CA SAN CLEMENTE

FY 2011 Grant 358 119 107 132 A

New Data Grant 358 122 105 131 A

Change (%) 0.0 2.8 -2.2 -0.8

CA SAN DIEGO

FY 2011 Grant 13,603 2,526 5,010 6,068 A

New Data Grant 11,999 2,521 4,494 4,984 A

Change (%) -11.8 -0.2 -10.2 -17.8

CA SAN DIEGO 

COUNTY

FY 2011 Grant 4,245 1,185 1,379 1,682 A

New Data Grant 3,772 1,193 1,173 1,406 A

Change (%) -11.2 0.8 -15.0 -16.4

CA SAN FRANCISCO

FY 2011 Grant 18,584 1,827 3,707 13,050 B

New Data Grant 18,181 1,645 3,703 12,833 B

Change (%) -2.2 -10.0 -0.2 -1.6

CA SAN JOAQUIN 

COUNTY

FY 2011 Grant 2,526 630 809 1,087 A

New Data Grant 2,639 639 913 1,087 A

Change (%) 4.4 1.4 12.8 0.0
52

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

CA SAN JOSE

FY 2011 Grant 9,151 1,865 4,522 2,764 A

New Data Grant 8,790 1,824 4,125 2,840 A

Change (%) -4.0 -2.2 -8.8 2.8

CA SAN LEANDRO

FY 2011 Grant 656 152 327 177 A

New Data Grant 629 164 292 173 A

Change (%) -4.2 7.8 -10.6 -2.4

CA SAN LUIS OBISPO 

COUNTY

FY 2011 Grant 1,887 479 434 974 A

New Data Grant 1,909 485 475 948 A

Change (%) 1.2 1.2 9.6 -2.6

CA SAN MARCOS 

CITY

FY 2011 Grant 635 156 247 232 A

New Data Grant 680 162 281 238 A

Change (%) 7.2 3.6 13.6 2.6

CA SAN MATEO

FY 2011 Grant 711 179 334 197 A

New Data Grant 727 187 365 175 A

Change (%) 2.4 4.4 9.2 -11.0

CA SAN MATEO 

COUNTY

FY 2011 Grant 2,564 748 1,104 712 A

New Data Grant 2,780 732 1,250 797 A

Change (%) 8.4 -2.0 13.2 12.0

CA SANTA ANA

FY 2011 Grant 6,217 658 3,263 2,295 A

New Data Grant 6,368 626 4,028 1,714 A

Change (%) 2.4 -5.0 23.4 -25.4

CA SANTA BARBARA

FY 2011 Grant 970 167 387 417 A

New Data Grant 888 170 367 351 A

Change (%) -8.4 2.0 -5.0 -15.8

CA SANTA BARBARA 

COUNTY

FY 2011 Grant 1,820 382 572 866 A

New Data Grant 1,741 384 637 720 A

Change (%) -4.4 0.4 11.4 -16.8

CA SANTA CLARA

FY 2011 Grant 979 217 489 274 A

New Data Grant 886 225 365 297 A

Change (%) -9.6 3.8 -25.4 8.2

CA SANTA CLARA 

COUNTY

FY 2011 Grant 1,585 288 790 507 B

New Data Grant 1,500 287 790 422 B

Change (%) -5.4 -0.2 0.0 -16.6

CA SANTA CLARITA

FY 2011 Grant 1,015 327 351 337 A

New Data Grant 1,215 340 496 379 A

Change (%) 19.8 4.0 41.4 12.6

CA SANTA CRUZ

FY 2011 Grant 551 110 147 294 A

New Data Grant 555 174 0 381 B

Change (%) 0.8 -40.8
53

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

CA SANTA MARIA

FY 2011 Grant 1,148 168 459 521 A

New Data Grant 1,316 192 691 433 A

Change (%) 14.6 14.2 50.6 -17.0

CA SANTA MONICA

FY 2011 Grant 1,250 182 468 600 B

New Data Grant 1,131 171 426 533 B

Change (%) -9.6 -6.2 -9.0 -11.0

CA SANTA ROSA

FY 2011 Grant 1,135 304 394 436 A

New Data Grant 1,367 324 489 554 A

Change (%) 20.4 6.2 24.0 27.2

CA SANTEE

FY 2011 Grant 287 107 81 99 A

New Data Grant 303 103 79 121 A

Change (%) 5.2 -3.8 -3.0 21.8

CA SEASIDE

FY 2011 Grant 368 66 168 134 A

New Data Grant 386 64 224 98 A

Change (%) 5.0 -3.2 33.6 -27.0

CA SIMI VALLEY

FY 2011 Grant 650 234 190 227 A

New Data Grant 613 240 184 189 A

Change (%) -5.8 2.4 -3.2 -16.6

CA SONOMA 

COUNTY

FY 2011 Grant 1,788 501 559 727 A

New Data Grant 1,758 497 588 672 A

Change (%) -1.6 -0.8 5.2 -7.6

CA SOUTH GATE

FY 2011 Grant 1,895 186 1,062 648 A

New Data Grant 1,772 182 1,086 504 A

Change (%) -6.4 -2.2 2.4 -22.2

CA SOUTH SAN 

FRANCISCO

FY 2011 Grant 543 121 311 111 A

New Data Grant 458 123 214 121 A

Change (%) -15.8 1.0 -31.2 9.4

CA STANISLAUS 

COUNTY

FY 2011 Grant 2,297 422 810 1,065 A

New Data Grant 2,212 428 782 1,001 A

Change (%) -3.8 1.4 -3.4 -6.0

CA STOCKTON

FY 2011 Grant 3,777 556 1,223 1,999 A

New Data Grant 3,479 562 1,287 1,630 A

Change (%) -7.8 1.2 5.2 -18.4

CA SUNNYVALE

FY 2011 Grant 1,118 259 608 251 A

New Data Grant 1,117 270 609 238 A

Change (%) 0.0 4.2 0.2 -5.2

CA THOUSAND 

OAKS

FY 2011 Grant 607 239 167 201 A

New Data Grant 637 244 195 197 A

Change (%) 4.8 2.2 16.8 -2.0
54

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

CA TORRANCE

FY 2011 Grant 1,054 271 472 310 A

New Data Grant 871 280 331 260 A

Change (%) -17.4 3.4 -30.0 -16.2

CA TULARE

FY 2011 Grant 646 111 209 326 A

New Data Grant 705 114 268 323 A

Change (%) 9.2 2.8 28.4 -0.8

CA TURLOCK

FY 2011 Grant 673 133 230 310 A

New Data Grant 542 132 165 245 A

Change (%) -19.4 -0.6 -28.4 -20.8

CA TUSTIN

FY 2011 Grant 731 140 391 200 A

New Data Grant 658 146 332 181 A

Change (%) -10.0 3.8 -15.0 -9.4

CA UNION CITY

FY 2011 Grant 585 141 291 153 A

New Data Grant 483 134 190 159 A

Change (%) -17.4 -4.8 -34.8 4.4

CA UPLAND

FY 2011 Grant 636 141 209 286 A

New Data Grant 602 142 268 191 A

Change (%) -5.4 0.8 28.6 -33.2

CA VACAVILLE

FY 2011 Grant 499 178 152 169 A

New Data Grant 462 178 111 173 A

Change (%) -7.2 0.2 -26.8 2.6

CA VALLEJO

FY 2011 Grant 1,051 222 422 408 A

New Data Grant 1,017 224 335 459 A

Change (%) -3.2 0.8 -20.6 12.6

CA VENTURA 

COUNTY

FY 2011 Grant 1,806 392 748 666 A

New Data Grant 1,602 391 700 511 A

Change (%) -11.2 -0.2 -6.4 -23.2

CA VICTORVILLE

FY 2011 Grant 841 214 209 418 A

New Data Grant 1,040 223 247 569 A

Change (%) 23.6 4.2 18.2 36.2

CA VISALIA

FY 2011 Grant 1,111 236 330 545 A

New Data Grant 1,133 240 365 528 A

Change (%) 2.0 1.6 10.6 -3.0

CA VISTA

FY 2011 Grant 1,071 179 451 441 A

New Data Grant 1,019 181 458 380 A

Change (%) -4.8 1.0 1.4 -13.8

CA WALNUT CREEK

FY 2011 Grant 285 124 79 83 A

New Data Grant 258 124 49 86 A

Change (%) -9.4 0.0 -37.8 3.6
55

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

CA WATSONVILLE

FY 2011 Grant 775 99 370 306 A

New Data Grant 827 99 444 284 A

Change (%) 6.8 0.0 19.8 -7.2

CA WEST COVINA

FY 2011 Grant 1,068 204 534 331 A

New Data Grant 991 205 460 326 A

Change (%) -7.2 0.4 -13.8 -1.2

CA WESTMINSTER

FY 2011 Grant 1,121 173 535 413 A

New Data Grant 962 173 510 279 A

Change (%) -14.2 -0.2 -4.6 -32.4

CA WHITTIER

FY 2011 Grant 842 158 383 301 A

New Data Grant 708 165 348 196 A

Change (%) -16.0 4.0 -9.2 -34.8

CA WOODLAND

FY 2011 Grant 527 107 216 204 A

New Data Grant 457 107 188 162 A

Change (%) -13.2 0.0 -13.0 -20.6

CA YORBA LINDA

FY 2011 Grant 245 128 56 62 A

New Data Grant 220 124 55 41 A

Change (%) -10.4 -3.2 -1.6 -33.6

CA YUBA CITY

FY 2011 Grant 512 119 156 236 A

New Data Grant 556 125 190 241 A

Change (%) 8.6 5.2 21.4 1.8

CO ADAMS COUNTY

FY 2011 Grant 1,359 413 395 551 A

New Data Grant 1,807 419 545 843 A

Change (%) 33.0 1.4 38.2 53.0

CO ARAPAHOE 

COUNTY

FY 2011 Grant 924 343 239 342 A

New Data Grant 1,168 347 258 563 A

Change (%) 26.4 1.2 7.6 64.6

CO ARVADA

FY 2011 Grant 461 209 65 187 A

New Data Grant 491 205 82 204 A

Change (%) 6.6 -2.0 26.2 9.2

CO AURORA

FY 2011 Grant 2,244 625 767 852 A

New Data Grant 2,882 627 788 1,467 A

Change (%) 28.4 0.2 2.8 72.2

CO BOULDER

FY 2011 Grant 853 194 129 531 A

New Data Grant 897 188 92 617 A

Change (%) 5.2 -3.0 -28.2 16.2

CO BROOMFIELD 

CITY/COUNTY

FY 2011 Grant 204 108 40 56 A

New Data Grant 207 108 19 81 A

Change (%) 1.4 -0.4 -53.6 44.6
56

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

CO CENTENNIAL

FY 2011 Grant 273 194 18 60 A

New Data Grant 335 194 18 123 A

Change (%) 22.4 -0.4 -4.0 104.0

CO COLORADO 

SPRINGS

FY 2011 Grant 2,328 773 472 1,083 A

New Data Grant 2,605 803 469 1,333 A

Change (%) 11.8 3.8 -0.6 23.0

CO DENVER

FY 2011 Grant 7,940 1,642 1,645 4,652 B

New Data Grant 7,804 1,844 1,690 4,270 B

Change (%) -1.8 12.2 2.8 -8.2

CO DOUGLAS 

COUNTY

FY 2011 Grant 758 557 71 130 A

New Data Grant 850 550 72 228 A

Change (%) 12.2 -1.2 1.6 74.6

CO EL PASO COUNTY

FY 2011 Grant 844 376 149 318 A

New Data Grant 977 377 165 435 A

Change (%) 15.8 0.0 10.4 37.0

CO FORT COLLINS

FY 2011 Grant 923 268 98 557 A

New Data Grant 1,096 278 62 757 A

Change (%) 18.6 3.4 -37.0 35.8

CO GRAND 

JUNCTION

FY 2011 Grant 329 113 47 169 A

New Data Grant 417 113 59 245 A

Change (%) 26.8 0.0 26.4 44.8

CO GREELEY

FY 2011 Grant 790 179 172 439 A

New Data Grant 894 179 155 560 A

Change (%) 13.2 0.0 -9.8 27.4

CO JEFFERSON 

COUNTY

FY 2011 Grant 987 469 152 366 A

New Data Grant 1,045 467 120 458 A

Change (%) 5.8 -0.4 -21.2 25.0

CO LAKEWOOD

FY 2011 Grant 820 274 194 352 A

New Data Grant 984 276 219 490 A

Change (%) 20.0 0.4 12.8 39.4

CO LONGMONT

FY 2011 Grant 493 171 128 195 A

New Data Grant 597 166 148 282 A

Change (%) 21.0 -2.8 16.0 45.0

CO LOVELAND

FY 2011 Grant 275 128 47 101 A

New Data Grant 323 129 29 165 A

Change (%) 17.4 0.6 -37.8 64.4

CO PUEBLO

FY 2011 Grant 1,513 371 401 742 B

New Data Grant 1,390 375 364 651 B

Change (%) -8.2 1.2 -9.2 -12.2
57

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

CO THORNTON

FY 2011 Grant 509 227 131 150 A

New Data Grant 640 229 154 257 A

Change (%) 25.8 0.6 17.4 71.2

CO WESTMINSTER

FY 2011 Grant 507 211 130 166 A

New Data Grant 587 205 102 280 A

Change (%) 15.8 -3.0 -21.4 68.6

CT BRIDGEPORT

FY 2011 Grant 3,009 526 1,226 1,258 B

New Data Grant 3,114 481 1,109 1,523 B

Change (%) 3.4 -8.4 -9.4 21.0

CT BRISTOL

FY 2011 Grant 575 83 92 400 B

New Data Grant 578 84 91 403 B

Change (%) 0.6 1.6 -0.4 0.6

CT DANBURY

FY 2011 Grant 566 120 0 446 B

New Data Grant 599 156 250 193 A

Change (%) 5.8 60.4

CT EAST HARTFORD

FY 2011 Grant 599 107 218 275 B

New Data Grant 584 122 178 284 B

Change (%) -2.6 14.4 -18.4 3.4

CT FAIRFIELD

FY 2011 Grant 511 32 148 331 B

New Data Grant 483 30 117 336 B

Change (%) -5.4 -6.8 -20.4 1.4

CT GREENWICH

FY 2011 Grant 862 51 230 580 B

New Data Grant 818 40 236 542 B

Change (%) -5.2 -22.6 2.4 -6.6

CT HAMDEN TOWN

FY 2011 Grant 496 88 45 364 B

New Data Grant 389 75 3 311 B

Change (%) -21.6 -14.8 -92.4 -14.6

CT HARTFORD

FY 2011 Grant 3,483 754 1,492 1,236 B

New Data Grant 3,985 663 1,451 1,871 B

Change (%) 14.4 -12.0 -2.8 51.4

CT MANCHESTER

FY 2011 Grant 608 91 86 432 B

New Data Grant 585 77 56 452 B

Change (%) -3.8 -14.8 -34.4 4.6

CT MERIDEN

FY 2011 Grant 845 133 234 478 B

New Data Grant 871 165 204 502 B

Change (%) 3.0 24.0 -12.8 5.0

CT MIDDLETOWN

FY 2011 Grant 380 66 20 294 B

New Data Grant 446 94 24 328 B

Change (%) 17.2 42.2 21.2 11.4
58

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

CT MILFORD TOWN

FY 2011 Grant 479 41 77 361 B

New Data Grant 501 39 116 346 B

Change (%) 4.6 -4.4 50.4 -4.2

CT NEW BRITAIN

FY 2011 Grant 1,717 238 661 818 B

New Data Grant 1,698 228 612 858 B

Change (%) -1.0 -4.2 -7.4 5.0

CT NEW HAVEN

FY 2011 Grant 3,362 583 1,312 1,468 B

New Data Grant 3,630 501 1,202 1,928 B

Change (%) 8.0 -14.0 -8.4 31.4

CT NEW LONDON

FY 2011 Grant 807 77 314 416 B

New Data Grant 759 65 289 405 B

Change (%) -5.8 -15.6 -7.8 -2.6

CT NORWALK

FY 2011 Grant 890 125 225 539 B

New Data Grant 953 122 192 639 B

Change (%) 7.2 -3.0 -14.8 18.6

CT NORWICH

FY 2011 Grant 911 86 265 560 B

New Data Grant 826 93 210 523 B

Change (%) -9.4 7.6 -20.8 -6.4

CT STAMFORD

FY 2011 Grant 1,046 194 228 623 B

New Data Grant 984 236 387 360 A

Change (%) -6.0 85.8

CT STRATFORD

FY 2011 Grant 637 52 233 352 B

New Data Grant 557 41 195 321 B

Change (%) -12.6 -21.6 -16.4 -8.8

CT WATERBURY

FY 2011 Grant 2,079 354 672 1,054 B

New Data Grant 2,108 384 612 1,112 B

Change (%) 1.4 8.4 -8.8 5.6

CT WEST HARTFORD

FY 2011 Grant 1,004 56 410 537 B

New Data Grant 907 62 369 477 B

Change (%) -9.6 9.4 -10.2 -11.2

CT WEST HAVEN

FY 2011 Grant 670 94 145 431 B

New Data Grant 648 94 106 448 B

Change (%) -3.2 -0.2 -26.8 4.0

DC DISTRICT OF 

COLUMBIA

FY 2011 Grant 16,329 2,311 6,840 7,178 B

New Data Grant 15,626 1,848 6,661 7,117 B

Change (%) -4.4 -20.0 -2.6 -0.8

DE DOVER

FY 2011 Grant 248 71 35 142 A

New Data Grant 254 69 30 154 A

Change (%) 2.6 -1.6 -13.2 8.6
59

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

DE NEW CASTLE 

COUNTY

FY 2011 Grant 2,144 892 342 911 A

New Data Grant 2,336 902 391 1,043 A

Change (%) 9.0 1.0 14.6 14.6

DE WILMINGTON

FY 2011 Grant 2,224 313 885 1,027 B

New Data Grant 2,390 286 893 1,211 B

Change (%) 7.4 -8.6 1.0 18.0

FL BOCA RATON

FY 2011 Grant 431 167 79 184 A

New Data Grant 443 163 40 239 A

Change (%) 2.8 -2.6 -48.8 29.8

FL BOYNTON 

BEACH

FY 2011 Grant 479 135 135 210 A

New Data Grant 494 132 125 238 A

Change (%) 3.0 -2.4 -7.0 13.0

FL BRADENTON

FY 2011 Grant 434 104 99 231 A

New Data Grant 444 96 138 211 A

Change (%) 2.2 -8.4 39.6 -8.8

FL BREVARD 

COUNTY

FY 2011 Grant 1,379 543 174 662 A

New Data Grant 1,323 552 169 601 A

Change (%) -4.0 1.8 -2.6 -9.2

FL BROWARD 

COUNTY

FY 2011 Grant 3,312 734 1,013 1,565 A

New Data Grant 2,811 720 730 1,361 A

Change (%) -15.2 -2.0 -27.8 -13.0

FL CAPE CORAL

FY 2011 Grant 624 298 77 250 A

New Data Grant 826 297 138 391 A

Change (%) 32.2 -0.2 79.6 56.6

FL CLEARWATER

FY 2011 Grant 838 205 174 460 A

New Data Grant 807 208 140 459 A

Change (%) -3.8 1.2 -19.4 0.0

FL COCOA

FY 2011 Grant 206 32 35 139 A

New Data Grant 225 33 56 136 A

Change (%) 9.2 3.4 58.6 -2.2

FL COCONUT 

CREEK

FY 2011 Grant 255 98 50 107 A

New Data Grant 262 102 48 112 A

Change (%) 2.8 4.4 -3.8 4.6

FL COLLIER COUNTY

FY 2011 Grant 1,949 541 586 822 A

New Data Grant 2,038 550 572 916 A

Change (%) 4.6 1.6 -2.4 11.6

FL CORAL SPRINGS

FY 2011 Grant 814 245 234 336 A

New Data Grant 657 233 162 263 A

Change (%) -19.2 -4.6 -30.8 -21.8
60

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

FL DAVIE

FY 2011 Grant 597 177 152 268 A

New Data Grant 583 177 126 280 A

Change (%) -2.4 0.0 -17.6 4.6

FL DAYTONA 

BEACH

FY 2011 Grant 738 123 124 491 A

New Data Grant 624 118 71 435 A

Change (%) -15.4 -4.6 -42.6 -11.4

FL DEERFIELD BEACH

FY 2011 Grant 669 145 178 346 A

New Data Grant 631 145 142 344 A

Change (%) -5.8 -0.4 -20.2 -0.6

FL DELRAY BEACH

FY 2011 Grant 524 125 150 249 A

New Data Grant 463 117 105 241 A

Change (%) -11.6 -6.8 -29.6 -3.0

FL DELTONA

FY 2011 Grant 450 161 91 197 A

New Data Grant 495 164 72 259 A

Change (%) 10.0 1.6 -21.6 31.6

FL ESCAMBIA 

COUNTY

FY 2011 Grant 1,883 479 261 1,143 A

New Data Grant 1,889 470 367 1,052 A

Change (%) 0.4 -1.8 40.4 -8.0

FL FORT PIERCE

FY 2011 Grant 625 82 139 403 A

New Data Grant 545 80 135 329 A

Change (%) -12.8 -2.6 -2.8 -18.4

FL FORT WALTON 

BEACH

FY 2011 Grant 127 36 21 70 A

New Data Grant 140 38 37 65 A

Change (%) 10.4 4.6 80.8 -7.8

FL FT LAUDERDALE

FY 2011 Grant 1,865 357 543 965 A

New Data Grant 1,736 319 469 948 A

Change (%) -7.0 -10.8 -13.6 -1.8

FL FT MYERS

FY 2011 Grant 681 125 171 385 A

New Data Grant 740 120 247 373 A

Change (%) 8.6 -4.0 44.6 -3.2

FL GAINESVILLE

FY 2011 Grant 1,258 225 127 905 A

New Data Grant 1,461 240 107 1,114 A

Change (%) 16.2 6.4 -16.0 23.2

FL HIALEAH

FY 2011 Grant 3,809 423 1,925 1,461 A

New Data Grant 2,250 433 524 1,293 A

Change (%) -41.0 2.4 -72.8 -11.4

FL HILLSBOROUGH 

COUNTY

FY 2011 Grant 5,417 1,646 1,328 2,442 A

New Data Grant 5,803 1,723 1,242 2,839 A

Change (%) 7.2 4.6 -6.6 16.2
61

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

FL HOLLYWOOD

FY 2011 Grant 1,363 276 446 641 A

New Data Grant 1,146 271 272 602 A

Change (%) -16.0 -1.6 -39.0 -6.0

