Research to Develop a Community Needs Index

by HUD USER

US Department of Housing and Urban Development
Office of Policy Development and Research
A
Research to Develop
Community Needs Index

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Research to Develop

A

Community Needs Index

Prepared for
U.S. Department of Housing and Urban Development
Office of Policy Development and Research
Prepared by
Econometrica, Inc.
Bethesda, MD
Frederick J. Eggers, Ph.D.
June 2007

Acknowledgements

Econometrica, Inc. conducted this research under contract to the U.S. Department of
Housing and Urban Development (HUD). Fred Eggers was the Principal Investigator;
Alex Thackeray and Fouad Moumen compiled and analyzed the data; and Larry
Campbell edited the report.

The Econometrica Team benefited from the assistance provided by many persons. James
Broughman, George Galster, Garth Taylor, and Christopher Walker served as consultants
to the project and contributed many helpful ideas and thoughtful criticisms. Kurt
Usowski, Craig Davis, and Ian Keith at HUD provided data and suggestions on how to
combine data from different sources. Harold Bunce, John Carruthers, Stanley Gimont,
Michael Hollar, Derek Hyra, and Kevin Neary of HUD contributed useful comments on
the first two drafts of the report; the final report is much better because of their insights.
The Team also appreciates the thoughts and opinions provided by the participants at the
Orientation Meeting and at the Expert Panel Meeting.

Special thanks are due to two people at HUD who made extraordinary contributions to
the success of this research. Marina Myhre served as the Government Technical
Representative. Her guidance, comments, and attentive management were crucial to the
success of the research. Todd Richardson shared his knowledge and experience
generously with the Team and also furnished the 2000 census data used in the analysis.
The U.S. Postal Service vacancy series used as a proxy for abandonment were created by
Todd Richardson.

FOREWORD
In order to understand the most efficient and fair way to allocate Community
Development Block Grant funds, HUD staff since 1976 have worked on developing
measures of community needs. This study, Research to Develop a Community Needs
Index, marks a further advance by developing an index that not only shows current needs
but also can be used to demonstrate changing community conditions.
The study draws on a number of public databases, including the American
Community Survey (ACS). It tests the feasibility of relying on the ACS for annual
information about community needs, and it devises a method to compare those needs
over time. Specifically this study used 2005 ACS data and other readily-available
sources in order to create an index of community needs. It then applied that index to
measure changes in community needs since 2000.
This study also develops and implements an innovative index of real fiscal
capacity, which measures the extent to which communities are capable of dealing with
their problems without federal assistance. To construct this index, the study compares
the ability of cities to raise revenue from various sources. The “real fiscal capacity”
index ranks 234 cities on the real resources that they could have used in order to solve
their community needs in 2005.
The study finds that it is possible to combine a “needs” index and a “fiscal
capacity” index for the purpose of measuring relative need for CDBG and other federal
support. This study was limited to exploratory and methodological issues.
Darlene F. Williams
Assistant Secretary for Policy
Development and Research

Contents
Executive Summary ........................................................................................................ viii
Identifying and Measuring Community Needs........................................................... ix
Finding Common Patterns among the Needs Indicators ............................................ xi
Comparing Community Needs across Time.............................................................xiii
Measuring Fiscal Capacity ........................................................................................ xv
Implications for Future Analysis .............................................................................. xvi
Recommendations for Future Work .......................................................................xviii
1. Introduction................................................................................................................... 1
1.1. HUD’s Mission and an Index of Community Needs........................................... 1
1.2. Policy Context ..................................................................................................... 2
1.3. History of Research into Community Needs....................................................... 3
1.4. Project Goals........................................................................................................ 4
1.5. Overview of Methodology and Organization of the Report................................ 5
2. Indicators of Community Needs.................................................................................. 9
2.1. Data Issues in Building an Index of Community Needs...................................... 9
2.1.1. Type and Size of Community................................................................ 9
2.1.2. Sources of Data Used to Measure City Needs..................................... 10
2.1.3. Time Consistence of Needs Indicators ................................................ 11
2.2. Selection of Needs Indicators ............................................................................ 11
2.2.1. How the Needs Indicators Were Selected ........................................... 11
2.2.2. Needs Indicators .................................................................................. 12
2.2.3. Other Indicators Considered But Not Used......................................... 18
2.3. Review of Indicators Prior to Index Building.................................................... 18
2.3.1. Correlations Among the Needs Indicators........................................... 18
2.3.2. Analysis of Means ............................................................................... 21
2.3.3. Analysis of the Crime Variables.......................................................... 22
2.4. Assessment of Needs Indicators ........................................................................ 23
3. Factor Analysis, Dimensions of Need, and a Community Needs Index ................. 25
3.1. A Brief Introduction to Factor Analysis ............................................................ 27
3.2. Application of Factor Analysis to Needs Indicators.......................................... 29
3.2.1 Anticipated Results from Factor Analysis............................................ 29
3.2.2. Initial Results from Factor Analysis.................................................... 30
3.2.3. Testing the Robustness of the Factor Analysis.................................... 31
3.2.4. Culling the Needs Indicators ............................................................... 33
3.2.5. Derivation of the Final Factors Used in this Report............................ 33
3.2.6. Calculating Factor Scores.................................................................... 36
iii

Contents
(continued)
3.3. Comparing Needs at Different Times................................................................ 37
3.4. Creating a Single-Valued Index of Community Needs ..................................... 39
3.4.1. Alternative Indices............................................................................... 39
3.4.2. Comparisons of Scores on Alternative Indices.................................... 41
3.4.3. Transformation of the Factor Score Functions into Functions of Needs
Indicators............................................................................................. 44
3.5. Summary of Factor Analysis ............................................................................. 45
4. Community Needs in 2000 and 2005 ......................................................................... 47
4.1. Changes in Community Needs for Cities with Populations of 65,000 or More 47
4.2. Comparison of Scores in 2000 and 2005 on Factor 1........................................ 49
4.3 Comparison of Scores in 2000 and 2005 on Factor 2........................................ 53
4.4. Comparison of Scores in 2000 and 2005 on Factor 3........................................ 56
4.5. Comparison of Scores in 2000 and 2005 on the Equal Weight Index............... 59
4.6. Summary............................................................................................................ 62
5. Measuring Fiscal Capacity......................................................................................... 65
5.1. General Approach.............................................................................................. 65
5.2. Variables Used to Measure Fiscal Capacity ...................................................... 67
5.3. Variations in Fiscal Capacity............................................................................. 70
5.4. Combining Need and Fiscal Capacity ............................................................... 73
5.4.1. Background.......................................................................................... 73
5.4.2. Simple Options for Combining the Measures ..................................... 74
5.5. An Index of Needs Adjusted for Capacity......................................................... 76
5.6. Summary............................................................................................................ 78
6. Implications ................................................................................................................. 79
6.1. ACS Data and Community Needs ..................................................................... 79
6.2. A Single-Valued Index of Community Needs................................................... 81
6.3. Intertemporal Comparisons of Needs ................................................................ 82
6.4. Factor Analysis Involving Different Geographies............................................. 83
6.5. Measuring Progress at the Tract Level .............................................................. 84
6.6. Measuring Fiscal Capacity ................................................................................ 86
6.7. Changing City Boundaries and Cost-of-Living Differences ............................. 87
6.8. Areas for Future Work....................................................................................... 88

iv

Contents
(continued)
Appendix A—Supplemental Tables ............................................................................ A-1
Appendix B—Using Regression Analysis to Develop a Community Needs Index.. B-1
B.1. General Concept, Model Specification, and Difficulties ................................ B-1
B.1.1. General Concept................................................................................ B-1
B.1.2. Model Specification .......................................................................... B-3
B.1.3. Difficulties ........................................................................................ B-4
B.2. Implementing the “Hedonic” Approach for City-Level Needs Indicators ..... B-4
B.2.1. Choice of Database ........................................................................... B-4
B.2.2. The Tract-Level Variables ................................................................ B-5
B.2.3. Variation Across Core-Based Statistical Areas ................................ B-8
B.2.4. City-Level Needs Indicators ............................................................. B-8
B.3. Modified Specification of the Hedonic Model ............................................. B-10
B.4. Interpreting the Coefficients of City-Level Needs Indicators....................... B-12
B.5. Use of the “Hedonic” Approach for Assessing Importance of Factors ........ B-13
B.6. Use of Information on City-Level Variables to Create Factor Weights ....... B-15
B.6.1. The Relationship between Beta Coefficients and Standardized Scoring
Coefficients..................................................................................... B-15
B.6.2. Extension of Experimental Use of Beta Coefficients to Guide Selection
of Weights....................................................................................... B-16
B.7. Insights Gained from the Hedonic-type Analysis......................................... B-17
Appendix C—Comparison of Factor Analysis for 2000 with Richardson Factor
Analysis for 2000........................................................................................................... C-1
C.1. Comparison of Index Structure....................................................................... C-1
C.2. Comparison of Need Scores............................................................................ C-3
C.3. Conclusion ...................................................................................................... C-5
References......................................................................................................................D-1

v

Tables

Table 1. Needs Indicators for Developing an Index of Community Needs..................... 13
Table 2. Comparison of Means for the Needs Indicators ................................................ 21
Table 3. Rotated Factor Loadings for Final Factor Analysis, Each Factor Sorted by
Loading .............................................................................................................. 35
Table 4. Correlations among Factor Scores..................................................................... 37
Table 5. Alternative Single-Valued Community Needs Indices...................................... 40
Table 6. Basic Statistics on the Alternative Indices......................................................... 41
Table 7. Correlations Among the Alternative Indices ..................................................... 42
Table 8. Comparison of Scores between Index 1 and Indices 2, 3, and 4 for 370 Cities 43
Table 9. Transformation of Factor-Scoring Coefficients into Scoring Coefficients for
Needs Indicators................................................................................................. 45
Table 10. Average Factor Scores and Average Equal Weight Index Scores in 2000 and
2005................................................................................................................. 48
Table 11. Changes in Factor 1 Scores between 2000 and 2005, by Region and
Population ....................................................................................................... 49
Table 12. Forty Cities with the Largest Increases in Factor 1 Scores, 2000-2005 .......... 51
Table 13. Forty Cities with the Largest Decreases in Factor 1 Scores, 2000-2005......... 52
Table 14. Changes in Factor 2 Scores between 2000 and 2005, by Region and Population
....................................................................................................................... 53
Table 15. Forty Cities with the Largest Increases in Factor 2 Scores, 2000-2005 .......... 54
Table 16. Forty Cities with the Largest Decreases in Factor 2 Scores, 2000-2005......... 55
Table 17. Changes in Factor 3 Scores between 2000 and 2005, by Region and
Population ........................................................................................................ 56
Table 18. Forty Cities with the Largest Increases in Factor 3 Scores, 2000-2005 .......... 57
Table 19. Forty Cities with the Largest Decreases in Factor 3 Scores, 2000-2005......... 58
Table 20. Changes in Equal Weight Index Scores between 2000 and 2005, by Region
and Population ................................................................................................ 59
Table 21. Forty Cities with the Largest Increases in Equal Weight Index Scores, 2000-
2005................................................................................................................. 60
Table 22. Forty Cities with the Largest Decreases in Equal Weight Index Scores, 2000-
2005................................................................................................................. 61
Table 23. Variables Used to Measure Fiscal Capacity .................................................... 69
Table 24. Twenty-five Cities with Greatest Real Fiscal Capacity................................... 71
Table 25. Twenty-five Cities with the Least Real Fiscal Capacity.................................. 71
Table 26. Real Fiscal Capacity for 25 Largest Cities with Data...................................... 73
Table 27. Impact of Combining Needs and Fiscal Capacity for 50 Cities with the
Highest Community Needs............................................................................. 77

vi

Tables
(continued)

Appendix A
Table A.1. Correlation Matrix for Needs Indicators...................................................... A-2
Table A.2. Initial Factor Analysis, 2005 Data: Factor Loading for Unrotated Factors A-4
Table A.3. Initial Factor Analysis, 2005 Data: Factor Loading for Varimax Orthogonal
Rotated Factors .......................................................................................... A-5
Table A.4. Factor Analysis Using 2000 Data: Unrotated and Rotated Factor Loading A-6
Table A.5. Factor Analysis Using 2005 Data, Rotated Factor Loadings for Large Cities
and Small Cities ......................................................................................... A-7
Table A.6. 2005 Factor Analysis Using VIOLCRIME Instead of PT1CRIME,
VARIMAX Rotated Factors ...................................................................... A-8
Table A.7. Standardized Scoring Coefficients, Based on 2005 Data Without Crime
Variables .................................................................................................... A-9
Table A.8. Alternative Index Scores............................................................................ A-10
Table A.9. Factor and Equal Weight Index Scores in 2005 and 2000 for 370 Cities.. A-23
Table A.10. Adjusted Needs Index for 234 Cities....................................................... A-37

Appendix B
Table B.1. Tract-Level Variables Used in Regressions................................................. B-6
Table B.2. Parameter Estimates for Tract-Level Variables ........................................... B-7
Table B.3. Estimated Coefficients for City-Level Needs Indicators ............................. B-9
Table B.4. Parameter Estimates for Tract-Level Variables with Modified Specification..
............................................................................................................... B-11
Table B.5. Estimated Coefficients for City-Level Needs Indicators with Modified
Specification ............................................................................................ B-12
Table B.6. Percentage Decline in Median House Prices Caused by a One-Standard-
Deviation Worsening in a Need Indicator .................................................. B-13
Table B.7. Estimated Coefficients for the Three Factors............................................. B-14
Table B.8. Percentage Change in Median House Prices Caused by a One-Standard-
Deviation Worsening in a Factor Score................................................... B-14
Table B.9. Beta Coefficients and Standardized Scoring Coefficients for Needs Indicators
with Valid Hedonic Coefficients ............................................................. B-16
Table B.10. Regressions Using All Tracts with Median House Values ...................... B-19
Table B.11. Regressions on Tracts with Less Dispersion in Median Prices................ B-25
Table B.12. Regressions on Tracts with Less Dispersion in Median Prices Using Factor
Scores....................................................................................................... B-31

Appendix C
Table C.1. Identical or Similar Indicators in Richardson and This Study ..................... C-3
Table C.2. Comparison of Richardson Index Scores and the Equal Weight Index
Scores......................................................................................................... C-4

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viii

Executive Summary

The U.S. Department of Housing and Urban Development (HUD) funded this research
for the purpose of developing an index of community needs. Such an index would take
information from various public databases on different types of community problems and
produce an overall assessment of the “neediness” of a community. As far back as 1976,
HUD devoted its own staff resources to studying community needs and devising ways to
synthesize various types of needs into an overall index of needs. HUD’s efforts have
been sporadic because the primary source of data on community needs has been the
decennial censuses, and thus new information on needs has been available only at 10-year
intervals. Now, the American Community Survey (ACS) will provide every year the
information that previously was available only from the decennial censuses. The annual
availability of information through the ACS makes a community needs index much more
valuable for HUD.

From HUD’s perspective, this research would provide the foundation for its future
analysis of community needs by:

• Testing the feasibility of relying on the ACS for annual information about
community needs.

• Devising a methodology to compare conditions in communities over time.

In the early stages of the research, HUD expanded the goals to include developing an
index of fiscal capacity and investigating an alternative methodology for constructing an
index.

Identifying and Measuring Community Needs

The first step in the research was to define the range of problems to be grouped together
as “community needs” and to identify “indicators” for each of the problems. In this
research, “community” means city and “needs” means the problems, experienced by
cities, that are relevant to HUD’s urban mission. The “indicators” are quantitative
measures available on a consistent basis for all or most of the cities studied.

HUD views its mission as including the support of community development. In its
Strategic Plan, HUD declares its concern about a wide variety of problems related to
strengthening communities; included among HUD’s concerns are housing conditions,
physical conditions, the quality of life, and economic opportunities. With this in mind,
the research team formulated a preliminary list of indicators that covered a broad array of
community ills. In selecting the indicators, the team reviewed measures used in previous
studies and data available from a variety of databases that contain information at the city
level or that could be manipulated to produce city-level measures. In a November 21,
2006, meeting involving the authors, HUD experts, consultants with previous experience
ix

in comparing conditions at the local level, representatives from the U.S. Government
Accountability Office, and representatives from the District of Columbia government, the
strengths and weaknesses of a variety of indicators were discussed and, in additional
consultation with HUD, a final list of 26 indicators was selected.

Eight indicators identify population groups that may have needs for city services beyond
those of the typical citizen. These include:

1. Poverty population.
2. Children living in poverty.
3. Persons over age 74 living in poverty.
4. Low-income population (excludes poverty population).
5. Single-parent families.
6. Adults without a high school diploma.
7. Working-age persons without a college degree.
8. Recent immigrants.

Four indicators identify problems with housing, housing markets, or housing finance.
These include:

9. Lack of affordable rental housing.
10. Overcrowded housing.
11. Older rental housing occupied by poor persons.
12. Mortgage-loan denial rate.

Three indicators identify the extent to which cities have seriously troubled
neighborhoods. These include:

13. Population living in high-poverty census tracts.
14. Population living in moderate-poverty census tracts.
15. Abandoned buildings.

Four indicators identify social and economic problems at the city level. These include:

16. Rate of violent crimes.
17. Rate of nonviolent crimes.
18. School-age population living in poverty.
19. Unemployment rate.

Four indicators identify conditions that might complicate a city’s efforts to deal with its
problems. These include:

20. Linguistic isolation.
21. City-metropolitan differences in minority population.
22. City-metropolitan differences in poverty rate.
23. City-metropolitan differences in median family income.
x

xi
Three indicators identify detrimental long-term trends. These include:

24. Excess infrastructure/loss of households.
25. Change in employment base.
26. Change in concentration of low-income families.

Table 1 in Chapter 2 defines each of these indicators more precisely and explains why
each was included. All the indicators are defined in percentage or ratio terms so that their
magnitude does not depend on city size. Also, all indicators are defined so that the more
serious the condition, the larger the value of the indicator.

Input from HUD and outside experts was used to choose the indicators. The list includes
several innovative indicators. The abandoned building indicator was developed by HUD
staff using a combination of census data and vacancy rates compiled by the United States
Postal Service (USPS). The lack of affordable rental housing indicator (#9) uses a
technique similar to that used by HUD to identify difficult development areas for the low-
income housing tax credit program. The mortgage denial rate is used for the first time in
this study. Variants of the city-metropolitan difference variables (#21 to #23) have been
used in previous studies, but this research uses a simplified definition that makes it easier
to calculate these indicators. All three long-trend indicators (#24 to #26) are new
formulations for this research.
1

The indicators require data from the ACS, the decennial censuses, the economic
censuses, USPS vacancy surveys, Home Mortgage Disclosure Act records, the FBI
Uniform Crime Report, and the Bureau of Labor Statistics Local Area Unemployment
Statistics. The 2005 ACS reported information on 473 cities (not including Puerto Rico)
with populations of 65,000 or more. The research attempted to calculate the 26 indicators
for each city. Information needed for individual indicators was missing for a number of
cities. The most serious missing data problem involved the crime data, which were not
available for 107 cities.

Finding Common Patterns among the Needs Indicators

The next step in the analysis was to determine the extent to which the needs indicators
can be distilled into a small (more manageable) number of underlying common “themes”
or components. The report uses factor analysis to search for common themes and to
produce a simpler way to observe how needs vary across communities. Previous HUD
research used factor analysis for this purpose.

1
A 27
th
indicator based on Housing Mortgage Discrimination Act data was identified—poor housing
appreciation in high-poverty neighborhoods—but it could not be implemented within the scope of the
project.

The research applied standard factor analysis techniques to the 26 indicators and
identified three dimensions of community needs. These include:

• Needs associated with poverty and structural problems (Factor 1).
• Needs associated with immigration and lack of affordable housing (Factor 2).
• Needs arising from limited economic prospects (Factor 3).

The robustness of the factor analysis was tested in several ways. First, factor analysis
was applied to the same indicators using 2000 data. The 2000 and 2005 analyses
identified factors that were nearly identical; this result confirmed that factors developed
using 2005 data could be applied to 2000 data on needs indicators. Second, the sample of
cities was split into those with populations of 200,000 or more and those with populations
of less than 200,000. Factor analysis applied separately to the two samples produced
results that were similar enough to suggest that the same pattern of needs apply across
different size classes of cities. Third, a different measure for violent crimes—one based
on occurrences rather than arrests—was substituted for the measure used in the initial
analysis. The results of the factor analysis did not seem to vary significantly when the
alternative measure was used. Fourth, the needs indicators were examined to see where
problems with missing data caused a large number of cities to drop out of the analysis.
Based on this examination, the two crime indicators were dropped. When factor analysis
was applied to the smaller set of needs indicators, the same factors were found as were
found with the full set of indicators. Eliminating the two crime variables increased the
number of cities examined from 292 to 370.