FL HOMESTEAD CITY

FY 2011 Grant 723 116 256 351 A

New Data Grant 839 117 240 482 A

Change (%) 16.0 0.8 -6.4 37.2

FL JACKSONVILLEDUVAL COUNTY

FY 2011 Grant 6,106 1,654 1,268 3,184 A

New Data Grant 6,039 1,664 1,055 3,320 A

Change (%) -1.2 0.6 -16.8 4.2

FL KISSIMMEE

FY 2011 Grant 556 121 181 254 A

New Data Grant 581 115 153 313 A

Change (%) 4.4 -5.0 -15.8 23.4

FL LAKE COUNTY

FY 2011 Grant 934 429 118 387 A

New Data Grant 1,139 378 158 603 A

Change (%) 22.0 -12.0 34.8 56.0

FL LAKELAND

FY 2011 Grant 702 181 111 409 A

New Data Grant 766 188 198 379 A

Change (%) 9.2 3.6 78.8 -7.4

FL LARGO

FY 2011 Grant 420 142 60 219 A

New Data Grant 488 150 73 266 A

Change (%) 16.2 5.8 21.4 21.4

FL LAUDERHILL

FY 2011 Grant 844 130 296 417 A

New Data Grant 758 129 208 421 A

Change (%) -10.2 -1.2 -29.8 1.0

FL LEE COUNTY

FY 2011 Grant 1,936 712 371 854 A

New Data Grant 2,325 775 530 1,020 A

Change (%) 20.0 9.0 42.8 19.4

FL MANATEE 

COUNTY

FY 2011 Grant 1,393 492 267 633 A

New Data Grant 1,730 511 390 828 A

Change (%) 24.2 3.8 45.8 30.8

FL MARCO ISLAND 

CITY

FY 2011 Grant 64 30 6 29 A

New Data Grant 90 32 25 34 A

Change (%) 40.0 4.0 354.8 17.6

FL MARGATE

FY 2011 Grant 374 105 112 157 A

New Data Grant 315 103 58 154 A

Change (%) -15.8 -2.4 -47.8 -1.8

FL MARION 

COUNTY

FY 2011 Grant 1,603 513 234 856 A

New Data Grant 1,683 516 223 943 A

Change (%) 5.0 0.6 -4.8 10.2
62

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

FL MELBOURNE

FY 2011 Grant 504 150 78 276 A

New Data Grant 498 147 61 290 A

Change (%) -1.2 -2.0 -22.0 5.0

FL MIAMI

FY 2011 Grant 7,503 837 3,135 3,531 A

New Data Grant 5,567 770 1,548 3,249 A

Change (%) -25.8 -8.0 -50.6 -8.0

FL MIAMI BEACH

FY 2011 Grant 1,572 170 734 668 A

New Data Grant 1,020 169 460 391 A

Change (%) -35.2 -0.6 -37.4 -41.6

FL MIAMI GARDENS 

CITY

FY 2011 Grant 1,264 211 450 602 A

New Data Grant 1,058 207 312 540 A

Change (%) -16.2 -2.2 -30.8 -10.4

FL MIAMI-DADE 

COUNTY

FY 2011 Grant 16,285 2,886 6,738 6,662 A

New Data Grant 11,896 2,905 3,323 5,668 A

Change (%) -27.0 0.6 -50.6 -15.0

FL MIRAMAR

FY 2011 Grant 702 211 283 208 A

New Data Grant 722 235 233 254 A

Change (%) 2.8 11.4 -17.8 22.0

FL NAPLES

FY 2011 Grant 101 43 16 43 A

New Data Grant 119 38 24 57 A

Change (%) 17.0 -12.2 52.8 33.4

FL NORTH MIAMI

FY 2011 Grant 1,115 112 509 494 A

New Data Grant 834 113 370 350 A

Change (%) -25.2 1.0 -27.2 -29.2

FL OCALA

FY 2011 Grant 442 107 63 272 A

New Data Grant 498 109 78 311 A

Change (%) 12.4 1.0 23.2 14.4

FL ORANGE 

COUNTY

FY 2011 Grant 5,556 1,541 1,421 2,595 A

New Data Grant 5,513 1,640 1,123 2,751 A

Change (%) -0.8 6.4 -21.0 6.0

FL ORLANDO

FY 2011 Grant 2,047 456 570 1,021 A

New Data Grant 2,059 459 509 1,091 A

Change (%) 0.6 0.8 -10.6 6.8

FL OSCEOLA 

COUNTY

FY 2011 Grant 1,081 402 246 433 A

New Data Grant 1,375 403 280 692 A

Change (%) 27.2 0.2 14.0 59.8

FL PALM BAY

FY 2011 Grant 560 195 102 263 A

New Data Grant 618 199 90 329 A

Change (%) 10.4 1.8 -11.2 25.0
63

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

FL PALM BEACH 

COUNTY

FY 2011 Grant 6,139 1,812 1,642 2,685 A

New Data Grant 6,521 1,901 1,572 3,047 A

Change (%) 6.2 5.0 -4.2 13.4

FL PANAMA CITY

FY 2011 Grant 377 124 167 86 B

New Data Grant 343 106 163 74 B

Change (%) -8.8 -14.2 -2.4 -13.8

FL PASCO COUNTY

FY 2011 Grant 2,366 873 297 1,197 A

New Data Grant 2,719 858 388 1,473 A

Change (%) 15.0 -1.6 30.6 23.2

FL PEMBROKE PINES

FY 2011 Grant 793 283 253 256 A

New Data Grant 742 298 139 304 A

Change (%) -6.4 5.2 -44.8 18.6

FL PENSACOLA

FY 2011 Grant 883 189 393 301 B

New Data Grant 774 150 405 219 B

Change (%) -12.4 -20.8 3.0 -27.0

FL PINELLAS 

COUNTY

FY 2011 Grant 2,676 938 379 1,359 A

New Data Grant 2,679 938 323 1,419 A

Change (%) 0.2 0.0 -14.8 4.4

FL PLANTATION

FY 2011 Grant 464 164 113 187 A

New Data Grant 425 164 77 185 A

Change (%) -8.4 -0.2 -32.0 -1.2

FL POLK COUNTY

FY 2011 Grant 2,677 784 550 1,344 A

New Data Grant 3,229 808 892 1,529 A

Change (%) 20.6 3.2 62.2 13.8

FL POMPANO 

BEACH

FY 2011 Grant 1,108 198 336 574 A

New Data Grant 966 193 274 499 A

Change (%) -12.8 -3.0 -18.4 -13.0

FL PORT ORANGE

FY 2011 Grant 255 105 29 121 A

New Data Grant 295 108 47 140 A

Change (%) 15.4 2.4 61.8 15.4

FL PORT ST LUCIE

FY 2011 Grant 630 299 87 245 A

New Data Grant 847 317 131 398 A

Change (%) 34.6 6.4 51.8 62.8

FL PUNTA GORDA

FY 2011 Grant 73 33 7 33 A

New Data Grant 74 32 0 42 A

Change (%) 2.0 -3.2 -100 28.2

FL SANFORD

FY 2011 Grant 405 99 81 225 A

New Data Grant 438 103 92 243 A

Change (%) 8.2 4.8 13.0 7.8
64

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

FL SARASOTA

FY 2011 Grant 506 101 114 291 A

New Data Grant 434 100 71 262 A

Change (%) -14.2 -0.4 -37.6 -9.8

FL SARASOTA 

COUNTY

FY 2011 Grant 1,348 605 164 578 A

New Data Grant 1,635 623 212 800 A

Change (%) 21.4 3.0 29.2 38.4

FL SEBASTIAN CITY

FY 2011 Grant 83 40 8 36 A

New Data Grant 100 42 7 50 A

Change (%) 20.0 6.2 -3.0 39.8

FL SEMINOLE 

COUNTY

FY 2011 Grant 1,766 700 348 718 A

New Data Grant 1,904 712 282 909 A

Change (%) 7.8 1.6 -18.8 26.8

FL ST PETERSBURG

FY 2011 Grant 1,993 472 390 1,130 A

New Data Grant 1,755 472 294 989 A

Change (%) -12.0 0.0 -24.6 -12.4

FL SUNRISE

FY 2011 Grant 661 173 200 287 A

New Data Grant 575 163 167 246 A

Change (%) -13.0 -6.2 -16.8 -14.4

FL TALLAHASSEE

FY 2011 Grant 1,743 334 215 1,195 A

New Data Grant 1,901 350 190 1,362 A

Change (%) 9.0 4.8 -11.6 14.0

FL TAMARAC

FY 2011 Grant 377 115 89 174 A

New Data Grant 344 117 65 163 A

Change (%) -8.8 1.0 -26.8 -6.0

FL TAMPA

FY 2011 Grant 3,389 665 845 1,879 A

New Data Grant 3,042 647 576 1,819 A

Change (%) -10.2 -2.6 -31.8 -3.2

FL TITUSVILLE

FY 2011 Grant 305 86 46 173 A

New Data Grant 280 84 34 161 A

Change (%) -8.4 -2.0 -25.0 -7.0

FL VOLUSIA 

COUNTY

FY 2011 Grant 1,746 559 252 935 A

New Data Grant 1,826 554 221 1,051 A

Change (%) 4.6 -0.8 -12.2 12.4

FL WEST PALM 

BEACH

FY 2011 Grant 990 192 273 525 A

New Data Grant 920 193 212 516 A

Change (%) -7.2 0.2 -22.4 -1.8

FL WINTER HAVEN

FY 2011 Grant 249 64 50 134 A

New Data Grant 344 65 95 183 A

Change (%) 38.2 1.4 90.2 36.4
65

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

GA ALBANY

FY 2011 Grant 1,039 146 188 704 A

New Data Grant 847 149 89 608 A

Change (%) -18.4 2.2 -52.4 -13.6

GA ATHENS-CLARKE 

COUNTY

FY 2011 Grant 1,332 225 181 926 A

New Data Grant 1,425 225 165 1,036 A

Change (%) 7.0 0.0 -9.0 11.8

GA ATLANTA

FY 2011 Grant 7,018 2,020 2,628 2,370 B

New Data Grant 8,741 1,906 4,037 2,797 B

Change (%) 24.6 -5.6 53.6 18.0

GA

AUGUSTARICHMOND 

COUNTY

FY 2011 Grant 2,040 386 342 1,312 A

New Data Grant 1,932 387 293 1,252 A

Change (%) -5.4 0.2 -14.4 -4.6

GA BRUNSWICK

FY 2011 Grant 396 95 203 98 B

New Data Grant 371 87 210 74 B

Change (%) -6.4 -8.8 3.6 -24.6

GA CHEROKEE 

COUNTY

FY 2011 Grant 788 415 111 263 A

New Data Grant 1,009 412 169 428 A

Change (%) 28.0 -0.8 53.0 62.8

GA CLAYTON 

COUNTY

FY 2011 Grant 1,916 530 565 821 A

New Data Grant 2,061 498 416 1,147 A

Change (%) 7.6 -6.0 -26.4 39.6

GA COBB COUNTY

FY 2011 Grant 2,969 1,252 662 1,054 A

New Data Grant 3,433 1,218 587 1,629 A

Change (%) 15.6 -2.8 -11.4 54.6

GA

COLUMBUSMUSCOGEE 

COUNTY

FY 2011 Grant 1,623 368 280 976 A

New Data Grant 1,580 580 578 422 B

Change (%) -2.6 -40.6

GA DALTON

FY 2011 Grant 368 65 147 156 A

New Data Grant 420 64 131 225 A

Change (%) 14.2 -1.6 -11.2 45.0

GA DE KALB COUNTY

FY 2011 Grant 5,193 1,365 1,537 2,291 A

New Data Grant 5,548 1,274 1,160 3,115 A

Change (%) 6.8 -6.6 -24.6 36.0

GA FULTON COUNTY

FY 2011 Grant 1,838 577 434 827 A

New Data Grant 1,903 524 327 1,053 A

Change (%) 3.6 -9.2 -24.6 27.2

GA GAINESVILLE

FY 2011 Grant 364 69 112 182 A

New Data Grant 426 65 147 214 A

Change (%) 17.2 -5.8 31.2 17.4
66

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

GA GWINNETT 

COUNTY

FY 2011 Grant 3,828 1,590 1,052 1,186 A

New Data Grant 4,842 1,577 1,035 2,229 A

Change (%) 26.4 -0.8 -1.6 88.0

GA HINESVILLE

FY 2011 Grant 271 59 54 158 A

New Data Grant 247 64 24 158 A

Change (%) -9.0 8.6 -54.8 0.0

GA JOHNS CREEK 

CITY

FY 2011 Grant 162 119 12 30 A

New Data Grant 254 148 27 79 A

Change (%) 56.8 23.8 118.0 162.2

GA MACON

FY 2011 Grant 1,184 179 163 842 A

New Data Grant 1,134 176 163 795 A

Change (%) -4.2 -1.6 0.2 -5.6

GA MARIETTA

FY 2011 Grant 627 129 181 317 A

New Data Grant 635 109 215 310 A

Change (%) 1.2 -15.8 19.2 -2.0

GA ROME

FY 2011 Grant 472 142 155 175 B

New Data Grant 431 150 146 135 B

Change (%) -8.6 5.8 -5.8 -22.8

GA ROSWELL

FY 2011 Grant 418 170 107 141 A

New Data Grant 468 170 106 192 A

Change (%) 12.2 0.4 -1.4 36.6

GA SANDY SPRINGS 

CITY

FY 2011 Grant 489 166 129 194 A

New Data Grant 473 181 115 177 A

Change (%) -3.4 9.4 -11.4 -8.8

GA SAVANNAH

FY 2011 Grant 2,408 580 1,117 710 B

New Data Grant 2,373 511 1,069 793 B

Change (%) -1.4 -12.0 -4.4 11.6

GA VALDOSTA

FY 2011 Grant 565 101 83 381 A

New Data Grant 586 105 76 405 A

Change (%) 3.8 4.2 -8.4 6.4

GA WARNER ROBINS

FY 2011 Grant 418 120 73 225 A

New Data Grant 486 128 60 298 A

Change (%) 16.4 7.0 -18.2 32.8

HI HONOLULU

FY 2011 Grant 8,786 1,755 4,079 2,952 A

New Data Grant 8,442 1,838 4,255 2,350 A

Change (%) -4.0 4.8 4.4 -20.4

IA AMES

FY 2011 Grant 445 110 37 299 A

New Data Grant 573 114 46 413 A

Change (%) 28.6 3.8 26.6 38.0
67

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

IA CEDAR FALLS

FY 2011 Grant 281 75 20 186 A

New Data Grant 294 76 7 212 A

Change (%) 4.8 1.2 -67.0 14.0

IA CEDAR RAPIDS

FY 2011 Grant 1,188 187 132 870 B

New Data Grant 1,222 278 135 809 B

Change (%) 3.0 49.2 2.8 -7.0

IA COUNCIL BLUFFS

FY 2011 Grant 978 123 290 565 B

New Data Grant 959 150 257 552 B

Change (%) -2.0 21.2 -11.2 -2.4

IA DAVENPORT

FY 2011 Grant 1,575 284 404 888 B

New Data Grant 1,490 284 409 797 B

Change (%) -5.4 0.2 1.2 -10.2

IA DES MOINES

FY 2011 Grant 3,844 462 1,416 1,966 B

New Data Grant 3,747 512 1,341 1,894 B

Change (%) -2.6 10.8 -5.2 -3.6

IA DUBUQUE

FY 2011 Grant 1,127 109 347 671 B

New Data Grant 1,042 102 332 609 B

Change (%) -7.6 -6.4 -4.4 -9.4

IA IOWA CITY

FY 2011 Grant 604 134 40 430 A

New Data Grant 716 131 48 538 A

Change (%) 18.6 -2.0 18.8 25.0

IA SIOUX CITY

FY 2011 Grant 1,740 195 637 908 B

New Data Grant 1,693 223 623 847 B

Change (%) -2.6 14.4 -2.2 -6.6

IA WATERLOO

FY 2011 Grant 1,262 196 512 554 B

New Data Grant 1,261 206 478 577 B

Change (%) 0.0 5.2 -6.6 4.2

IA WEST DES 

MOINES

FY 2011 Grant 213 110 30 73 A

New Data Grant 249 109 34 106 A

Change (%) 16.8 -0.8 15.2 44.0

ID BOISE

FY 2011 Grant 1,176 398 239 539 A

New Data Grant 1,283 397 205 682 A

Change (%) 9.0 -0.2 -14.4 26.4

ID COEUR D'ALENE

FY 2011 Grant 282 85 44 153 A

New Data Grant 294 85 57 152 A

Change (%) 4.4 0.4 27.6 -0.2

ID IDAHO FALLS

FY 2011 Grant 370 107 73 190 A

New Data Grant 352 110 52 191 A

Change (%) -4.8 2.4 -28.4 0.2
68

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

ID LEWISTON

FY 2011 Grant 242 77 0 166 B

New Data Grant 256 83 0 173 B

Change (%) 5.6 8.0 4.4

ID MERIDIAN

FY 2011 Grant 231 132 31 68 A

New Data Grant 288 145 48 95 A

Change (%) 24.8 9.2 56.6 40.6

ID NAMPA

FY 2011 Grant 491 157 112 222 A

New Data Grant 784 157 178 449 A

Change (%) 59.6 0.2 58.2 102.4

ID POCATELLO

FY 2011 Grant 453 106 76 270 A

New Data Grant 412 105 33 274 A

Change (%) -9.0 -1.8 -57.2 1.6

IL ALTON CITY

FY 2011 Grant 908 117 442 349 B

New Data Grant 882 111 450 320 B

Change (%) -2.8 -4.8 1.8 -8.2

IL ARLINGTON 

HEIGHTS

FY 2011 Grant 255 141 47 66 A

New Data Grant 272 145 48 79 A

Change (%) 6.8 2.4 2.8 19.4

IL AURORA

FY 2011 Grant 1,111 334 353 423 A

New Data Grant 1,324 382 319 623 A

Change (%) 19.2 14.2 -9.6 47.2

IL BELLEVILLE

FY 2011 Grant 667 100 184 383 B

New Data Grant 626 89 138 399 B

Change (%) -6.2 -10.8 -25.0 4.2

IL BERWYN

FY 2011 Grant 1,258 89 397 772 B

New Data Grant 1,149 101 300 747 B

Change (%) -8.6 13.8 -24.4 -3.2

IL BLOOMINGTON

FY 2011 Grant 547 103 0 444 B

New Data Grant 624 144 0 480 B

Change (%) 14.0 39.8 8.0

IL BOLINGBROOK

FY 2011 Grant 286 137 67 82 A

New Data Grant 346 141 86 118 A

Change (%) 21.0 3.2 29.2 44.2

IL CHAMPAIGN

FY 2011 Grant 698 155 72 471 A

New Data Grant 788 156 78 553 A

Change (%) 12.8 0.6 9.2 17.4

IL CHICAGO

FY 2011 Grant 75,816 11,749 30,984 33,084 B

New Data Grant 80,761 10,425 32,207 38,128 B

Change (%) 6.6 -11.2 4.0 15.2
69

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

IL CHICAGO 

HEIGHTS

FY 2011 Grant 547 120 264 163 B

New Data Grant 589 130 259 200 B

Change (%) 7.6 8.0 -1.8 22.6

IL CICERO

FY 2011 Grant 1,328 278 297 753 B

New Data Grant 1,532 234 238 1,061 B

Change (%) 15.4 -16.0 -19.8 40.8

IL COOK COUNTY

FY 2011 Grant 8,859 3,014 2,345 3,499 A

New Data Grant 9,603 3,098 2,409 4,095 A

Change (%) 8.4 2.8 2.8 17.0

IL DANVILLE

FY 2011 Grant 883 119 380 384 B

New Data Grant 936 153 364 419 B

Change (%) 6.0 27.6 -4.0 9.2

IL DECATUR

FY 2011 Grant 1,403 274 512 617 B

New Data Grant 1,391 261 498 632 B

Change (%) -0.8 -4.8 -2.6 2.4

IL DEKALB

FY 2011 Grant 376 88 52 235 A

New Data Grant 462 85 71 307 A

Change (%) 22.8 -4.4 34.6 30.6

IL DES PLAINES

FY 2011 Grant 300 110 97 93 A

New Data Grant 302 113 80 110 A

Change (%) 0.8 2.4 -17.2 18.0

IL DOWNERS 

GROVE

FY 2011 Grant 199 24 0 175 B

New Data Grant 184 33 0 152 B

Change (%) -7.2 37.0 -13.2

IL DU PAGE 

COUNTY

FY 2011 Grant 3,136 1,325 889 922 A

New Data Grant 3,349 1,300 883 1,167 A

Change (%) 6.8 -1.8 -0.8 26.6

IL EAST ST LOUIS

FY 2011 Grant 1,644 230 1,173 241 B

New Data Grant 1,651 201 1,175 274 B

Change (%) 0.4 -12.6 0.2 13.6

IL ELGIN

FY 2011 Grant 769 208 301 261 A

New Data Grant 779 209 239 331 A

Change (%) 1.4 0.4 -20.4 27.0

IL EVANSTON

FY 2011 Grant 1,753 159 515 1,080 B

New Data Grant 1,782 122 542 1,118 B

Change (%) 1.6 -23.0 5.2 3.6

IL GRANITE CITY

FY 2011 Grant 715 75 368 272 B

New Data Grant 718 83 371 265 B

Change (%) 0.4 10.6 0.6 -2.6
70

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

IL HOFFMAN 

ESTATES

FY 2011 Grant 272 104 91 78 A

New Data Grant 291 100 98 93 A

Change (%) 6.8 -3.6 8.0 19.6

IL JOLIET

FY 2011 Grant 840 285 169 385 A

New Data Grant 1,037 284 238 515 A

Change (%) 23.6 -0.4 40.6 33.8

IL KANE COUNTY

FY 2011 Grant 1,079 538 234 308 A

New Data Grant 1,187 527 239 421 A

Change (%) 10.0 -2.0 2.2 36.8

IL KANKAKEE

FY 2011 Grant 542 118 184 240 B

New Data Grant 534 147 170 217 B

Change (%) -1.4 24.0 -7.4 -9.4

IL LAKE COUNTY

FY 2011 Grant 2,296 1,137 456 703 A

New Data Grant 2,604 1,115 567 921 A

Change (%) 13.4 -2.0 24.4 31.0

IL MADISON 

COUNTY

FY 2011 Grant 1,265 334 18 914 B

New Data Grant 1,204 415 152 637 A

Change (%) -4.8 91.0

IL MCHENRY 

COUNTY

FY 2011 Grant 1,190 644 211 335 A

New Data Grant 1,429 624 274 532 A

Change (%) 20.0 -3.2 29.6 58.8

IL MOLINE

FY 2011 Grant 819 86 264 469 B

New Data Grant 745 78 250 417 B