The factor analysis based on 24 needs indicators is the one used for all the analyses in the
remainder of the report. Factor scores were computed for each city on each factor by
multiplying a set of standardized scoring coefficients derived from the factor analysis by
the standardized value of the needs indicators for the city and summing the products. A
standardized value for a needs indicator is obtained by subtracting a mean value from the
value of the indicator for that city and dividing the difference by a standard deviation.

The explicit goal of this project was to develop a single-valued index of community
needs. The report compared six alternative single-valued indices constructed by using
various linear combinations of the scores on the three factors. The report was unable to
find any statistical, programmatic, or logical reasons that made a compelling case for
choosing one index over any of the others. Statistically, an equal weight index—an index
formed by giving each of the three factors a weight of 1/3—produces results that are very
similar to the results from the other indices that vary the weights given to the factors. 

For this reason, the report uses the equal weight index in all the analyses involving a
single-valued index.

High correlation across all cities does not mean that the ranking of some cities is not
substantially different depending upon the index used. If HUD were to use one of these
indices to allocate funds to cities, the choice of index would be of great concern to
individual cities. But, if HUD is interested primarily in analyzing the variation in needs
xii

across cities and over time, then the results from the equal weight index will be similar to
those from any index that applies reasonable weights to the factor scores.

Comparing Community Needs across Time

This research developed a methodology for applying factor analysis to data on needs at
two points in time and successfully implemented the methodology. There are two keys to
carrying out intertemporal comparisons correctly.

• First, the dimensions of need identified in the base year must still be relevant in
the comparison year.

o The comparison of factor analyses using 2000 and 2005 data confirmed
that the same factors applied in 2000 and 2005.

• Second, needs must be measured relative to conditions in the year in which the
factor analysis is performed—that is, the means and standard deviations from the
year used to derive the standardized scoring coefficients must be used to
standardize the needs indicators in both years.

o Since the report uses 2005 data to identify the factors, the report uses the
means and standard deviations calculated on data for the 24 needs
indicators in 2005 to standardize the values of the needs indicators in both
2000 and 2005.

Using this technique, the report compares conditions in cities in 2000 with conditions in
2005 using each of the factors and the equal weight index. The scores are positively
related to needs—that is, for each factor and for the equal weight index, an increase in the
score means that a city is worse off in 2005 than in 2000.

Between 2000 and 2005, cities—on average—became worse off with respect to poverty
and structural problems as well as immigration and housing affordability problems, but
improved with respect to the limited economic prospects factor.

• Regional differences appeared on the individual factors, such as:

o The Northeast had the highest average scores on the poverty and
structural problems factor in both 2000 and 2005 and the largest increase
in average scores between the two years. The West had the lowest
average scores on this factor in both years and the smallest increase
between the two years.

o For the immigration and housing affordability factor, the average scores
of cities in the Northeast and West were higher than the national average
xiii

in both 2000 and 2005. Cities in the Northeast had the highest average
change between 2000 and 2005.

o Cities in the Northeast had the lowest scores on the limited economic
prospects factor in 2000 and showed the greatest improvement between
2000 and 2005.

• Differences by class size of cities were less common. For example:

o There appeared to be a systematic relationship between the scores on the
poverty and structural problems factor and city size. The average score
declined by size class in both 2000 and 2005. The change in scores was
approximately the same for all the size classes, except for cities with
populations between 500,000 and one million, which had a slightly higher
increase in average scores.

o With the exception of cities with over one million residents, there
appeared to be little relationship between population size and the
prevalence of problems related to immigration and housing affordability.
The largest cities had an average score of 0.70 or more in both 2000 and
2005; the national average was 0.00 in 2005.

• There were also some interesting patterns in the lists of cities with the biggest
increases in scores (becoming worse off) and the lists of cities with the biggest
decreases in scores (becoming better off).

o Some of the worse off cities on the poverty and structural problems factor
experienced big increases on this factor between 2000 and 2005; the cities
were Camden, Detroit, Cleveland, Rochester, Reading, and Syracuse.

o Compared with other states, California had the most cities—95—among
the 370 scored. Still, California cities appeared in higher than expected
proportions on the list of the 40 biggest losers and gainers. One would
expect, proportionally, 10 cities from California on each list. Instead:

− Twenty-four of the 40 cities with the biggest improvements on the
poverty and structural problems factor were California cities.
− Fifteen of the 40 cities with the worse changes on the immigration
and housing affordability factor were California cities.
− The five cities with the largest improvements on the immigration
and housing affordability factor, and 18 of the top 40, were
California cities.

The equal weight index showed that, on average, community needs decreased slightly
between 2000 and 2005. According to the index, conditions were stable or got better in
202 of 370 cities. However, the report notes that the observed improvement appears to
xiv

be related strongly to the substantial increase in the proportion of adults with a high
school diploma between 2000 and 2005, a fact that was questioned when the report
reviewed data on each of the indicators.

Measuring Fiscal Capacity

The federal government, in general, and HUD in particular, are interested in developing
an index of community needs because they want to know the extent to which
communities require federal assistance. But a needs index answers only one-half of this
question; the federal government also needs to know the extent to which communities are
capable of dealing with their problems without federal assistance.

The report develops and implements an index of real fiscal capacity. To construct the
index, the report compares cities on their ability to raise revenue from various sources,
including assistance from state governments. Then, the report translates the potential
revenue into real terms by dividing total potential revenue by the average annual wage for
government employees calculated at the metropolitan-area level. Using real capacity
adjusts for differences across cities in the costs of responding to community needs. The
real fiscal capacity index ranks 266 cities on the real resources that they can potentially
use to solve community needs in 2005.

The report also develops a technique for combining the equal weight index of community
needs with the index of real fiscal capacity to obtain an adjusted needs index that looks at
both needs and capacity. The report calculates adjusted needs index scores for 234 cities
in 2005.

The most important findings from the research on fiscal capacity are:

• It is possible to construct an index of real fiscal capacity, which is a very
important advancement in analyzing the need for federal assistance.

• The index of real fiscal capacity is sensitive to both income and wage rates.
Places with high income or lower government wages are more likely to have high
real fiscal capacity. High-income places can generally afford more government
services because they can raise more tax revenue; places with low government
wages can generally afford more government services because every tax dollar
goes further in providing services.

• The index is negatively correlated with the equal weight index of community
needs. Cities with high community needs are more likely to have low real fiscal
capacity.

xv

• It is possible to combine a needs index and a fiscal capacity index. The adjusted
needs index developed in this chapter produced different rankings from the equal
weight index of community needs. But, in general, the change in rankings was
not great, probably because of the negative correlation between the two
component indices.

Implications for Future Analysis

One objective of this research was to test whether the ACS data would support the same
type of analysis that HUD had conducted using long-form data. The answer to this
question is “yes.” In the future, HUD can depend on the ACS to monitor conditions in
cities and counties. The report successfully uses ACS data to construct useful measures
of community needs using factor analysis. Of the 24 needs indicators used in the final
factor analysis, 16 used ACS data, one used ACS data combined with long-form data,
and four used long-form data. All five indicators that used either long-form data or a
combination of ACS and long-form data should be available in the future from the ACS.

The following are some issues and open questions that HUD will have to keep in mind in
future work using the ACS.

• The reporting rules used in the ACS are similar to those used for the long form of
the decennial census. But, because the ACS sample size is smaller, the rules can
result in more frequent suppression of data.

• The Census Bureau has established, as a general policy, releasing for the ACS all
tabulations prepared for the 2000 long-form data. However, some special
tabulations of long-form data have not yet been released. HUD should probably
contact the Census Bureau to make sure that these tabulations are not forgotten.

• The ACS has not released data on persons in group quarters yet. So, there has
been no experience with the usefulness of the tabulations or the reliability of the
data.

• The Census Bureau will make revisions to the ACS questionnaire, and revisions
always create the possibility of discontinuities in the data.

An explicit goal of this project was to develop a single-valued index of community needs.
The research achieved this objective, but the outcome was only a qualified success. The
report was unable to find any statistical, programmatic, or logical reasons that make a
compelling case for choosing one index over any of the others. At HUD’s request, the
report examined the use of regression analysis to provide definitive guidance in
weighting the factors or the needs indicators. However, the prominent role of housing
affordability in Factor 2 and in two or three of the needs indicators undermined attempts
to apply the regression results directly. The regression approach did provide some
xvi

insights on deriving weights, but the report could not explore the full implication of these
insights.

HUD indicated early on in the project that it was interested in the lessons from this
research that could be applied to measuring needs at the tract level. The Administration
has proposed creating a special fund within the CDBG program to award communities
for making progress in reducing neighborhood distress. Such a proposal would require a
community needs measure at the neighborhood level. Since ACS data will be available
at the census-tract level beginning in 2010, it was hoped that the experience gained here
in constructing a city-level index using ACS data would be useful to HUD in developing
a neighborhood-level index.

This research laid the ground work for a measure of progress at the census-tract level in
three important ways: the identification of needs indicators, the successful application of
factor analysis to the needs indicators, and the development of a methodology for making
intertemporal comparisons of needs. Despite these useful insights, HUD will need to do
a lot of conceptual and empirical work to develop a technique capable of measuring
progress at the local level. The obstacles include:

• Several of the needs indicators used at the city level would not be applicable at
the tract level because of the absence of data at the tract level or because the
concepts behind the indicators are more applicable at the city level than at the
tract level.

• Because of the substantial change in the number and type of indicators, a new
factor analysis would have to be performed at the tract level. This factor analysis
is likely to identify different dimensions of need than the three identified at the
city level in this report.

• The ACS has lower sampling rates than the long-form survey in the decennial
censuses. This raises concerns about data suppression at the tract level and about
measurement errors.

• On the conceptual side, a clear distinction needs to be made between measuring a
change in needs and measuring how local government actions have reduced
community needs. Conceptually, one would like to control for outside influences
so that cities would not benefit from favorable external conditions or suffer from
unfavorable external conditions. In this respect, measuring needs is simpler than
measuring progress.

• At the tract level, gentrification can give the appearance of progress in reducing
needs, but progress is not really achieved because many people with needs are
forced to relocate with their needs still unmet.

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xviii
Recommendations for Future Work

The most important area for future work is to expand and improve upon the list of needs
indicators. This report uses a well-conceived, broad-based, and carefully defined set of
needs indicators that provide the basis for a useful factor analysis. However, the greatest
payoff for understanding community needs is likely to come from improving these
indicators and filling in some missing gaps. Future work should concentrate on getting
good measures of education and health needs and, most important, on getting better
measures of the impact of long-term economic forces on cities.

Page 1
1. Introduction

1.1. HUD’s Mission and an Index of Community Needs

The U.S. Department of Housing and Urban Development (HUD) funded the research
reported in this document for the purpose of developing an index of community needs.
Such an index would take data from various sources on different types of community
problems and produce an overall assessment of the “neediness” of a community.

Consistent with the “Urban Development” portion of its name, HUD views its mission as
including the support of community development. In its latest Strategic Plan, HUD
identifies the following five objectives under the goal of strengthening communities:
2

• Assist disaster recovery in the Gulf Coast region.

• Enhance sustainability of communities by expanding economic opportunities.

• Foster a suitable living environment in communities by improving physical
conditions and quality of life.

• End chronic homelessness and move homeless families and individuals to
permanent housing.

• Address housing conditions that threaten health.

These objectives, particularly the last four, indicate HUD’s concern with a wide variety
of problems that confront local governments. An accurate and reliable index of
community needs would help HUD carry out its responsibilities in several ways. These
include:

• An index would enable HUD to rank communities by the extent of their needs.

• Such a ranking would help HUD develop equitable formulas for distributing
funds to communities.

• An index would also enable HUD to track whether a community’s needs are
improving or getting worse over time.

• Information on the components that enter into the calculation of an index score
would help HUD diagnose the type of problems facing communities in general
and individual communities.

2
HUD Strategic Plan FY 2006 – FY 2011, U.S. Department of Housing and Urban Development, March
31, 2006.

Page 2

• Construction of an index would help HUD understand how the various kinds of
community problems relate to one another and the extent to which they represent
the same or different types of need.

As far back as 1976, HUD devoted its own staff resources to studying community needs
and devising ways to synthesize various types of needs into an overall index of needs.
HUD’s efforts have been sporadic because the primary source of data on community
needs has been the decennial censuses, and thus new information on needs has been
available only at 10-year intervals. Now the American Community Survey (ACS) will
provide every year the information that previously was available only from the decennial
censuses.
3
The annual availability of information through the ACS makes a community
needs index much more valuable for HUD. For this reason, HUD contracted with
Econometrica, Inc. to build upon HUD’s previous research to develop a community
needs index that could be implemented with ACS and other contemporary data to provide
yearly information on community needs.

1.2. Policy Context

In February 2005, HUD issued a report (Richardson 2005) that measured community
needs and analyzed how well the current Community Development Block Grant (CDBG)
formula distributes funds with respect to community needs. This report also presented
alternative formulas that would distribute CDBG funds more equitably with respect to
community needs. This was the sixth in a series of reports on the CDBG formula, but it
was the first report that HUD prepared without being requested to do so by Congress.
The Administration subsequently proposed changes in the CDBG allocation mechanism.

In April 2005, the U.S. Government Accountability Office (GAO) presented to Congress
the results of its study of the CDBG formula and testified that the allocation mechanism
could be improved. In June 2006, GAO officials testified on the Administration’s
proposal and explained how GAO planned to respond to a request from Congress to
assess the CDBG formula. The GAO created an expert panel using its National Academy
of Sciences connection. The panel was asked to examine:

• HUD’s construction of a needs index as a criterion for measuring community
needs, including HUD’s factor analysis and the specific indicators of need
included in its index.

3
The ACS revolutionizes the way the federal government collects demographic data. The ACS collects
virtually the same information annually that the long form of the decennial census collected at 10-year
intervals, but the ACS has a lower sampling rate than the long form. In 2006, the Census Bureau released
data from the 2005 ACS for most places with populations of 65,000 or more and, thereafter, plans to
release ACS data every year for those places. Beginning in 2008, it will release 3-year moving average
data for all places with populations of 20,000 or more. Thereafter, it plans to release 3-year moving average
data every year for these places. Beginning in 2010, it will release 5-year moving average data for all
places, including census tracts and block groups, and, thereafter, plans to release 5-year moving average
data every year for all places.

• The development of an evaluation criterion for GAO to use that accounts for the
potential mismatch between a jurisdiction’s community needs and its economic
and fiscal capacity to meet that need.

As of the date of this report, GAO has not completed work on its study.

A community needs index based on ACS data would be valuable in assessing proposed
changes to the CDBG formula arising from the Administration or GAO.

The Administration’s proposal also contained a provision that would create a special fund
within the CDBG program to award communities for making progress in reducing
neighborhood distress. Such a proposal would require a community-needs measure at the
neighborhood level. Since ACS data will be available at the census-tract level beginning
in 2010, the experience gained here in constructing a city-level index using ACS data
should be useful to HUD in developing a neighborhood-level index.

1.3. History of Research into Community Needs

Between 1976 and 2005, HUD personnel conducted five studies of community needs:

• 1976: An Evaluation of the Community Development Block Grant Formula,
prepared by Harold L. Bunce.

• 1979: City Need and Community Development Funding, prepared by Harold L.
Bunce and Robert L. Goldberg.

• 1983: Effects of the 1980 Census on Community Development Funding, prepared
by Harold L. Bunce, Sue G. Neal, and John L. Gardner.

• 1995: Effect of the 1990 Census on CDBG Program Funding, prepared by Kevin
Neary and Todd Richardson.

• 2005: CDBG Formula Targeting to Community Needs, prepared by Todd
Richardson.

These five studies had three common characteristics: First, each study focused on
whether the formula used to distribute CDBG funds was doing so effectively and
equitably. Second, each study gathered data from a variety of sources on conditions in
communities receiving CDBG funding. Variables were selected to measure problems
that communities are allowed to use CDBG funds to treat. Third, each study used factor
analysis to search out underlying patterns among the need variables and to simplify the
data for calculating an index.

Page 3

1.4. Project Goals

The primary goal of the project is to use 2005 ACS data and other data to create an index
of community needs that has the following properties:

• HUD can use the index to evaluate the needs of cities with populations of 65,000
or more as of 2005.

• In 2007 and every year thereafter, when the Census Bureau releases new ACS
data, HUD can enter the new data into the index and update its assessment of city
needs.

• HUD can rely on the index to track changes in the needs of individual cities over
time.

• The index, with minor modifications as may be required, can be used to evaluate
the needs of smaller cities and urban counties when more detailed ACS data
become available in 2008.

Using an index to compare needs at two different points in time extends previous work
with community needs indices and requires a revised methodology. Each new wave of
ACS data will provide new information on individual community needs and the
opportunity to construct a new community needs index. Since each index creates its own
frame of reference, a single frame of reference has to be selected and criteria developed
to ensure the validity of the chosen frame.

HUD intended Econometrica, Inc. to build the index using the factor analysis approach
employed in HUD’s previous work on need indices. However, the project has some
secondary research goals, which are to:

• Examine whether previous approaches should be modified to take into account
community boundary changes and cost-of-living differences across communities.

• Explore an alternative approach for creating a needs index based on hedonic-like
regression models.

• Explore the development of an index of the capacity of communities to deal with
problems.

As noted in Section 1.2, the Administration has proposed a special fund within the CDBG
program to award communities for making progress in reducing neighborhood distress.
HUD hopes to use the lessons from this project to provide insights into developing a
means to measure progress at the neighborhood level. In the concluding chapter, the
report discusses how the experience from this research at the city level could be applied
in creating an index of neighborhood distress at the census tract level.

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1.5. Overview of Methodology and Organization of the Report

The process of developing a community needs index involves a number of steps, each of
which has its own conceptual issues that must be resolved. The principal steps that an
analyst must undertake are:

A. The analyst must establish what concepts should be included in the notion of
“community needs.” For the purpose of this research, “community” means city or
county. The residents of cities and counties experience a wide range of problems.
The first step in developing a community needs index is to identify the subset of
these problems that cities and counties have the responsibility of alleviating and
that are consistent with the “urban development” mission of HUD.

B. The analyst must create valid measures of these concepts. For each concept in
Step A, the analyst must find data that adequately represent the problem, that are
reliable, and that are available for all the communities being studied. Care must
be taken to avoid conceptual errors such as measuring the consequences of not
dealing with problems instead of measuring the problems themselves.

C. The ability of a city or county to deal with community needs depends upon the
resources available to the city or county, that is, on its fiscal capacity, and on
conditions, such as long-run economic decline or racial segregation, that may
make problems more difficult to resolve. The analyst must be able to identify
complicating conditions, determine how to measure them, and figure out how to
relate them to direct measures of needs. In addition, the analyst must determine
whether it is feasible to measure a community’s capacity to meet its needs and, if
so, how to relate capacity to needs.

D. The outcome of Steps A, B, and C should be a set of variables that measures
needs and complicating conditions for the universe of communities being studied.
Next, the analyst must determine the extent to which these measures can be
distilled into a small (more manageable) number of underlying common “themes”
or components. The report uses factor analysis to search for common themes and
to produce a simpler way to observe how needs vary across communities.

E. Next, the analyst must decide how the various components of need should be
weighted in the creation of a summary index of need. One can look to previous
research, relevant legislative guidance, “common-sense rules of thumb,” or other
methods to combine the output from Step D into a single index.

F. Finally, the analyst must figure out how to use the components of need developed
in Step D or the single index developed in Step E to measure needs at a different
point in time.

This report discusses how each of these steps was accomplished and what was learned in
resolving the issues involved in each step. The report provides a list of needs indicators
Page 5

that are generally available for all cities with populations of 65,000 or more and that will
be available in the future for smaller cities and for counties. From the needs indicators, a
set of three factors that summarizes the types of needs associated with the needs
indicators was found, and the report used alternative ways to combine the factors into a
single index. The report applies the factors and a single-valued index based on the
factors to explore relative need among 370 cities in 2005 and to monitor changes in need
from 2000 to 2005.

This report contains the following six chapters:

1. Introduction.

2. Indicators of Community Needs – Chapter 2 identifies cities as the entities being
studied and defines the range of conditions to be considered as “needs” at the city
level. It deals with all of the issues involved in Steps A and B and the issues in
Step C associated with conditions that make it more difficult for cities to deal with
problems. After examining data on various measures of need, we selected 26
needs indicators to be used in the factor analysis.

3. Factor Analysis, Dimensions of Need, and a Community Needs Index – Chapter 3
applies factor analysis to the data on needs indicators and identifies three common
themes (factors) that encompass the conditions measured by the needs indicators.
The chapter combines these three factors into a single index using four alternative
sets of weights for the factors and compares the alternatives indices. Finally, the
chapter develops the methodology to apply the factor analysis developed using
2005 data to measure city needs in 2000. The chapter deals with the issues
involved in Steps D, E, and F. As such, it lays out the methodology used in this
study and proposed to be used with future rounds of ACS data.

4. Community Needs in 2000 and 2005 – Chapter 4 applies the results from Chapter
3 to examine how cities differ in needs in 2005 and how city needs changed from
2000 to 2005.

5. Measuring Fiscal Capacity – Chapter 5 develops a methodology for measuring
fiscal capacity, implements the methodology for 292 cities in 2005, and explores
how one could combine a measure of fiscal capacity with a community needs
index to obtain a complete picture of the relative dependence of cities on federal
aid. This chapter deals with the issues involved in Step C associated with fiscal
capacity.

6. Implications – Chapter 6 summarizes the lessons learned in Chapters 2 through 5
and applies them to the three main objectives of this study: developing techniques
for measuring community needs that can be used with future rounds of ACS data,
developing techniques for tracking changes in needs for individual communities,
and exploring ways to measure progress in resolving needs at the neighborhood
level.
Page 6

Chapter 4 contains the most important empirical results—a comparison of needs in 370
cities in 2000 and 2005. Chapter 5 presents the empirical findings related to fiscal
capacity and the joint consideration of community needs and fiscal capacity.

This project involved a substantial amount of methodological work, both in
conceptualizing and implementing the analysis. Chapter 2 contains the conceptual work
related to the selection of needs indicators. Chapter 3 presents the methodology involved
in the factor analysis and in applying factor analysis in multiple time periods. Chapter 5
describes the rationale and processes involved in constructing an index of real fiscal
capacity and in combining the index of community needs and the index of real fiscal
capacity. Appendix B describes the methodology behind the hedonic-type analysis and
contains the results of that work.

Appendix A contains supplemental tables. Appendix C compares the 2000 factor
analysis performed by Richardson in his 2005 study with a 2000 factor analysis using the
needs indicators developed in this study.

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Page 8

2. Indicators of Community Needs

HUD designed this study to test the use of ACS data to measure a variety of community
needs and track changes in needs over time. This focus shaped the choice of data, time
period, and type of communities used in the analysis. The first section of this chapter
discusses these choices. The second section describes the range of problems and
conditions considered as community needs, proposes a set of needs indicators, and
explains why certain variables were not included as needs indicators. The third section
examines data on the proposed indicators to test whether they are reliable measures of
need prior to the factor analysis in Chapter 3. The fourth section contains our assessment
of the accuracy and comprehensiveness of the needs indictors.

2.1. Data Issues in Building an Index of Community Needs

This section discusses the issues involved in gathering data for an index of community
needs. 

2.1.1. Type and Size of Community

In this report, community will mean a city with a population in 2005 of 65,000 or more.
The recent availability of ACS data motivated this research and, as of now, the Census
Bureau has released ACS data only for states and for cities and counties with populations
of 65,000 or more. Data on places with populations of 20,000 or more will become
available in 2008.

Counties are not included in the analysis for two reasons: non-urban counties probably
have a different mix of problems, and including them with cities and urban counties
could produce misleading results. Second, in the community development area, HUD
usually deals with units of governments, which means that it may work with both a
county and with cities within that county. This creates data difficulties because,
depending upon the issue, HUD may want data at the county level that relate to
community needs for both the overall county and individual cities within the county. In
awarding CDBG funds to an urban county, HUD considers only the portion of the county
outside of cities that receives CDBG funds directly from HUD. Conceptually, it would
be possible to construct ACS estimates for these pieces of counties, but the current
65,000-minimum-population rule and confidentiality constraints that limit reporting in
individual tables would eliminate numerous urban counties from the analysis. For these
reasons, HUD excluded counties from the analysis.

The Census Bureau lists 499 places for which it has released ACS data, including 7 in
Puerto Rico. We dropped the seven places in Puerto Rico because of problems with
particular variables. For five cities (Indianapolis, Louisville, Nashville, Augusta, and
Page 9

Page 10
Athens), the information is reported for “balance” of the jurisdiction. Based on
correspondence with the Census Bureau, these data cover the consolidated city and
county, but omit incorporated places within the consolidated city/county. It is possible
that the city/county government is responsible for community needs in the omitted
incorporated areas, but there is nothing in the ACS data to confirm or deny responsibility.
These five places are included in the analysis. Twenty-seven of the 499 places are
“census designated places” (CDPs). Of these, one (Honolulu) is a CDBG central city,
and six are CDBG noncentral city entitlement cities. We kept these seven places.
4

New Orleans and other Gulf Coast cities present special problems. Two-thirds of the
ACS data for these areas were collected prior to Hurricane Katrina and one-third
afterwards, although response rates were probably low. If the primary purpose of this
study were to rank cities by need, we might have eliminated many of the Gulf Coast
cities because their needs today are probably much different from their needs measured
by the 2005 data.
5
But, as noted previously, this study concentrates mainly on
developing techniques for measuring need. The ACS data provide a reasonable good
measure of conditions in these places prior to Katrina and, therefore, we included them as
part of the universe of places used to test the techniques.

After these adjustments, the analysis begins with 472 cities; the actual number included
in any analysis depends upon how many cities have data for all the measures used in that
analysis. Of the 472 cities, 235 are principal cities, and 137 are suburban cities.

2.1.2. Sources of Data Used to Measure City Needs

The types of analyses that are used to construct a community needs index require that the
data be defined and collected consistently across cities. This means that we must rely on
national databases and ignore valuable local data sources.
6
The databases used are:

• The 2005 American Community Survey.
• The 2000 decennial census.
• The 1997 and 2002 Economic Censuses.
• United States Postal Service (USPS) vacancy data for 2006.
• 2005 Home Mortgage Disclosure Act data.
• FBI Uniform Crime Report data for 2000 and 2004.

4
Initially we also retained an eighth CDP—Arlington, VA—which is a CDBG urban county. However,
Arlington dropped out of the analysis at an early stage because of missing information for some variables.
5
The Census Bureau published a report using ACS data to compared conditions in Gulf Coast states prior
to and after Katrina. The report provides data at the state level, distinguishing between the set of counties
designated as disaster areas and the balance of the state. See
http://www.census.gov/acs/www/Products/Profiles/gulf_coast/index.htm.
6
Examples of relevant local records are city records that bear on neighborhood conditions, such as building
code violations or abandoned cars, and county records that bear on real estate conditions, such as property
transactions, property valuations, housing court, and evictions. Unfortunately, the methods for collecting
and storing these sources of information are not standardized.

• Bureau of Labor Statistics Local Area Unemployment Statistics for 2000 and
2005.

Information from other sources—principally, the 2002 Census of Local Governments and
the 2001 Residential Finance Survey—was used to adjust data from the ACS and the
Economic Censuses to create measures of fiscal capacity.

2.1.3. Time Consistence of Needs Indicators

To encompass as wide a range of city problems as feasible, we combined ACS data with
data from other nationally available sources. In almost every instance, we used the
version of those data collected in 2005 or as close to 2005 as possible. The most recently
released FBI uniform crime data were compiled in 2004. The most recent Economic
Census covered calendar year 2002. HUD used USPS data (2006) to construct an
estimate of abandoned structures. Because of the extensive work involved in calculating
this estimate, HUD used the most recent data (2006) and decided not to construct a
separate 2005 estimate for this study.

In some cases, the Census Bureau has not yet produced tables for the ACS that it
published for the 2000 decennial census; in other cases, comparable ACS tables are
published, but the tables were empty for a number of our 472 cities because of small
sample sizes. Tables reporting tenure, age of structure, and poverty status jointly are
examples of the first situation, and the table on overcrowded housing is an example of the
second situation. In these situations, we used data from the 2000 census. We also used
2000 census data for those indicators of need that require information at the census-tract
level.

The ACS will be adding tables in upcoming years, and census-tract data will be available
in 2010.

2.2. Selection of Needs Indicators

2.2.1. How the Needs Indicators Were Selected

Econometrica team members met with HUD to discuss the research at an Orientation
Meeting on October 12, 2006. One of the issues on the agenda was the range of city
problems to be considered in gathering data for the needs index. As noted in Chapter 1,
the previous HUD studies had focused on needs that are eligible for assistance under the
Community Development Block Grant program. The question posed to HUD was
whether to focus strictly on problems that can be treated using CDBG funds or to take a
wider perspective on community needs. The goals of the CDBG program are broad, and
very few things are excluded de facto as eligible activities. So, using CDBG eligibility as
the criterion would not significantly limit the types of needs included in the analysis.
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Page 12
Nevertheless, the participants in the Orientation Meeting agreed that the study should
adopt a broad definition of community needs—that is, a definition that included needs
beyond those typically eligible for unrestricted funding under the CDBG program. The
participants reasoned that HUD’s mission extends to most problem areas that affect cities
and other communities.

Given this direction, we investigated a wide range of data sources and developed a list of
potential variables for discussion at an expert panel meeting on November 21, 2006. The
list drew upon the variables used in the previous HUD studies and ideas developed by
GAO for its ongoing study.

The participants in the November expert panel meeting stressed certain principles in
selecting variables, including:

• Variables should clearly relate to city-level needs.

• Proxies should be avoided in deference to direct measures of need.

• Failure of a city to respond to a problem should not be considered a need.

• Variables should be defined to avoid spurious needs, such as the low income of
college students who receive support from their families.

Using these principles, the panel rejected a number of variables on the list and suggested
some additions to the list. In some cases, the panel suggested we investigate alternative
measures of particular needs and make the final selection after reviewing the data. A
revised list of variables was submitted on December 4, 2006, and work was begun on
collecting data to implement the variables. Discussion continued via e-mail on how to
construct useful measures from the data collected under the Home Mortgage Disclosure
Act (HMDA).

2.2.2. Needs Indicators

Table 1 identifies the 27 needs indicators that HUD and Econometrica jointly selected.
The list includes variables related to population subgroups with special needs, housing
needs, social needs, neighborhood needs, economic needs, conditions that make it more
difficult for cities to respond to various needs, and indicators of unfavorable long-run
trends. These needs indicators deal with the broad range of problems covered by the five
objectives relating to supporting community development in HUD’s strategic plan. Table
1 classifies each indicator with respect to the category of problem that it measures.
However, many of the indicators relate to more than one type of problem.

Table 1. Needs Indicators for Developing an Index of Community Needs

Page 13

Variable (Short-name Used
in Tables)
Comments Definition
Populations with Needs: The first eight indicators identify subgroups in the populations that may have specialized needs that require the attention and
resources of city governments.
POOR PERSONS
(POORPERS)
Poverty in cities has always been a central concern to HUD. The
CDBG program requires that cities use 70 percent of program
funds to benefit low and moderate income persons. In line with
previous research, we eliminate poor college students on the
grounds that most receive support from their parents that is not
included in income.
Ratio of persons age 3 and over not enrolled in college
who live in households with below poverty incomes to all
persons age 3 and over who live in households.
POOR CHILDREN
(POORCHILD)
Children living in poor households require different and perhaps
more city-supplied services than poor working age adults.
Percent of persons under 18 (children) in the household
population living in households with below poverty
incomes
POOR ELDERLY
(POOROVER74)
The elderly require different services and perhaps more city-
supplied services than poor working age adults. We chose “over
74” rather than “over 64” for two reasons: (1) with long life spans,
“over 74” seems to be a better identifier of “the elderly” who are
likely to have special needs, and (2) it is less correlated with overall
poor population (POORPERS) and therefore more likely to identify
different types of needs.
Percent of persons over 74 living in households with
below poverty incomes
LOW INCOME HOUSEHOLDS
(LWINCHHDS)
This variable was added to pick up low-income households whose
incomes exceed the poverty level. The CDBG emphasis on low
and moderate income persons argues for including more than just
the poverty level population.
Percent of persons living in households with incomes
greater than the poverty level and less than 50 percent of
area median income. Note the decennial census does not
contain the table needed to calculate this variable, thus
the 2005 ACS data are use for both 2000 and 2005.
SINGLE-PARENT FAMILIES
WITH CHILDREN
(SGLPRNTFAM)
Single-parent households frequently require city-supplied services
and unsupervised children in some of these households may
create neighborhood problems. We chose “single-parent” over
“female-headed” because the needs associated with these families
are not limited to “female-headed” families. Previous studies had
used female-headed families.
Percent of families that are single parent-headed with own
children under 18.
UNEDUCATED POPULATION
(UNEDUCADULTS)
Adults without a high school diploma generally have lower skills
than other workers and may require some support and training
during periods of unemployment and may not have adequate
preparation for post-employment living support.
Percent of household population over 18 without a high
school diploma.
UNDEREDUCATED
WORKING AGE
POPULATION
(UNDEREDWORKAGE)
These workers are more vulnerable to being unemployed and have
greater difficulty finding new jobs.
Percent of household population over 24 and less than 65
without a college degree.
RECENT IMMIGRANT
POPULATION
(RCNTIMMIG)
Language problems and cultural differences create adjustment
problems for many members of this group.
Percent of household population that is foreign born and
entered the United States within the last 15 years.

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Table 1. Needs Indicators for Developing an Index of Community Needs (continued)
Variable (Short-name Used in
Tables)
Comments Definition
Housing Needs: The next five indicators identify problems with housing, housing markets, or housing finance that require city attention or resources or
reduce the attractiveness of a city.
LACK OF AFFORDABLE
RENTAL HOUSING
(LACKAFFDRENTALS)
There is no good measure of affordability problems in rental housing.
HUD has successfully used a close variant of this measure to identify
cities where housing costs relative to income justify additional
assistance under the Low Income Housing Tax Credit.
Ratio of median gross rent (city) to median family income
(city).
OVERCROWDED HOUSING
(OVERCROWD2000)
A comprehensive study by the British Government has found
potential links between overcrowded housing and health and
development problems.
Percent of households living in units where the number of
person per room is 1.01 or greater. Note this variable is
available only for 2000 and the 2000 values are used for
both 2000 and 2005.
POOR QUALITY HOUSING
(PR70RENTPOV)
Previous studies have used the percent of housing built prior to 1940
as both an indicator of deteriorated housing and older infrastructure.
This variable has been criticized for being an inaccurate indicator of
either housing or infrastructure deterioration. Richardson (2005)
found that the percent of the housing stock that (1) was built prior to
1970, (2) was rental, and (3) was occupied by a household with
below poverty income was a better indicator of poor quality housing.
We use the Richardson (2005) indicator. We have a separate
indicator of infrastructure problems.
Percent of occupied housing units built prior to 1970 and
occupied by a poor renter household. Note this variable is
available only for 2000 and the 2000 values are used for
both 2000 and 2005.
DENIAL RATE FOR
MORTGAGE APPLICATIONS
(DENIAL)
This variable identifies cities where lenders are restricting credit
because of poor appreciation prospects or some combination of
inadequate income or credit problems on the part of potential buyers.
Percent of loan applications denied.
POOR HOUSING
APPRECIATION IN HIGH
POVERTY NEIGHBORHOODS
(POORAPPRECHIGH-
POVNGHS)
HUD and panel members wanted a variable that could discriminate
between poor neighborhoods and poor neighborhoods with poor
appreciation potential.
Percent change between 2000 and 2005 in average
mortgage amount on loans in high poverty neighborhoods.
(As explained in the text, this variable was not calculated.)
Neighborhood Needs: The next three indicators identify the extent to which cities may have seriously troubled neighborhoods.
HIGH POVERTY
NEIGHBORHOODS
(PCTPOPHIGH-
POVNGHS
Previous research has found that social problems are markedly
greater in neighborhoods with a high percentage of poor persons.
The research typically uses 40 percent as the crucial percentage.
Percent of city population living in census tracts with poverty
rates of 40 percent or higher. Note this variable is available
only for 2000 and the 2000 values are used for both 2000
and 2005.
MODERATE POVERTY
NEIGHBORHOODS
(PCTPOPMOD-
POVNGHS)
Since there is a relationship between concentrated poverty and
neighborhood problems, we included this variable to identify
neighborhoods – other than the highest poverty neighborhoods –
where the poverty concentration may be a problem.
Percent of city population living in census tracts with poverty
rates greater than or equal to 20 percent but less than 40
percent. Note this variable is available only for 2000 and
the 2000 values are used for both 2000 and 2005.

Table 1. Needs Indicators for Developing an Index of Community Needs (continued)

Page 15

Variable (Short-name Used in
Tables)
Comments Definition
ABANDONMENT
(PCTVACMOD-
POVCITY)
Abandoned buildings are a blighting influence and could affect
community health. HUD has always wanted a reliable measure of
abandoned building but data on abandonment are neither universally
nor consistently collected at the city level. To solve this problem,
HUD analysts have collected data on vacant housing units from the
USPS and have counted the number of such structures in moderate
to high poverty neighborhood under the presumption that vacant units
in such neighborhood have a high probability of being or becoming
abandoned.
Ratio of vacant housing units (from 2006 USPS surveys) in
tracts with 20 percent or more poor (identified from 2000
census) to total housing units in city (from 2005 ACS). Note
only one version of this variable is available and is used for
both 2000 and 2005.
City-Wide Social or Economic Problems: The next four indicators identify social or economic problems at the city level.
PART 1 CRIME
(PT1CRIME)
Part 1 crimes include violent crimes and serious nonviolent crimes.
Crimes of this nature are a social problem in themselves and have a
blighting influence on neighborhoods.
Number of part 1 crimes per 100,000 population. Based on
2004 FBI data on arrests for murder, rape, burglary, motor
vehicle theft, arson, and other part 1 crimes - see
http://www.fbi.gov/ucr/cius_04/appendices/appendix_02.html
for definitions of Part 1 and Part 2 crimes.
PART 2 CRIME
(PT2CRIME)
Part 2 crimes include offenses that are less serious but that
nevertheless reduce the quality of life of city residents.
Number of part 2 crimes per 100,000 population. Based on
2004 FBI data on arrests for forgery, fraud, simple assault,
prostitution, drug offenses, drunkenness, disorderly conduct,
and other part 2 crimes - see
http://www.fbi.gov/ucr/cius_04/appendices/appendix_02.html
for definitions of Part 1 and Part 2 crimes.
POOR SCHOOL AGED
POPULATION
(SCHPOPPOOR)
This variable was included as a measure of the problems faced by a
city in carrying out its responsibility to provide quality education to its
youth.
Percent of the school aged population (between 5 and 17)
living in households with below poverty income.
UNEMPLOYMENT RATE
(UNEMPCEN)
We chose the unemployment rate measured by the ACS and the
decennial census over a Bureau of Labor Statistics variable for two
reasons: (1) its is calculated from sample data not estimated by a
model and (2) its less precise definition of labor force may be
successful at capturing disguised unemployment, that is, the
unemployment of persons who have left the labor force because of
discouragement.
Percent of household population over 16 that is unemployed
and looking for work (in labor force). This variable is
calculated from the 2000 decennial census or the ACS.
Conditions that Complicate Dealing with Other Problems: The next four variables were added to identify conditions that might make it more difficult for
cities to deal with the needs of people, housing, neighborhoods, or general social or economic problems.
LINGUISTICALLY ISOLATION
(LINGISOL)
This variable identifies language difficulties that may complicate a
city’s efforts to provide services and may generate the need for
additional services.
Percent of households in which all adults (high school age
and older) have some limitation in communicating in English.
(A household is classified as “linguistically isolated” if no
household members age 14 years and over spoke only
English, and no household members age 14 years and over
who spoke a language other than English spoke English
“Very well.”)

Page 16
Table 1. Needs Indicators for Developing an Index of Community Needs (continued)
Variable (Short-name Used in
Tables)
Comments Definition
MEASURE OF RACIAL
DISSIMILARITY
(MINCON)
Cities in highly segregated metropolitan areas may experience
additional difficulties in providing ordinary services and will have to
deal with segregation and its consequences. The CDBG program
has as an objective promoting an increase in the diversity of
neighborhoods.
Ratio of minority population rate in city to minority population
rate in metropolitan area
INDEX OF ECONOMIC
DISSIMILARITY
(POVCON)
Cities in areas where the poverty population is concentrated may
experience additional difficulties in providing ordinary services and
will have to deal with poverty concentration and its consequences.
The CDBG program has as an objective reducing the isolation of
income groups.
Poverty rate in city divided by poverty rate in metropolitan
area
LOCAL FISCAL DISPARITY
(MEDINCCBS2CITY)
Disparity in incomes between central cities and suburbs make it
difficult for central cities to meet their needs and can create disparity
problems that affect the entire local economy.
Median family income of metro area relative to median
family income of jurisdiction
Long-run Decline: The final three indicators identify cities that are suffering from long-run decline.
EXCESS INFRASTRUCTURE
(EXCSINFRA)
The panel initially focused on this variable as a good indicator of the
extent to which a city may be faced with maintaining more
infrastructure than it needs. The indicator also identifies declining
cities.
Ratio of maximum population measured in households
(without reference to boundary changes) at 1970, 1980,
1990, and 2000 to current population
CHANGE IN EMPLOYMENT
BASE
(CHNGEMPLOYBASE)
This variable focuses on the recent performance of the city economy.
It compares growth in the labor force to growth in actual jobs within
the city.
The ratio of two ratios: the first ratio is labor force in 2005 to
labor force in 2000 from BLS; the second ratio is jobs in the
city from the 2002 economic census to jobs in the city from
the 1997 economic census.
CHANGE IN THE
CONCENTRATION OF LOW
INCOME FAMILIES
(CHGLOWINCCON)
This variable measures how well incomes in the city are keeping
pace with incomes throughout the country with special attention to a
city’s relative share of low income families.
Calculate the proportion of families in a city that have
incomes in the bottom quintile for all families in the country
and then take the ratio of this proportion in 2005 (or 2000)
and divides by the proportion in 1970 (based on 1969
income).