Change (%) -9.0 -9.8 -5.0 -11.0

IL MOUNT 

PROSPECT

FY 2011 Grant 325 103 131 92 A

New Data Grant 273 104 73 95 A

Change (%) -16.0 1.8 -44.0 3.8

IL NAPERVILLE

FY 2011 Grant 433 278 57 99 A

New Data Grant 468 273 58 137 A

Change (%) 8.0 -1.6 1.2 38.4

IL NORMAL

FY 2011 Grant 369 102 19 249 A

New Data Grant 459 101 40 317 A

Change (%) 24.2 -0.8 115.8 27.6

IL NORTH 

CHICAGO

FY 2011 Grant 273 63 84 126 A

New Data Grant 235 73 20 141 B

Change (%) -14.2 -42.0

IL OAK LAWN

FY 2011 Grant 247 102 40 104 A

New Data Grant 264 109 51 105 A

Change (%) 7.2 6.8 26.2 0.4
71

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

IL OAK PARK

FY 2011 Grant 1,726 61 483 1,182 B

New Data Grant 1,672 54 486 1,132 B

Change (%) -3.2 -11.8 0.8 -4.2

IL PALATINE 

VILLAGE

FY 2011 Grant 368 129 130 109 A

New Data Grant 400 132 119 149 A

Change (%) 8.6 2.6 -9.0 37.0

IL PEKIN

FY 2011 Grant 387 62 109 216 B

New Data Grant 369 72 98 199 B

Change (%) -4.8 15.0 -10.2 -7.8

IL PEORIA

FY 2011 Grant 1,744 427 500 817 B

New Data Grant 1,784 365 489 930 B

Change (%) 2.4 -14.4 -2.4 14.0

IL RANTOUL

FY 2011 Grant 332 29 262 41 B

New Data Grant 327 45 248 34 B

Change (%) -1.4 54.8 -5.2 -17.0

IL ROCK ISLAND

FY 2011 Grant 1,059 114 497 448 B

New Data Grant 1,017 112 475 430 B

Change (%) -4.0 -1.8 -4.4 -4.0

IL ROCKFORD

FY 2011 Grant 1,924 430 413 1,080 B

New Data Grant 2,151 603 447 1,100 B

Change (%) 11.8 40.4 8.2 1.8

IL SCHAUMBURG 

VILLAGE

FY 2011 Grant 308 138 92 78 A

New Data Grant 303 143 65 95 A

Change (%) -1.6 3.8 -30.0 22.4

IL SKOKIE

FY 2011 Grant 471 71 284 116 B

New Data Grant 504 89 294 121 B

Change (%) 7.0 25.4 3.8 3.8

IL SPRINGFIELD

FY 2011 Grant 1,163 271 89 803 B

New Data Grant 1,146 327 98 721 B

Change (%) -1.4 20.6 10.8 -10.2

IL ST CLAIR 

COUNTY

FY 2011 Grant 1,316 443 222 651 B

New Data Grant 1,277 403 183 692 A

Change (%) -3.0 56.2

IL URBANA

FY 2011 Grant 421 77 52 292 A

New Data Grant 407 80 48 279 A

Change (%) -3.6 2.8 -8.2 -4.4

IL WAUKEGAN

FY 2011 Grant 951 175 351 424 A

New Data Grant 781 172 265 344 A

Change (%) -17.8 -2.0 -24.8 -18.8
72

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

IL WHEATON CITY

FY 2011 Grant 210 105 39 65 A

New Data Grant 193 102 20 71 A

Change (%) -8.0 -3.4 -48.2 9.2

IL WILL COUNTY

FY 2011 Grant 1,329 827 150 352 A

New Data Grant 1,685 801 291 594 A

Change (%) 26.8 -3.2 93.8 68.8

IN ANDERSON

FY 2011 Grant 871 162 206 503 B

New Data Grant 902 188 211 503 B

Change (%) 3.6 15.8 2.4 0.2

IN BLOOMINGTON

FY 2011 Grant 762 139 38 584 A

New Data Grant 869 155 35 678 A

Change (%) 14.0 11.4 -8.2 16.2

IN CARMEL

FY 2011 Grant 181 134 13 34 A

New Data Grant 227 153 11 64 A

Change (%) 25.4 13.6 -19.6 90.2

IN COLUMBUS

FY 2011 Grant 266 65 0 200 B

New Data Grant 247 85 32 130 A

Change (%) -7.0 98.6

IN EAST CHICAGO

FY 2011 Grant 1,235 166 708 361 B

New Data Grant 1,258 176 698 385 B

Change (%) 1.8 6.2 -1.4 6.6

IN ELKHART

FY 2011 Grant 683 146 93 444 B

New Data Grant 790 208 113 469 B

Change (%) 15.6 42.6 21.2 5.6

IN EVANSVILLE

FY 2011 Grant 2,686 338 1,239 1,109 B

New Data Grant 2,687 374 1,201 1,112 B

Change (%) 0.0 10.6 -3.0 0.2

IN FORT WAYNE

FY 2011 Grant 1,926 539 0 1,387 B

New Data Grant 2,213 655 0 1,558 B

Change (%) 15.0 21.6 12.4

IN GARY

FY 2011 Grant 3,313 551 2,148 614 B

New Data Grant 3,606 577 2,300 729 B

Change (%) 8.8 4.6 7.0 18.8

IN GOSHEN

FY 2011 Grant 251 54 0 197 B

New Data Grant 274 80 0 193 B

Change (%) 9.0 48.4 -1.8

IN HAMILTON 

COUNTY

FY 2011 Grant 581 394 47 140 A

New Data Grant 734 367 91 276 A

Change (%) 26.4 -6.8 93.6 97.2
73

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

IN HAMMOND

FY 2011 Grant 2,114 249 1,139 726 B

New Data Grant 2,097 298 1,062 736 B

Change (%) -0.8 19.6 -6.8 1.4

IN INDIANAPOLIS

FY 2011 Grant 8,670 1,924 2,284 4,463 B

New Data Grant 9,580 2,441 2,026 5,112 B

Change (%) 10.4 27.0 -11.2 14.6

IN KOKOMO

FY 2011 Grant 913 124 318 470 B

New Data Grant 963 181 309 473 B

Change (%) 5.4 45.4 -3.0 0.6

IN LA PORTE

FY 2011 Grant 453 49 132 273 B

New Data Grant 411 56 118 237 B

Change (%) -9.4 15.6 -10.6 -13.2

IN LAFAYETTE

FY 2011 Grant 587 154 0 433 B

New Data Grant 675 208 0 467 B

Change (%) 15.0 34.6 7.8

IN LAKE COUNTY

FY 2011 Grant 1,204 565 196 444 A

New Data Grant 1,451 588 198 664 A

Change (%) 20.4 4.2 1.2 49.8

IN MICHIGAN CITY

FY 2011 Grant 655 85 283 287 B

New Data Grant 671 104 287 280 B

Change (%) 2.6 23.0 1.6 -2.4

IN MISHAWAKA

FY 2011 Grant 496 95 0 401 B

New Data Grant 473 99 19 356 B

Change (%) -4.6 3.6 9860.4 -11.2

IN MUNCIE

FY 2011 Grant 1,284 298 440 546 B

New Data Grant 1,318 326 399 593 B

Change (%) 2.6 9.4 -9.4 8.6

IN NEW ALBANY

FY 2011 Grant 661 106 241 314 B

New Data Grant 662 114 248 300 B

Change (%) 0.2 7.4 3.0 -4.4

IN SOUTH BEND

FY 2011 Grant 2,552 368 1,183 1,001 B

New Data Grant 2,549 426 1,194 929 B

Change (%) -0.2 15.6 1.0 -7.2

IN TERRE HAUTE

FY 2011 Grant 1,648 211 612 825 B

New Data Grant 1,661 245 587 829 B

Change (%) 0.8 16.0 -4.2 0.6

IN WEST LAFAYETTE

FY 2011 Grant 399 61 18 320 A

New Data Grant 429 57 14 358 A

Change (%) 7.4 -6.4 -22.0 11.8
74

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

KS JOHNSON 

COUNTY

FY 2011 Grant 1,067 594 156 317 A

New Data Grant 1,292 600 209 483 A

Change (%) 21.0 1.0 33.6 52.6

KS KANSAS CITY

FY 2011 Grant 2,257 524 843 890 B

New Data Grant 2,277 536 785 956 B

Change (%) 1.0 2.2 -6.8 7.4

KS LAWRENCE

FY 2011 Grant 730 178 72 480 A

New Data Grant 850 169 58 623 A

Change (%) 16.4 -5.0 -19.4 29.8

KS LEAVENWORTH

FY 2011 Grant 326 60 0 266 B

New Data Grant 330 71 0 259 B

Change (%) 1.4 18.4 -2.6

KS MANHATTAN 

CITY

FY 2011 Grant 472 102 37 333 A

New Data Grant 630 101 101 428 A

Change (%) 33.4 -1.4 174.8 28.4

KS OVERLAND PARK

FY 2011 Grant 593 338 88 166 A

New Data Grant 663 334 93 236 A

Change (%) 11.8 -1.2 5.2 41.8

KS SHAWNEE

FY 2011 Grant 208 119 33 56 A

New Data Grant 248 120 31 98 A

Change (%) 19.6 0.6 -5.4 74.6

KS TOPEKA

FY 2011 Grant 1,802 310 718 774 B

New Data Grant 1,805 383 659 764 B

Change (%) 0.2 23.4 -8.4 -1.4

KS WICHITA

FY 2011 Grant 2,580 720 523 1,337 A

New Data Grant 2,881 737 551 1,593 A

Change (%) 11.6 2.4 5.2 19.2

KY ASHLAND

FY 2011 Grant 625 84 321 220 B

New Data Grant 579 77 309 192 B

Change (%) -7.4 -8.6 -3.6 -12.6

KY BOWLING 

GREEN

FY 2011 Grant 512 109 66 336 A

New Data Grant 556 112 64 380 A

Change (%) 8.6 2.4 -2.2 12.8

KY COVINGTON

FY 2011 Grant 1,495 164 594 737 B

New Data Grant 1,580 157 612 811 B

Change (%) 5.6 -4.0 3.0 10.0

KY ELIZABETHTOWN

FY 2011 Grant 144 47 17 80 A

New Data Grant 164 55 10 99 A

Change (%) 13.8 16.0 -41.4 24.4
75

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

KY HENDERSON

FY 2011 Grant 230 92 0 137 B

New Data Grant 240 55 33 151 A

Change (%) 4.4 63.6

KY HOPKINSVILLE

FY 2011 Grant 261 62 26 173 A

New Data Grant 281 61 27 193 A

Change (%) 7.8 -1.8 5.4 11.8

KY LEXINGTONFAYETTE

FY 2011 Grant 1,904 573 207 1,124 A

New Data Grant 2,235 570 252 1,413 A

Change (%) 17.4 -0.6 21.8 25.6

KY LOUISVILLE

FY 2011 Grant 10,777 938 262 2,202 4,370 3,006 A/B

New Data Grant 7,482 1,837 2,107 3,538 B

Change (%) -30.6 -16.6 -51.8 17.8

KY OWENSBORO

FY 2011 Grant 494 176 101 218 B

New Data Grant 477 110 61 306 A

Change (%) -3.4 74.0

LA ALEXANDRIA

FY 2011 Grant 600 94 69 436 A

New Data Grant 534 92 108 334 A

Change (%) -11.0 -2.6 56.6 -23.6

LA BATON ROUGE

FY 2011 Grant 3,752 734 620 2,398 A

New Data Grant 3,624 741 569 2,314 A

Change (%) -3.4 1.0 -8.2 -3.4

LA BOSSIER CITY

FY 2011 Grant 478 122 73 284 A

New Data Grant 482 118 86 278 A

Change (%) 0.8 -3.4 18.4 -1.8

LA HOUMATERREBONNE

FY 2011 Grant 1,111 211 210 690 A

New Data Grant 988 216 227 546 A

Change (%) -11.0 2.0 8.0 -20.8

LA JEFFERSON 

PARISH

FY 2011 Grant 3,234 726 675 1,833 A

New Data Grant 2,810 705 564 1,541 A

Change (%) -13.0 -2.8 -16.4 -16.0

LA KENNER

FY 2011 Grant 605 131 140 334 A

New Data Grant 512 129 126 257 A

Change (%) -15.4 -2.0 -9.8 -23.0

LA LAFAYETTE

FY 2011 Grant 1,480 345 262 873 A

New Data Grant 1,357 362 206 789 A

Change (%) -8.2 4.8 -21.2 -9.6

LA LAKE CHARLES

FY 2011 Grant 782 282 296 204 B

New Data Grant 737 271 279 187 B

Change (%) -5.8 -4.2 -5.6 -8.4
76

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

LA MONROE

FY 2011 Grant 788 100 124 564 A

New Data Grant 793 297 360 136 B

Change (%) 0.6 -47.4

LA NEW ORLEANS

FY 2011 Grant 14,914 2,762 7,335 4,816 B

New Data Grant 12,187 1,345 7,335 3,507 B

Change (%) -18.2 -51.4 0.0 -27.2

LA SHREVEPORT

FY 2011 Grant 2,347 385 396 1,566 A

New Data Grant 2,005 384 293 1,328 A

Change (%) -14.6 -0.2 -26.0 -15.2

LA SLIDELL

FY 2011 Grant 179 53 21 104 A

New Data Grant 185 52 17 116 A

Change (%) 3.6 -1.8 -20.8 11.2

LA ST TAMMANY 

PARISH

FY 2011 Grant 999 375 131 492 A

New Data Grant 1,125 380 207 538 A

Change (%) 12.6 1.4 57.8 9.2

LA THIBODAUX

FY 2011 Grant 194 70 71 53 B

New Data Grant 160 53 67 39 B

Change (%) -17.6 -24.0 -5.8 -25.4

MA ARLINGTON

FY 2011 Grant 1,174 36 416 721 B

New Data Grant 1,147 34 392 721 B

Change (%) -2.2 -6.6 -5.8 -0.2

MA ATTLEBORO

FY 2011 Grant 424 54 0 370 B

New Data Grant 391 60 0 331 B

Change (%) -7.6 12.4 -10.6

MA BARNSTABLE

FY 2011 Grant 319 88 0 231 B

New Data Grant 308 57 0 251 B

Change (%) -3.4 -35.8 9.0

MA BOSTON

FY 2011 Grant 17,497 2,303 5,021 10,173 B

New Data Grant 17,882 2,038 5,231 10,613 B

Change (%) 2.2 -11.4 4.2 4.4

MA BROCKTON

FY 2011 Grant 1,386 283 198 905 B

New Data Grant 1,424 218 183 1,023 B

Change (%) 2.8 -23.0 -7.6 13.0

MA BROOKLINE

FY 2011 Grant 1,469 109 309 1,051 B

New Data Grant 1,385 127 271 987 B

Change (%) -5.8 16.6 -12.4 -6.0

MA CAMBRIDGE

FY 2011 Grant 2,797 238 662 1,897 B

New Data Grant 3,094 268 688 2,139 B

Change (%) 10.6 12.2 3.8 12.8
77

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

MA CHICOPEE

FY 2011 Grant 1,163 139 456 567 B

New Data Grant 1,149 153 452 544 B

Change (%) -1.2 9.8 -0.8 -4.0

MA FALL RIVER

FY 2011 Grant 2,742 325 740 1,676 B

New Data Grant 2,772 305 746 1,721 B

Change (%) 1.2 -6.2 0.8 2.8

MA FITCHBURG

FY 2011 Grant 1,037 119 281 637 B

New Data Grant 1,038 133 295 610 B

Change (%) 0.2 12.0 5.4 -4.4

MA FRAMINGHAM

FY 2011 Grant 484 108 0 375 B

New Data Grant 487 92 0 395 B

Change (%) 0.8 -15.0 5.4

MA GLOUCESTER

FY 2011 Grant 726 55 103 568 B

New Data Grant 699 38 120 541 B

Change (%) -3.8 -31.4 16.8 -4.8

MA HAVERHILL

FY 2011 Grant 958 111 101 746 B

New Data Grant 991 127 102 762 B

Change (%) 3.4 15.0 1.4 2.0

MA HOLYOKE

FY 2011 Grant 1,211 213 484 514 B

New Data Grant 1,198 203 479 515 B

Change (%) -1.0 -4.6 -0.8 0.2

MA LAWRENCE

FY 2011 Grant 1,583 363 449 771 B

New Data Grant 1,466 343 364 759 B

Change (%) -7.4 -5.6 -18.8 -1.6

MA LEOMINSTER

FY 2011 Grant 464 82 0 382 B

New Data Grant 438 65 11 363 B

Change (%) -5.4 -21.2 -5.0

MA LOWELL

FY 2011 Grant 2,173 360 424 1,388 B

New Data Grant 2,172 317 382 1,473 B

Change (%) 0.0 -11.8 -10.0 6.0

MA LYNN

FY 2011 Grant 2,301 306 679 1,315 B

New Data Grant 2,472 304 627 1,542 B

Change (%) 7.4 -1.0 -7.8 17.2

MA MALDEN

FY 2011 Grant 1,408 108 381 919 B

New Data Grant 1,321 115 329 877 B

Change (%) -6.2 6.4 -13.6 -4.6

MA MEDFORD

FY 2011 Grant 1,589 72 525 992 B

New Data Grant 1,586 84 505 998 B

Change (%) -0.2 16.0 -3.8 0.6
78

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

MA NEW BEDFORD

FY 2011 Grant 2,741 391 785 1,565 B

New Data Grant 2,772 358 716 1,699 B

Change (%) 1.2 -8.6 -8.8 8.6

MA NEWTON

FY 2011 Grant 2,028 71 677 1,280 B

New Data Grant 1,894 74 652 1,168 B

Change (%) -6.6 4.0 -3.6 -8.8

MA NORTHAMPTON

FY 2011 Grant 686 53 208 426 B

New Data Grant 661 57 202 402 B

Change (%) -3.8 8.4 -2.8 -5.6

MA PEABODY CITY

FY 2011 Grant 420 53 0 366 B

New Data Grant 398 62 0 336 B

Change (%) -5.2 15.6 -8.2

MA PITTSFIELD

FY 2011 Grant 1,369 107 556 706 B

New Data Grant 1,257 124 515 618 B

Change (%) -8.2 15.4 -7.2 -12.4

MA PLYMOUTH 

TOWN

FY 2011 Grant 358 56 0 302 B

New Data Grant 361 54 0 307 B

Change (%) 0.8 -3.8 1.6

MA QUINCY

FY 2011 Grant 1,893 133 503 1,257 B

New Data Grant 1,928 146 472 1,310 B

Change (%) 1.8 10.0 -6.2 4.2

MA REVERE CITY

FY 2011 Grant 764 145 107 512 B

New Data Grant 704 101 99 504 B

Change (%) -7.8 -30.6 -6.8 -1.6

MA SALEM

FY 2011 Grant 1,042 80 219 743 B

New Data Grant 1,014 83 212 719 B

Change (%) -2.6 3.4 -3.0 -3.2

MA SOMERVILLE

FY 2011 Grant 2,586 198 821 1,567 B

New Data Grant 2,735 198 811 1,726 B

Change (%) 5.8 -0.2 -1.2 10.2

MA SPRINGFIELD

FY 2011 Grant 3,718 713 1,330 1,676 B

New Data Grant 4,120 729 1,327 2,064 B

Change (%) 10.8 2.2 -0.2 23.2

MA TAUNTON

FY 2011 Grant 822 117 75 630 B

New Data Grant 752 102 68 582 B

Change (%) -8.6 -13.0 -10.0 -7.6

MA WALTHAM

FY 2011 Grant 966 79 283 604 B

New Data Grant 1,056 114 273 670 B

Change (%) 9.4 43.8 -3.6 11.0
79

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

MA WESTFIELD

FY 2011 Grant 404 89 0 315 B

New Data Grant 357 83 0 273 B

Change (%) -11.8 -6.6 -13.2

MA WORCESTER

FY 2011 Grant 4,141 614 1,216 2,310 B

New Data Grant 4,456 539 1,205 2,712 B

Change (%) 7.6 -12.4 -1.0 17.4

MA WEYMOUTH 

TOWN

FY 2011 Grant 744 65 230 449 B

New Data Grant 744 64 224 456 B

Change (%) 0.2 -1.6 -2.4 1.6

MA YARMOUTH

FY 2011 Grant 125 46 14 65 A

New Data Grant 106 46 18 42 A

Change (%) -14.8 -0.4 32.4 -35.0

MD ANNAPOLIS

FY 2011 Grant 310 94 0 216 B

New Data Grant 238 59 0 179 B

Change (%) -23.4 -37.8 -17.2

MD ANNE ARUNDEL 

COUNTY

FY 2011 Grant 1,899 936 265 698 A

New Data Grant 1,964 962 322 680 A

Change (%) 3.4 2.8 21.6 -2.6

MD BALTIMORE

FY 2011 Grant 21,039 3,028 9,650 8,361 B

New Data Grant 20,066 2,242 9,658 8,166 B

Change (%) -4.6 -26.0 0.0 -2.4

MD BALTIMORE 

COUNTY

FY 2011 Grant 3,748 1,527 547 1,674 A

New Data Grant 4,052 1,552 689 1,810 A

Change (%) 8.2 1.6 26.2 8.2

MD BOWIE CITY

FY 2011 Grant 146 103 15 28 A

New Data Grant 185 106 34 46 A

Change (%) 26.4 2.2 126.6 62.0

MD CUMBERLAND

FY 2011 Grant 905 88 371 445 B

New Data Grant 811 68 359 385 B

Change (%) -10.4 -23.0 -3.2 -13.6

MD FREDERICK

FY 2011 Grant 352 79 0 273 B

New Data Grant 344 80 0 264 B

Change (%) -2.4 0.6 -3.2

MD GAITHERSBURG

FY 2011 Grant 407 116 161 131 A

New Data Grant 410 116 153 142 A

Change (%) 0.6 -0.4 -5.0 8.6

MD HAGERSTOWN

FY 2011 Grant 839 138 188 513 B

New Data Grant 743 129 186 428 B

Change (%) -11.6 -6.4 -1.2 -16.6
80

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

MD HARFORD 

COUNTY

FY 2011 Grant 948 469 103 376 A

New Data Grant 963 472 102 389 A

Change (%) 1.6 0.6 -1.2 3.4

MD HOWARD 

COUNTY

FY 2011 Grant 1,061 545 183 334 A

New Data Grant 1,005 553 121 331 A

Change (%) -5.4 1.6 -34.0 -0.8

MD MONTGOMERY 

COUNTY

FY 2011 Grant 4,663 1,741 1,407 1,516 A

New Data Grant 4,284 1,739 1,192 1,353 A

Change (%) -8.2 -0.2 -15.2 -10.8

MD

PRINCE 

GEORGES 

COUNTY