Page 17
We defined each indicator in percentage or per capita terms or as a ratio, so that the value
of the indicator would depend only on conditions in a city and not on city size. We also
defined each indicator in such a way that an increase in the value of the indicator means
that conditions measured by that indicator have worsened. Consistent definition of the
indicators will make it easier to interpret the factor analysis in Chapter 3.

The poverty variable (POORPERS) is based on an estimate from the ACS, using a
national poverty-level income, of the number of poor persons.
7
It is reasonable to expect
that the consequences of poverty are greater in high-cost areas than in low-cost areas.
Because the count is based on data available only to the Census Bureau, there is no easy
way to adjust these data for cost-of-living differences.

UNEDUCADULTS and UNDEREDWORKAGE were developed based on similar but
slightly different concerns, and therefore are defined using different age qualifications.
UNEDUCATDULTS refers to all persons over age 18, whereas UNDEREDWORKAGE
refers to the 18-65 years-old population. In both cases, lack of education was considered
to place persons at greater risk of unemployment, and therefore the focus on working age
is appropriate for both variables. In addition, persons without a high school education
may not have had the earning capacity during their working years to adequately prepare
themselves for retirement. Therefore, UNEDUCADULTS also focuses on persons over
age 65. This different focus creates some problems in Appendix B, but the rationale
seems reasonable.

The recent immigrant population variable (RCNTIMMIG) counts all persons who were
foreign born and immigrated to the United States during the previous 15 years, including
both citizens and non-citizens. According to the Census Bureau:
8

The American Community Survey questionnaires do not ask about
immigration status. The population surveyed includes all people who
indicated that the United States was their usual place of residence on the
survey date. The foreign-born population includes naturalized U.S.
citizens, Lawful Permanent Residents (immigrants), temporary migrants
(e.g., foreign students), humanitarian migrants (e.g., refugees), and
unauthorized migrants (people illegally present in the United States).

Legal and illegal immigrant households can present similar problems for local
governments, most notably, language difficulties in the workplace, language difficulties
in schools, and the need for medical services. To the extent that the Census Bureau is
successful in including illegal immigrants in the ACS, they should be counted in this
indicator.

HMDA data were used to construct the DENIAL RATE FOR MORTGAGE
APPLICATIONS indicator.

7
There are separate poverty levels for Alaska and Hawaii.
8
American Community Survey, Puerto Rico Community Survey, 2005 Subject Definitions, page 31,
http://www.census.gov/acs/www/UseData/Def.htm.

Page 18

We also planned to use HMDA data to identify low-income neighborhoods with stagnant
or declining housing markets. This is the POOR HOUSING APPRECIATION IN HIGH
POVERTY NEIGHBORHOODS indicator listed in Table 1. The construction of this
indicator proved to be too complicated for the limited scope of this project, and therefore
this indicator is not used in the subsequent analysis.
9

2.2.3. Other Indicators Considered But Not Used

We considered a large number of potential indicators and excluded many for various
reasons. The following two exclusions deserve additional discussion:

• Persons with a Disability Limiting Employment: We had originally planned to
use this variable. We dropped it because changes in the skip pattern used to ask
this question appear to have produced a substantial downward shift in the
percentage between 2000 and 2005.

• Decline of the Middle Class: We constructed an indicator that focused on the
proportion of families in a city that are middle-income families. We defined
middle income as having an income higher than the incomes of the poorest 20
percent of American families but lower than the richest 20 percent of American
families. We took the ratio of this proportion in 2005 (or 2000) to the ratio in
1970 to determine whether the city was gaining or losing middle-class families.
We decided not to use this variable because a city can have a lower proportion of
middle-class families as a result of growing poorer or growing richer. Among the
100 cities that had the largest decline in middle-income families between 1970
and 2005, 34 also had a decline in the proportion of poor families. In these 34
cities, only the proportion of rich families was growing. We used CHANGE IN
THE CONCENTRATION OF LOW-INCOME FAMILIES as an indicator of
long-term trends instead of the decline in the middle-class indicator.

2.3. Review of Indicators Prior to Index Building

2.3.1. Correlations Among the Needs Indicators

After gathering data on the needs indicators for both 2000 and 2005, we examined the
distribution of each variable and its correlation with the other variables to determine

9
Construction of the variable would require HMDA data from two different years. The main problem is
that the 2000 HMDA data do not contain a variable to identify mobile homes. In constructing the DENIAL
variable, we eliminated investor loans and mobile-home loans because of concern that their inclusion
would affect the results. We had the same concern about the PRICE APPRECIATION indicator. To
eliminate mobile homes would require matching the HMDA data to a list prepared by HUD analysts of
lenders who specialize in mobile-home lending.

whether the indicator is performing as anticipated and to uncover any problems with the
indicator. It was this analysis that led to the elimination of the employment disability and
decline of the middle-class indicators discussed above.

The first test in the correlation analysis was to determine if any of the needs indicators is
highly correlated with population. As noted, we defined the indicators so that their
values should be independent of city size and, therefore, we expected to find low
correlations between population and the various needs indicators. None of the 26
variables had a correlation with population greater in absolute value than 0.20.

Next, we examined the correlations among the needs indicators. This analysis provides
some prior indication of how the factor analysis will sort the indicators and can identify
problem with the indicators as implemented. Table A.1 in Appendix A reports these
correlations. The most interesting findings from the correlation analysis were:

• POORPERS has correlations of 0.60 or higher with 15 other indicators. These
include all but two of the variables that use income or poverty in their definitions,
but also include SGLPRNTFAM, DENIAL, UNEMPCEN, MINCON,
UNEDUCADULTS, and EMPLOYDISB.

• UNEMPCEN seems to correlate with the same variables with which POORPERS
correlates, but at lower rates.

• OVERCROWD_2000, the crime variables, and RCNTIMMIG have low
correlations with POORPERS.

• RCNTIMMIG has correlations of 0.60 or higher only with LINGISOL and
OVERCROWD_2000.

• POOROVER74 is weakly related to all the other variables.

• The crime indicators (PT1CRIME and PT2CRIME) correlate most highly with
each other, but the correlation is only 0.53. These crime indicators correlate
weakly with all the other variables.

The low correlation of the crime variables with each other is puzzling and, as we shall see
in Chapter 3, these variables do not perform well in the factor analysis. We discuss these
indicators more in Section 2.3.3.

MEDINCCBS2CITY, POVCON, and MINCON are indicators that measure conditions
recognized in previous studies as either problems in themselves or as factors that
complicate the solution of other problems. While the concepts behind POVCON and
MINCON are not new, the definitions of these indicators are new. Richardson (2005)
used a dissimilarity index to measure the extent of racial segregation. We considered
using dissimilarity indices to measure both racial and income segregation, but were
persuaded in the November 21 meeting that the definitions in Table 1 were simpler to
Page 19

implement and provided much the same information. The correlation analysis reveals no
problems with these variables as defined.

• MEDINCCBS2CITY correlates highly with the other variables we used to
characterize city/suburb differences: POVCON (0.86) and MINCON (0.73). It
also correlates highly with the poverty variables.

Six of the needs indicators in Table 1 were defined for the first time in this study; these
are: LACKAFFDRENTALS, DENIAL, PCTVACMODPOVCITY, EXCSINFRA,
CHNGEMPLOYBASE, and CHGLOWINCCON.

• LACKAFFDRENTALS correlates most highly with UNEDUCADULTS (0.66), a
somewhat surprising result. It has only modest to low correlations with the other
housing variables: OVERCROWD_2000 (0.58), PR70RENTPOV (0.49),
DENIAL (0.28), and PCTVACMODPOVTOCITY (0.15).
LACKAFFDRENTALS appears to pick up different types of housing problems
than the other housing indicators.

• DENIAL correlates most highly with PCTVACMODPOVTOCITY (0.76) and
has correlations above 0.60 with some of the indicators of population groups with
special needs and with UNEMPCEN, MINCON, and MEDINCCBS2CITY.

• PCTVACMODPOVTOCITY correlates most highly with DENIAL (0.76) and
has correlations of greater that 0.60 with seven other indicators, including poor
persons, poor children, poor school-aged children, excess infrastructure, poor
quality rental housing, and minority concentration.

• EXCSINFRA correlates most highly with PCTVACMODPOVTOCITY (0.64)
and has correlations over 0.50 only with DENIAL and PR70RENTPOV.
DENIAL appears to be picking up some “older city” problems.

• CHNGEMPLOYBASE does not correlate highly with any of the other variables;
its highest correlations are with the education variables, UNDEREDWORKAGE
(0.18) and UNEDUCADULTS (0.13). The correlation results for
CHNGEMPLOYBASE are not disturbing. It is intended to identify a type of
need different from that of the other indicators and some association with
education limitations should be expected.

• CHGLOWINCCON has modest correlations with three variables and low
correlations with the remaining variables; its highest correlations are with
MEDINCCBS2CITY (0.47), DENIAL (0.44), and LWINCHHDS (0.42).
Page 20

2.3.2. Analysis of Means

After the correlation analysis, we compared the means of the indicators in 2000 and 2005
for two reasons: to identify data errors and to obtain a sense of how conditions changed
between 2000 and 2005. Table 2 reports these comparisons.

Table 2. Comparison of Means for the Needs Indicators
Variable
2005
Mean
2000
Mean
Absolute
Difference
Percent
Difference
POORPERS 0.140 0.125 0.0148 11.8%
POORCHILD 0.207 0.181 0.0265 14.7%
POOROVER74 0.109 0.108 0.0002 0.2%
SGLPRNTFAM 0.181 0.160 0.0201 12.5%
UNEDUCADULTS 0.169 0.204 -0.0350 -17.2%
UNDEREDWORKAGE 0.691 0.715 -0.0239 -3.3%
RCNTIMMIG 0.099 0.091 0.0081 8.9%
LACKAFFDRENTALS 0.180 0.159 0.0208 13.1%
SCHPOPPOOR 0.198 0.175 0.0233 13.3%
UNEMPCEN 0.076 0.066 0.0096 14.5%
LINGISOL 0.071 0.060 0.0109 18.0%
MINCON 1.281 1.255 0.0267 2.1%
POVCON 1.196 1.139 0.0573 5.0%
MEDINCCBS2CITY 1.125 1.081 0.0436 4.0%
EXCSINFRA 1.025 1.015 0.0105 1.0%
CHGLOWINCCON 1.245 1.310 0.0650 5.2%
PT1CRIME 957.036 1004.970 -47.9337 -4.8%
PT2CRIME 4133.420 4311.42 -178.0000 -4.1%
LWINCHHDS 0.299 0.299 Same data Same data
OVERCROWD_2000 0.083 0.083 Same data Same data
PR70RENTPOV 0.052 0.052 Same data Same data
PCTVACMODPOVCITY 0.010 0.010 Same data Same data
DENIAL 0.220 0.220 Same data Same data
PCTPOPHIGHPOVNGHS 0.026 0.026 Same data Same data
PCTPOPMODPOVNGHS 0.175 0.175 Same data Same data
CHNGEMPLBASE 0.836 0.836 Same data Same data

For the last eight indicators in Table 2, we were unable to calculate values for both 2000
and 2005, and therefore used the same values of these variables for each city in both
years. We had data only for 2000 for OVERCROWD_2000, PR70RENTPOV,
PCTPOPHIGHPOVNGHS, and PCTPOPMODPOVNGHS. We had data only for 2005
for LWINCHHDS, PCTVACMODPOVCITY, DENIAL, and CHNGEMPLBASE.
Therefore, Table 2 does not calculate the absolute or percent differences for the means of
these eight indicators.

Only two of the changes in means are surprising. The 17-percent decline in uneducated
adults seems remarkably large for social statistics that frequently change at glacial rates.
However, the national data show a 25-percent decline in this ratio. The Census Bureau
Page 21

Page 22
report comparing the 2000 decennial census with the ACS-type C2SS collected in 2000
reveals no substantial differences between the two surveys on educational attainment.
The ACS Subject Definition document mentions changes in the questions prior to 1999,
but no changes that would have affected the 2000 comparisons. The 18-percent increase
in linguistic isolation also seems large. The base is small, and the national change was a
16-percent increase.

Except for the crime and education indicators, all the needs indicators have higher mean
values in 2005. This suggests that conditions in cities, on average, worsened over the
period from 2000 to 2005.

2.3.3. Analysis of the Crime Variables

The results involving the crime variables were puzzling. The 2004 versions of
PT1CRIME and PT2CRIME had only a 0.53 correlation with each other and had low
correlations with all the other variables. Both indicators had low loading on all the
factors and, as noted in Chapter 3, the test of sampling adequacy indicated that
PT1CRIME was not a good candidate for factor analysis. All the previous studies used
some indicator of crimes, and our panel of experts concurred in the inclusion of the two
crime measures among the needs indicators. So, at HUD’s request, we investigated why
the crime indicators did not perform better.

First, we examined the correlations between our crime variables in 2000 and 2004. It
turns out that PT1CRIME as measured in 2000 had a correlation of only 0.20 with
PT1CRIME measured in 2004, and PT2CRIME as measured in 2000 had a correlation of
only 0.27 with PT2CRIME measured in 2004. These low correlations heighten our
concern about the crime data.

The FBI distinguishes between Part 1 crimes and Part 2 crimes in its Uniform Crime
Report data. In the Part 1 crime data, the FBI reports separately on: criminal homicide,
forcible rape, robbery, aggravated assault, burglary, larceny-theft, motor vehicle theft,
and arson. Normally, the FBI data on Part 1 crimes include both known offenses and
those cleared by arrests, but the data we received contained only arrest records. In
retrospect, we are not sure why the Department of Justice gave us only arrest data for Part
1 crimes. The Uniform Crime Report data on Part 2 crimes are based solely on arrest
data; perhaps the DOJ data staff provided the Part 1 and Part 2 data on a matching
basis—that is, using information from arrests only.

From the FBI Web site, we downloaded data on Part 1 crimes for 2005; these data
include both arrests and known occurrences.
10
The correlation between these data, which
the FBI Web site label “violent crime,” and our PTICRIME variable (based on 2004 data)
was 0.20. It appears that either there are substantial differences between the arrest data

10
http://www.fbi.gov/ucr/05cius/data/table_08.html.

and the combination of arrests and known offenses, or there are problems with our arrest
data. (We checked the programs used to read the data carefully.)

The correlations between VIOLCRIME and the other needs indicators are also reported
in Table A.1 in Appendix A. VIOLCRIME has correlations between 0.5 and 0.6 with
DENIAL, PCTVACMODPOVCITY, POORCHILD, POORPERS, SCHPOPPOOR, and
SGLPRNTFAM; all the other correlations are less than 0.5. While VIOLENT crime has
stronger associations with other needs indicators than PT1CRIME, its correlations with
the other indicators are modest to low.

2.4. Assessment of Needs Indicators

Table 1 includes eight indicators that are designed to identify population groups that may
have needs for city services beyond those of the typical citizen. The variables measure
separately the poor and low-income persons, poor children, the elderly poor, single-
parent households, persons with limited education, and immigrants. The ACS provides
detailed information on important subgroups, and it should be possible to implement all
eight indicators in future years for cities of all sizes and counties, including HUD-defined
urban counties.

Table 1 lists five indicators of problems with housing, but we were only able to
implement four of the indicators. Of these four, two used 2000 census data. ACS data
were used only for LACKAFFDRENTALS. In the future, however, HUD should be able
to use ACS data to estimate the overcrowded housing variable and the poor quality rental
housing variable as well as this variable for cities of all sizes and for counties. Also in
the future, HUD will be able to estimate the DENIAL indicator using HMDA data and
perhaps will be able to use these data to create a useful indicator that identifies high-
poverty neighborhoods with poor appreciation of owner-occupied housing. At this time,
the housing indicators are limited in scope, but some of these limitations appear to be
temporary.

Table 1 contains three indicators of the extent of neighborhood problems. All three
variables are based on tract-level data from the 2000 decennial census. But beginning in
2010, HUD should be able to employ ACS tract-level data in these indicators. The
primary indicator is proportion of the population living in high-poverty tracts, defined as
tracts with a poverty rate of 40 percent or more. The urban literature has highlighted
these neighborhoods as having problems related to the concentration of poverty. The
abandonment variable was developed by HUD in an attempt to identify problems
associated with abandoned buildings. The distribution and correlation analyses suggest
that the abandoned building indicator is working reasonably well.

Table 1 contains only four indicators of city-wide social or economic problems. As noted
above and discussed further in Chapter 3, the crime variables seem to be surprisingly
unrelated to other indicators of need. We were unable to find any city-level data on
health needs, and we were able to find only one measure to associate with problems in
Page 23

education. For our education indicator, we chose percentage of the school-age population
that is poor, because it appears to represent the problem facing a city in providing quality
education. There were three reasons for the lack of more measures related to education:
inconsistent definition of measures across schools and school districts, difficulty
converting the education data provided by the federal government for schools and school
districts into city-level measures, and concern that some of the measures represented
failures on the part of cities to meet their education responsibilities rather than difficulties
of the education challenges presented to cities. With respect to the third reason, test
scores are an example of a measure that could indicate either poor performance or
difficult-to-educate populations.

Table 1 contains measures of four conditions—LINGISOL, MEDINCCBS2CITY,
POVCON, and MINCON—that we believe complicate the ability of cities to deal with
their problems. All four indicators can be easily calculated using ACS data and should be
available in the future for all cities and for all counties.

Table 1 contains three indicators of unfavorable long-run trends that affect cities.
EXCSINFRA both measures a condition that is a problem in its own right—having to
maintain more infrastructure per household than the typical city—and identifies cities in
long-run decline—that is, cities that are losing households. CHGLOWINCCON
identifies cities that are losing their middle- and upper-income households. Both
indicators represent important dimensions of long-run change at the city level. A third
dimension is economic vitality. The indicator chosen to represent this dimension is
limited in three ways: the indicator changes only every 5 years; its calculation requires
the integration of data from two different sources; and the confidentiality rules applied to
the Economic Census data introduce noise into the calculation.

At this stage, we believe that we have very good indicators of the needs associated with
certain demographic groups, fairly good indicators of housing problems, good indicators
of neighborhood-level problems, only marginally adequate measures of city-wide social
and economic problems, good measures of complicating conditions, and adequate
measures of two out of three of the key long-term trends. As more ACS data become
available, the problems with the housing indicators should be eliminated, and all of the
indicators should be available for all cities, for counties, and for HUD-defined urban
counties.

Future work to improve indicators of community needs should focus on:

• Getting good measures of education and health needs.
• Testing other formulations of the national crime data to find a more robust crime
indicator.
• Getting a better measure of the impact of long-term economic forces on cities.

Page 24

Page 25
3. Factor Analysis, Dimensions of Need, and a
Community Needs Index

In developing an index of community needs, analysts face two problems. These can be
succinctly, and fairly accurately, described as “too much data” and “not enough data.”
11

The ACS and other national data sets provide a wealth of information on persons and
housing, the local economy, and some social problems such as crime. Using these
sources, we constructed 26 indicators of need. We could have included many more, such
as per capita income, persons with disabilities, and retail and wholesale jobs.
12
The
previous chapter described how we worked with HUD and other experts to narrow down
the list of potential indicators. Now having selected indicators, we need to figure out
how to make sense of so much information. How does a one-percentage-point higher
unemployment rate compare in neediness to a three-percentage-point higher poverty rate
or 15 more serious crimes per 100,000 population? Does the overcrowded housing
indicator simply indicate low income or high housing prices and therefore duplicate the
poverty or lack of affordable housing indicators, or does it represent a different type of
problem? Questions like these need to be answered in order to create a mathematical
formula that translates the needs indicators into a single number.

On the other hand, despite all the data available from the Census Bureau and other
sources, our information on community needs is incomplete. While we have multiple
indicators of needs related to specific demographic groups and to housing, we have
limited information in other areas. As Section 2.4 points out, we have only one indicator
of education problems and no indicators of health problems. We also have only one
indicator of long-term economic trends, and it has limitations. The available economic
data are limited by the timeliness of the every 5-year economic census and, even more so,
by the confidentiality requirements that result in suppressed data for many useful
variables—for example, the total number of jobs in a city. It would be comforting to
know that the some of other 24 indictors were providing reasonable proxies for the type
of problems measured by our lone education and economic indicators.

Previous research has turned to factor analysis to solve the problems discussed in the two
preceding paragraphs—that is, to aggregate multiple measures into simpler indicators of
need and to identify distinct dimensions of need that hopefully span the full range of
problems that affect cities. This chapter carries out factor analysis using the indicators

11
The advent of the ACS eliminated a third problem that seriously limited previous efforts to measure
community needs: the absence of timely data.
12
We did not use per capita income because we consider it a measure of capacity, and this analysis
considers need and capacity separately. We did not use persons with disabilities because the consensus at
the November 21 meeting was that employment disabilities, and not disabilities in general, is the more
relevant needs indicator. As mentioned in Chapter 2, the persons with employment disabilities indicator
was dropped because of suspected data problems. We did not use retail and wholesale jobs because that
data series provides an incomplete picture of economic activity, and the data needed to properly balance the
picture do not exist.

developed in Chapter 2; it explains factor analysis and explains how we use the results of
factor analysis to create a single-valued community needs index and to track conditions
in cities over time. Chapter 4 uses the results of our factor analysis to compare the needs
of different cities and to see how the needs of individual cities changed from 2000 to
2005.

The discussion in this chapter is organized as follows:

• Section 3.1 provides a simple explanation of factor analysis.

• Section 3.2 uses factor analysis to reduce the needs indicators developed in
Chapter 2 to three independent dimensions of need—based, for the most part, on
2005 data. The long discussion in Section 3.2 is divided into six subsections:

o Section 3.2.1 lists the expectations we carried into the factor analysis;
these expectations guided the choices we made during factor analysis.

o Section 3.2.2 reports the results using all 26 needs indicators. The
footnotes in this Section give technical details on how we did the factor
analysis.

o Section 3.2.3 discusses tests that we ran to determine whether the factor
analysis would support the uses to which we plan to put it.

o Section 3.2.4 considers the possibility of dropping some needs indicators
to increase the number of cities for which we can report results.

o Section 3.2.5 carries out a second factor analysis using fewer needs
indicators. The factors developed in this section are used in Section 3.4 to
construct a single-valued community needs index and in Chapter 4 to
compare conditions in different cities in 2000 and 2005. Table 3 presents
the factor loading for these factors.

o Section 3.2.6 explains how we calculated factor scores for each city.

• Section 3.3 explains the problems involved in using factor analysis to compare
needs across time and describe the methodology we used to make comparisons
between needs in 2000 and 2005.

• Section 3.4 explains the problems involved in combining the separate dimensions
of need that are the product of factor analysis into a single-valued index of
community needs. We consider six alternative ways to combine the dimensions
and compare results from the alternative indices.

• Section 3.5 provides a brief summary of the chapter.

Page 26

Page 27
3.1. A Brief Introduction to Factor Analysis

Factor analysis is a statistical technique that examines multifaceted data to find simpler,
underlying patterns. Researchers apply factor analysis to data sets that have three
characteristics: (1) there are a sizable number of units being observed (472 cities in our
case); (2) there are a variety of pieces of information (variables) on each unit (26 needs
indicators in our case); and (3) there are reasons to believe that there are certain natural
groupings among the variables that reflect common contributory sources.
13
The third
characteristic is the defining characteristic of factor analysis. The first two characteristics
provide the data structure needed for the analysis, while the third characteristic motivates
the analysis. The goal of factor analysis is to uncover patterns in the data that can be
characterized in a useful fashion.

Factor analysis achieves “data reduction”—that is, it replaces a large number of variables
with a smaller number that approximates the range of joint variation found in the data set
with the larger number of variables. There are other statistical techniques that result in
data reduction, most notably, principal components analysis. We could have applied
principal components analysis to our database of 26 indicators for 472 cities. The results
would have looked similar to those from factor analysis. The output from both factor
analysis and principal components analysis would be three or four new variables that
represent the range of variation found in the data set with 26 indicators. But the
techniques used to obtain the smaller set of variables are different; the statistical
properties of the new variables are different; and the interpretation of the new variables is
different.
14
The fundamental difference between the two techniques is the difference in
interpretation. Factor analysis posits the existence of unobservable “causes” that produce
the correlations among the original variables; principal components analysis does not
look for underlying causes. Because of this different orientation, the two techniques use
different statistical algorithms. The principal components algorithm attempts to explain
as much of the variance in the original data as possible, while the factor analysis
algorithm attempts to explain the correlation among the variables in the original data.

The output of factor analysis is in the format of the table that follows this paragraph. The
factor loadings are numbers between 1 and -1 that relate each unobserved factor to the
observed variables. For reading the rows, the larger the factor loading, the more
important a factor is in determining the value of that variable. For reading the columns,
the larger the factor loading, the stronger the association is between that factor and those
variables. Understanding what variables are associated with a factor helps the analyst

13
In factor analysis, the assumption is that every variable V
i
is a result of factors that act on two or more of
the variables and other unique circumstances that act on that variable only. Statistically, this assumption
translates into the following equation: V
i
= Σα
j
F
j
+ U
i
, where U
i
are influences unique to that variable and F
j

are the common factors. Factor analysis assumes that there are common underlying forces that are
providing joint causation, but it cannot prove their existence or identify what they are.
14
See Jae-On Kim and Charles W. Mueller, FACTOR ANALYSIS Statistical Methods and Practical Issue,
Series: Quantitative Applications in the Social Sciences, Number 14, Sage Publications, 1978, pp. 14-23,
for a discussion of the similarities and differences between factor analysis and principal components
analysis.

understand the nature of the unobserved factor. In the work described in this chapter, we
will pay particular attention to the factor loadings.

Factor 1 Factor 2 Factor 3 Factor 4
Variable 1 Factor loading Factor loading Factor loading Factor loading
Variable 2 Factor loading Factor loading Factor loading Factor loading
Variable 3 Factor loading Factor loading Factor loading Factor loading
Variable 4 Factor loading Factor loading Factor loading Factor loading
Variable 5 Factor loading Factor loading Factor loading Factor loading
Variable 6 Factor loading Factor loading Factor loading Factor loading
Variable 7 Factor loading Factor loading Factor loading Factor loading
Variable 8 Factor loading Factor loading Factor loading Factor loading

Unlike most statistical techniques, factor analysis does not produce definitive results in
the following three senses:

• The mathematical formulas involved in factor analysis will always produce a
table like the one above, whether or not there are unobserved causes at work in
determining the value of the variables. Although there is a test to determine
whether data are suitable for factor analysis, there is no test to prove the existence
of a factor.

• The mathematic formulas involved in factor analysis will identify many possible
“factors” that may or may not be real. Although there are techniques for deciding
on how many of the possible factors to use, the choice always involves some
judgment.

• The factor loadings are not unique. After determining how many factors to select,
one can apply certain statistical techniques and produce different tables of the
type above from the same data. Each of the tables identifies the same number of
factors, and those factors explain the same amount of variation in the data. The
tables differ in factor loading, and therefore offer different perspectives on the
possible underlying factors. Choosing which of the possible tables to use also
requires some judgment.

Using factor analysis, we will find three factors that appear to underlie the 26 needs
indicators. We interpret these factors as measuring more fundamental “dimensions of
need” than the 26 individual needs indicators—that is, we view the “factors” or
“dimensions” as logical aggregations of needs that have similar origins or that occur
together. Using the needs indicators, factor analysis provides a technique to generate a
“score” for each city on each factor. We will build a needs index around these three
factor scores. This last step also requires judgment since nothing in the factor analysis
process tells us how much importance to give to each factor.

Page 28

3.2. Application of Factor Analysis to Needs Indicators

3.2.1 Anticipated Results from Factor Analysis

The need to exercise judgment at various stages in performing factor analysis makes it
essential to have a clear sense before beginning the analysis of what to expect to find in
the way of factors. For this reason, HUD and Econometrica team members discussed
their expectations regarding the factors that would be revealed. Based on previous
experiences with factor analysis and an understanding of the forces affecting cities, HUD
and Econometrica anticipated finding up to four factors and expected those factors to be
related to poverty, immigration, economic decline, and city/suburb disparities.

Every previous study that has used factor analysis to identify dimensions of community
needs has found a “poverty” factor, and “poverty” has always been the first factor—that
is, the factor that accounts for most of the variation among the chosen needs indicators.
One reason for the primacy of the poverty factor is that the national data sources,
particularly the decennial census (and now the ACS), contain an abundance of data that
relate to various forms or manifestations of poverty. Among our 26 needs indicators, we
have an indicator of overall poverty, poor elderly persons, poor children, and poor
school-age children. Our measures also include single-parent families—a group that
generally has lower income—and households with incomes higher than the poverty level
but lower than 50 percent of metropolitan-area median income. In addition, we measure
the percent of the population living in neighborhoods with poverty levels of 40 percent or
more, and the percent living in neighborhoods with poverty levels between 20 and 40
percent. In choosing these needs indicators, we were careful to avoid needless
duplication. For example, we measured poverty among the elderly, poverty among
children, and poverty among school-age children in addition to overall poverty, because
we thought each of these groups required somewhat different responses from local
government. Also, previous research has found that social problems are greater where
poverty is concentrated, particularly in neighborhoods where the poverty rate is 40
percent or more. We did not include female-headed households along with single-parent
households.

Immigration, economic decline, and city/suburb disparities can be thought of as
conditions that generate problems. “Poverty” can be thought of both as a condition that
generates problems and as a problem in itself that results from other underlying causes.
Poverty could result from a variety of causes: lack of human capital (that is, poor
education); market imperfections, such as discrimination or a spatial mismatch between
jobs and housing; or general economic decline. In evaluating the findings from factor
analysis, we will look at “poverty” in both ways.

Page 29

Page 30
3.2.2. Initial Results from Factor Analysis

Using 2005 data on the 26 needs indicators for the 472 cities, we performed a standard
factor analysis using the factor analysis feature of the statistical program known as
SAS™. We were not able to calculate values for all 26 needs indicators for every city.
Missing crime data was the primary reason for dropping cities. In other cases, the
missing data resulted from suppression of data by the Census Bureau—either to protect
the confidentiality of respondents or because the sample sizes were too small to justify
reporting the results. Because of missing data, only 292 cities were used in the initial
factor analysis.

The procedures in SAS™ first examine the data to determine whether there are sufficient
relationships among the indicators to justify factor analysis. The 2005 data seem to be
well-suited to factor analysis. The SAS™-provided measure of sampling adequacy was
strong for all the variables except CHNGEMPLBASE and PT1CRIME.
15
Only the
PT1CRIME measure was considered unacceptable and then only marginally so; the
implication is that PT1CRIME is not determined by any of the factors that appear to be
related to the other indicators. The overall measure of sample adequacy was considered
strong.

Any factor analysis program, such as SAS™, reports factors in the order in which they
help explain the variation in the data.
16
In deciding how many of the reported factors to
use, we considered two rules. The first rule selects factors until the group selected
account for all of the variation. This approach led to the selection of seven factors.
Examination of the factor loading indicated that after the first three or four factors, the
remaining factors had no useful interpretation. Therefore, we used a second rule that
indicated that only three factors should be considered.
17
The three factors selected
appeared to be readily interpretable. The first factor appeared to be associated with
poverty, central city/suburb disparities, and long-run decline; the second factor appeared
to be associated with immigration and housing affordability; and the third factor appeared
to be associated with more immediate limited economic prospects. The factor loading for
these factors is reported in Table A.2 in Appendix A.
18

Next, we used a feature available in all factor analysis programs and “rotated” the factors.
Rotation is a process that finds alternative factor loadings that are equivalent to the initial

15
This is known as the Kaiser test. The Kaiser measure was strong (0.78 or better) for all the variables
except CHNGEMPLBASE (0.60) and PT1CRIME (0.49). The overall Kaiser measure was 0.91, which is
considered strong.
16
We used the “principal factor” approach to extract factors.
17
The second rule is known as the Eigenvalue rule. An Eigenvalue is computed for each factor, and only
factors with an Eigenvalue greater than one are chosen. In simplest terms, an Eigenvalue greater than 1
means that a factor explains more than the average amount of variation explained by all the factors.
18
These initial loadings are called the “unrotated” loadings because the next step in the factor analysis
process is to “rotate” the factors to produce alternative loadings that may be more easily interpreted.

Page 31
loading but may be more easily interpreted.
19
We chose to obtain “orthogonal” factors,
that is, factors that are uncorrelated with each other.
20
Having factors that are statistically
uncorrelated with each other is useful in developing an index, because it enables us to
consider the components of the index as independent contributors to overall need.
21

Table A.3 in Appendix A reports the factor loading for these orthogonal factors.

Despite the small differences between the rotated and unrotated factors, the rotated
factors provide a somewhat clearer interpretation, such as:

• Factor 1 could be interpreted as a “poverty” factor, but it can also be interpreted
as a “city/suburb disparities combined with long-term decline” factor.

• Factor 2 is a combined “immigration and housing affordability” factor.

• Factor 3 is a “weak economy” factor because of the importance of the two
education indicators, viewed in a human capital context, and the change in
employment base indicator.

Statistically, there is no reason to favor rotated factors over unrotated factors or the
interpretation attributed to one set of factors over another. Both sets of factors explain
the same amount of variation in the data; both satisfy the test for sample adequacy and
the same criterion for selecting factors. Fortunately, the interpretations of the unrotated
and rotated factors are sufficiently close that use of the rotated factors raises no concerns.

3.2.3. Testing the Robustness of the Factor Analysis

Before proceeding further with the analysis, we carried out three important tests. First,
we repeated the factor analysis using 2000 data. A prime objective of the research, as
specified by HUD, is to develop an index that can be used to track conditions in cities
over time. To do this, we need to be confident that the factors that appear to explain the
2005 data are also capable of explaining the 2000 data. To test this, we compared the
factor loadings from factor analysis on the 2000 data with the factor loading from factor
analysis on 2005 data. Table A.4 in Appendix A reports the results of this analysis.
22

19
The factor loading are equivalent in the sense that they explain the same amount of variation in the data
and represent the same hypothetical factors. The mathematical techniques used in factor analysis involve
the solution to a matrix algebra problem that has no unique solution; the unrotated and rotated factor
loading are alternative solutions.
20
We employed the Varimax rotation, which maximizes the variance of the squared factor loading for each
factor.
21
While the orthogonal factors are derived in such a way that the unobserved factors are uncorrelated, the
factor scores will not be uncorrelated.
22
Because of missing information or other difficulties, some of the needs indicators have the same values
in the 2000 data as in the 2005 data. These are: CHNGEMPLBASE, DENIAL, OVERCROWD,
LWINCHHDS, PCTPOPHIGHPOVNGHS, PCTPOPMODPOVNGHS, PCTVACMODPOVCITY, and
PR70RENTPOV.

Page 32
The factor patterns in Table A.4 are close, but not identical, to those in Tables A.2 and
A.3. In 2000 data, PR1CRIME (arrests related to more serious crimes) loads most
heavily on Factor 1, and UNEDUCADULTS (adults without a high school diploma)
loads most heavily on rotated Factor 2 instead of Factor 3. When we look at the entire
pattern of factor loadings, only the PT1CRIME loadings are noticeably different. In our
opinion, the patterns of factor loadings are close enough to justify applying the 2005
factor scoring coefficients to 2000 data. Section 3.3 explains how one should perform a
similar test if, for example, one were to use the 2005 factor analysis to compare
conditions in 2005 and 2010.

The second test examined whether the same factors explain conditions in large cities and
small cities. Splitting the sample of cities roughly in half, we ran the factor analysis
separately for cities with 200,000 or more residents and cities with less than 200,000
residents. Table A.5 in Appendix A reports the results for the rotated factors.

The main difference between factor analysis applied to large and small cities is that the
rule used to select factors for the large cities calls for using four factors. The new fourth
factor is most strongly associated with declining household population as measured by
EXCSINFRA, but EXCSINFRA still loads strongly on Factor 1. There are three other
noteworthy changes from the analysis involving all cities, including:

• The change in employment base indicator (CHNGEMPLBASE) is more strongly
associated with Factor 2 for large cities than Factor 3.

• The lack of affordable rental indicator (LACKAFFDRENTALS) is more strongly
associated with Factor 1 than Factor 2 for the large cities.

• The uneducated adults indicator (UNEDUCADULTS) is more strongly associated
with Factor 2 than Factor 3 for large cities.

Despite these differences, we believe that it is appropriate to apply factor analysis to the
combined database that includes both large and small cities. The fourth factor adds little
to the analysis and the other differences are minor.
23
Therefore, with only minor
reservations, we proceed with the factor analysis.

As noted in Section 2.3.3, we have some concerns about the quality of the crime data.
For this reason, we replaced PT1CRIME with VIOLCRIME and carried out the factor
analysis again. Table A.5 in Appendix A reports the results. Previously, PT1CRIME had
very low loading on all three factors; now VIOLCRIME has a loading of 0.56 on Factor
1. Despite the higher loading, VIOLCRIME is only the 16
th
most important of the 26
needs indicators for this factor.
24
A comparison of Table A.6 with Table A.5 shows that

23
The test of sampling adequacy suggests that CHNGEMPLBASE is not an appropriate variable for
inclusion in either the large city or small city analysis.
24
In Richardson’s 2000 analysis, the comparable crime variable had higher factor loadings but was not
among the most important variables in defining either the unrotated or rotated factors. See Todd
Richardson (2007), “Analyzing a Community Development Needs Index,” in Cityscape.

Page 33
replacing PT1CRIME with VIOLCRIME had virtually no effect on the interpretation of
the three factors.

3.2.4. Culling the Needs Indicators

After the initial results, we explored alternative ways to structure the factor analysis. In
particular, we considered dropping variables that had a large number of missing values in
order to increase the number of cities in the analysis. The crime indicators (PT1CRIME
and PT2CRIME) were missing for 107 cities.
25
The next four indicators in terms of
missing values were: (1) change in the low-income concentration (CHGLOWINCCON),
with 42 missing values; (2) the relative concentration of minorities in the central city
(MINCON), with 36 missing values; (3) the proportion of immigrants who entered the
United States in the last 15 years (RCNTIMMIG), with 25 missing values; and (4)
change in the employment base over a recent 5-year period (CHNGEMPLBASE), with
17 missing values.

We decided to eliminate the crime indicators because they had low loadings on all the
factors; the test of sampling adequacy indicated that, at least, PT1CRIME was not a good
candidate for factor analysis; and the analysis in Section 2.3.3 suggests that there may be
some problems with PT1CRIME. We decided to keep the remaining variables because
all four were important to the interpretation of the factors on which they loaded. In
addition, MINCON and RCNTIMMG had high factor loadings on their respective
factors. Eliminating the crime indicators increased the number of cities in the factor
analysis from 292 to 370, with a total population of 83,246,832 in 2005.

3.2.5. Derivation of the Final Factors Used in this Report

We reran the factor analysis for those 370 cities using 24 needs indicators. Again, the
rule we used in the initial analysis and our judgment led us to select three factors. Table
3 contains the factor loadings for those three factors based on an orthogonal rotation. To
make it easier to interpret each factor, we ranked the indicators by their loadings on each
factor. The final factors (below) are easy to interpret:

• Factor 1: Three of the four indicators that identify various types of poverty loaded
heavily on Factor 1. These are the overall proportion of poor persons
(POORPERS), the proportion of children living in households with poverty
incomes (POORCHILD), and the proportion of school-age children living in
households with poverty incomes (SCHPOPPOOR). The proportion of persons
over 74 living in poverty (POOROVER74) had a modest loading of Factor 1, but
this indicator loaded more heavily on this factor than either of the other factors.
The indicators that related neighborhood poverty also load heavily on Factor 1.

25
Early in the project, HUD had suggested experimenting with excluding some indicators to determine
whether their exclusion made any difference to the analysis.

Page 34
The three measures of city/suburb disparity load heavily on Factor 1; these are
POVCON, MEDINCCBS2CITY, and MINCON. Finally, although the two
indicators of long-term trends, EXCSINFRA and CHGLOWINCCON, have
modest loadings on Factor 1, it is the factor on which they load most heavily.

• Factor 2: The proportion of households that are linguistically isolated
(LINGISOL) and the proportion of the population who are recent immigrants to
the United States (RCNTIMMIG) load heavily on Factor 2. Overcrowded
housing (OVERCROWD2000) and the lack of affordable rental housing also load
heavily on Factor 2. The proportion of adults without a high school diploma
(UNEDUCADULTS) loads heavily on Factor 2.

• Factor 3: Only the proportion of adults between 25 and 65 years of age without a
college degree (UNDEREDWORKAGE) and the proportion of adults without a
high school diploma (UNEDUCADULTS) load heavily on Factor 3, although
UNEDUCADULTS loads slightly more heavily on Factor 2. The change in the
employment base over a recent 5-year period (CHNGEMPLBASE) has its highest
loading on this factor. The proportion of mortgage applications that are denied
(DENIAL) also has a modest loading on this factor.