FY 2011 Grant 5,457 1,502 1,873 2,082 A

New Data Grant 4,802 1,549 1,487 1,766 A

Change (%) -12.0 3.2 -20.6 -15.2

MD SALISBURY

FY 2011 Grant 274 55 34 185 A

New Data Grant 271 59 64 148 A

Change (%) -1.0 6.8 86.6 -19.8

ME AUBURN

FY 2011 Grant 580 57 173 351 B

New Data Grant 547 59 166 323 B

Change (%) -5.6 3.4 -3.8 -8.0

ME BANGOR

FY 2011 Grant 937 104 337 495 B

New Data Grant 838 92 310 437 B

Change (%) -10.6 -12.2 -8.2 -11.8

ME BIDDEFORD

FY 2011 Grant 443 59 94 290 B

New Data Grant 452 52 92 308 B

Change (%) 2.0 -11.0 -2.2 6.2

ME CUMBERLAND 

COUNTY

FY 2011 Grant 1,544 210 0 1,334 B

New Data Grant 1,614 269 0 1,344 B

Change (%) 4.6 28.0 0.8

ME LEWISTON

FY 2011 Grant 948 109 328 512 B

New Data Grant 854 146 301 407 B

Change (%) -10.0 34.6 -8.2 -20.4

ME PORTLAND CITY

FY 2011 Grant 1,941 187 574 1,180 B

New Data Grant 1,895 193 521 1,181 B

Change (%) -2.4 3.4 -9.4 0.2

MI BATTLE CREEK

FY 2011 Grant 1,191 157 538 496 B

New Data Grant 1,255 186 519 550 B

Change (%) 5.4 18.4 -3.4 11.0

MI BAY CITY

FY 2011 Grant 1,312 113 583 616 B

New Data Grant 1,235 116 558 562 B

Change (%) -5.8 2.8 -4.4 -8.8
81

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

MI BENTON 

HARBOR

FY 2011 Grant 421 99 224 98 B

New Data Grant 497 94 229 174 B

Change (%) 18.0 -5.8 2.6 77.0

MI CANTON TWP

FY 2011 Grant 312 156 57 100 A

New Data Grant 357 174 44 139 A

Change (%) 14.2 11.8 -23.0 39.2

MI CLINTON TWP

FY 2011 Grant 467 186 88 193 A

New Data Grant 546 187 72 288 A

Change (%) 16.8 0.6 -19.0 48.8

MI DEARBORN

FY 2011 Grant 2,035 332 1,045 659 B

New Data Grant 1,728 331 854 543 B

Change (%) -15.0 -0.2 -18.2 -17.6

MI DEARBORN 

HEIGHTS

FY 2011 Grant 1,031 75 862 95 B

New Data Grant 954 107 759 88 B

Change (%) -7.6 43.4 -12.0 -7.0

MI DETROIT

FY 2011 Grant 33,531 5,131 19,941 8,460 B

New Data Grant 37,533 5,414 22,017 10,103 B

Change (%) 12.0 5.6 10.4 19.4

MI EAST LANSING

FY 2011 Grant 537 88 51 398 A

New Data Grant 510 94 40 376 A

Change (%) -5.2 6.4 -22.4 -5.6

MI FARMINGTON 

HILLS

FY 2011 Grant 330 152 62 116 A

New Data Grant 386 154 62 171 A

Change (%) 16.8 1.0 -0.6 47.0

MI FLINT

FY 2011 Grant 3,947 685 2,301 961 B

New Data Grant 4,028 708 2,370 950 B

Change (%) 2.0 3.4 3.0 -1.2

MI GENESEE 

COUNTY

FY 2011 Grant 1,595 575 204 816 A

New Data Grant 1,869 591 267 1,010 A

Change (%) 17.2 2.8 30.8 23.8

MI GRAND RAPIDS

FY 2011 Grant 3,684 626 908 2,150 B

New Data Grant 3,856 732 947 2,178 B

Change (%) 4.6 16.8 4.4 1.2

MI HOLLAND

FY 2011 Grant 325 72 40 213 B

New Data Grant 326 75 48 202 B

Change (%) 0.0 3.6 21.6 -5.2

MI JACKSON

FY 2011 Grant 1,269 147 535 588 B

New Data Grant 1,290 165 522 604 B

Change (%) 1.6 12.4 -2.4 2.8
82

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

MI KALAMAZOO

FY 2011 Grant 1,675 351 630 693 B

New Data Grant 1,764 416 597 751 B

Change (%) 5.4 18.4 -5.4 8.4

MI KENT COUNTY

FY 2011 Grant 1,403 665 205 533 A

New Data Grant 1,644 661 172 812 A

Change (%) 17.2 -0.8 -16.0 52.4

MI LANSING

FY 2011 Grant 2,018 419 601 997 B

New Data Grant 2,043 506 576 960 B

Change (%) 1.2 20.8 -4.2 -3.6

MI LINCOLN PARK

FY 2011 Grant 793 65 580 148 B

New Data Grant 729 88 524 117 B

Change (%) -8.0 36.6 -9.8 -21.0

MI LIVONIA

FY 2011 Grant 329 173 46 110 A

New Data Grant 317 187 23 107 A

Change (%) -3.6 8.2 -50.0 -2.8

MI MACOMB 

COUNTY

FY 2011 Grant 1,497 711 230 557 A

New Data Grant 1,776 720 242 813 A

Change (%) 18.6 1.4 5.2 46.0

MI MIDLAND

FY 2011 Grant 219 79 15 125 A

New Data Grant 241 81 27 133 A

Change (%) 10.2 2.4 89.2 6.2

MI MONROE

FY 2011 Grant 486 58 165 263 B

New Data Grant 461 58 168 235 B

Change (%) -5.0 0.6 1.8 -10.6

MI MUSKEGON

FY 2011 Grant 911 153 382 377 B

New Data Grant 930 189 383 358 B

Change (%) 2.0 24.0 0.4 -5.2

MI MUSKEGON HTS

FY 2011 Grant 421 75 222 124 B

New Data Grant 463 89 227 147 B

Change (%) 10.0 19.4 2.0 18.6

MI NILES

FY 2011 Grant 308 34 119 155 B

New Data Grant 293 47 112 134 B

Change (%) -4.6 38.6 -5.8 -13.2

MI NORTON 

SHORES

FY 2011 Grant 129 25 44 61 B

New Data Grant 116 36 32 48 B

Change (%) -10.2 45.8 -27.0 -21.0

MI OAKLAND 

COUNTY

FY 2011 Grant 3,504 1,654 589 1,261 A

New Data Grant 3,952 1,660 643 1,649 A

Change (%) 12.8 0.4 9.2 30.8
83

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

MI PONTIAC

FY 2011 Grant 1,416 303 715 398 B

New Data Grant 1,602 324 782 496 B

Change (%) 13.2 6.8 9.4 24.6

MI PORT HURON

FY 2011 Grant 793 113 295 385 B

New Data Grant 737 137 293 308 B

Change (%) -7.0 21.2 -0.8 -20.2

MI PORTAGE

FY 2011 Grant 192 90 27 76 A

New Data Grant 240 89 25 126 A

Change (%) 24.8 -0.6 -7.2 66.6

MI REDFORD

FY 2011 Grant 932 55 777 100 B

New Data Grant 880 63 718 99 B

Change (%) -5.6 14.6 -7.6 -0.8

MI ROSEVILLE

FY 2011 Grant 536 80 358 99 B

New Data Grant 556 101 341 114 B

Change (%) 3.8 26.2 -4.4 15.6

MI ROYAL OAK

FY 2011 Grant 1,244 54 798 392 B

New Data Grant 1,230 66 780 383 B

Change (%) -1.2 23.4 -2.2 -2.4

MI SAGINAW

FY 2011 Grant 2,275 367 1,153 755 B

New Data Grant 2,381 362 1,178 842 B

Change (%) 4.6 -1.4 2.2 11.4

MI SOUTHFIELD

FY 2011 Grant 456 146 109 201 A

New Data Grant 465 138 75 252 A

Change (%) 2.0 -5.2 -30.8 25.2

MI ST CLAIR SHORES

FY 2011 Grant 862 49 684 129 B

New Data Grant 882 83 677 122 B

Change (%) 2.2 69.0 -1.2 -5.2

MI STERLING 

HEIGHTS

FY 2011 Grant 605 246 131 228 A

New Data Grant 700 250 148 302 A

Change (%) 15.6 1.8 12.8 32.4

MI TAYLOR

FY 2011 Grant 443 115 82 246 A

New Data Grant 459 122 83 254 A

Change (%) 3.8 6.2 2.2 3.2

MI WARREN

FY 2011 Grant 762 259 148 356 A

New Data Grant 908 258 172 477 A

Change (%) 19.0 -0.2 16.2 34.2

MI WASHTENAW 

COUNTY

FY 2011 Grant 1,990 544 337 1,110 A

New Data Grant 2,127 538 243 1,346 A

Change (%) 6.8 -1.0 -28.0 21.2
84

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

MI WATERFORD 

TOWNSHIP

FY 2011 Grant 323 137 57 129 A

New Data Grant 406 138 60 207 A

Change (%) 25.8 1.0 5.6 61.0

MI WAYNE COUNTY

FY 2011 Grant 5,685 965 2,241 2,480 B

New Data Grant 5,174 1,093 1,776 2,305 B

Change (%) -9.0 13.2 -20.8 -7.0

MI WESTLAND

FY 2011 Grant 1,073 123 858 93 B

New Data Grant 1,019 170 754 95 B

Change (%) -5.0 38.8 -12.0 2.6

MI WYOMING

FY 2011 Grant 420 137 108 176 A

New Data Grant 524 139 82 304 A

Change (%) 24.8 1.8 -24.2 72.6

MN ANOKA COUNTY

FY 2011 Grant 1,025 521 173 332 A

New Data Grant 1,215 520 245 451 A

Change (%) 18.6 -0.2 41.4 36.0

MN BLOOMINGTON

FY 2011 Grant 361 160 84 118 A

New Data Grant 408 160 83 165 A

Change (%) 12.8 -0.4 -0.8 40.6

MN COON RAPIDS

FY 2011 Grant 272 121 48 103 A

New Data Grant 316 119 67 130 A

Change (%) 16.2 -1.6 39.4 26.2

MN DAKOTA 

COUNTY

FY 2011 Grant 1,550 803 267 480 A

New Data Grant 1,727 805 290 633 A

Change (%) 11.4 0.2 8.8 31.8

MN DULUTH

FY 2011 Grant 2,568 266 950 1,351 B

New Data Grant 2,402 286 906 1,209 B

Change (%) -6.4 7.6 -4.6 -10.4

MN EDEN PRAIRIE

FY 2011 Grant 233 121 46 67 A

New Data Grant 273 117 64 92 A

Change (%) 17.0 -2.8 40.0 36.8

MN HENNEPIN 

COUNTY

FY 2011 Grant 2,187 982 492 712 A

New Data Grant 2,479 987 515 977 A

Change (%) 13.4 0.6 4.6 37.2

MN MANKATO CITY

FY 2011 Grant 347 116 0 232 B

New Data Grant 399 163 0 236 B

Change (%) 14.8 40.6 1.8

MN MINNEAPOLIS

FY 2011 Grant 12,043 1,310 4,244 6,489 B

New Data Grant 11,983 1,415 4,183 6,385 B

Change (%) -0.4 8.0 -1.4 -1.6
85

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

MN MINNETONKA

FY 2011 Grant 169 98 24 47 A

New Data Grant 176 96 16 63 A

Change (%) 3.6 -2.4 -32.8 35.2

MN MOORHEAD

FY 2011 Grant 259 71 24 164 A

New Data Grant 242 73 10 158 A

Change (%) -6.6 3.2 -57.4 -3.4

MN NORTH 

MANKATO CITY

FY 2011 Grant 80 18 0 63 B

New Data Grant 66 17 0 48 B

Change (%) -18.2 -0.8 -23.0

MN PLYMOUTH

FY 2011 Grant 238 141 39 59 A

New Data Grant 259 136 28 95 A

Change (%) 8.6 -3.4 -27.0 60.6

MN RAMSEY COUNTY

FY 2011 Grant 929 436 162 331 A

New Data Grant 1,070 431 170 468 A

Change (%) 15.2 -1.0 4.8 41.4

MN ROCHESTER

FY 2011 Grant 524 200 92 232 A

New Data Grant 561 206 88 267 A

Change (%) 7.0 2.8 -3.8 14.8

MN ST CLOUD

FY 2011 Grant 432 151 0 281 B

New Data Grant 566 127 52 387 A

Change (%) 31.0 156.0

MN ST LOUIS 

COUNTY

FY 2011 Grant 2,201 219 848 1,134 B

New Data Grant 1,924 205 826 892 B

Change (%) -12.6 -6.4 -2.4 -21.4

MN ST PAUL

FY 2011 Grant 7,217 913 2,367 3,937 B

New Data Grant 7,080 965 2,259 3,855 B

Change (%) -2.0 5.6 -4.6 -2.0

MN WASHINGTON 

COUNTY

FY 2011 Grant 582 335 73 174 A

New Data Grant 678 337 81 260 A

Change (%) 16.6 0.6 10.4 49.6

MN WOODBURY CITY

FY 2011 Grant 157 111 19 27 A

New Data Grant 192 119 20 53 A

Change (%) 22.0 7.8 3.8 93.4

MO BLUE SPRINGS

FY 2011 Grant 206 108 18 80 A

New Data Grant 238 101 28 108 A

Change (%) 15.0 -6.0 57.2 34.0

MO COLUMBIA

FY 2011 Grant 774 198 61 516 A

New Data Grant 900 209 95 596 A

Change (%) 16.2 5.8 56.8 15.4
86

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

MO FLORISSANT

FY 2011 Grant 201 98 32 71 A

New Data Grant 242 101 49 92 A

Change (%) 20.4 2.8 54.6 29.0

MO INDEPENDENCE

FY 2011 Grant 683 204 0 479 B

New Data Grant 830 225 145 460 A

Change (%) 21.6 125.0

MO JEFFERSON CITY

FY 2011 Grant 290 85 14 191 B

New Data Grant 256 84 0 172 B

Change (%) -12.0 -1.2 -100 -10.2

MO JEFFERSON 

COUNTY

FY 2011 Grant 1,034 422 148 464 A

New Data Grant 1,179 421 160 598 A

Change (%) 14.0 -0.4 8.6 28.8

MO JOPLIN

FY 2011 Grant 587 136 109 342 B

New Data Grant 543 154 103 286 B

Change (%) -7.6 12.8 -5.2 -16.4

MO KANSAS CITY

FY 2011 Grant 7,655 1,307 2,898 3,450 B

New Data Grant 8,480 1,439 3,090 3,951 B

Change (%) 10.8 10.0 6.6 14.6

MO LEES SUMMIT

FY 2011 Grant 287 167 25 95 A

New Data Grant 324 176 25 123 A

Change (%) 12.8 5.2 -0.6 29.4

MO O'FALLON

FY 2011 Grant 230 152 25 53 A

New Data Grant 258 153 33 72 A

Change (%) 11.8 0.4 33.2 35.2

MO SPRINGFIELD

FY 2011 Grant 1,219 305 125 789 A

New Data Grant 1,324 308 133 883 A

Change (%) 8.6 1.0 6.4 12.0

MO ST CHARLES

FY 2011 Grant 285 127 31 127 A

New Data Grant 317 127 31 160 A

Change (%) 11.4 0.0 -1.8 26.0

MO ST JOSEPH

FY 2011 Grant 1,572 190 543 839 B

New Data Grant 1,493 201 521 771 B

Change (%) -5.0 5.6 -4.0 -8.2

MO ST LOUIS

FY 2011 Grant 17,829 1,760 9,609 6,460 B

New Data Grant 18,894 1,524 9,903 7,467 B

Change (%) 6.0 -13.4 3.0 15.6

MO ST LOUIS 

COUNTY

FY 2011 Grant 5,089 1,412 599 3,078 B

New Data Grant 5,120 1,561 465 3,094 B

Change (%) 0.6 10.6 -22.4 0.6
87

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

MO ST CHARLES 

COUNTY

FY 2011 Grant 686 402 75 209 A

New Data Grant 703 410 64 229 A

Change (%) 2.4 1.8 -14.2 9.4

MS BILOXI

FY 2011 Grant 511 146 255 111 B

New Data Grant 440 102 268 70 B

Change (%) -14.0 -30.0 5.2 -37.0

MS GULFPORT

FY 2011 Grant 682 137 122 423 A

New Data Grant 616 131 115 370 A

Change (%) -9.8 -4.6 -6.0 -12.4

MS HATTIESBURG

FY 2011 Grant 568 102 69 396 A

New Data Grant 647 89 103 455 A

Change (%) 14.0 -13.4 49.2 14.8

MS JACKSON

FY 2011 Grant 2,257 338 449 1,469 A

New Data Grant 2,074 335 360 1,380 A

Change (%) -8.0 -1.2 -19.8 -6.0

MS MOSS POINT

FY 2011 Grant 155 27 30 98 A

New Data Grant 117 26 17 73 A

Change (%) -24.8 -2.0 -42.4 -25.6

MS PASCAGOULA

FY 2011 Grant 274 46 47 181 A

New Data Grant 227 43 28 156 A

Change (%) -17.0 -5.8 -41.6 -13.6

MT BILLINGS

FY 2011 Grant 646 205 76 366 A

New Data Grant 639 201 75 363 A

Change (%) -1.0 -1.8 -0.6 -0.6

MT GREAT FALLS

FY 2011 Grant 845 169 295 381 B

New Data Grant 806 158 296 352 B

Change (%) -4.6 -6.2 0.2 -7.6

MT MISSOULA

FY 2011 Grant 574 133 64 376 A

New Data Grant 629 129 42 459 A

Change (%) 9.8 -3.4 -34.8 22.0

NC ASHEVILLE

FY 2011 Grant 1,098 218 298 582 B

New Data Grant 1,029 259 215 556 B

Change (%) -6.2 18.6 -28.0 -4.4

NC BURLINGTON

FY 2011 Grant 399 100 81 218 A

New Data Grant 516 96 133 287 A

Change (%) 29.4 -3.4 64.8 31.2

NC CARY

FY 2011 Grant 452 264 75 113 A

New Data Grant 471 261 68 142 A

Change (%) 4.0 -1.4 -9.0 25.2
88

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

NC CHAPEL HILL

FY 2011 Grant 530 104 125 302 A

New Data Grant 460 110 30 320 A

Change (%) -13.0 6.6 -75.6 6.0

NC CHARLOTTE

FY 2011 Grant 4,358 1,362 1,012 1,985 A

New Data Grant 4,951 1,410 1,020 2,521 A

Change (%) 13.6 3.6 0.8 27.0

NC CONCORD

FY 2011 Grant 355 130 69 157 A

New Data Grant 472 152 107 212 A

Change (%) 32.6 17.2 56.6 35.0

NC CUMBERLAND 

COUNTY

FY 2011 Grant 923 312 502 109 B

New Data Grant 959 229 143 586 A

Change (%) 4.0 87.8

NC DURHAM

FY 2011 Grant 1,764 443 385 936 A

New Data Grant 1,837 440 380 1,017 A

Change (%) 4.2 -0.6 -1.4 8.6

NC FAYETTEVILLE

FY 2011 Grant 1,398 401 238 760 A

New Data Grant 1,503 387 218 898 A

Change (%) 7.6 -3.6 -8.2 18.2

NC GASTONIA

FY 2011 Grant 579 141 96 342 A

New Data Grant 650 138 128 383 A

Change (%) 12.2 -2.0 33.6 12.2

NC GOLDSBORO

FY 2011 Grant 360 74 47 239 A

New Data Grant 380 70 50 260 A

Change (%) 5.6 -5.2 7.0 8.8

NC GREENSBORO

FY 2011 Grant 1,740 492 330 918 A

New Data Grant 2,121 520 335 1,266 A

Change (%) 22.0 5.8 1.8 37.8

NC GREENVILLE

FY 2011 Grant 744 158 79 506 A

New Data Grant 876 163 83 630 A

Change (%) 17.8 3.0 4.0 24.4

NC HICKORY

FY 2011 Grant 288 80 63 144 A

New Data Grant 340 77 63 200 A

Change (%) 18.2 -3.8 -0.4 38.4

NC HIGH POINT

FY 2011 Grant 708 200 118 390 A

New Data Grant 891 201 118 572 A

Change (%) 26.0 0.6 0.4 46.6

NC JACKSONVILLE

FY 2011 Grant 483 156 83 244 A

New Data Grant 411 135 53 223 A

Change (%) -14.8 -13.2 -36.6 -8.6
89

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

NC KANNAPOLIS

FY 2011 Grant 322 80 76 166 B

New Data Grant 397 137 81 180 B

Change (%) 23.2 70.6 5.8 8.2

NC LENOIR

FY 2011 Grant 132 35 15 83 A

New Data Grant 165 35 18 112 A

Change (%) 25.6 1.8 24.2 35.8

NC MECKLENBURG 

COUNTY

FY 2011 Grant 433 243 50 140 A

New Data Grant 488 193 79 216 A

Change (%) 12.6 -20.6 57.4 54.2

NC MORGANTON

FY 2011 Grant 134 33 27 75 A

New Data Grant 177 33 18 126 A

Change (%) 32.2 -1.0 -30.6 69.4

NC RALEIGH

FY 2011 Grant 2,256 784 424 1,048 A

New Data Grant 2,750 779 470 1,501 A

Change (%) 21.8 -0.8 10.8 43.2

NC ROCKY MOUNT

FY 2011 Grant 602 115 96 391 A

New Data Grant 549 111 85 354 A

Change (%) -8.8 -3.8 -11.2 -9.6

NC SALISBURY

FY 2011 Grant 258 82 27 149 B

New Data Grant 285 65 62 157 A

Change (%) 10.2 91.6

NC WAKE COUNTY

FY 2011 Grant 1,329 648 181 501 A

New Data Grant 1,596 657 333 606 A

Change (%) 20.0 1.4 84.6 21.0

NC WILMINGTON

FY 2011 Grant 802 196 83 523 A

New Data Grant 889 205 80 603 A

Change (%) 10.8 4.8 -2.8 15.2

NC WINSTON-SALEM

FY 2011 Grant 1,703 444 312 947 A

New Data Grant 2,123 443 443 1,237 A

Change (%) 24.6 -0.4 42.0 30.6

ND BISMARCK

FY 2011 Grant 313 118 37 158 A

New Data Grant 316 118 30 168 A

Change (%) 1.0 -0.2 -18.8 6.4

ND FARGO

FY 2011 Grant 621 185 74 362 A

New Data Grant 718 203 82 432 A

Change (%) 15.6 10.2 10.8 19.2

ND GRAND FORKS

FY 2011 Grant 376 99 44 233 A