Table 3. Rotated Factor Loadings for Final Factor Analysis, Each Factor Sorted by Loadings

Factor 1:
Poverty and
Structural
Problems
Factor 2:
Immigration
and
Housing
Affordability
Factor 3:
Limited
Economic
Prospects
POORPERS 0.92728 LINGISOL 0.91800 UNDEREDWORKAGE 0.78216
POORCHILD 0.91442 RCNTIMMIG 0.85752 UNEDUCADULTS 0.54446
POVCON 0.89969 OVERCROWD2000 0.82505 DENIAL 0.43440
SCHPOPPOOR 0.89838 LACKAFFDRENTALS 0.64700 UNEMPCEN 0.28849
PR70RENTPOV 0.87989 UNEDUCADULTS 0.59240 OVERCROWD2000 0.25630
SGLPRNTFAM 0.85591 LWINCHHDS 0.32419 PCTPOPMODPOVNGHS 0.24034
MEDINCCBS2CITY 0.85455 MEDINCCBS2CITY 0.29163 CHNGEMPLBASE 0.23059
LWINCHHDS 0.85063 PCTPOPMODPOVNGHS 0.27201 CHGLOWINCCON 0.20700
MINCON 0.78498 UNDEREDWORKAGE 0.20307 POORPERS 0.18606
PCTVACMODPOVCITY 0.77777 POOROVER74 0.20099 SGLPRNTFAM 0.17529
PCTPOPMODPOVNGHS 0.77370 CHGLOWINCCON 0.18643 LACKAFFDRENTALS 0.15992
DENIAL 0.73175 PR70RENTPOV 0.14105 PCTVACMODPOVCITY 0.15615
PCTPOPHIGHPOVNGHS 0.68063 POORPERS 0.12609 POORCHILD 0.13772
UNEMPCEN 0.63363 POVCON 0.10734 SCHPOPPOOR 0.11026
EXCSINFRA 0.58278 SCHPOPPOOR 0.09173 LINGISOL 0.09723
UNEDUCADULTS 0.47999 POORCHILD 0.06533 MEDINCCBS2CITY 0.09539
POOROVER74 0.45757 UNEMPCEN 0.06183 PCTPOPHIGHPOVNGHS 0.05213
LACKAFFDRENTALS 0.44787 CHNGEMPLBASE 0.02851 POOROVER74 0.03401
CHGLOWINCCON 0.36726 PCTPOPHIGHPOVNGHS 0.02778 EXCSINFRA 0.01141
UNDEREDWORKAGE 0.35296 SGLPRNTFAM 0.01532 MINCON -0.00458
LINGISOL 0.04718 MINCON 0.00792 LWINCHHDS -0.00897
OVERCROWD2000 -0.01856 EXCSINFRA -0.10178 PR70RENTPOV -0.14311
RCNTIMMIG -0.11242 DENIAL -0.15728 RCNTIMMIG -0.15913
CHNGEMPLBASE -0.16812 PCTVACMODPOVCITY -0.28525 POVCON -0.16557
Page 35

Page 36
Based on these loadings, we ascribe these interpretations to the three factors:

• Factor 1 is the poverty-structural problems factor.

• Factor 2 is the immigration-housing factor.

• Factor 3 is the limited economic prospects factor. The low education of the work
force, combined with recent declines in jobs relative to the labor force, result in
this label. We think the modest loading of DENIAL and the unemployment rate
(UNEMPCEN) are consistent with this interpretation.

Factor 3 is the least well-defined factor. Lack of clear definition probably results from
the paucity of good information on economic trends in cities that was discussed in the
conclusion to Chapter 2.

Chapter 4 compares cities based on these three factors and on an index derived from
these factors in Section 3.5.

3.2.6. Calculating Factor Scores

Having identified three common dimensions of need among cities with populations of
65,000 or more, the next step is to calculate a score for each city on each factor so that we
can compare the need level in different cities on each dimension. A factor-loading table,
such as Table 3, provides information on the relationship between the unobserved factors
and the observed needs indicators. This information is useful in characterizing the
unobserved factors, but it cannot be used to estimate the factors. In general, factors are
not linear combinations of the variables used to identify them.
26

Techniques have been developed to use the observed variables to create linear
approximations of the unobserved factors. These techniques first transform the observed
variables into standardized form and then use one of several methods to create a set of
“standardized scoring coefficients.”
27
The standardized variables are multiplied by the
standardized scoring coefficient to provide a linear approximation of each factor and,
using this approximation, to create a score for each city on each factor. To derive the
standardized scoring coefficients, we used a technique that employs a regression method
to minimize the squared deviation between the “estimated” factors and the unobserved
factors. Table A.7 in Appendix A presents the standardized scoring coefficients.

Because the scores are linear combinations of standardized indicators, the expected value
of the score for each scored factor is zero. Because we have defined each indicator such
that higher values indicate worse conditions on that indicator, score values greater than
zero indicate higher-than-average problems. The unobserved factors are uncorrelated

26
This is another example of how factor analysis differs from principal components analysis. By
definition, principal components are linear combinations of the variables that they represent.
27
A standardized value is calculated by the formula: (value – mean value)/standard deviation.

Page 37
with each other, but because the standardized scoring coefficients only create linear
approximations of the unobserved factors, the set of factor scores have non-zero
correlations. Table 4 shows that the three sets of factor scores are almost uncorrelated—
an indication that the scores measure distinctly different conditions.

Table 4. Correlations among Factor Scores
28

Correlations Factor 1 Factor 2 Factor 3
Factor 1 1.000 0.012 0.003
Factor 2
1.000 0.016
Factor 3
1.000

3.3. Comparing Needs at Different Times

Typically analysts use factor analysis to compare conditions in different cities at a given
point in time—for example, to compare the community needs of Denver and Wichita in
2005. But, often analysts want to know whether conditions in a given city have improved
or worsened between two points in time. For example, does Denver have more
community needs in 2005 than it had in 2000? Factor analysis can also be used for this
purpose, but analysts need to take some conceptual issues into account.

A factor score is calculated as the weighted sum of the number of standard deviations
above (+) or below (-) the mean for each need indicator. The weights are the factor
scoring coefficients calculated as part of the factor analysis. With each new wave of
ACS data, there will be new means and standard deviations for the needs indicators and,
if the factor analysis is repeated, new factor scoring coefficients. Potentially a new factor
analysis could even reveal new factors or major changes in factor definitions. With so
many possible changes in the inputs used to compare conditions between the two time
periods, it is important to define a process that yields a result that has a clear
interpretation.

The approach we proposed and used has the following steps:

1. Choose a base year. We used 2005 as the base year because the project focused
on using the 2005 ACS data. The comparison year is 2000.

2. Derive factors in the base year and save the standardized scoring coefficients to
use as weights in both the base year and the comparison year. Table A.7 contains
the standardized scoring coefficients.

3. Do a new factor analysis with each new comparison year. Use this to determine
whether conditions have changed so much as to make the use of the base-year
factor analysis no longer legitimate. Section 3.2.4 reports the comparison we
made between the 2000 and 2005 factor analyses. If the base-year factors appear

28
Because we have adhered strictly to the protocols involved in factor analysis, we treat the factor scores as
cardinal measurements and therefore use Pearson correlation coefficients.

Page 38
to be the same as the factors in the comparison year, proceed to the following
steps.

4. Translate the needs indicators into standardized form in both the base year and the
comparison year using the means and standard deviations calculated in the base
year. This step is crucial because the standardized scoring coefficients derived
from the base year are designed to produce factor scores using standardized needs
indicators, defined by the means and standard deviations of the base year. In this
way, conditions on each need indicator are measured by the distance from the
mean of that indicator in the base year using the base-year standard deviation as
the unit of measure. Therefore, conditions on each need indicator are measured
consistently in both years.

5. Compute the weighted sum of the standardized needs indicators in both the base
year and the comparison year. Subtract the base-year score from the comparison-
year score. A positive difference indicates that community needs on that factor
increased between the base year and the comparison year; a negative difference
indicates that community needs on that factor decreased between the base year
and the comparison year.

6. For each factor, compute the mean factor score for all cities in the base year and
in the comparison year. Subtract the mean factor score in the base year from the
mean factor score in the comparison year. This difference indicates whether
community needs as measured by that factor have improved or worsened on
average. This comparison is not weighted by the size of the cities.

There are two important points that need to be made about Steps 5 and 6:

• While Steps 5 and 6 are described in terms of a single factor, the same procedure
could be applied to a single-value needs index that is calculated as a linear
combination of the factors. Instead of using the standardized scoring coefficients
for a single factor as the weights in Step 5, one would use a linear combination of
the standardized scoring coefficients as weights. The same linear combination
would be applied to the standardized scoring coefficients as the one applied to the
factor scores in computing the single-valued index.

• In both Steps 5 and 6, we use the differences between the scores rather the ratio of
the scores. Because the scores can be both positive and negative, the ratio of the
scores will not produce a consistent ranking.
29

Chapter 4 presents the results of Steps 1 through 6.

Steps 1 through 6 should produce reasonable results for comparisons between points in
time that are close together. Over longer time periods, it is possible that factor analysis

29
Section 5.4.2 discusses this issue in more detail with respect to combining a measure of fiscal capacity
with a single-valued needs index.

Page 39
will not produce a similar set of factors using the base-year and comparison-year data on
the needs indicators. Using the experience with price indices as a guide, we suggest the
following approach to handling this problem:

• If the comparison-year factor structure is no longer consistent with the base-year
structure, then use the comparison-year factor structure as a new baseline. This is
analogous to using a new basket of goods and services for a price index. One
could continue to report the old index along with the new index. This approach
used the method applied to price indices until the introduction of chain-linked
indicators.

We considered whether it would be possible to create the equivalent of a chain-linked
indicator to handle this situation. Chain-linked indices have two key characteristics: they
allow the weights to evolve over time, and applying the technique year-by-year over a
period of years produces the same result as applying it to the beginning and end years of
the period. To achieve these two characteristics, chain-link indicators use geometric
means instead of arithmetic means. Unfortunately, factor scoring is based on arithmetic
averaging instead of geometric averaging. Therefore, we cannot construct a chain-link
index to compare needs over time.

3.4. Creating a Single-Valued Index of Community Needs

3.4.1. Alternative Indices

The explicit goal of this project is to produce an index of community needs—a formula
that will assign one number to each city to indicate its relative need. The needs index
will be a function of the three factor scores; but because the factor scores are linear
combinations of the needs indicators, it will also be a function of the 24 needs indicators.

Section 3.1 noted that factor analysis provides no information that can be used to choose
weights to combine the factors into a single-valued index. In this section, we construct
six alternative indices; some are based on external rationales while others are created to
test how sensitive the index results are to the choice of weights. Table 5 defines the
alternative indices and the reasons we constructed them.

Page 40

Table 5. Alternative Single-Valued Community Needs Indices
Index Index Name
Factor 1
(Poverty and
Structural
Problems)
Weight
Factor 2
(Immigration and
Housing
Affordability)
Weight
Factor 3
(Limited
Economic
Prospects)
Weight
Rationale for Index
1 Equal weight 1/3 1/3 1/3
This index treats all three factors the same. It is the standard to
which we compared the other indices.
2
Triple weight to
poverty and
structural
problems
0.60 0.20
0.20

Legislation provides virtually no guidance in choosing weights.
However, the CDBG statute does give precedence to “the
development of viable urban communities, by providing decent
housing and suitable living environment and expanding economic
opportunities, principally for persons of low and moderate income
[emphasis added].” Therefore, we provide a triple weight to the
factor that relates to poverty. We decided on triple weights so that
Indices 2 and 3 would parallel Index 4.
3
Triple weight to
immigration and
housing
affordability
factor
0.20 0.60 0.20
Indices 2 and 4 provide extra weights to Factors 1 and 3
respectively. We added this index to see what happens when we
add extra weight to Factor 2 alone.
4
Triple weight to
limited
economic
prospects factor
or
hedonic weights
0.20 0.20 0.60
The hedonic analysis in Section B.3 in Appendix B indicates that
Factor 3 should receive three times the weight of Factor 1. It
provides no information on how to weight Factor 2; we gave Factor 2
the same weight as Factor 1.
5
Richardson
weights
0.80 0.15 0.05
Richardson chose these weights for his unrotated factors. Our
rotated factors do not match well with either Richardson’s unrotated
or rotated factors but they are more similar to his unrotated factors.
6
Partial hedonic
weights
0.60 0.28 0.12
Using the hedonic analysis in Appendix B, we chose these weights
to obtain the closest match between the weighted sum of the scoring
coefficients for eight needs indicators to beta coefficients for those
variables in the hedonic-type equation. See Section B.4 for an
explanation of how we derived these weights.

Page 41
The equal weight index (Number 1) is the standard to which we compare the other
indices; it treats all the factors equally. We have some rationale for Indices 2, 4, 5, and 6.
Index 2 puts a triple weight on the poverty and structural problems factor because the
CDBG legislation emphasizes assistance to low- and moderate-income person indices.
Index 5 uses the same weights that Richardson employed when he constructed an index
based on 2000 census data. Richardson used four unrotated factors, which he identified
with poverty, immigration, high poverty concentration, and income growth. He gave
these factors weights of 0.80, 0.15, 0.05, and 0.00 respectively. Our Factors 1 and 2
correspond roughly to Richardson’s first two factors; there appears to be little overlap
between our Factor 3 and Richardson’s third factor. We based Index 5 on the Richardson
weights. The hedonic analysis reported in Appendix B provides some guidance on
weighting the factors. A regression involving the factor scores suggests that Factor 3
should be receive three times the weight of Factor 1 but provides no guidance on what
weight should be given to Factor 2. We incorporated this information into the weights
for Index 4. A separate regression involving the 24 needs indicators provided useful
information on how eight of the indicators affect property values. We incorporated this
information into the weights for Index 6. Index 3 was added to test the sensitivity of the
results to added weight to Factor 2.

3.4.2. Comparisons of Scores on Alternative Indices

Table A.8 in Appendix A contains the scores on all six indices for the 370 cities for
which we computed factor scores. Table 6 presents some key statistics on the indices.
Indices 2, 5, and 6 have larger ranges than the other three indices because these indices
give a heavy weight to the poverty and structural problems factor and because the scores
for that factor have a larger range than the scores for the other two factors.
30
The equal
weight index has the smallest standard deviation while the Richardson index has the
largest.

Table 6. Basic Statistics on the Alternative Indices

Index 1:
Equal
Weight
Index 2:
Triple
Weight to
Poverty
and
Structural
Problems
Index 3:
Triple Weight
to Immigration
and Housing
Affordability
Factor
Index 4:
Hedonic
Weights
Index 5:
Richardson
Weights
Index 6:
Partial
Hedonic
Weights
Mean -0.01 -0.01 -0.01 -0.02 -0.01 -0.01
Variance 0.28 0.38 0.36 0.36 0.57 0.39
Std Dev 0.53 0.61 0.60 0.60 0.76 0.62
Max 2.03 3.02 2.45 1.62 3.82 3.11
Min -1.21 -1.26 -0.96 -1.91 -1.34 -1.21
Range 3.25 4.29 3.41 3.53 5.17 4.32

30
The ranges for the three factor scores are: 6.0, 5.0, and 5.2.

Page 42
Table 7 presents the correlations between population and the six indices and the
correlations among the indices. This table shows that all the indices except the hedonic
index (Index 4) have some correlation with population. Larger cities appear to have more
need; this effect is small in all cases. The needs indicators were defined in per capita or
percentage terms so that they would be independent of city size, and Table A.1 in
Appendix A shows that correlations between population and the needs indicators were
very low, ranging from -0.06 to 0.17. So the small positive correlations between five of
the index scores and population suggest that larger cities have somewhat greater
community needs.
31

Table 7. Correlations Among the Alternative Indices

Population
Index 1:
Equal
Weight
Index 2:
Triple
Weight to
Poverty
and
Structural
Problems
Index 3:
Triple
Weight to
Immigration
and
Housing
Affordability
Factor
Index 4:
Hedonic
Weights
Index 5:
Richardson
Weights
Index 6:
Partial
Hedonic
Weights
Population 1.000 0.122 0.152 0.140 0.026 0.163 0.172
Index 1 0.122 1.000 0.874 0.875 0.872 0.719 0.863
Index 2 0.152 0.874 1.000 0.644 0.641 0.964 0.987
Index 3 0.140 0.875 0.644 1.000 0.649 0.492 0.704
Index 4 0.026 0.872 0.641 0.649 1.000 0.420 0.565
Index 5 0.163 0.719 0.964 0.492 0.420 1.000 0.964
Index 6 0.172 0.863 0.987 0.704 0.565 0.964 1.000

The correlations between the equal weight index (Index 1) and all the other indices are
strong.
32
The Richardson index (Index 5) has the lowest correlation with the equal
weight index (0.719); the other four indices have correlations of approximately 0.87.
This suggests that an equal weight index is a reasonable approximation to a wide range of
weighted indices.

Weighting does affect the scoring. While the equal weight index correlates well with the
other indices, the correlations among the weighted indices vary more. We focus on
Indices 2, 3, and 4 because they represent, respectively, emphasizing Factors 1, 2, or 3
heavily. Correlations among these indices are in the range of 0.60 to 0.65. The
Richardson index weighs Factor 1 very highly and gives small weight to the other two
factors. It correlates highly with Indices 2 and 6, which also weigh Factor 1 highly but
has correlations in the 0.40 to 0.50 range with Indices 3 and 4.

Table 8 compares the scores on the equal weight index (Index 1) to the score from
Indices 2, 3, and 4, which successively give triple weight to Factors 1, 2, and 3. Scores
on Index 1 varied from a high of 2.03 to a low of -1.21, a range of 3.25 points. The

31
The correlations between population and the factor scores were: 0.148 for Factor 1, 0.127 for Factor 2,
and -0.065 for Factor 3.
32
The Spearman rank-order correlations among the six indices are very close to the Pearson correlations
reported in Table 7 and display the same pattern.

scores for all the cities on Indices 2, 3, and 4 are within 1.00 points of their scores on
Index 1; the scores for over 90 percent of the cities are within 0.50 points of their scores
on Index 1; and the scores for over 60 percent of the cities are within 0.25 points of their
scores on Index 1. Camden had the largest difference in scores between Indices 1 and 2;
it scored 0.99 points higher on Index 2. Despite this large difference in scores, Camden
was the city with the highest score on both Index 1 and Index 2. Miami had the largest
difference in scores between Indices 1 and 3; it scored 0.87 points higher on Index 3.
Miami was ranked as the 56
th
most needy city on Index 1 and was ranked as the 16
th
most
needy city on Index 3. Cambridge, MA had the largest difference in scores between
Indices 1 and 4; it scored 0.96 points lower on Index 4. Cambridge was ranked as the
357
th
most needy city on Index 1 and was ranked as the 368
th
most needy city on Index 4.

Table 8. Comparison of Scores between Index 1 and Indices 2, 3, and 4 for
370 Cities
Absolute
Difference
between Score
on Index 1 and
Score on --
Index 2: Triple Weight
to Poverty and
Structural Problems
Index 3: Triple Weight to
Immigration and Housing
Affordability Factor
Index 4: Triple Weigh to
Limited Economic
Prospects
Mean 0.23 0.23 0.23
Std Dev 0.18 0.18 0.18
Max 0.99 0.87 0.96
Number of
Cities whose
Score on Index
1 is
Index 2 Index 3 Index 4
Within 1.00 370 370 370
Within 0.50 339 338 340
Within 0.25 234 223 230

The data in Tables 6, 7, and 8 indicate that, from a statistical perspective, an equal weight
index provides scores and rankings similar to those provided by indices that weigh the
factor scores unevenly. The numeric and rank-order correlations between Index 1 and
Indices 2, 3, and 4 are all above 0.85. Using Indices 2, 3, or 4, rather than Index 1, would
affect the scores of 60 percent of the cities by less than 0.25 points. For this reason, we
will use the equal weight index as our single-valued index in comparing conditions across
cities and between 2000 and 2005 in Chapter 4.

Statistical closeness does not mean that the ranking of some cities are not substantially
different depending upon the index used. Washington, DC had the biggest change in
ranking between Index 1 and Index 2; it is ranked 243
rd
on Index 1 and 80
th
on Index 2.
Sunnyvale, CA had the biggest change in ranking between Index 1 and Index 3; it is
ranked 284
th
on Index 1 and 98
th
on Index 3. Providence had the biggest change in
ranking between Index 1 and Index 4; it is ranked 48
th
on Index 1 and 256
th
on Index 4.
If HUD were to use one of these indices to allocate funds to cities, the choice of index
would be of great concern to individual cities. But, if HUD is interested primarily in
analyzing the variation in needs across cities and over time, then the results from the
equal weight index will be similar to those from any index that applies reasonable
weights to the factor scores.
Page 43

Page 44
3.4.3. Transformation of the Factor Score Functions into Functions
of Needs Indicators

Because the factor scores are linear combinations of the needs indicators, the choice of
index determines which needs indicators will have the greatest impact on the index
score.
33
Interpreting the indices in terms of the needs indicators helps identify the cities
that might do best or worst on a particular Index.

Table 9 uses the standardized scoring coefficients in Table A.7 to transform Indices 1
through 4 from weighted sums of factor scores into weighted sums of the 24 needs
indicators. The entries in Table 9 tell how much increase in the relevant index score
would result from a one standard deviation increase in need on a given need indicator.
SCHPOPPOOR, LWINCHHDS, MEDINCCBS2CITY, MINCON, and EXCSINFRA are
needs indicators that contribute to high scores on Index 2. As expected, RCNTIMMG,
LINGSOL, OVERCROWD_2000, and LACKAFFRDRENTALS contribute to high
scores on Index 3. DENIAL, CHNGEMPLBASE, UNDEREDWORKAGE,
UNEDUCADULTS, and UNEMPCEN contribute to high scores on Index 4.

33
Increases in some needs indicators—for example, POOROVER74—would decrease the index score for
that city. The factor loading and the standardized scoring coefficients take into account correlations among
the needs indicators, and thus some of the loading and some of the scoring coefficients are negative.

Table 9. Transformation of Factor-Scoring Coefficients into Scoring
Coefficients for Needs Indices
Need Indicator
Index 1: Equal
Weight
Index 2: Triple
Weight to
Poverty and
Structural
Problems
Index 3: Triple
Weight to
Immigration and
Housing
Affordability Factor
Index 4: Triple
Weight to
Limited
Economic
Prospects
POORPERS 0.1413 0.1596 0.0881 0.1761
POORCHILD 0.025 0.0505 -0.0378 0.0633
SCHPOPPOOR 0.0070 0.0525 0.0392 -0.0707
POOROVER74 -0.0061 -0.0008 -0.0040 -0.0135
LWINCHHDS 0.0078 0.0160 0.0442 -0.0367
SGLPRNTFAM 0.0170 0.0342 0.0016 0.0152
PCTPOPHIGHPOVNGHS -0.0126 -0.0019 -0.0116 -0.0244
PCTPOPMODPOVNGHS 0.0249 0.0185 0.0331 0.0232
PCTVACMODPOVCITY -0.0006 0.0365 -0.0530 0.0148
MEDINCCBS2CITY 0.0970 0.1397 0.0988 0.0525
MINCON 0.0038 0.0193 -0.0077 -0.0003
POVCON -0.1085 -0.0261 -0.0628 -0.2367
EXCSINFRA 0.0082 0.0247 -0.0040 0.0039
CHGLOWINCCON 0.0134 -0.0010 0.0056 0.0357
RCNTIMMIG 0.0178 0.0076 0.1093 -0.0637
LINGISOL 0.086 0.039 0.1966 0.0241
OVERCROWD_2000 0.0659 0.0325 0.1052 0.0601
LACKAFFDRENTALS 0.0290 0.0107 0.0630 0.0132
PR70RENTPOV -0.0288 0.0432 -0.0136 -0.1160
DENIAL 0.0516 0.0530 -0.0116 0.1134
CHNGEMPLBASE 0.0159 0.0066 0.0114 0.0297
UNDEREDWORKAGE 0.1037 0.0395 0.0424 0.2292
UNEDUCADULTS 0.1688 0.0849 0.1533 0.2682
UNEMPCEN 0.0129 0.0156 -0.0038 0.0270

3.5. Summary of Factor Analysis

This chapter applied standard factor techniques to a set of 26 needs indicators developed
in Chapter 2. The majority of these needs indicators use data from the 2005 American
Community Survey. The factor analysis identified three dimensions that represent
community needs in 2005 for the 292 cities for which we have data.

We tested the factor analysis results in three ways. First, we compared the factor analysis
using 2005 data for most needs indicators to factor analysis using 2000 data for most
needs indicators. The two analyses identified factors that were nearly identical. This
process gives us confidence that we could apply factors developed using 2005 data to
2000 data on needs indicators. Second, we split the sample of cities into those with
populations of 200,000 or more and those with populations of less than 200,000. Factor
analysis applied separately to the two samples produced results that were very similar.
These results gave us some confidence that community needs are similar in larger and
Page 45

smaller cities. Finally, we substituted a different measure for violent crimes than the
measure used in the initial analysis (PT1CRIME). The results of the factor analysis did
not seem to vary significantly when the alternative measure of violent crimes was used.
This relieved some concerns we had about the original measure of violent crimes.

Next, we examined the needs indicators to see where problems with missing data caused
a large number of cities to drop out of the analysis. Based on this examination, we
eliminated PT1CRIME and PT2CRIME from the set of indicators and reran the factor
analysis. When applied to the smaller set of needs indicators, factor analysis identified
the same factors found with the full set of indicators. Eliminating these two variables
increased the number of cities included from 292 to 370. This factor analysis is the one
that we use for the remainder of the analysis in the report. We interpret the factors to
represent the needs associated with:

• Poverty and structural problems,
• Immigration and lack of affordability housing, and
• Limited economic prospects.

The first two factors are well-defined; the third factor is weakly defined. We ascribed the
weak definition of the limited economic prospects factor to the lack of multifaceted data
on economic conditions and trends in cities.

In Section 3.3, we discussed technical issues in applying factors developed at one point to
data on the same needs indicators at a different point in time. This provided the
conceptual background for the comparisons in Chapter 4.

Finally, we examined six alternative single-valued needs indicators based on linear
combinations of scores from the three factors. Examining the correlations among the
indices and other statistics, we concluded that an equal weight index would provide
adequate information on the variation in community needs across cities and across time.
We use the equal weight index in Chapters 4 and 5.

Page 46

4. Community Needs in 2000 and 2005

While this research project has multiple objectives, the two principal goals are to test the
feasibility of using ACS data to measure community needs and to test the feasibility of
measuring changes in community needs over time. Chapter 2 identified 26 indicators of
problems at the city level, most of which either use ACS data or will be capable of using
ACS data once the ACS begins to release 5-year moving average data for census tracts.
Chapter 3 performed factor analysis using 24 of the 26 indicators and identified three
factors that track different dimensions of community needs. Chapter 3 also examined
several single-valued indices based on the three factors and explained how to apply factor
analysis in different years.

This chapter compares conditions in 370 cities in 2000 and 2005 using each of the three
factors and also using the equal weight index developed in Chapter 3. The chapter
examines changes in each factor between 2000 and 2005 to obtain a fuller picture of how
conditions in individual cities are changing. The equal weight index provides a
convenient summary of these changes. As noted in Chapter 3, the results from the equal
weight index are similar in scope and general details to that from other indices that weigh
the factors unequally. While unequal weighting can markedly change the scoring of
individual cities, the overall patterns are more stable.

The reader should keep in mind the following facts about how the analysis in this chapter
was carried out: (1) 2005 data were used to identify the factors and to develop
standardized scoring coefficients; (2) the standardized scoring coefficients were applied
to standardized data on 24 indicators in 2000 and 2005; and (3) standardization of the
indicators was achieved in both 2000 and 2005 by taking the value of the indicator in the
relevant year and subtracting the mean value of the indicator in 2005 and dividing the
difference by the standard deviation of the indicator in 2005. Table A.9 in Appendix A
presents the results of these calculations for 2000 and 2005 for the three factors and for
the equal weight index for all 370 cities.

Section 4.1 looks at the how conditions changed on average for the 370 cities between
2000 and 2005. Sections 4.2, 4.3, and 4.4 examine changes across individual cities for
each of the three factors. Section 4.5 uses the equal weight index to compare changes in
overall community needs. Section 4.6 contains a summary of findings.

4.1. Changes in Community Needs for Cities with Populations of
65,000 or More

Table 10 computes the average score on each factor in 2000 and 2005 and the average
score on the equal weight index in both years. On each factor and on the index, an
increase in the scores (a positive change) indicates an increase in community needs while
a decrease in scores (a negative change) indicates a decrease in community needs.
Page 47

Before looking at the numbers in Table 10, it is important to call attention to two
previous results. Table 9 indicated that, of the 24 needs indicators, UNEDUCADULTS
has the largest impact on the equal weight index. (Table A.7 in Appendix A indicates
that UNEDUCADULTS also has a strong impact on the scoring for Factor 3.) Table 2
noted that the mean of the unstandardized data for UNEDUCADULTS declined by 17
percent between 2000 and 2005. The discussion of Table 2 expressed surprise at the size
of this decline, but examination of Census Bureau reports comparing the decennial
census with the ACS failed to find any indication of problems with this variable. In
addition, the observed decline for the cities studied closely paralleled the decline in the
data for the entire United States. Because we found no evidence of problems with this
variable, we included it among the needs indicators.

Table 10. Average Factor Scores and Average Equal Weight Index Scores
in 2000 and 2005
Factor 1 (Poverty
and Structural
Problems)
Factor 2 (Immigration
and Housing
Affordability)
Factor 3 (Limited
Economic
Prospects)
Equal
Weight
Index
Mean -
2005
-0.006 0.004 -0.034 -0.012
Mean -
2000
-0.154 -0.067 0.192 -0.010
Change 0.149 0.070 -0.226 -0.002
Ratio of
change to
standard
deviation in
2005
16.1% 7.8% -25.4% -0.5%
Number of
cities worse
off
283 231 44 168
Number of
cities no
worse off
87 139 326 202

Table 10 shows that, on average, community needs—as measured by the equal weight
index—decreased slightly between 2000 and 2005. This decline was due solely to
improvement in the needs represented by Factor 3, the limited economic prospects factor.
As discussed above, the improvement in Factor 3 and the equal weight index can be
attributed to the substantial reduction in the percentage of adults without a high school
diploma (UNEDUCADULTS) between 2000 and 2005.

Table 10 also shows that community needs related to poverty and structural problems
(Factor 1) and immigration and housing affordability (Factor 2) worsened between 2000
and 2005. Conditions worsened most with respect to poverty and structural problems.
The average city experienced a move of 1/6 of a standard deviation up in the score on this
factor whereas the average city experienced a move of only 1/12 of a standard deviation
up in the score on the immigration and housing affordability factor. Consistent with the
relative size of the average changes, the number of cities that were worse off (had higher
scores) in 2005 was larger for Factor 1 than Factor 2.

Page 48

Page 49
Correlation analysis found little evidence of a relationship between changes in the score
on one factor and changes in the scores on either of the other two factors between 2000
and 2005. Changes in the score of Factor 1 have a correlation of 0.18 with changes in the
scores of Factor 2. The other two pairings have negative correlations of -0.01 and -0.06.
34

In addition, there was no relationship between population and changes in the scores on
any of the factors or on the score for the equal weight index.

4.2. Comparison of Scores in 2000 and 2005 on Factor 1

The Factor 1 scores rank cities on community needs related to poverty and structural
problems. Between 2000 and 2005, 283 of the 370 cities became worse off on this
dimension of need. Table 11 shows how the scores on this factor varied by region and
by size class of cities.

Table 11. Changes in Factor 1 Scores between 2000 and 2005, by Region
and Population
Region
Number of
Cities
2000
Factor 1
2005
Factor 1
Difference
South 110 -0.11 0.03 0.14
West 150 -0.48 -0.40 0.08
Midwest 69 -0.06 0.18 0.24
Northeast 41 0.77 1.05 0.28
Population
1,000,000+ 9 0.46 0.59 0.13
500,000-999,999 21 0.33 0.53 0.20
300,000-499,999 23 0.30 0.44 0.14
200,000-299,999 37 0.07 0.23 0.16
100,000-199,999 124 -0.22 -0.08 0.15
under 100,000 156 -0.32 -0.18 0.14
All cities 370 -0.15 -0.01 0.15

The Northeast has the highest average scores on the poverty and structural problems
factor in both 2000 and 2005 and the largest increase in average scores between the two
years. The West region has the lowest average scores on this factor in both years and the
smallest increase between the two years. Using 2005 as the standard, only the average
scores in the Northeast were above the average for 2005 in 2000; by 2005, all regions
except the West had above-average scores.

There appears to be a systematic relationship between the scores on the poverty and
structural problems factor and city size. The average score declined by size class in both
2000 and 2005. The change in scores is approximately the same for all the size classes—
except for cities with populations between 500,000 and a million, which have a slight
higher increase in average scores.

34
As expected, scores of a factor in 2000 are highly correlated (approximately 0.97) with scores on the
same factor in 2005.

Table 12 lists the 40 cities that had the largest increase in need on this factor between
2000 and 2005. The list contains cities that already had serious problems related to this
factor in 2000—such as Camden, Detroit, Cleveland, Rochester, Reading, and
Syracuse—and cities that were relatively well off on Factor 1 in 2000—such as
Redwood, CA; West Covina, CA; Hillsboro, OR; Garland, TX; Upland, CA; and Cedar
Rapids. The latter cities moved up sharply in the ranking on Factor 1. The Northeast
region is heavily represented among those cities with the largest increases in Factor 1
scores. Of the 41 Northeast cities, 14 are on the list of 40 cities with the largest increase
in Factor 1 needs between 2000 and 2005.

Page 50

Table 12. Forty Cities with the Largest Increases in Factor 1 Scores,
2000-2005

City State
2005
Population
2000
Factor 1
2005
Factor 1
Difference
2000
Rank
2005
Rank
1 Lawrence city Massachusetts 82,191 1.88 2.89 1.01 12 5
2 Hillsboro city Oregon 82,732 -0.74 0.19 0.94 276 120
3 Camden city New Jersey 73,305 3.62 4.51 0.89 1 1
4 Reading city Pennsylvania 81,302 2.04 2.89 0.85 8 6
5 Passaic city New Jersey 68,422 0.93 1.76 0.83 32 20
6 Scranton city Pennsylvania 67,314 0.28 1.07 0.80 91 42
7 Redwood City city California 81,195 -1.00 -0.25 0.74 312 204
8 Gainesville city Florida 100,879 0.24 0.93 0.69 97 52
9 West Covina city California 116,371 -0.97 -0.33 0.64 309 223
10 Baton Rouge city Louisiana 205,442 0.79 1.44 0.64 45 27
11 Dayton city Ohio 132,679 1.92 2.56 0.64 11 9
12 Springfield city Massachusetts 146,948 1.32 1.96 0.64 22 18
13 Birmingham city Alabama 222,154 1.82 2.46 0.64 14 11
14 Hammond city Indiana 72,507 0.35 0.97 0.62 84 50
15 Cleveland city Ohio 414,534 2.52 3.14 0.62 4 2
16 Somerville city Massachusetts 74,869 -0.12 0.48 0.60 152 85
17 Cedar Rapids city Iowa 119,670 -0.62 -0.04 0.58 255 171
18 Nampa city Idaho 67,112 -0.27 0.31 0.58 180 111
19 Allentown city Pennsylvania 105,231 0.89 1.46 0.57 38 26
20 Albany city New York 78,402 1.53 2.10 0.57 18 13
21 Detroit city Michigan 836,056 2.59 3.13 0.54 3 3
22 Gresham city Oregon 95,334 -0.43 0.11 0.54 222 138
23 Syracuse city New York 132,495 2.02 2.55 0.53 10 10
24 Pueblo city Colorado 101,302 0.35 0.86 0.51 82 57
25 Avondale city Arizona 61,666 -0.42 0.08 0.50 219 144
26 Bryan city Texas 56,277 0.09 0.59 0.50 119 75
27 Lansing city Michigan 119,675 0.57 1.06 0.49 67 43
28 Rochester city New York 189,312 2.42 2.90 0.48 6 4
29 Vancouver city Washington 155,488 -0.31 0.17 0.47 190 126
30 Garland city Texas 235,750 -0.73 -0.27 0.46 273 210
31 Lowell city Massachusetts 96,876 0.65 1.10 0.44 56 38
32 South Bend city Indiana 97,070 0.65 1.08 0.44 57 41
33 Milwaukee city Wisconsin 556,948 1.29 1.72 0.43 24 22
34 Lynn city Massachusetts 83,419 0.66 1.09 0.43 54 39
35 Pawtucket city Rhode Island 72,896 0.50 0.92 0.42 72 53
36 Toledo city Ohio 285,937 0.72 1.14 0.42 47 34
37 High Point city N Carolina 101,852 -0.15 0.26 0.42 163 112
38 Upland city California 74,420 -0.70 -0.29 0.41 265 214
39 Rockford city Illinois 139,173 0.26 0.67 0.41 92 71
40 Tyler city Texas 87,687 0.09 0.50 0.40 117 82

Page 51

Table 13 lists the 40 cities that experienced the greatest improvement on the poverty and
structural problems factor between 2000 and 2005.

Table 13. Forty Cities with the Largest Decreases in Factor 1 Scores,
2000-2005

City State
2005
Population
2000
Factor 1
2005
Factor 1
Difference
2000
Rank
2005
Rank
1 Miami city Florida 361,701 0.84 0.10 -0.73 42 140
2 Glendale city California 194,620 -0.38 -0.88 -0.50 209 306
3 Turlock city California 74,883 -0.38 -0.84 -0.46 210 298
4 Rialto city California 93,284 -0.26 -0.67 -0.41 178 277
5 Pomona city California 161,257 0.16 -0.24 -0.40 111 203
6 Oceanside city California 162,259 -0.58 -0.98 -0.40 247 320
7 Wilmington city N Carolina 91,207 0.30 -0.05 -0.35 88 173
8 Alexandria city Virginia 133,479 -0.51 -0.85 -0.34 232 303
9 Redding city California 89,362 -0.19 -0.51 -0.32 170 255
10 Westminster city California 97,946 -0.77 -1.08 -0.31 281 338
11 Santa Monica city California 82,777 -0.74 -1.00 -0.27 274 324
12 Hemet city California 77,076 0.01 -0.26 -0.27 131 206
13 Pompano Beach Florida 94,892 -0.02 -0.28 -0.26 135 213
14 Bethlehem city Pennsylvania 68,144 0.20 -0.04 -0.24 100 167
15 Richmond city Virginia 180,757 1.22 0.98 -0.24 26 48
16 Long Beach city California 463,956 0.62 0.38 -0.24 62 104
17 San Marcos city California 77,445 -0.71 -0.94 -0.23 270 314
18 Escondido city California 133,017 -0.46 -0.69 -0.22 226 280
19 Columbia city S Carolina 88,450 0.93 0.72 -0.21 34 67
20 Deltona city Florida 85,979 -0.86 -1.04 -0.18 298 330
21 Newark city New Jersey 254,217 2.21 2.02 -0.18 7 16
22 Peoria city Illinois 102,136 0.86 0.68 -0.18 41 70
23 Fullerton city California 142,064 -0.84 -1.00 -0.16 292 323
24 Quincy city Massachusetts 84,080 -0.69 -0.85 -0.16 262 302
25 Stockton city California 278,515 0.52 0.38 -0.14 69 102
26 Orange city California 137,994 -1.00 -1.14 -0.14 315 350
27 Inglewood city California 120,204 0.63 0.48 -0.14 60 84
28 Suffolk city Virginia 77,922 -0.26 -0.39 -0.13 179 235
29 Chino city California 69,732 -1.16 -1.29 -0.13 338 364
30 McKinney city Texas 92,337 -0.95 -1.07 -0.13 307 334
31 Merced city California 65,391 0.68 0.55 -0.12 52 77
32 El Cajon city California 92,507 0.06 -0.06 -0.12 123 177
33 North Las Vegas Nevada 165,061 -0.35 -0.47 -0.12 201 247
34 Melbourne city Florida 76,373 -0.45 -0.58 -0.12 225 267
35 Riverside city California 294,059 -0.37 -0.49 -0.12 204 253
36 Buena Park city California 76,062 -0.82 -0.94 -0.12 289 315
37 Simi Valley city California 116,722 -1.18 -1.30 -0.12 340 365
38 Berkeley city California 90,432 0.19 0.08 -0.11 104 145
39 Clearwater city Florida 108,382 -0.35 -0.46 -0.10 202 245
40 Modesto city California 202,971 -0.29 -0.39 -0.10 188 234
Page 52

Twenty-four of the 40 cities are in California; 11 others are in the South region. Only
Long Beach and Miami have populations over 300,000. Interesting cases include
Newark, which moved from 7
th
highest score in 2000 to the 16
th
highest score in 2005,
and Richmond, which moved from the 26
th
highest score to the 48
th
highest.

4.3 Comparison of Scores in 2000 and 2005 on Factor 2

The Factor 2 scores rank cities on community needs related to immigration and the
housing affordability. Between 2000 and 2005, 231 of the 370 cities became worse off
on this dimension of need. Table 14 shows how the scores on this factor varied by region
and by size class of cities.