New Data Grant 432 102 32 298 A

Change (%) 15.0 2.8 -26.6 27.8
90

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

NE BELLEVUE

FY 2011 Grant 224 99 34 91 A

New Data Grant 325 97 88 141 A

Change (%) 45.4 -2.0 155.2 55.4

NE LINCOLN

FY 2011 Grant 1,598 456 0 1,142 B

New Data Grant 1,754 498 242 1,014 A

Change (%) 9.8 122.2

NE OMAHA

FY 2011 Grant 4,192 911 530 2,751 B

New Data Grant 5,167 1,106 998 3,063 B

Change (%) 23.2 21.4 88.2 11.4

NH DOVER

FY 2011 Grant 319 46 0 272 B

New Data Grant 276 45 0 232 B

Change (%) -13.2 -3.0 -15.0

NH MANCHESTER

FY 2011 Grant 1,729 234 292 1,202 B

New Data Grant 1,789 270 274 1,245 B

Change (%) 3.6 15.4 -6.2 3.6

NH NASHUA

FY 2011 Grant 667 121 0 546 B

New Data Grant 614 110 0 504 B

Change (%) -8.0 -9.4 -7.8

NH PORTSMOUTH

FY 2011 Grant 594 40 248 307 B

New Data Grant 534 26 240 268 B

Change (%) -10.2 -34.2 -3.2 -12.6

NH ROCHESTER

FY 2011 Grant 276 50 0 227 B

New Data Grant 253 72 0 182 B

Change (%) -8.4 43.8 -19.8

NJ ASBURY PARK

FY 2011 Grant 412 106 119 188 B

New Data Grant 465 87 121 257 B

Change (%) 13.0 -17.4 2.0 37.0

NJ ATLANTIC CITY

FY 2011 Grant 1,224 199 622 403 B

New Data Grant 1,228 170 610 448 B

Change (%) 0.4 -14.6 -1.8 11.0

NJ ATLANTIC 

COUNTY

FY 2011 Grant 1,186 412 256 518 A

New Data Grant 1,130 414 239 476 A

Change (%) -4.8 0.6 -6.6 -8.0

NJ BAYONNE

FY 2011 Grant 1,729 132 663 934 B

New Data Grant 1,577 128 591 858 B

Change (%) -8.8 -3.0 -11.0 -8.0

NJ BERGEN COUNTY

FY 2011 Grant 9,879 916 3,140 5,822 B

New Data Grant 8,958 897 2,934 5,128 B

Change (%) -9.4 -2.2 -6.6 -12.0
91

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

NJ BLOOMFIELD

FY 2011 Grant 1,067 58 426 582 B

New Data Grant 876 54 372 450 B

Change (%) -18.0 -8.4 -12.8 -22.6

NJ BRICK TOWNSHIP

FY 2011 Grant 319 152 47 120 A

New Data Grant 308 145 45 118 A

Change (%) -3.4 -5.0 -3.6 -1.4

NJ BRIDGETON

FY 2011 Grant 373 103 83 187 B

New Data Grant 343 88 73 182 B

Change (%) -8.0 -14.6 -11.6 -3.0

NJ BURLINGTON 

COUNTY

FY 2011 Grant 1,432 737 202 492 A

New Data Grant 1,416 738 182 496 A

Change (%) -1.2 0.2 -10.0 0.8

NJ CAMDEN

FY 2011 Grant 2,540 565 1,213 761 B

New Data Grant 2,452 528 1,207 718 B

Change (%) -3.4 -6.6 -0.6 -5.8

NJ CAMDEN 

COUNTY

FY 2011 Grant 2,394 370 271 1,753 B

New Data Grant 2,299 375 276 1,647 B

Change (%) -4.0 1.6 2.0 -6.0

NJ CHERRY HILL

FY 2011 Grant 425 58 301 66 B

New Data Grant 426 56 287 82 B

Change (%) 0.2 -1.8 -4.6 23.6

NJ CLIFTON

FY 2011 Grant 1,317 104 563 650 B

New Data Grant 1,162 125 474 563 B

Change (%) -11.8 20.0 -15.8 -13.4

NJ EAST ORANGE

FY 2011 Grant 1,565 278 635 652 B

New Data Grant 1,467 273 631 562 B

Change (%) -6.4 -1.6 -0.6 -13.8

NJ EDISON

FY 2011 Grant 578 193 223 162 A

New Data Grant 616 193 189 234 A

Change (%) 6.6 0.0 -15.4 44.6

NJ ELIZABETH

FY 2011 Grant 1,901 442 456 1,003 B

New Data Grant 2,091 241 1,233 618 A

Change (%) 10.0 39.6

NJ ESSEX COUNTY

FY 2011 Grant 5,698 409 1,916 3,374 B

New Data Grant 4,999 323 1,709 2,967 B

Change (%) -12.2 -21.2 -10.8 -12.0

NJ EWING 

TOWNSHIP

FY 2011 Grant 210 41 46 122 B

New Data Grant 215 53 49 113 B

Change (%) 2.6 28.6 5.4 -7.2
92

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

NJ FRANKLIN 

TOWNSHIP

FY 2011 Grant 292 116 87 89 A

New Data Grant 263 120 58 85 A

Change (%) -9.8 3.8 -33.0 -5.2

NJ GLOUCESTER 

COUNTY

FY 2011 Grant 1,280 293 0 987 B

New Data Grant 1,205 333 0 872 B

Change (%) -5.8 13.8 -11.8

NJ GLOUCESTER 

TWP

FY 2011 Grant 313 125 49 138 A

New Data Grant 296 125 67 105 A

Change (%) -5.4 -0.4 35.0 -24.4

NJ HAMILTON

FY 2011 Grant 554 76 89 389 B

New Data Grant 548 72 106 370 B

Change (%) -1.2 -5.2 18.6 -4.8

NJ HAMMONTON

FY 2011 Grant 147 24 17 106 B

New Data Grant 89 28 0 62 B

Change (%) -39.0 16.8 -100 -41.6

NJ HOWELL 

TOWNSHIP

FY 2011 Grant 197 100 26 72 A

New Data Grant 177 98 11 68 A

Change (%) -10.0 -1.2 -56.6 -5.8

NJ HUDSON 

COUNTY

FY 2011 Grant 3,412 468 782 2,162 B

New Data Grant 3,009 383 578 2,048 B

Change (%) -11.8 -18.2 -26.0 -5.2

NJ IRVINGTON

FY 2011 Grant 1,022 220 413 389 B

New Data Grant 1,005 166 429 410 B

Change (%) -1.6 -24.4 3.8 5.4

NJ JACKSON 

TOWNSHIP

FY 2011 Grant 180 103 22 55 A

New Data Grant 218 106 47 65 A

Change (%) 21.0 2.8 116.4 18.0

NJ JERSEY CITY

FY 2011 Grant 5,931 930 2,150 2,851 B

New Data Grant 5,943 726 2,034 3,183 B

Change (%) 0.2 -22.0 -5.4 11.6

NJ LAKEWOOD 

TOWNSHIP

FY 2011 Grant 709 138 169 402 A

New Data Grant 979 179 289 511 A

Change (%) 38.0 29.8 71.2 27.0

NJ LONG BRANCH

FY 2011 Grant 465 110 80 275 B

New Data Grant 468 82 104 282 B

Change (%) 0.6 -25.4 30.0 2.6

NJ MIDDLESEX 

COUNTY

FY 2011 Grant 1,747 744 478 525 A

New Data Grant 1,779 764 416 599 A

Change (%) 1.8 2.8 -13.0 14.0
93

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

NJ MIDDLETOWN

FY 2011 Grant 260 43 0 217 B

New Data Grant 234 45 0 189 B

Change (%) -10.2 4.4 -13.0

NJ MILLVILLE

FY 2011 Grant 264 85 0 179 B

New Data Grant 284 106 1 177 B

Change (%) 7.6 24.6 -1.0

NJ MONMOUTH 

COUNTY

FY 2011 Grant 2,729 504 0 2,225 B

New Data Grant 2,521 445 0 2,076 B

Change (%) -7.6 -11.6 -6.6

NJ MORRIS COUNTY

FY 2011 Grant 2,049 286 0 1,762 B

New Data Grant 1,768 240 0 1,527 B

Change (%) -13.8 -16.0 -13.4

NJ NEW 

BRUNSWICK

FY 2011 Grant 725 100 223 403 A

New Data Grant 787 106 342 339 A

Change (%) 8.4 6.6 53.4 -16.0

NJ NEWARK

FY 2011 Grant 7,836 1,567 4,126 2,143 B

New Data Grant 7,450 1,159 4,054 2,237 B

Change (%) -5.0 -26.0 -1.8 4.4

NJ NORTH BERGEN 

TOWNSHIP

FY 2011 Grant 695 135 110 450 B

New Data Grant 539 108 30 401 B

Change (%) -22.4 -20.2 -72.4 -11.0

NJ OCEAN CITY

FY 2011 Grant 273 22 0 251 B

New Data Grant 347 20 0 327 B

Change (%) 27.2 -6.4 30.2

NJ OCEAN COUNTY

FY 2011 Grant 1,112 529 107 476 A

New Data Grant 1,161 506 155 500 A

Change (%) 4.4 -4.2 44.4 5.0

NJ OLD BRIDGE 

TOWNSHIP

FY 2011 Grant 294 127 77 90 A

New Data Grant 261 126 64 71 A

Change (%) -11.4 -1.2 -16.8 -21.2

NJ PARSIPPANYTROY HILLS TWP

FY 2011 Grant 255 98 89 67 A

New Data Grant 235 103 81 51 A

Change (%) -7.8 5.0 -9.2 -24.4

NJ PASSAIC

FY 2011 Grant 1,116 301 176 639 B

New Data Grant 1,467 135 779 554 A

Change (%) 31.4 84.2

NJ PASSAIC 

COUNTY

FY 2011 Grant 950 126 0 824 B

New Data Grant 903 131 0 772 B

Change (%) -5.0 4.0 -6.4
94

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

NJ PATERSON

FY 2011 Grant 2,684 685 874 1,125 B

New Data Grant 2,712 676 843 1,193 B

Change (%) 1.0 -1.2 -3.6 6.0

NJ PERTH AMBOY

FY 2011 Grant 646 173 105 369 B

New Data Grant 569 155 73 341 B

Change (%) -12.0 -10.4 -30.6 -7.4

NJ SAYREVILLE

FY 2011 Grant 206 82 58 67 A

New Data Grant 223 82 67 74 A

Change (%) 8.0 0.8 16.2 9.8

NJ SOMERSET 

COUNTY

FY 2011 Grant 1,133 179 0 954 B

New Data Grant 1,011 147 0 864 B

Change (%) -10.8 -18.4 -9.4

NJ TOMS RIVER 

TOWNSHIP

FY 2011 Grant 409 187 47 175 A

New Data Grant 392 176 77 140 A

Change (%) -4.2 -6.2 64.2 -20.4

NJ TRENTON

FY 2011 Grant 2,771 363 1,102 1,306 B

New Data Grant 2,623 339 1,058 1,227 B

Change (%) -5.4 -6.8 -4.0 -6.0

NJ UNION CITY

FY 2011 Grant 1,269 301 205 763 B

New Data Grant 974 128 479 367 A

Change (%) -23.2 22.0

NJ UNION COUNTY

FY 2011 Grant 4,869 425 1,937 2,507 B

New Data Grant 4,475 389 1,813 2,274 B

Change (%) -8.0 -8.6 -6.4 -9.4

NJ UNION 

TOWNSHIP

FY 2011 Grant 635 47 296 292 B

New Data Grant 514 48 250 216 B

Change (%) -19.0 3.0 -15.6 -25.8

NJ VINELAND

FY 2011 Grant 497 114 117 266 A

New Data Grant 438 117 99 222 A

Change (%) -11.8 2.2 -15.2 -16.4

NJ WASHINGTON 

TOWNSHIP

FY 2011 Grant 166 101 12 53 A

New Data Grant 164 94 17 54 A

Change (%) -1.0 -7.0 42.8 1.2

NJ WAYNE 

TOWNSHIP

FY 2011 Grant 173 104 19 51 A

New Data Grant 182 105 23 53 A

Change (%) 4.6 1.2 26.4 4.0

NJ WOODBRIDGE

FY 2011 Grant 650 96 257 297 B

New Data Grant 594 95 223 276 B

Change (%) -8.8 -1.6 -13.0 -7.2
95

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

NM ALBUQUERQUE

FY 2011 Grant 3,996 1,023 876 2,098 A

New Data Grant 4,233 1,052 861 2,319 A

Change (%) 6.0 2.8 -1.6 10.6

NM FARMINGTON

FY 2011 Grant 389 84 98 208 A

New Data Grant 395 88 121 185 A

Change (%) 1.4 5.4 24.0 -10.8

NM LAS CRUCES

FY 2011 Grant 916 181 145 591 A

New Data Grant 836 188 104 543 A

Change (%) -8.8 4.2 -28.0 -8.0

NM RIO RANCHO

FY 2011 Grant 301 160 50 92 A

New Data Grant 386 169 61 156 A

Change (%) 28.2 5.6 23.6 69.6

NM SANTA FE

FY 2011 Grant 525 143 120 262 A

New Data Grant 569 131 134 303 A

Change (%) 8.4 -8.6 12.0 16.0

NV CARSON CITY

FY 2011 Grant 396 107 116 173 A

New Data Grant 398 107 90 201 A

Change (%) 0.6 -0.2 -22.2 16.2

NV CLARK COUNTY

FY 2011 Grant 5,799 1,651 1,925 2,222 A

New Data Grant 6,501 1,721 2,114 2,666 A

Change (%) 12.2 4.2 9.8 20.0

NV HENDERSON

FY 2011 Grant 1,042 496 203 344 A

New Data Grant 1,247 497 236 514 A

Change (%) 19.6 0.2 16.4 49.6

NV LAS VEGAS

FY 2011 Grant 4,632 1,097 1,563 1,971 A

New Data Grant 4,638 1,125 1,480 2,033 A

Change (%) 0.2 2.6 -5.4 3.2

NV NORTH LAS 

VEGAS

FY 2011 Grant 1,532 434 509 590 A

New Data Grant 1,880 418 780 682 A

Change (%) 22.8 -3.6 53.2 15.6

NV RENO

FY 2011 Grant 1,847 425 641 782 A

New Data Grant 1,971 434 604 932 A

Change (%) 6.6 2.2 -5.8 19.2

NV SPARKS

FY 2011 Grant 567 173 208 186 A

New Data Grant 643 174 201 268 A

Change (%) 13.6 0.8 -3.2 44.2

NY ALBANY

FY 2011 Grant 3,364 397 1,261 1,705 B

New Data Grant 3,361 396 1,185 1,781 B

Change (%) 0.0 -0.4 -6.0 4.4
96

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

NY AMHERST TOWN

FY 2011 Grant 525 223 55 247 A

New Data Grant 549 236 39 274 A

Change (%) 4.6 5.6 -28.8 11.0

NY AUBURN

FY 2011 Grant 984 91 325 568 B

New Data Grant 915 85 308 522 B

Change (%) -7.0 -7.2 -5.2 -8.0

NY BABYLON TOWN

FY 2011 Grant 1,161 427 245 489 A

New Data Grant 1,025 412 248 366 A

Change (%) -11.8 -3.6 1.4 -25.2

NY BINGHAMTON

FY 2011 Grant 2,111 231 870 1,011 B

New Data Grant 1,942 212 816 914 B

Change (%) -8.0 -8.2 -6.2 -9.6

NY BUFFALO

FY 2011 Grant 14,541 1,585 6,614 6,341 B

New Data Grant 14,974 1,361 6,607 7,006 B

Change (%) 3.0 -14.2 -0.2 10.4

NY CHEEKTOWAGA 

TOWN

FY 2011 Grant 971 128 484 359 B

New Data Grant 991 136 460 395 B

Change (%) 2.0 6.8 -5.0 9.8

NY CLAY TOWN

FY 2011 Grant 258 114 27 117 A

New Data Grant 213 112 14 87 A

Change (%) -17.2 -1.2 -48.6 -25.6

NY COLONIE TOWN

FY 2011 Grant 356 75 0 281 B

New Data Grant 360 78 0 281 B

Change (%) 1.0 4.6 0.0

NY DUNKIRK

FY 2011 Grant 521 62 191 268 B

New Data Grant 517 59 181 277 B

Change (%) -0.8 -4.2 -5.4 3.4

NY DUTCHESS 

COUNTY

FY 2011 Grant 1,330 236 0 1,093 B

New Data Grant 1,334 237 0 1,097 B

Change (%) 0.2 0.2 0.4

NY ELMIRA

FY 2011 Grant 1,238 133 506 599 B

New Data Grant 1,147 121 498 528 B

Change (%) -7.4 -9.0 -1.6 -11.8

NY ERIE COUNTY

FY 2011 Grant 2,761 295 533 1,933 B

New Data Grant 2,551 317 422 1,812 B

Change (%) -7.6 7.4 -20.8 -6.2

NY GLEN FALLS

FY 2011 Grant 510 45 175 289 B

New Data Grant 477 36 161 280 B

Change (%) -6.4 -19.4 -8.2 -3.4
97

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

NY GREECE

FY 2011 Grant 376 181 37 158 A

New Data Grant 422 185 40 197 A

Change (%) 12.4 2.6 8.2 24.4

NY HAMBURG 

TOWN

FY 2011 Grant 451 53 76 322 B

New Data Grant 420 67 58 295 B

Change (%) -6.8 26.2 -24.0 -8.4

NY HUNTINGTON 

TOWN

FY 2011 Grant 815 391 112 312 A

New Data Grant 795 138 0 658 B

Change (%) -2.4 -56.0

NY IRONDEQUOIT

FY 2011 Grant 922 59 415 448 B

New Data Grant 852 78 382 392 B

Change (%) -7.6 32.0 -8.0 -12.4

NY ISLIP TOWN

FY 2011 Grant 1,850 653 464 733 A

New Data Grant 1,629 647 456 527 A

Change (%) -12.0 -0.8 -1.8 -28.2

NY ITHACA

FY 2011 Grant 743 184 165 393 B

New Data Grant 789 187 160 442 B

Change (%) 6.2 1.6 -3.2 12.4

NY JAMESTOWN

FY 2011 Grant 1,272 128 418 726 B

New Data Grant 1,120 115 387 619 B

Change (%) -12.0 -10.6 -7.4 -14.8

NY KINGSTON

FY 2011 Grant 742 75 269 398 B

New Data Grant 744 68 245 431 B

Change (%) 0.2 -9.4 -9.2 8.6

NY MIDDLETOWN

FY 2011 Grant 527 92 117 318 B

New Data Grant 517 85 86 347 B

Change (%) -2.0 -8.4 -26.4 9.0

NY MONROE 

COUNTY

FY 2011 Grant 1,737 365 0 1,372 B

New Data Grant 1,777 425 0 1,352 B

Change (%) 2.4 16.4 -1.4

NY MOUNT VERNON

FY 2011 Grant 1,651 203 566 881 B

New Data Grant 1,506 146 571 789 B

Change (%) -8.8 -27.8 0.8 -10.4

NY NASSAU 

COUNTY

FY 2011 Grant 14,136 1,383 6,545 6,208 B

New Data Grant 13,833 1,102 6,568 6,163 B

Change (%) -2.2 -20.4 0.4 -0.8

NY NEW ROCHELLE

FY 2011 Grant 1,519 155 513 851 B

New Data Grant 1,470 139 465 866 B

Change (%) -3.2 -10.6 -9.4 1.8
98

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

NY NEW YORK

FY 2011 Grant 163,359 35,217 41,200 86,942 B

New Data Grant 168,010 27,435 42,510 98,065 B

Change (%) 2.8 -22.0 3.2 12.8

NY NEWBURGH

FY 2011 Grant 768 148 229 391 B

New Data Grant 771 123 215 433 B

Change (%) 0.4 -16.6 -6.4 10.8

NY NIAGARA FALLS

FY 2011 Grant 2,287 226 1,279 782 B

New Data Grant 2,386 193 1,270 923 B

Change (%) 4.4 -14.4 -0.8 18.0

NY ONONDAGA 

COUNTY

FY 2011 Grant 2,061 281 315 1,466 B

New Data Grant 1,882 296 220 1,366 B

Change (%) -8.8 5.2 -30.2 -6.8

NY ORANGE 

COUNTY

FY 2011 Grant 1,569 287 0 1,282 B

New Data Grant 1,562 258 0 1,304 B

Change (%) -0.4 -10.0 1.6

NY POUGHKEEPSIE

FY 2011 Grant 934 140 350 445 B

New Data Grant 878 121 303 454 B

Change (%) -6.0 -13.0 -13.4 2.0

NY ROCHESTER

FY 2011 Grant 8,718 1,155 3,386 4,177 B

New Data Grant 8,808 1,047 3,279 4,482 B

Change (%) 1.0 -9.2 -3.2 7.4

NY ROCKLAND 

COUNTY

FY 2011 Grant 1,888 565 503 819 A

New Data Grant 2,136 584 697 855 A

Change (%) 13.2 3.2 38.6 4.4

NY ROME

FY 2011 Grant 1,089 100 551 438 B

New Data Grant 1,026 95 536 394 B

Change (%) -5.8 -4.4 -2.6 -10.2

NY SARATOGA 

SPRINGS

FY 2011 Grant 334 45 0 289 B

New Data Grant 379 37 0 342 B

Change (%) 13.4 -17.6 18.2

NY SCHENECTADY

FY 2011 Grant 2,317 259 764 1,294 B

New Data Grant 2,165 216 690 1,259 B

Change (%) -6.6 -16.6 -9.8 -2.6

NY SUFFOLK 

COUNTY

FY 2011 Grant 3,335 1,442 530 1,364 A

New Data Grant 3,209 1,407 548 1,253 A

Change (%) -3.8 -2.4 3.6 -8.2

NY SYRACUSE

FY 2011 Grant 5,586 791 2,321 2,474 B

New Data Grant 5,288 703 2,193 2,391 B

Change (%) -5.4 -11.2 -5.4 -3.4
99

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

NY TONAWANDA 

TOWN

FY 2011 Grant 1,723 112 1,074 537 B

New Data Grant 1,624 127 1,029 468 B

Change (%) -5.8 13.8 -4.2 -12.8

NY TROY

FY 2011 Grant 1,869 183 672 1,014 B

New Data Grant 1,793 189 626 979 B

Change (%) -4.0 3.2 -6.8 -3.6

NY UNION TOWN

FY 2011 Grant 1,292 132 535 625 B

New Data Grant 1,190 122 492 575 B

Change (%) -7.8 -7.0 -8.0 -8.0

NY UTICA

FY 2011 Grant 2,634 299 1,158 1,177 B

New Data Grant 2,456 278 1,084 1,093 B

Change (%) -6.8 -7.0 -6.4 -7.2

NY WEST SENECA

FY 2011 Grant 320 45 85 191 B

New Data Grant 302 51 68 184 B

Change (%) -5.8 13.2 -20.2 -3.6

NY WESTCHESTER 

COUNTY

FY 2011 Grant 5,379 544 749 4,085 B

New Data Grant 5,072 440 723 3,910 B

Change (%) -5.6 -19.2 -3.6 -4.2

NY WHITE PLAINS

FY 2011 Grant 828 108 230 490 B

New Data Grant 929 98 228 602 B

Change (%) 12.2 -9.2 -0.4 23.0

NY YONKERS

FY 2011 Grant 3,390 635 1,065 1,690 B

New Data Grant 3,302 488 1,094 1,720 B

Change (%) -2.6 -23.2 2.8 1.8

OH AKRON

FY 2011 Grant 6,191 780 2,862 2,549 B

New Data Grant 6,248 813 2,903 2,532 B

Change (%) 1.0 4.2 1.4 -0.6

OH ALLIANCE

FY 2011 Grant 627 81 258 288 B

New Data Grant 632 68 255 309 B

Change (%) 0.8 -16.4 -1.0 7.2

OH BARBERTON

FY 2011 Grant 685 77 303 305 B

New Data Grant 675 94 296 285 B

Change (%) -1.6 21.8 -2.4 -6.6

OH BOWLING 

GREEN

FY 2011 Grant 271 56 13 203 A

New Data Grant 290 58 12 220 A

Change (%) 7.0 4.0 -5.6 8.4

OH BUTLER COUNTY

FY 2011 Grant 1,048 483 87 478 A

New Data Grant 1,246 497 134 615 A

Change (%) 19.0 2.8 54.6 28.6
100

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

OH CANTON

FY 2011 Grant 2,617 316 1,152 1,149 B

New Data Grant 2,775 356 1,195 1,225 B

Change (%) 6.0 12.6 3.8 6.6

OH CINCINNATI

FY 2011 Grant 11,752 1,471 5,266 5,015 B

New Data Grant 12,502 1,451 5,607 5,444 B

Change (%) 6.4 -1.4 6.4 8.6

OH CLEVELAND

FY 2011 Grant 21,655 2,584 11,039 8,031 B

New Data Grant 22,540 2,338 11,271 8,931 B

Change (%) 4.0 -9.6 2.2 11.2

OH CLEVELAND 

HEIGHTS

FY 2011 Grant 1,602 111 592 898 B

New Data Grant 1,625 147 571 907 B

Change (%) 1.4 32.2 -3.6 1.0

OH COLUMBUS

FY 2011 Grant 5,845 1,487 745 3,613 A

New Data Grant 7,061 1,517 1,133 4,410 A

Change (%) 20.8 2.0 52.2 22.0

OH CUYAHOGA 

COUNTY

FY 2011 Grant 3,874 603 1,083 2,188 B

New Data Grant 3,386 753 621 2,012 B

Change (%) -12.6 24.8 -42.6 -8.0

OH CUYAHOGA 

FALLS

FY 2011 Grant 643 63 261 319 B

New Data Grant 661 86 270 304 B

Change (%) 2.6 36.6 3.6 -4.8

OH DAYTON

FY 2011 Grant 5,742 754 2,998 1,990 B

New Data Grant 5,781 779 3,094 1,908 B

Change (%) 0.6 3.2 3.2 -4.2

OH EAST CLEVELAND

FY 2011 Grant 1,022 180 409 433 B

New Data Grant 1,268 165 481 623 B

Change (%) 24.0 -8.4 17.6 43.6

OH ELYRIA

FY 2011 Grant 636 135 135 365 B

New Data Grant 656 157 133 365 B

Change (%) 3.2 16.4 -1.4 0.0

OH EUCLID

FY 2011 Grant 974 107 595 273 B

New Data Grant 985 132 558 295 B

Change (%) 1.0 23.8 -6.2 8.0

OH FAIRBORN

FY 2011 Grant 236 61 21 153 A

New Data Grant 240 62 10 168 A

Change (%) 1.8 1.6 -52.2 9.4

OH FRANKLIN 

COUNTY

FY 2011 Grant 1,658 798 170 690 A

New Data Grant 2,023 806 344 873 A

Change (%) 22.0 1.0 102.6 26.6
101

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

OH HAMILTON CITY

FY 2011 Grant 1,338 168 574 596 B

New Data Grant 1,441 224 563 655 B

Change (%) 7.8 33.0 -1.8 9.8

OH HAMILTON 

COUNTY

FY 2011 Grant 2,914 572 227 2,115 B

New Data Grant 3,047 663 332 2,053 B

Change (%) 4.6 15.8 46.2 -3.0

OH KENT

FY 2011 Grant 271 54 19 198 A

New Data Grant 296 56 6 234 A

Change (%) 9.2 3.2 -66.6 18.2

OH KETTERING

FY 2011 Grant 524 56 354 114 B

New Data Grant 513 79 310 123 B

Change (%) -2.2 41.6 -12.2 8.0

OH LAKE COUNTY

FY 2011 Grant 1,281 211 363 707 B

New Data Grant 1,338 272 367 700 B

Change (%) 4.4 28.8 1.0 -1.0

OH LAKEWOOD

FY 2011 Grant 2,018 105 613 1,300 B

New Data Grant 1,930 132 577 1,222 B

Change (%) -4.4 26.0 -6.0 -6.0

OH LANCASTER

FY 2011 Grant 510 78 96 337 B

New Data Grant 488 97 72 319 B

Change (%) -4.4 25.4 -24.8 -5.4

OH LIMA

FY 2011 Grant 1,125 180 487 459 B

New Data Grant 1,064 195 463 406 B

Change (%) -5.4 8.6 -5.0 -11.4

OH LORAIN

FY 2011 Grant 1,129 244 416 469 B

New Data Grant 1,265 306 480 479 B

Change (%) 12.0 25.2 15.4 2.2

OH MANSFIELD

FY 2011 Grant 903 159 271 473 B

New Data Grant 869 147 282 440 B

Change (%) -3.8 -7.6 4.2 -7.0

OH MARIETTA

FY 2011 Grant 401 47 140 214 B

New Data Grant 378 56 137 185 B

Change (%) -5.6 20.2 -2.2 -13.6

OH MASSILLON

FY 2011 Grant 660 69 198 393 B

New Data Grant 654 83 199 372 B

Change (%) -0.8 21.4 0.6 -5.4

OH MENTOR

FY 2011 Grant 159 100 10 48 A

New Data Grant 186 91 11 84 A

Change (%) 17.0 -9.4 2.0 75.4
102

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

OH MIDDLETOWN

FY 2011 Grant 627 136 146 345 B

New Data Grant 735 196 175 364 B

Change (%) 17.4 44.4 19.8 5.6

OH MONTGOMERY 

COUNTY

FY 2011 Grant 1,642 641 173 827 A

New Data Grant 1,875 661 218 996 A

Change (%) 14.2 3.0 26.0 20.4

OH NEWARK

FY 2011 Grant 775 124 192 460 B

New Data Grant 797 168 183 447 B

Change (%) 2.8 35.8 -4.8 -2.8

OH PARMA

FY 2011 Grant 944 88 589 267 B

New Data Grant 862 96 520 247 B

Change (%) -8.6 9.2 -11.8 -7.6

OH SANDUSKY

FY 2011 Grant 747 89 280 378 B

New Data Grant 759 103 272 384 B

Change (%) 1.6 16.4 -3.0 1.6

OH SPRINGFIELD

FY 2011 Grant 1,813 223 776 813 B

New Data Grant 1,765 260 778 727 B

Change (%) -2.6 16.4 0.2 -10.6

OH STARK COUNTY

FY 2011 Grant 1,329 255 32 1,042 B

New Data Grant 1,182 322 0 860 B

Change (%) -11.2 26.2 -100 -17.4

OH STEUBENVILLE

FY 2011 Grant 678 80 376 222 B

New Data Grant 637 83 369 185 B

Change (%) -6.0 4.8 -1.8 -16.8

OH SUMMIT COUNTY

FY 2011 Grant 909 500 77 331 A

New Data Grant 1,056 516 94 446 A

Change (%) 16.2 3.2 21.4 34.6

OH TOLEDO

FY 2011 Grant 7,048 1,159 2,438 3,452 B

New Data Grant 7,682 1,244 2,733 3,706 B

Change (%) 9.0 7.4 12.0 7.4

OH WARREN

FY 2011 Grant 1,220 187 577 456 B

New Data Grant 1,200 208 587 404 B

Change (%) -1.6 11.6 1.8 -11.4

OH WARREN 

COUNTY

FY 2011 Grant 649 390 52 207 A

New Data Grant 830 396 84 350 A

Change (%) 27.8 1.6 59.8 69.2

OH YOUNGSTOWN

FY 2011 Grant 3,640 404 2,221 1,016 B

New Data Grant 3,758 404 2,250 1,104 B

Change (%) 3.2 0.0 1.4 8.6
103

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

OK EDMOND

FY 2011 Grant 370 157 44 169 A

New Data Grant 410 157 37 216 A

Change (%) 11.0 0.2 -15.8 27.8

OK ENID

FY 2011 Grant 519 142 130 246 B

New Data Grant 454 152 106 196 B

Change (%) -12.4 6.6 -18.4 -20.4

OK LAWTON

FY 2011 Grant 779 176 129 474 A

New Data Grant 765 187 137 441 A

Change (%) -1.8 6.0 6.6 -6.8

OK MIDWEST CITY

FY 2011 Grant 437 111 64 262 A

New Data Grant 425 105 69 251 A

Change (%) -2.8 -5.2 8.4 -4.4

OK MOORE CITY

FY 2011 Grant 259 104 47 108 A

New Data Grant 311 106 86 118 A

Change (%) 20.0 2.2 83.4 9.4

OK NORMAN

FY 2011 Grant 778 211 92 475 A

New Data Grant 828 214 98 516 A

Change (%) 6.6 1.4 6.6 8.8

OK OKLAHOMA CITY

FY 2011 Grant 4,771 1,083 906 2,781 A

New Data Grant 4,878 1,118 953 2,806 A

Change (%) 2.2 3.2 5.2 1.0

OK SHAWNEE

FY 2011 Grant 364 101 75 188 B

New Data Grant 337 114 80 144 B

Change (%) -7.2 13.0 6.0 -23.4

OK TULSA

FY 2011 Grant 3,323 753 666 1,903 A

New Data Grant 3,595 756 698 2,142 A

Change (%) 8.2 0.4 4.8 12.6

OK TULSA COUNTY

FY 2011 Grant 1,172 504 177 491 A

New Data Grant 1,367 511 280 577 A

Change (%) 16.6 1.2 58.2 17.4

OR ASHLAND

FY 2011 Grant 186 41 19 127 A

New Data Grant 184 67 0 118 B

Change (%) -1.0 -47.2

OR BEAVERTON

FY 2011 Grant 554 181 165 209 A

New Data Grant 639 173 197 269 A

Change (%) 15.2 -4.2 19.4 28.6

OR BEND

FY 2011 Grant 400 149 61 189 A

New Data Grant 397 148 59 190 A

Change (%) -0.8 -1.2 -2.8 0.2
104

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

OR CLACKAMAS 

COUNTY

FY 2011 Grant 1,931 750 406 775 A

New Data Grant 2,189 733 451 1,006 A

Change (%) 13.4 -2.4 11.0 29.8

OR CORVALLIS

FY 2011 Grant 490 100 68 322 A

New Data Grant 516 105 42 369 A

Change (%) 5.4 5.4 -37.8 14.4

OR EUGENE

FY 2011 Grant 1,254 296 159 799 A

New Data Grant 1,356 301 161 894 A

Change (%) 8.2 1.6 1.2 12.0

OR GRESHAM

FY 2011 Grant 801 198 212 391 A

New Data Grant 972 204 302 467 A

Change (%) 21.4 3.0 42.2 19.4

OR HILLSBORO

FY 2011 Grant 602 185 195 223 A

New Data Grant 731 177 240 314 A

Change (%) 21.2 -4.4 23.0 41.0

OR MEDFORD

FY 2011 Grant 562 142 116 304 A

New Data Grant 617 144 148 325 A

Change (%) 9.8 1.6 27.8 6.8

OR MULTNOMAH 

COUNTY

FY 2011 Grant 281 113 55 113 A

New Data Grant 324 88 89 147 A

Change (%) 15.2 -21.8 61.4 29.6

OR PORTLAND

FY 2011 Grant 9,032 1,424 1,509 6,099 B

New Data Grant 8,637 1,557 1,278 5,802 B

Change (%) -4.4 9.4 -15.2 -4.8

OR SALEM

FY 2011 Grant 1,313 301 336 676 A

New Data Grant 1,326 298 358 670 A

Change (%) 1.0 -0.8 6.6 -1.0

OR SPRINGFIELD

FY 2011 Grant 553 111 112 330 A

New Data Grant 507 115 85 308 A

Change (%) -8.2 3.4 -24.2 -6.6

OR WASHINGTON 

COUNTY

FY 2011 Grant 1,819 670 441 708 A

New Data Grant 2,071 664 493 914 A

Change (%) 13.8 -0.8 11.8 29.0

PA ABINGTON

FY 2011 Grant 783 42 371 370 B

New Data Grant 718 35 346 337 B

Change (%) -8.2 -16.6 -6.6 -9.0

PA ALLEGHENY 

COUNTY

FY 2011 Grant 14,918 1,409 6,232 7,277 B

New Data Grant 14,110 1,367 5,959 6,783 B

Change (%) -5.4 -3.0 -4.4 -6.8
105

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

PA ALLENTOWN

FY 2011 Grant 2,506 398 686 1,422 B

New Data Grant 2,182 445 540 1,197 B

Change (%) -13.0 11.8 -21.4 -15.8

PA ALTOONA

FY 2011 Grant 1,815 179 724 912 B

New Data Grant 1,642 153 709 781 B

Change (%) -9.6 -14.8 -2.0 -14.4

PA BEAVER COUNTY

FY 2011 Grant 3,535 342 1,562 1,631 B

New Data Grant 3,308 316 1,549 1,443 B

Change (%) -6.4 -7.6 -0.8 -11.6

PA BENSALEM 

TOWNSHIP

FY 2011 Grant 340 114 75 152 A

New Data Grant 322 117 80 125 A

Change (%) -5.4 2.6 6.8 -17.4

PA BERKS COUNTY

FY 2011 Grant 2,442 285 0 2,156 B

New Data Grant 2,336 342 0 1,994 B

Change (%) -4.4 19.8 -7.6

PA BETHLEHEM

FY 2011 Grant 1,526 210 502 814 B

New Data Grant 1,343 182 464 697 B

Change (%) -12.0 -13.6 -7.4 -14.4

PA BRISTOL 

TOWNSHIP

FY 2011 Grant 620 88 437 94 B

New Data Grant 592 78 419 94 B

Change (%) -4.4 -11.6 -4.0 0.2

PA BUCKS COUNTY

FY 2011 Grant 2,147 381 0 1,766 B

New Data Grant 1,874 384 0 1,490 B

Change (%) -12.8 0.6 -15.6

PA CARLISLE

FY 2011 Grant 385 46 80 259 B

New Data Grant 327 33 76 218 B

Change (%) -15.0 -29.0 -5.4 -15.6

PA CHESTER

FY 2011 Grant 1,285 195 730 359 B

New Data Grant 1,322 224 755 343 B

Change (%) 2.8 14.6 3.4 -4.6

PA CHESTER 

COUNTY

FY 2011 Grant 2,488 465 0 2,023 B

New Data Grant 2,348 530 0 1,818 B

Change (%) -5.6 14.0 -10.2

PA CUMBERLAND 

COUNTY

FY 2011 Grant 1,229 231 0 999 B

New Data Grant 1,123 214 0 909 B

Change (%) -8.6 -7.4 -9.0

PA DAUPHIN 

COUNTY

FY 2011 Grant 1,367 248 0 1,118 B

New Data Grant 1,284 271 0 1,013 B

Change (%) -6.2 9.0 -100 -9.4
106

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

PA DELAWARE 

COUNTY

FY 2011 Grant 3,830 505 1,028 2,296 B

New Data Grant 3,530 487 987 2,055 B

Change (%) -7.8 -3.6 -4.0 -10.6

PA EASTON

FY 2011 Grant 868 79 270 519 B

New Data Grant 812 80 258 473 B

Change (%) -6.6 0.4 -4.4 -8.8

PA ERIE

FY 2011 Grant 3,201 391 1,303 1,507 B

New Data Grant 3,130 411 1,297 1,423 B

Change (%) -2.2 5.0 -0.4 -5.6

PA HARRISBURG

FY 2011 Grant 1,877 250 902 725 B

New Data Grant 2,093 247 859 987 B

Change (%) 11.4 -1.4 -4.8 36.2

PA HAVERFORD

FY 2011 Grant 904 37 409 458 B

New Data Grant 836 24 397 414 B

Change (%) -7.6 -35.0 -2.8 -9.6

PA HAZLETON

FY 2011 Grant 864 69 330 465 B

New Data Grant 713 70 278 364 B

Change (%) -17.4 2.2 -15.6 -21.8

PA JOHNSTOWN

FY 2011 Grant 1,419 123 743 554 B

New Data Grant 1,339 116 737 486 B

Change (%) -5.6 -5.4 -0.8 -12.4

PA LANCASTER CITY

FY 2011 Grant 1,628 236 453 939 B

New Data Grant 1,575 241 393 941 B

Change (%) -3.2 2.2 -13.2 0.2

PA LANCASTER 

COUNTY

FY 2011 Grant 3,042 515 0 2,527 B

New Data Grant 2,812 543 0 2,269 B

Change (%) -7.6 5.6 -10.2

PA LEBANON

FY 2011 Grant 762 82 261 419 B

New Data Grant 697 93 240 364 B

Change (%) -8.6 13.4 -8.2 -13.2

PA LEHIGH COUNTY

FY 2011 Grant 1,313 163 0 1,150 B

New Data Grant 1,278 177 0 1,101 B

Change (%) -2.6 8.4 -4.2

PA LOWER MERION

FY 2011 Grant 1,093 53 398 642 B

New Data Grant 1,016 59 382 575 B

Change (%) -7.0 12.0 -4.0 -10.4

PA LUZERNE 

COUNTY

FY 2011 Grant 4,567 440 1,169 2,957 B

New Data Grant 4,296 418 1,104 2,775 B

Change (%) -6.0 -5.2 -5.6 -6.2
107

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

PA MCKEESPORT

FY 2011 Grant 1,103 112 577 414 B

New Data Grant 1,091 112 596 382 B

Change (%) -1.0 0.4 3.2 -7.6

PA MILLCREEK 

TOWNSHIP

FY 2011 Grant 218 101 12 105 A

New Data Grant 219 103 11 105 A

Change (%) 0.6 2.0 -10.0 0.6

PA MONTGOMERY 

COUNTY

FY 2011 Grant 3,333 460 0 2,873 B

New Data Grant 3,212 525 0 2,688 B

Change (%) -3.6 14.2 -6.4

PA NORRISTOWN

FY 2011 Grant 911 111 331 470 B

New Data Grant 852 104 294 454 B

Change (%) -6.4 -6.0 -11.0 -3.4

PA NORTHAMPTON 

COUNTY

FY 2011 Grant 1,624 172 0 1,452 B

New Data Grant 1,624 167 0 1,456 B

Change (%) 0.0 -2.8 0.4

PA PENN HILLS

FY 2011 Grant 686 74 416 196 B

New Data Grant 770 83 427 259 B

Change (%) 12.2 13.4 2.8 32.0

PA PHILADELPHIA

FY 2011 Grant 46,187 7,094 18,235 20,858 B

New Data Grant 43,091 6,485 18,096 18,510 B

Change (%) -6.8 -8.6 -0.8 -11.2

PA PITTSBURGH

FY 2011 Grant 15,038 1,348 7,439 6,251 B

New Data Grant 14,651 1,146 7,379 6,126 B

Change (%) -2.6 -15.0 -0.8 -2.0

PA READING

FY 2011 Grant 2,805 436 830 1,538 B

New Data Grant 2,622 485 723 1,414 B

Change (%) -6.6 11.2 -12.8 -8.0

PA SCRANTON

FY 2011 Grant 3,098 228 1,191 1,678 B

New Data Grant 2,788 232 1,117 1,440 B

Change (%) -10.0 1.4 -6.2 -14.2

PA SHARON

FY 2011 Grant 627 59 290 279 B

New Data Grant 599 48 293 258 B

Change (%) -4.6 -18.8 1.2 -7.4

PA STATE COLLEGE

FY 2011 Grant 644 77 110 457 A

New Data Grant 568 81 108 379 A

Change (%) -11.8 5.0 -2.0 -17.0

PA UPPER DARBY

FY 2011 Grant 1,776 157 762 857 B

New Data Grant 1,531 172 696 663 B

Change (%) -13.8 9.6 -8.6 -22.6
108

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

PA WASHINGTON 

COUNTY

FY 2011 Grant 3,899 412 1,357 2,130 B

New Data Grant 3,620 377 1,327 1,917 B

Change (%) -7.2 -8.6 -2.2 -10.0

PA WESTMORELAND 

COUNTY

FY 2011 Grant 4,155 530 1,104 2,522 B

New Data Grant 3,890 503 1,025 2,362 B

Change (%) -6.4 -5.0 -7.2 -6.4

PA WILKES-BARRE

FY 2011 Grant 1,752 149 680 923 B

New Data Grant 1,736 163 660 913 B

Change (%) -1.0 9.6 -3.0 -1.2

PA WILLIAMSPORT

FY 2011 Grant 1,151 128 421 601 B

New Data Grant 1,112 130 411 570 B

Change (%) -3.4 1.8 -2.2 -5.2

PA YORK

FY 2011 Grant 1,531 204 517 810 B

New Data Grant 1,359 230 465 663 B

Change (%) -11.2 13.2 -10.0 -18.2

PA YORK COUNTY

FY 2011 Grant 2,365 330 0 2,035 B

New Data Grant 2,205 393 0 1,812 B

Change (%) -6.8 19.2 -11.0

PR AGUADILLA 

MUNICIPIO

FY 2011 Grant 1,681 131 318 1,232 A

New Data Grant 1,351 118 163 1,071 A

Change (%) -19.6 -10.2 -48.8 -13.0

PR ARECIBO 

MUNICIPIO

FY 2011 Grant 2,472 199 505 1,767 A

New Data Grant 1,809 186 179 1,444 A

Change (%) -26.8 -6.4 -64.6 -18.2

PR BAYAMÓN 

MUNICIPIO

FY 2011 Grant 4,109 423 992 2,694 A

New Data Grant 3,009 401 575 2,033 A

Change (%) -26.8 -5.2 -42.0 -24.6

PR CABO ROJO 

MUNICIPIO

FY 2011 Grant 1,122 106 243 774 A

New Data Grant 992 98 148 745 A

Change (%) -11.6 -7.0 -39.0 -3.8

PR CAGUAS 

MUNICIPIO

FY 2011 Grant 3,054 277 729 2,048 A

New Data Grant 2,252 275 353 1,624 A

Change (%) -26.2 -0.6 -51.6 -20.8

PR CANOVANAS 

MUNICIPIO

FY 2011 Grant 1,207 93 289 825 A

New Data Grant 838 92 174 573 A

Change (%) -30.6 -1.2 -40.0 -30.6

PR CAROLINA 

MUNICIPIO

FY 2011 Grant 3,502 362 943 2,198 A

New Data Grant 2,731 341 757 1,633 A

Change (%) -22.0 -5.8 -19.6 -25.8
109

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

PR CAYEY 

MUNICIPIO

FY 2011 Grant 1,178 91 255 832 A

New Data Grant 864 93 159 612 A

Change (%) -26.6 1.8 -37.6 -26.4

PR CIDRA 

MUNICIPIO

FY 2011 Grant 1,073 95 278 700 A

New Data Grant 846 84 146 616 A

Change (%) -21.2 -12.0 -47.4 -12.0

PR FAJARDO 

MUNICIPIO

FY 2011 Grant 873 82 191 599 A

New Data Grant 741 71 146 524 A

Change (%) -15.2 -13.0 -23.8 -12.6

PR GUAYAMA 

MUNICIPIO

FY 2011 Grant 1,112 88 230 794 A

New Data Grant 915 87 186 642 A

Change (%) -17.6 -0.2 -19.4 -19.0

PR GUAYNABO 

MUNICIPIO

FY 2011 Grant 1,727 199 462 1,066 A

New Data Grant 1,229 189 201 840 A

Change (%) -28.8 -5.2 -56.6 -21.2

PR HUMACAO 

MUNICIPIO

FY 2011 Grant 1,413 118 321 974 A

New Data Grant 1,202 113 251 838 A

Change (%) -15.0 -4.4 -21.8 -14.0

PR ISABELA 

MUNICIPIO

FY 2011 Grant 1,183 94 226 863 A

New Data Grant 1,000 88 96 816 A

Change (%) -15.6 -6.2 -57.6 -5.4

PR JUANA DIAZ 

MUNICIPIO

FY 2011 Grant 1,433 103 327 1,002 A

New Data Grant 1,202 98 294 810 A

Change (%) -16.2 -5.4 -10.0 -19.2

PR MANATI 

MUNICIPIO

FY 2011 Grant 1,194 97 271 825 A

New Data Grant 1,020 85 202 732 A

Change (%) -14.6 -12.6 -25.4 -11.2

PR MAYAGÜEZ 

MUNICIPIO

FY 2011 Grant 2,562 178 597 1,787 A

New Data Grant 1,877 172 239 1,466 A

Change (%) -26.8 -3.6 -60.0 -18.0

PR PONCE 

MUNICIPIO

FY 2011 Grant 4,648 345 962 3,342 A

New Data Grant 3,436 321 546 2,569 A

Change (%) -26.0 -7.0 -43.2 -23.2

PR RIO GRANDE 

MUNICIPIO

FY 2011 Grant 1,293 111 334 849 A

New Data Grant 1,007 105 233 670 A

Change (%) -22.0 -5.4 -30.2 -21.2

PR SAN GERMÁN 

MUNICIPIO

FY 2011 Grant 942 73 231 638 A

New Data Grant 748 68 110 570 A

Change (%) -20.6 -6.0 -52.6 -10.6
110

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

PR SAN JUAN 

MUNICIPIO

FY 2011 Grant 9,328 813 2,412 6,103 A

New Data Grant 6,975 2,827 3,448 700 B

Change (%) -25.2 -53.6

PR SAN SEBASTIÁN 

MUNICIPIO

FY 2011 Grant 1,250 94 267 890 A

New Data Grant 1,017 82 101 834 A

Change (%) -18.6 -12.8 -62.0 -6.2

PR TOA ALTA 

MUNICIPIO

FY 2011 Grant 1,407 163 369 875 A

New Data Grant 1,321 143 220 959 A

Change (%) -6.0 -12.4 -40.4 9.6

PR TOA BAJA 

MUNICIPIO

FY 2011 Grant 1,912 183 425 1,304 A

New Data Grant 1,549 173 329 1,048 A

Change (%) -19.0 -5.8 -22.6 -19.6

PR TRUJILLO ALTO 

MUNICIPIO

FY 2011 Grant 1,420 171 355 894 A

New Data Grant 1,091 144 194 753 A

Change (%) -23.2 -15.4 -45.4 -15.8

PR VEGA BAJA 

MUNICIPIO

FY 2011 Grant 1,553 126 326 1,100 A

New Data Grant 1,229 115 151 963 A

Change (%) -20.8 -8.6 -53.8 -12.4

PR YAUCO 

MUNICIPIO

FY 2011 Grant 1,322 94 307 921 A

New Data Grant 1,081 81 178 822 A

Change (%) -18.2 -13.8 -42.0 -10.8

RI CRANSTON

FY 2011 Grant 1,012 115 252 645 B

New Data Grant 1,049 104 238 708 B

Change (%) 3.8 -9.6 -5.8 9.8

RI EAST 

PROVIDENCE

FY 2011 Grant 749 87 181 481 B

New Data Grant 756 82 192 482 B

Change (%) 1.0 -6.2 6.6 0.2

RI PAWTUCKET

FY 2011 Grant 1,891 256 621 1,014 B

New Data Grant 1,985 213 615 1,157 B

Change (%) 5.0 -17.0 -0.8 14.2

RI PROVIDENCE

FY 2011 Grant 5,143 985 1,745 2,413 B

New Data Grant 5,274 740 1,628 2,905 B

Change (%) 2.6 -24.8 -6.6 20.4

RI WARWICK

FY 2011 Grant 864 106 227 531 B

New Data Grant 971 112 242 617 B

Change (%) 12.4 5.8 6.4 16.2

RI WOONSOCKET

FY 2011 Grant 1,219 173 341 704 B

New Data Grant 1,278 161 360 757 B

Change (%) 4.8 -7.2 5.4 7.6
111

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

SC AIKEN

FY 2011 Grant 195 57 16 123 A

New Data Grant 211 57 15 139 A

Change (%) 7.8 -0.2 -3.2 13.0

SC ANDERSON

FY 2011 Grant 652 104 435 113 B

New Data Grant 632 103 433 96 B

Change (%) -3.0 -0.8 -0.6 -14.4

SC CHARLESTON

FY 2011 Grant 965 370 0 596 B

New Data Grant 960 337 0 623 B

Change (%) -0.6 -9.0 4.6

SC CHARLESTON 

COUNTY

FY 2011 Grant 1,853 479 268 1,106 A

New Data Grant 1,724 481 219 1,024 A

Change (%) -7.0 0.6 -18.0 -7.4

SC COLUMBIA

FY 2011 Grant 1,128 250 147 731 A

New Data Grant 973 366 199 408 B

Change (%) -13.8 -49.8

SC FLORENCE

FY 2011 Grant 305 62 42 200 A

New Data Grant 252 71 27 153 A

Change (%) -17.4 14.8 -35.6 -23.4

SC GREENVILLE

FY 2011 Grant 927 178 470 278 B

New Data Grant 923 180 499 244 B

Change (%) -0.4 1.2 6.2 -12.6

SC GREENVILLE 

COUNTY

FY 2011 Grant 2,207 775 318 1,114 A

New Data Grant 2,596 774 337 1,486 A

Change (%) 17.6 -0.2 6.0 33.4

SC HORRY COUNTY

FY 2011 Grant 1,403 462 192 749 A

New Data Grant 2,081 478 555 1,048 A

Change (%) 48.4 3.6 188.6 40.0

SC LEXINGTON 

COUNTY

FY 2011 Grant 1,370 509 167 694 A

New Data Grant 1,516 519 198 798 A

Change (%) 10.6 2.2 19.0 15.0

SC RICHLAND 

COUNTY

FY 2011 Grant 1,265 431 196 638 A

New Data Grant 1,316 453 140 723 A

Change (%) 4.0 5.0 -28.6 13.2

SC ROCK HILL

FY 2011 Grant 435 134 68 233 A

New Data Grant 544 128 79 337 A

Change (%) 25.2 -4.6 16.6 44.6

SC SPARTANBURG

FY 2011 Grant 678 184 328 166 B

New Data Grant 696 145 361 190 B

Change (%) 2.8 -21.4 10.2 14.8
112

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

SC SPARTANBURG 

COUNTY

FY 2011 Grant 1,183 408 177 598 A

New Data Grant 1,298 417 181 701 A

Change (%) 9.8 2.2 2.2 17.2

SC SUMMERVILLE

FY 2011 Grant 203 87 26 90 A

New Data Grant 225 84 41 101 A

Change (%) 10.6 -4.4 57.6 11.6

SC SUMTER

FY 2011 Grant 338 74 39 224 A

New Data Grant 348 78 33 237 A

Change (%) 3.0 5.2 -17.6 6.0

SD RAPID CITY

FY 2011 Grant 452 130 64 258 A

New Data Grant 522 131 80 312 A

Change (%) 15.6 1.0 24.2 20.8

SD SIOUX FALLS

FY 2011 Grant 768 305 111 352 A

New Data Grant 905 297 153 455 A

Change (%) 17.8 -2.8 37.8 29.6

TN BRISTOL

FY 2011 Grant 216 77 8 132 B

New Data Grant 175 51 14 110 A

Change (%) -18.8 42.8

TN CHATTANOOGA

FY 2011 Grant 1,594 566 300 727 B

New Data Grant 1,822 600 325 896 B

Change (%) 14.4 6.0 8.4 23.2

TN CLARKSVILLE

FY 2011 Grant 739 241 129 369 A

New Data Grant 869 256 128 484 A

Change (%) 17.6 6.4 -0.6 31.0

TN CLEVELAND

FY 2011 Grant 304 78 29 197 A

New Data Grant 373 80 58 236 A

Change (%) 22.8 2.2 102.2 19.4

TN FRANKLIN CITY

FY 2011 Grant 253 117 38 98 A

New Data Grant 255 120 19 115 A

Change (%) 0.8 2.8 -50.4 18.0

TN JACKSON

FY 2011 Grant 529 123 66 339 A

New Data Grant 595 126 56 413 A

Change (%) 12.6 2.0 -14.6 21.8

TN JOHNSON CITY

FY 2011 Grant 445 122 32 291 A

New Data Grant 496 122 47 326 A

Change (%) 11.2 -0.2 46.6 12.2

TN KINGSPORT

FY 2011 Grant 366 87 19 261 A

New Data Grant 375 93 36 246 A

Change (%) 2.4 7.4 89.2 -5.6
113

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

TN KNOX COUNTY

FY 2011 Grant 937 445 69 423 A

New Data Grant 1,069 449 123 497 A

Change (%) 14.2 1.0 77.6 17.6

TN KNOXVILLE

FY 2011 Grant 1,694 358 139 1,197 A

New Data Grant 1,751 345 105 1,301 A

Change (%) 3.4 -3.6 -24.0 8.6

TN MEMPHIS

FY 2011 Grant 7,320 1,308 1,403 4,609 A

New Data Grant 7,387 1,247 1,319 4,821 A

Change (%) 1.0 -4.6 -6.0 4.6

TN MORRISTOWN

FY 2011 Grant 266 54 43 168 A

New Data Grant 267 56 37 174 A

Change (%) 0.6 3.6 -14.6 3.4

TN MURFREESBORO

FY 2011 Grant 589 203 67 319 A

New Data Grant 755 210 99 446 A

Change (%) 28.0 3.0 47.0 40.0

TN NASHVILLEDAVIDSON

FY 2011 Grant 4,508 1,229 783 2,496 A

New Data Grant 4,778 1,208 753 2,817 A

Change (%) 6.0 -1.6 -4.0 12.8

TN OAK RIDGE

FY 2011 Grant 237 62 163 12 B

New Data Grant 254 75 139 41 B

Change (%) 7.4 20.0 -15.0 252.8

TN SHELBY COUNTY

FY 2011 Grant 906 471 106 328 A

New Data Grant 1,055 541 95 419 A

Change (%) 16.6 15.0 -10.4 27.6

TX ABILENE

FY 2011 Grant 984 227 185 572 A

New Data Grant 982 226 192 564 A

Change (%) -0.2 -0.4 3.6 -1.4

TX ALLEN

FY 2011 Grant 241 163 32 46 A

New Data Grant 305 162 60 82 A

Change (%) 26.6 -0.2 90.0 78.2

TX AMARILLO

FY 2011 Grant 1,624 366 382 876 A

New Data Grant 1,627 368 349 911 A

Change (%) 0.2 0.4 -8.8 4.0

TX ARLINGTON

FY 2011 Grant 2,876 735 999 1,143 A

New Data Grant 3,229 705 1,007 1,518 A

Change (%) 12.2 -4.2 0.8 32.8

TX AUSTIN

FY 2011 Grant 6,878 1,520 2,105 3,252 A

New Data Grant 7,503 1,524 2,116 3,864 A

Change (%) 9.0 0.2 0.4 18.8
114

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

TX BAYTOWN CITY

FY 2011 Grant 749 137 250 362 A

New Data Grant 687 138 235 313 A

Change (%) -8.4 1.0 -5.8 -13.6

TX BEAUMONT

FY 2011 Grant 1,654 459 860 334 B

New Data Grant 1,461 409 736 316 B

Change (%) -11.6 -11.0 -14.4 -5.4

TX BEXAR COUNTY

FY 2011 Grant 1,592 485 386 722 A

New Data Grant 1,712 702 327 683 A

Change (%) 7.6 44.8 -15.2 -5.4

TX BRAZORIA 

COUNTY

FY 2011 Grant 1,706 438 505 763 A

New Data Grant 1,886 434 646 806 A

Change (%) 10.6 -1.0 27.8 5.8

TX BROWNSVILLE

FY 2011 Grant 2,966 342 876 1,748 A

New Data Grant 3,337 337 1,120 1,879 A

Change (%) 12.6 -1.4 27.8 7.6

TX BRYAN

FY 2011 Grant 853 144 211 497 A

New Data Grant 938 147 232 558 A

Change (%) 10.0 1.8 10.0 12.2

TX CARROLLTON

FY 2011 Grant 723 250 260 213 A

New Data Grant 835 230 302 304 A

Change (%) 15.4 -8.0 16.0 42.6

TX COLLEGE 

STATION

FY 2011 Grant 1,029 168 110 752 A

New Data Grant 1,097 181 73 843 A

Change (%) 6.6 8.0 -34.0 12.2

TX CONROE

FY 2011 Grant 529 113 171 245 A

New Data Grant 620 108 219 292 A

Change (%) 17.2 -4.0 28.0 19.4

TX CORPUS CHRISTI

FY 2011 Grant 3,055 556 816 1,683 A

New Data Grant 2,906 588 726 1,592 A

Change (%) -4.8 5.8 -11.2 -5.4

TX DALLAS

FY 2011 Grant 15,882 2,512 6,072 7,297 A

New Data Grant 16,139 2,309 5,630 8,200 A

Change (%) 1.6 -8.0 -7.2 12.4

TX DALLAS COUNTY

FY 2011 Grant 1,923 681 566 676 A

New Data Grant 2,203 695 547 961 A

Change (%) 14.6 2.0 -3.2 42.2

TX DENISON

FY 2011 Grant 334 69 125 139 B

New Data Grant 336 78 139 119 B

Change (%) 0.8 13.0 11.0 -14.2
115

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

TX DENTON

FY 2011 Grant 803 237 152 414 A

New Data Grant 932 219 134 579 A

Change (%) 16.0 -8.0 -11.8 39.8

TX EDINBURG

FY 2011 Grant 848 140 225 483 A

New Data Grant 1,042 149 304 590 A

Change (%) 22.8 6.2 34.8 22.0

TX EL PASO

FY 2011 Grant 7,676 1,200 2,105 4,371 A

New Data Grant 7,780 1,251 1,969 4,560 A

Change (%) 1.4 4.4 -6.4 4.4

TX EULESS CITY

FY 2011 Grant 343 103 127 112 A

New Data Grant 451 99 181 171 A

Change (%) 31.6 -4.2 42.4 52.4

TX FLOWER MOUND 

TOWN

FY 2011 Grant 198 136 18 45 A

New Data Grant 204 125 17 63 A

Change (%) 3.0 -8.2 -6.6 40.6

TX FORT BEND 

COUNTY

FY 2011 Grant 1,818 706 511 601 A

New Data Grant 2,138 775 539 823 A

Change (%) 17.6 9.8 5.6 37.0

TX FORT WORTH

FY 2011 Grant 6,153 1,407 1,826 2,921 A

New Data Grant 6,536 1,429 1,702 3,405 A

Change (%) 6.2 1.6 -6.8 16.6

TX FRISCO

FY 2011 Grant 267 198 29 40 A

New Data Grant 368 226 43 99 A

Change (%) 37.8 14.0 49.2 148.8

TX GALVESTON

FY 2011 Grant 1,317 259 559 499 B

New Data Grant 1,411 215 650 546 B

Change (%) 7.2 -17.0 16.2 9.4

TX GARLAND

FY 2011 Grant 1,887 429 788 669 A

New Data Grant 1,971 437 661 872 A

Change (%) 4.4 2.0 -16.0 30.4

TX GRAND PRAIRIE

FY 2011 Grant 1,264 316 455 493 A

New Data Grant 1,395 338 392 665 A

Change (%) 10.4 7.0 -14.0 34.8

TX GRAPEVINE

FY 2011 Grant 236 99 67 70 A

New Data Grant 248 89 58 100 A

Change (%) 4.8 -9.6 -13.4 42.8

TX HARLINGEN

FY 2011 Grant 864 126 248 490 A

New Data Grant 905 125 208 572 A

Change (%) 4.8 -1.0 -16.2 16.8
116

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

TX HARRIS COUNTY

FY 2011 Grant 9,860 3,062 3,143 3,655 A

New Data Grant 11,688 3,401 3,431 4,856 A

Change (%) 18.6 11.0 9.2 32.8

TX HIDALGO 

COUNTY

FY 2011 Grant 7,639 776 2,141 4,722 A

New Data Grant 8,641 810 3,095 4,736 A

Change (%) 13.2 4.4 44.6 0.2

TX HOUSTON

FY 2011 Grant 27,343 4,365 9,998 12,979 A

New Data Grant 27,161 4,048 9,601 13,512 A

Change (%) -0.6 -7.2 -4.0 4.2

TX IRVING

FY 2011 Grant 2,056 397 947 712 A

New Data Grant 2,104 417 789 898 A

Change (%) 2.4 5.0 -16.6 26.2

TX KILLEEN

FY 2011 Grant 884 231 262 391 A

New Data Grant 1,039 247 236 557 A

Change (%) 17.6 6.8 -10.0 42.6

TX LAREDO

FY 2011 Grant 3,291 437 1,042 1,811 A

New Data Grant 3,753 455 1,410 1,888 A

Change (%) 14.0 4.2 35.2 4.2

TX LEAGUE CITY

FY 2011 Grant 269 139 55 75 A

New Data Grant 345 161 68 116 A

Change (%) 28.4 16.2 24.4 54.0

TX LEWISVILLE

FY 2011 Grant 525 203 159 163 A

New Data Grant 613 184 178 251 A

Change (%) 16.6 -9.6 12.2 54.0

TX LONGVIEW

FY 2011 Grant 692 151 142 399 A

New Data Grant 712 155 192 365 A

Change (%) 2.8 2.8 34.4 -8.4

TX LUBBOCK

FY 2011 Grant 2,100 437 426 1,237 A

New Data Grant 2,238 443 512 1,284 A

Change (%) 6.6 1.4 20.0 3.8

TX MARSHALL

FY 2011 Grant 381 107 146 128 B

New Data Grant 338 93 149 96 B

Change (%) -11.2 -13.2 1.8 -24.6

TX MCALLEN

FY 2011 Grant 1,619 256 486 878 A

New Data Grant 1,795 250 521 1,024 A

Change (%) 10.8 -2.0 7.2 16.6

TX MCKINNEY CITY

FY 2011 Grant 499 247 93 159 A

New Data Grant 738 253 161 324 A

Change (%) 47.8 2.4 72.4 103.8
117

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

TX MESQUITE

FY 2011 Grant 813 258 261 295 A

New Data Grant 910 270 252 389 A

Change (%) 11.8 4.4 -3.6 32.0

TX MIDLAND

FY 2011 Grant 830 210 196 424 A

New Data Grant 784 214 182 388 A

Change (%) -5.6 2.0 -7.0 -8.6

TX MISSION

FY 2011 Grant 765 133 200 432 A

New Data Grant 965 149 321 495 A

Change (%) 26.0 11.4 60.6 14.6

TX MISSOURI CITY

FY 2011 Grant 271 145 65 61 A

New Data Grant 413 130 96 187 A

Change (%) 52.2 -10.2 46.8 205.6

TX MONTGOMERY 

COUNTY

FY 2011 Grant 1,690 703 341 647 A

New Data Grant 2,194 718 513 963 A

Change (%) 29.8 2.2 50.4 48.8

TX NEW BRAUNFELS

FY 2011 Grant 319 108 73 138 A

New Data Grant 361 111 84 165 A

Change (%) 13.2 3.0 16.4 19.4

TX NORTH 

RICHLAND HILLS

FY 2011 Grant 299 128 79 92 A

New Data Grant 317 122 52 142 A

Change (%) 6.0 -4.4 -34.0 54.4

TX ODESSA

FY 2011 Grant 1,028 195 251 582 A

New Data Grant 896 193 245 459 A

Change (%) -12.8 -1.2 -2.4 -21.2

TX ORANGE

FY 2011 Grant 382 87 235 60 B

New Data Grant 366 79 243 44 B

Change (%) -4.2 -8.8 3.2 -26.6

TX PASADENA

FY 2011 Grant 1,733 282 659 792 A

New Data Grant 1,780 287 672 820 A

Change (%) 2.6 2.0 2.0 3.6

TX PEARLAND

FY 2011 Grant 277 167 49 61 A

New Data Grant 319 176 48 95 A

Change (%) 15.2 5.4 -1.0 54.8

TX PHARR

FY 2011 Grant 1,016 128 304 584 A

New Data Grant 1,333 136 481 716 A

Change (%) 31.2 6.0 58.4 22.6

TX PLANO

FY 2011 Grant 1,142 529 279 334 A

New Data Grant 1,195 501 219 475 A

Change (%) 4.6 -5.2 -21.4 42.2
118

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

TX PORT ARTHUR

FY 2011 Grant 1,235 303 722 210 B

New Data Grant 1,194 233 742 219 B

Change (%) -3.4 -23.0 2.8 4.0

TX RICHARDSON

FY 2011 Grant 581 200 181 200 A

New Data Grant 633 191 168 273 A

Change (%) 9.0 -4.2 -7.2 36.4

TX ROUND ROCK

FY 2011 Grant 386 204 99 83 A

New Data Grant 541 193 163 185 A

Change (%) 40.2 -5.6 65.0 122.4

TX ROWLETT

FY 2011 Grant 192 110 36 46 A

New Data Grant 251 108 58 85 A

Change (%) 30.6 -1.2 60.0 83.0

TX SAN ANGELO

FY 2011 Grant 809 178 164 467 A

New Data Grant 782 180 148 454 A

Change (%) -3.4 0.8 -9.4 -2.8

TX SAN ANTONIO

FY 2011 Grant 13,226 2,656 3,741 6,830 A

New Data Grant 12,959 2,559 3,174 7,226 A

Change (%) -2.0 -3.6 -15.2 5.8

TX SAN BENITO

FY 2011 Grant 456 49 136 271 A

New Data Grant 447 47 150 250 A

Change (%) -2.0 -4.6 10.8 -7.8

TX SAN MARCOS

FY 2011 Grant 480 103 86 290 A

New Data Grant 642 87 77 479 A

Change (%) 33.8 -15.8 -11.2 64.8

TX SHERMAN

FY 2011 Grant 293 74 64 155 A

New Data Grant 324 74 58 192 A

Change (%) 10.6 0.0 -9.0 23.8

TX SUGAR LAND

FY 2011 Grant 307 158 66 83 A

New Data Grant 347 152 58 136 A

Change (%) 13.0 -3.8 -11.0 63.4

TX TARRANT 

COUNTY

FY 2011 Grant 2,448 935 617 897 A

New Data Grant 2,859 964 667 1,227 A

Change (%) 16.8 3.2 8.2 36.8

TX TEMPLE

FY 2011 Grant 467 116 93 258 A

New Data Grant 434 127 79 227 A

Change (%) -7.0 9.6 -14.4 -12.0

TX TEXARKANA

FY 2011 Grant 415 72 63 281 A

New Data Grant 332 127 121 84 B

Change (%) -19.8 -54.6
119

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

TX TEXAS CITY

FY 2011 Grant 386 85 89 212 A

New Data Grant 405 87 114 204 A

Change (%) 4.8 2.2 28.2 -4.0

TX TRAVIS COUNTY

FY 2011 Grant 790 350 204 236 A

New Data Grant 1,001 345 259 398 A

Change (%) 26.8 -1.4 26.8 68.2

TX TYLER

FY 2011 Grant 859 191 186 481 A

New Data Grant 976 187 210 580 A

Change (%) 13.6 -2.2 12.4 20.4

TX VICTORIA

FY 2011 Grant 566 122 139 306 A

New Data Grant 588 121 158 309 A

Change (%) 3.8 -1.2 14.0 1.2

TX WACO

FY 2011 Grant 1,511 244 288 980 A

New Data Grant 1,524 241 278 1,005 A

Change (%) 0.8 -1.4 -3.4 2.6

TX WICHITA FALLS

FY 2011 Grant 1,285 273 643 369 B

New Data Grant 1,219 266 584 369 B

Change (%) -5.2 -2.6 -9.2 -0.2

TX WILLIAMSON 

COUNTY

FY 2011 Grant 993 516 197 280 A

New Data Grant 1,242 526 279 437 A

Change (%) 25.0 2.0 41.4 56.0

UT CLEARFIELD

FY 2011 Grant 210 55 45 110 A

New Data Grant 243 58 49 136 A

Change (%) 16.0 5.8 9.2 24.0

UT DAVIS COUNTY

FY 2011 Grant 673 375 112 186 A

New Data Grant 832 382 208 243 A

Change (%) 23.6 1.8 85.0 30.4

UT LAYTON

FY 2011 Grant 311 129 66 116 A

New Data Grant 315 130 62 124 A

Change (%) 1.2 0.6 -6.8 6.6

UT LOGAN

FY 2011 Grant 536 96 115 325 A

New Data Grant 548 93 98 358 A

Change (%) 2.4 -3.0 -15.2 10.2

UT OGDEN

FY 2011 Grant 1,007 262 277 468 B

New Data Grant 1,029 306 271 452 B

Change (%) 2.2 17.0 -2.2 -3.4

UT OREM

FY 2011 Grant 556 184 125 247 A

New Data Grant 664 170 151 343 A

Change (%) 19.6 -7.6 21.0 39.2
120

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

UT PROVO

FY 2011 Grant 1,542 232 371 939 A

New Data Grant 1,719 217 395 1,107 A

Change (%) 11.4 -6.4 6.6 17.8

UT SALT LAKE CITY

FY 2011 Grant 3,688 576 1,267 1,845 B

New Data Grant 3,599 531 1,191 1,877 B

Change (%) -2.4 -7.8 -6.0 1.8

UT SALT LAKE 

COUNTY

FY 2011 Grant 2,257 906 530 820 A

New Data Grant 2,553 898 653 1,002 A

Change (%) 13.2 -0.8 23.2 22.2

UT SANDY CITY

FY 2011 Grant 359 188 53 118 A

New Data Grant 402 169 71 162 A

Change (%) 12.0 -10.2 33.6 37.4

UT ST GEORGE

FY 2011 Grant 439 140 100 199 A

New Data Grant 512 141 160 211 A

Change (%) 16.6 0.2 60.8 6.0

UT TAYLORSVILLE

FY 2011 Grant 334 114 101 119 A

New Data Grant 382 113 102 166 A

Change (%) 14.2 -1.0 1.4 39.4

UT UTAH COUNTY

FY 2011 Grant 1,143 605 215 323 A

New Data Grant 1,417 580 351 486 A

Change (%) 24.0 -4.2 63.2 50.8

UT WEST JORDAN

FY 2011 Grant 450 203 107 140 A

New Data Grant 497 200 129 168 A

Change (%) 10.4 -1.4 20.6 20.0

UT WEST VALLEY

FY 2011 Grant 845 242 274 329 A

New Data Grant 1,049 250 408 392 A

Change (%) 24.2 3.2 48.6 19.2

VA ALEXANDRIA

FY 2011 Grant 1,143 290 456 397 A

New Data Grant 760 270 187 303 A

Change (%) -33.4 -7.0 -59.0 -23.6

VA ARLINGTON 

COUNTY

FY 2011 Grant 1,610 444 646 521 A

New Data Grant 1,370 267 442 661 B

Change (%) -15.0 -48.6

VA BLACKSBURG

FY 2011 Grant 580 83 26 471 A

New Data Grant 494 82 11 402 A

Change (%) -14.8 -0.8 -59.6 -14.6

VA BRISTOL

FY 2011 Grant 259 57 101 101 B

New Data Grant 284 68 96 120 B

Change (%) 9.6 18.6 -4.8 19.2
121

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

VA CHARLOTTESVILLE

FY 2011 Grant 479 210 25 244 B

New Data Grant 477 84 57 336 A

Change (%) -0.6 59.8

VA CHESAPEAKE

FY 2011 Grant 1,108 430 176 501 A

New Data Grant 981 428 145 408 A

Change (%) -11.4 -0.4 -17.8 -18.8

VA CHESTERFIELD 

COUNTY

FY 2011 Grant 1,136 593 136 407 A

New Data Grant 1,319 610 188 521 A

Change (%) 16.2 2.8 38.8 28.0

VA CHRISTIANSBURG

FY 2011 Grant 94 38 6 50 A

New Data Grant 121 41 13 68 A

Change (%) 28.2 6.8 102.8 34.8

VA COLONIAL 

HEIGHTS

FY 2011 Grant 78 34 12 32 A

New Data Grant 78 34 10 34 A

Change (%) -1.0 -2.6 -15.4 6.0

VA DANVILLE

FY 2011 Grant 973 198 427 348 B

New Data Grant 924 183 431 310 B

Change (%) -5.0 -7.4 1.0 -11.0

VA FAIRFAX COUNTY

FY 2011 Grant 5,418 2,054 1,796 1,569 A

New Data Grant 4,949 2,129 1,241 1,578 A

Change (%) -8.6 3.6 -30.8 0.6

VA FREDERICKSBURG

FY 2011 Grant 209 56 0 154 B

New Data Grant 156 54 0 102 B

Change (%) -25.2 -2.8 -33.4

VA HAMPTON

FY 2011 Grant 973 279 164 531 A

New Data Grant 1,148 265 329 553 A

Change (%) 18.0 -5.0 101.2 4.2

VA HARRISONBURG

FY 2011 Grant 488 87 48 352 A

New Data Grant 536 94 109 332 A

Change (%) 9.8 8.0 125.2 -5.8

VA HENRICO 

COUNTY

FY 2011 Grant 1,335 573 202 560 A

New Data Grant 1,564 592 219 753 A

Change (%) 17.2 3.2 8.2 34.6

VA HOPEWELL

FY 2011 Grant 197 69 47 81 B

New Data Grant 230 87 51 92 B

Change (%) 16.6 26.0 8.0 13.8

VA LOUDOUN 

COUNTY

FY 2011 Grant 870 582 125 163 A

New Data Grant 1,018 602 181 236 A

Change (%) 17.0 3.4 44.4 44.6
122

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

VA LYNCHBURG

FY 2011 Grant 746 198 105 444 B

New Data Grant 716 239 76 401 B

Change (%) -4.0 21.0 -27.4 -9.6

VA NEWPORT NEWS

FY 2011 Grant 1,469 373 251 845 A

New Data Grant 1,287 348 189 750 A

Change (%) -12.4 -6.8 -24.8 -11.2

VA NORFOLK

FY 2011 Grant 4,718 862 2,804 1,052 B

New Data Grant 4,373 660 2,625 1,088 B

Change (%) -7.4 -23.4 -6.4 3.4

VA PETERSBURG

FY 2011 Grant 620 136 277 206 B

New Data Grant 672 104 277 290 B

Change (%) 8.4 -23.8 0.0 41.2

VA PORTSMOUTH

FY 2011 Grant 1,641 326 912 402 B

New Data Grant 1,600 264 937 399 B

Change (%) -2.4 -19.0 2.8 -0.8

VA PRINCE WILLIAM 

COUNTY

FY 2011 Grant 1,758 827 408 523 A

New Data Grant 2,116 875 555 686 A

Change (%) 20.4 5.8 36.0 31.2

VA RADFORD

FY 2011 Grant 185 31 9 144 A

New Data Grant 160 32 4 124 A

Change (%) -13.6 1.2 -52.6 -14.2

VA RICHMOND

FY 2011 Grant 4,394 848 1,573 1,973 B

New Data Grant 4,396 764 1,534 2,099 B

Change (%) 0.0 -10.0 -2.4 6.4

VA ROANOKE

FY 2011 Grant 1,671 312 642 717 B

New Data Grant 1,651 307 592 752 B

Change (%) -1.2 -1.8 -7.8 4.8

VA SUFFOLK

FY 2011 Grant 505 162 53 291 A

New Data Grant 479 163 59 258 A

Change (%) -5.2 0.8 10.8 -11.4

VA VIRGINIA BEACH

FY 2011 Grant 2,196 838 402 955 A

New Data Grant 1,991 844 267 879 A

Change (%) -9.4 0.8 -33.4 -8.0

VA WINCHESTER

FY 2011 Grant 229 63 0 166 B

New Data Grant 251 68 0 183 B

Change (%) 9.6 7.4 10.4

VT BURLINGTON

FY 2011 Grant 812 148 184 480 B

New Data Grant 703 151 131 421 B

Change (%) -13.4 2.0 -28.8 -12.4
123

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

WA ANACORTES

FY 2011 Grant 96 24 0 72 B

New Data Grant 102 22 0 81 B

Change (%) 6.8 -9.4 12.0

WA AUBURN

FY 2011 Grant 419 123 92 204 A

New Data Grant 523 135 136 252 A

Change (%) 25.0 10.2 48.8 23.2

WA BELLEVUE

FY 2011 Grant 655 245 189 221 A

New Data Grant 640 236 164 241 A

Change (%) -2.2 -3.6 -13.6 9.0

WA BELLINGHAM

FY 2011 Grant 766 271 0 495 B

New Data Grant 797 302 0 496 B

Change (%) 4.2 11.4 0.2

WA BREMERTON

FY 2011 Grant 512 140 103 269 B

New Data Grant 445 122 67 257 B

Change (%) -13.0 -13.4 -35.2 -4.4

WA CLARK COUNTY

FY 2011 Grant 1,236 525 217 494 A

New Data Grant 1,476 519 328 630 A

Change (%) 19.4 -1.2 51.0 27.4

WA

EAST 

WENATCHEE 

CITY

FY 2011 Grant 104 24 22 58 A

New Data Grant 117 25 31 61 A

Change (%) 12.4 4.6 40.6 5.0

WA EVERETT

FY 2011 Grant 837 192 239 405 A

New Data Grant 864 199 232 433 A

Change (%) 3.2 3.4 -3.0 6.8

WA FEDERAL WAY

FY 2011 Grant 652 166 211 274 A

New Data Grant 646 172 167 307 A

Change (%) -0.8 3.8 -20.8 11.8

WA KENNEWICK

FY 2011 Grant 528 131 145 252 A

New Data Grant 574 143 145 287 A

Change (%) 8.8 8.6 0.6 13.6

WA KENT CITY

FY 2011 Grant 710 165 223 321 A

New Data Grant 801 178 257 366 A

Change (%) 12.8 7.8 15.0 14.0

WA KING COUNTY

FY 2011 Grant 4,047 1,595 1,015 1,437 A

New Data Grant 4,209 1,555 945 1,709 A

Change (%) 4.0 -2.4 -6.8 19.0

WA KITSAP COUNTY

FY 2011 Grant 1,034 395 190 449 A

New Data Grant 968 409 140 419 A

Change (%) -6.4 3.4 -26.4 -6.6
124

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

WA LAKEWOOD

FY 2011 Grant 578 112 152 314 A

New Data Grant 528 112 118 298 A

Change (%) -8.6 0.6 -22.6 -5.2

WA LONGVIEW

FY 2011 Grant 329 71 59 200 A

New Data Grant 344 71 48 225 A

Change (%) 4.2 0.2 -18.8 12.6

WA MOUNT VERNON

FY 2011 Grant 294 62 90 143 A

New Data Grant 328 61 117 150 A

Change (%) 11.4 -1.6 30.2 5.0

WA OLYMPIA

FY 2011 Grant 353 105 0 248 B

New Data Grant 365 119 0 246 B

Change (%) 3.2 13.0 -0.8

WA PASCO

FY 2011 Grant 563 113 191 258 A

New Data Grant 652 115 201 336 A

Change (%) 15.8 1.6 5.2 30.0

WA PIERCE COUNTY

FY 2011 Grant 2,707 1,027 565 1,115 A

New Data Grant 2,847 1,022 519 1,305 A

Change (%) 5.2 -0.4 -8.0 17.0

WA RENTON CITY

FY 2011 Grant 420 120 130 169 A

New Data Grant 499 175 130 194 A

Change (%) 18.8 46.2 -0.4 14.4

WA RICHLAND

FY 2011 Grant 248 92 45 111 A

New Data Grant 247 93 28 126 A

Change (%) -0.2 0.8 -38.2 14.4

WA SEATTLE

FY 2011 Grant 10,729 1,352 2,751 6,626 B

New Data Grant 10,400 1,274 2,753 6,374 B

Change (%) -3.0 -5.8 0.0 -3.8

WA SHORELINE

FY 2011 Grant 313 102 83 127 A

New Data Grant 269 102 44 123 A

Change (%) -13.8 0.2 -47.2 -3.2

WA SNOHOMISH 

COUNTY

FY 2011 Grant 2,792 1,116 680 995 A

New Data Grant 2,882 1,141 566 1,175 A

Change (%) 3.2 2.2 -16.8 18.0

WA SPOKANE

FY 2011 Grant 3,439 641 869 1,929 B

New Data Grant 3,192 658 767 1,766 B

Change (%) -7.2 2.6 -11.6 -8.4

WA SPOKANE 

COUNTY

FY 2011 Grant 1,406 513 207 685 A

New Data Grant 1,451 506 196 749 A

Change (%) 3.2 -1.4 -5.4 9.4
125

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

WA TACOMA

FY 2011 Grant 2,639 631 284 1,725 B

New Data Grant 2,502 586 276 1,640 B

Change (%) -5.2 -7.2 -2.6 -4.8

WA VANCOUVER

FY 2011 Grant 1,219 320 288 610 A

New Data Grant 1,264 312 254 698 A

Change (%) 3.8 -2.6 -11.8 14.4

WA WENATCHEE

FY 2011 Grant 304 58 96 149 A

New Data Grant 226 62 38 127 A

Change (%) -25.6 5.4 -60.4 -15.2

WA YAKIMA

FY 2011 Grant 1,073 166 340 567 A

New Data Grant 995 176 297 523 A

Change (%) -7.2 5.8 -12.6 -7.8

WI APPLETON

FY 2011 Grant 565 78 23 463 B

New Data Grant 545 126 0 419 B

Change (%) -3.6 61.2 -100 -9.6

WI BELOIT

FY 2011 Grant 615 90 160 365 B

New Data Grant 547 123 149 276 B

Change (%) -11.0 36.2 -6.8 -24.4

WI DANE COUNTY

FY 2011 Grant 1,032 195 0 837 B

New Data Grant 1,097 474 194 430 A

Change (%) 6.2 120.0

WI EAU CLAIRE

FY 2011 Grant 578 164 0 414 B

New Data Grant 579 205 0 374 B

Change (%) 0.2 25.2 -9.8

WI FOND DU LAC

FY 2011 Grant 531 63 85 383 B

New Data Grant 502 85 72 346 B

Change (%) -5.4 35.2 -16.0 -9.8

WI GREEN BAY

FY 2011 Grant 925 221 144 561 B

New Data Grant 962 274 99 589 B

Change (%) 4.0 23.8 -31.0 5.0

WI JANESVILLE

FY 2011 Grant 488 80 0 408 B

New Data Grant 459 123 0 336 B

Change (%) -6.0 53.8 -17.6

WI KENOSHA

FY 2011 Grant 966 176 52 738 B

New Data Grant 945 253 29 664 B

Change (%) -2.2 43.6 -43.8 -10.2

WI LA CROSSE

FY 2011 Grant 925 171 253 502 B

New Data Grant 961 211 243 507 B

Change (%) 3.8 23.4 -3.8 1.0
126

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

WI MADISON

FY 2011 Grant 1,798 618 0 1,180 B

New Data Grant 2,044 450 321 1,273 A

Change (%) 13.6 106.0

WI MILWAUKEE

FY 2011 Grant 15,282 2,609 6,349 6,324 B

New Data Grant 16,159 2,578 6,328 7,253 B

Change (%) 5.8 -1.2 -0.4 14.6

WI MILWAUKEE 

COUNTY

FY 2011 Grant 1,537 219 72 1,245 B

New Data Grant 1,419 281 79 1,059 B

Change (%) -7.6 28.2 9.2 -15.0

WI NEENAH

FY 2011 Grant 195 27 20 148 B

New Data Grant 200 33 18 150 B

Change (%) 2.4 18.8 -11.8 1.2

WI OSHKOSH

FY 2011 Grant 775 120 50 606 B

New Data Grant 750 142 21 587 B

Change (%) -3.2 18.8 -57.4 -3.2

WI RACINE

FY 2011 Grant 1,786 235 648 903 B

New Data Grant 1,813 256 670 886 B

Change (%) 1.6 9.2 3.4 -1.8

WI SHEBOYGAN

FY 2011 Grant 982 87 262 633 B

New Data Grant 891 96 235 560 B

Change (%) -9.2 10.8 -10.2 -11.6

WI SUPERIOR

FY 2011 Grant 784 74 295 415 B

New Data Grant 726 71 283 372 B

Change (%) -7.4 -3.6 -4.2 -10.4

WI WAUKESHA

FY 2011 Grant 387 70 0 317 B

New Data Grant 412 103 0 309 B

Change (%) 6.2 46.6 -2.6

WI WAUKESHA 

COUNTY

FY 2011 Grant 920 607 92 221 A

New Data Grant 990 613 89 288 A

Change (%) 7.6 1.0 -3.2 30.0

WI WAUSAU

FY 2011 Grant 640 89 157 394 B

New Data Grant 598 82 144 372 B

Change (%) -6.6 -7.8 -8.4 -5.4

WI WAUWATOSA

FY 2011 Grant 1,086 37 501 548 B

New Data Grant 972 40 477 455 B

Change (%) -10.6 7.6 -4.8 -17.0

WI WEST ALLIS

FY 2011 Grant 1,253 83 523 647 B

New Data Grant 1,262 125 511 626 B

Change (%) 0.8 50.6 -2.2 -3.4
127

Grant Allocation Change Due to:

State Grantee Name

Grant 

Amount 

($000)

Population

($000) 

Overcrowded

Units

($000) 

Persons in 

Poverty

($000)

Growth Lag

($000)

Pre-1940

Housing

($000)

Formula

Type

WV CHARLESTON

FY 2011 Grant 1,678 183 981 514 B

New Data Grant 1,555 165 948 442 B

Change (%) -7.4 -9.8 -3.4 -14.0

WV HUNTINGTON

FY 2011 Grant 1,876 254 955 667 B

New Data Grant 1,804 257 936 611 B

Change (%) -3.8 1.0 -2.0 -8.4

WV MARTINSBURG

FY 2011 Grant 351 62 71 218 B

New Data Grant 274 56 67 152 B

Change (%) -21.8 -10.6 -5.6 -30.4

WV MORGANTOWN

FY 2011 Grant 486 186 43 257 B

New Data Grant 393 148 48 197 B

Change (%) -19.2 -20.2 11.2 -23.4

WV PARKERSBURG

FY 2011 Grant 954 135 445 374 B

New Data Grant 898 154 438 306 B

Change (%) -5.8 14.2 -1.6 -18.0

WV VIENNA CITY

FY 2011 Grant 106 18 44 44 B

New Data Grant 104 19 40 44 B

Change (%) -2.0 7.8 -9.0 1.4

WV WEIRTON

FY 2011 Grant 453 44 303 106 B

New Data Grant 410 45 283 83 B

Change (%) -9.4 1.6 -6.6 -21.4

WV WHEELING

FY 2011 Grant 1,338 113 642 583 B

New Data Grant 1,262 92 634 536 B

Change (%) -5.6 -18.6 -1.2 -8.0

WY CASPER

FY 2011 Grant 387 117 62 208 B

New Data Grant 291 88 50 152 B

Change (%) -24.8 -24.6 -18.4 -27.0

WY CHEYENNE

FY 2011 Grant 483 96 115 272 B

New Data Grant 420 91 84 246 B

Change (%) -13.0 -5.6 -27.0 -9.8
131

U.S. Department of Housing and Urban Development

Office of Policy Development and Research

Washington, DC 20410-6000

December 2011