Table 14. Changes in Factor 2 Scores between 2000 and 2005, by Region
and Population
Region
Number of
Cities
2000
Factor 2
2005
Factor 2
Difference
South
110 -0.40 -0.33 0.07
West
150 0.34 0.41 0.06
Midwest
69 -0.63 -0.59 0.04
Northeast
41 0.28 0.41 0.14
Population
1,000,000+
9 0.70 0.76 0.06
500,000-999,999
21 -0.14 -0.10 0.04
300,000-499,999
23 0.21 0.23 0.02
200,000-299,999
37 -0.27 -0.16 0.11
100,000-199,999
124 -0.12 -0.05 0.07
under 100,000
156 -0.05 0.02 0.08
All cities
370 -0.07 0.00 0.07

Cities in the Northeast experienced the greatest worsening of conditions on this factor, an
increase of 0.14 standard deviations, which was twice the national average. With the
exception of cities with over a million residents, there appears to be little relationship
between population size and the prevalence of problems related to immigration and
housing affordability. The largest cities had an average score of 0.70 or more in both
2000 and 2005.

Table 15 lists the 40 cities that had the greatest increase in the score on Factor 2.
California, Texas, and Florida account for 26 of the 40 cities. None of the cities on this
list had populations above 300,000. Only Salinas, CA and Lawrence, MA had been
ranked in the top 20 in 2000, and only five of these cities had been ranked in the top 50 in
2000. Mesquite, TX and Cape Coral, FL had the biggest increase in rank on this factor.
Mesquite moved from 248
th
to 153
rd
while Cape Coral moved from 244
th
to 179
th
.
Page 53

Table 15. Forty Cities with the Largest Increases in Factor 2 Scores,
2000-2005

City State
2005
Population
2000
Factor 2
2005
Factor 2
Difference
2000
Rank
2005
Rank
1 Deerfield Beach Florida 71,599 0.27 1.11 0.84 102 47
2 Mesquite city Texas 126,895 -0.58 0.02 0.61 248 153
3 Camden city New Jersey 73,305 0.77 1.36 0.59 61 29
4 Union City California 65,239 1.16 1.74 0.58 32 20
5 San Bernardino California 204,552 1.00 1.56 0.55 41 24
6 Trenton city New Jersey 77,471 0.15 0.69 0.54 115 72
7 Redwood City California 81,195 0.82 1.34 0.52 56 30
8 Aurora city Illinois 170,490 0.46 0.96 0.49 83 58
9 Reading city Pennsylvania 81,302 0.12 0.62 0.49 119 83
10 Hemet city California 77,076 0.11 0.60 0.49 122 85
11 Lowell city Massachusetts 96,876 0.64 1.10 0.46 67 49
12 Rialto city California 93,284 0.65 1.11 0.46 66 48
13 Garland city Texas 235,750 0.26 0.72 0.46 104 68
14 Palmdale city California 145,800 0.22 0.66 0.44 108 77
15 Gresham city Oregon 95,334 -0.09 0.35 0.44 151 105
16 Salinas city California 156,950 2.14 2.56 0.43 10 4
17 Cape Coral city Florida 134,388 -0.58 -0.16 0.42 244 179
18 Fremont city California 210,387 0.86 1.26 0.40 54 34
19 Hollywood city Florida 138,412 0.29 0.68 0.39 99 73
20 Turlock city California 74,883 0.32 0.71 0.39 95 69
21 Newark city New Jersey 254,217 1.27 1.65 0.38 29 22
22 Tracy city California 82,218 -0.20 0.18 0.38 172 126
23 Pompano Beach Florida 94,892 0.18 0.54 0.36 110 88
24 Lawrence city Massachusetts 82,191 2.05 2.41 0.35 13 7
25 Worcester city Massachusetts 154,398 0.12 0.47 0.35 120 95
26 Antioch city California 103,339 -0.28 0.07 0.35 183 145
27 Pasadena city Texas 150,180 0.70 1.05 0.34 63 52
28 Fairfield city California 102,642 -0.13 0.21 0.34 154 122
29 Hesperia city California 79,714 -0.19 0.15 0.34 170 131
30 Kent city Washington 84,979 0.09 0.43 0.34 124 98
31 Victorville city California 93,042 -0.04 0.29 0.32 142 115
32 Salem city Oregon 142,006 -0.32 0.00 0.32 187 157
33 Palm Bay city Florida 90,102 -0.56 -0.25 0.31 239 191
34 Bloomington city Indiana 55,406 -0.47 -0.16 0.31 213 178
35 Wyoming city Michigan 68,960 -0.52 -0.21 0.31 226 187
36 Irving city Texas 212,262 0.85 1.16 0.31 55 40
37 Costa Mesa city California 105,333 0.88 1.19 0.31 51 38
38 Scranton city Pennsylvania 67,314 -0.95 -0.65 0.30 337 274
39 Lewisville city Texas 81,484 -0.37 -0.08 0.29 199 166
40 Bryan city Texas 56,277 -0.02 0.27 0.29 138 117

Page 54

Table 16 lists the 40 cities that had the largest decrease in scores for the immigration and
housing affordability factor.

Table 16. Forty Cities with the Largest Decreases in Factor 2 Scores,
2000-2005

City State
2005
Population
2000
Factor 2
2005
Factor 2
Difference
2000
Rank
2005
Rank
1 Pasadena city California 129,400 0.92 0.34 -0.58 47 107
2 Santa Barbara city California 90,708 0.63 0.14 -0.49 69 134
3 San Marcos city California 77,445 0.79 0.33 -0.47 57 110
4 Baldwin Park city California 84,812 2.82 2.36 -0.46 4 8
5 Santa Ana city California 302,302 3.81 3.39 -0.42 1 3
6 Fort Lauderdale Florida 141,307 0.14 -0.26 -0.40 116 196
7 Elizabeth city New Jersey 121,137 2.32 1.93 -0.39 6 16
8 Alhambra city California 76,309 2.50 2.15 -0.35 5 12
9 McKinney city Texas 92,337 -0.27 -0.60 -0.33 182 260
10 Hayward city California 135,474 1.31 1.00 -0.30 28 57
11 Killeen city Texas 98,434 -0.65 -0.91 -0.27 271 332
12 Mountain View California 69,427 1.11 0.87 -0.24 39 61
13 Alameda city California 77,058 0.31 0.11 -0.20 96 139
14 San Jose city California 887,330 1.31 1.12 -0.19 27 45
15 Southfield city Michigan 75,053 -0.67 -0.87 -0.19 276 321
16 New Bedford city Massachusetts 84,898 0.30 0.10 -0.19 98 140
17 Chico city California 71,298 -0.36 -0.55 -0.19 195 249
18 Waukesha city Wisconsin 62,690 -0.72 -0.91 -0.19 291 330
19 Glendale city California 194,620 2.29 2.11 -0.18 8 13
20 Evanston city Illinois 62,258 -0.47 -0.65 -0.18 215 275
21 Westminster city Colorado 99,305 -0.54 -0.72 -0.18 234 290
22 Upland city California 74,420 -0.11 -0.29 -0.17 153 199
23 Chattanooga city Tennessee 139,158 -1.07 -1.24 -0.17 353 365
24 San Francisco city California 719,077 1.12 0.95 -0.17 37 59
25 Carson city California 92,156 0.66 0.49 -0.17 64 93
26 Suffolk city Virginia 77,922 -0.84 -1.00 -0.17 320 344
27 North Las Vegas Nevada 165,061 0.99 0.82 -0.16 42 63
28 Simi Valley city California 116,722 -0.38 -0.54 -0.16 201 245
29 Sioux City city Iowa 78,395 -0.58 -0.74 -0.16 245 298
30 Lorain city Ohio 65,476 -0.76 -0.91 -0.15 305 331
31 Roanoke city Virginia 90,074 -0.91 -1.06 -0.15 331 347
32 Pawtucket city Rhode Island 72,896 0.49 0.34 -0.14 79 108
33 Westland city Michigan 80,284 -0.68 -0.82 -0.14 277 315
34 Peoria city Illinois 102,136 -1.21 -1.35 -0.14 364 369
35 Honolulu CDP Hawaii 362,252 0.71 0.57 -0.14 62 87
36 Round Rock city Texas 81,639 -0.51 -0.65 -0.14 225 273
37 Newton city Massachusetts 82,383 -0.43 -0.57 -0.14 208 254
38 Los Angeles city California 3,731,437 2.01 1.88 -0.14 14 18
39 Fargo city North Dakota 88,809 -0.79 -0.92 -0.13 309 333
40 Buena Park city California 76,062 1.16 1.03 -0.13 33 55
Page 55

The five cities with the largest decreases, and 18 of the top 40, are in California; the
California cities include three very large cities—Los Angeles, San Jose, and San
Francisco. Five of the 10 cities with the highest scores on this factor in 2000 were among
the 40 cities with the largest decreases. Fort Lauderdale had the greatest change in rank,
moving from 116
th
in 2000 to 196
th
in 2005.

4.4. Comparison of Scores in 2000 and 2005 on Factor 3

The Factor 3 scores rank cities on community needs related to limited economic
prospects. Between 2000 and 2005, the average score on Factor 3 declined, indicating
that conditions improved on average for cities on this dimension of need. Table 17 shows
how the changes in scores for this factor varied by region and size class of cities.

Table 17. Changes in Factor 3 Scores between 2000 and 2005,
by Region and Population
Region
Number of
Cities
2000
Factor 3
2005
Factor 3
Difference
South 110 0.29 0.08 -0.21
West 150 0.26 0.02 -0.24
Midwest 69 0.05 -0.10 -0.14
Northeast 41 -0.07 -0.44 -0.36
Population
1,000,000+ 9 0.21 -0.08 -0.29
500,000-999,999 21 -0.16 -0.40 -0.24
300,000-499,999 23 0.09 -0.12 -0.21
200,000-299,999 37 0.22 0.03 -0.19
100,000-199,999 124 0.28 0.07 -0.21
under 100,000 156 0.17 -0.07 -0.24
All cities 370 0.19 -0.03 -0.23

On average, cities in every region and in every size class improved on this factor between
2000 and 2005. Cities in the Northeast had the lowest scores on this factor in 2000 and
showed the greatest improvement between 2000 and 2005. There does not appear to be
any consistent relationship between city size and either the Factor 3 scores or the changes
in the Factor 3 scores.

Table 18 lists the 40 cities that had the largest increase in community needs on the limited
economic prospects factor between 2000 and 2005. Only 44 of the 370 cities became
worse off on this factor. For 24 of these cities, the increases in Factor 3 scores were
negligible, 0.10 standard deviations or less. Carrollton, TX; Gastonia, NC; and Cedar
Rapids, IA had the largest increases. These increases combined with the general pattern
of decreases created some large changes in the rankings on Factor 3. Carrollton moved
from 214
th
to 125
th
; Gastonia moved from 70
th
to 27
th
; and Cedar Rapids moved from
268
th
to 194
th
.
Page 56

Table 18. Forty Cities with the Largest Increases in Factor 3 Scores,
2000-2005

City State
2005
Population
2000
Factor 3
2005
Factor 3
Difference
2000
Rank
2005
Rank
1 Carrollton city Texas 122,699 0.12 0.42 0.30 214 125
2 Gastonia city N Carolina 72,183 0.91 1.14 0.23 70 27
3 Cedar Rapids city Iowa 119,670 -0.23 -0.01 0.22 268 194
4 Lawton city Oklahoma 79,486 0.96 1.15 0.19 63 26
5 Southfield city Michigan 75,053 0.43 0.62 0.19 162 91
6 Westminster city California 97,946 0.71 0.88 0.17 114 48
7 Irving city Texas 212,262 0.35 0.51 0.16 175 107
8 Arlington city Texas 348,965 0.48 0.63 0.16 152 85
9 Mesquite city Texas 126,895 1.47 1.62 0.15 19 5
10 Topeka city Kansas 117,326 0.31 0.45 0.14 184 122
11 Sioux City Iowa 78,395 0.77 0.90 0.13 101 44
12 Glendale city California 194,620 -0.62 -0.49 0.13 312 266
13 Bloomington city Indiana 55,406 -2.34 -2.21 0.13 366 364
14 Champaign city Illinois 65,600 -1.62 -1.51 0.11 355 348
15 High Point city N Carolina 101,852 0.65 0.76 0.11 122 66
16 Lorain city Ohio 65,476 1.42 1.53 0.11 21 8
17 Cary town N Carolina 107,446 -1.26 -1.16 0.10 344 331
18 Fayetteville city N Carolina 128,777 0.72 0.82 0.10 111 56
19 Lakewood city Colorado 142,434 0.05 0.14 0.09 234 173
20 Turlock city California 74,883 1.04 1.14 0.09 50 28
21 Midland city Texas 100,799 1.02 1.11 0.09 54 31
22 Dayton city Ohio 132,679 0.55 0.64 0.08 141 84
23 Orlando city Florida 221,299 -0.21 -0.13 0.07 264 220
24 Killeen city Texas 98,434 0.62 0.69 0.07 130 76
25 Chico city California 71,298 -0.93 -0.87 0.06 334 312
26 Gresham city Oregon 95,334 0.45 0.50 0.05 157 110
27 Plano city Texas 251,648 -0.72 -0.68 0.05 324 291
28 Berkeley city California 90,432 -3.26 -3.22 0.04 370 369
29 Tempe city Arizona 166,171 -0.64 -0.61 0.04 316 278
30 Wichita city Kansas 354,582 0.36 0.39 0.03 174 134
31 Garland city Texas 235,750 1.14 1.17 0.03 41 23
32 Toledo city Ohio 285,937 1.00 1.03 0.03 57 34
33 Hemet city California 77,076 1.34 1.37 0.03 24 13
34 Madison city Wisconsin 203,704 -2.04 -2.02 0.02 365 362
35 Pontiac city Michigan 59,472 1.13 1.16 0.02 42 24
36 Lubbock city Texas 199,789 0.68 0.70 0.02 115 74
37 Aurora city Colorado 291,317 0.37 0.40 0.02 170 133
38 Spokane city Washington 192,777 -0.11 -0.09 0.02 254 209
39 Clearwater city Florida 108,382 0.14 0.16 0.02 208 170
40 Cleveland city Ohio 414,534 0.72 0.73 0.01 110 70

Page 57

Table 19 lists the 40 cities that showed the greatest improvement on the limited economic
prospects factor.

Table 19. Forty Cities with the Largest Decreases in Factor 3 Scores,
2000-2005

City State
2005
Population
2000
Factor 3
2005
Factor 3
Difference
2000
Rank
2005
Rank
1 Deerfield Beach Florida 71,599 0.67 -0.27 -0.94 116 245
2 Davie town Florida 88,683 0.56 -0.20 -0.76 137 228
3 Indio city California 65,091 1.65 0.89 -0.76 12 46
4 Tustin city California 79,811 0.31 -0.40 -0.71 185 260
5 Newark city New Jersey 254,217 0.44 -0.24 -0.67 160 236
6 Hawthorne city California 100,754 1.41 0.74 -0.67 22 68
7 Upland city California 74,420 0.40 -0.27 -0.67 164 241
8 Allentown city Pennsylvania 105,231 0.21 -0.44 -0.65 197 262
9 Worcester city Massachusetts 154,398 -0.50 -1.14 -0.64 300 330
10 Lawrence city Massachusetts 82,191 -0.24 -0.88 -0.64 272 314
11 Miami Beach city Florida 84,086 -0.07 -0.70 -0.63 248 296
12 Jersey City New Jersey 246,335 -0.49 -1.11 -0.62 298 327
13 Baltimore city Maryland 608,481 0.20 -0.41 -0.62 200 261
14 Alhambra city California 76,309 -0.03 -0.64 -0.61 243 284
15 Cambridge city Massachusetts 81,260 -2.67 -3.28 -0.60 367 370
16 Cranston city Rhode Island 77,025 0.75 0.17 -0.57 105 169
17 Miramar city Florida 115,444 0.66 0.09 -0.57 118 182
18 Birmingham city Alabama 222,154 0.82 0.26 -0.56 90 155
19 Fayetteville city Arkansas 58,839 -0.89 -1.44 -0.55 329 342
20 Napa city California 73,085 0.21 -0.33 -0.55 196 254
21 Pittsburgh city Pennsylvania 284,366 -0.22 -0.76 -0.54 265 305
22 Carson city California 92,156 1.56 1.02 -0.54 15 35
23 Washington city
District of
Columbia 515,118 -1.74 -2.28 -0.54 359 366
24 Santa Maria city California 88,817 1.19 0.65 -0.54 35 83
25 Camden city New Jersey 73,305 0.77 0.23 -0.54 100 159
26 Hollywood city Florida 138,412 0.46 -0.07 -0.53 153 202
27 Santa Fe city New Mexico 66,453 -0.22 -0.75 -0.53 267 301
28 Fall River city Massachusetts 97,612 1.10 0.58 -0.52 44 97
29 Suffolk city Virginia 77,922 0.96 0.45 -0.51 62 121
30 Pleasanton city California 67,018 -0.68 -1.19 -0.50 321 332
31 Lowell city Massachusetts 96,876 -0.33 -0.83 -0.50 280 310
32 Portsmouth city Virginia 95,183 0.80 0.30 -0.50 92 149
33 Bellingham city Washington 69,057 -0.94 -1.44 -0.49 335 341
34 Livermore city California 87,054 -0.09 -0.58 -0.49 252 275
35 Paterson city New Jersey 148,353 0.61 0.13 -0.48 131 177
36 Baldwin Park city California 84,812 1.99 1.50 -0.48 3 9
37 New York City New York 7,956,113 -0.60 -1.08 -0.48 310 326
38 Savannah city Georgia 117,478 0.49 0.01 -0.48 149 190
39 Lafayette city Louisiana 108,175 0.39 -0.08 -0.47 167 207
40 Clovis city California 80,529 0.85 0.38 -0.47 82 135
Page 58

Deerfield Beach, FL—the city that experienced the greatest worsening of Factor 2—
showed the greatest improvement on Factor 3. New York, Washington, Pittsburgh,
Newark, Jersey City, and Birmingham were among the cities with the largest decreases in
Factor 3 scores. There were 13 Northeastern cities on the list, nine more than would have
been expected by chance.

4.5. Comparison of Scores in 2000 and 2005 on the Equal
Weight Index

Table 20 shows how the changes in scores for the equal weight index varied by region
and size class of cities. According to the equal weight index, conditions improved in the
West, were stable in the South, and worsened in the Midwest and Northeast. Overall
conditions were generally stable; if the differences in the last column were carried to
three decimal places as was done in Table 10, the difference for all cities would be -.002,
a very small improvement at the national level. There was no consistent pattern in the
changes by city size. According to the equal weight index, conditions were stable or got
better in 202 cities.

Table 20. Changes in Equal Weight Index Scores between 2000 and 2005,
by Region and Population
Region Number of Cities
2000 Equal Weight
Index
2005 Equal Weight
Index Difference
South 110 -0.08 -0.07 0.00
West 150 0.04 0.01 -0.03
Midwest 69 -0.21 -0.17 0.05
Northeast 41 0.33 0.34 0.02
Population
1,000,000+ 9 0.46 0.42 -0.03
500,000-999,999 21 0.01 0.01 0.00
300,000-499,999 23 0.20 0.18 -0.02
200,000-299,999 37 0.01 0.03 0.02
100,000-199,999 124 -0.02 -0.02 0.00
under 100,000 156 -0.07 -0.07 -0.01
All cities 370 -0.01 -0.01 0.00

Table 21 lists the 40 cities that experienced the biggest worsening of conditions between
2000 and 2005. Nineteen of the 40 are cities in the Northeast and Midwest; the
proportionate share of the 40 from these two regions would be 11. Dallas is the only city
with 500,000 or more population. Camden, Passaic, and Lawrence, MA had high scores
in 2000 and experienced big increases between 2000 and 2005. We were able to score 11
cities in North Carolina; four of them made the list of worst change in overall condition.

Page 59

Table 21. Forty Cities with the Largest Increases in Equal Weight Index
Scores, 2000-2005

City State
2005
Population
2000
EW
Index
2005
EW
Index
Difference
2000
Rank
2005
Rank
1 Reading city Pennsylvania 81,302 0.93 1.34 0.40 17 6
2 Gresham city Oregon 95,334 -0.02 0.32 0.34 169 89
3 Garland city Texas 235,750 0.22 0.54 0.31 107 51
4 Camden city New Jersey 73,305 1.72 2.03 0.31 2 1
5 Redwood City city California 81,195 -0.18 0.12 0.30 228 141
6 Mesquite city Texas 126,895 -0.02 0.28 0.29 167 101
7 Springfield city Massachusetts 146,948 0.52 0.81 0.29 50 27
8 Dayton city Ohio 132,679 0.35 0.64 0.28 83 38
9 Union City city California 65,239 0.03 0.29 0.26 153 96
10 Hillsboro city Oregon 82,732 -0.17 0.09 0.26 226 147
11 Lawrence city Massachusetts 82,191 1.23 1.47 0.24 10 5
12 Bloomington city Indiana 55,406 -0.90 -0.66 0.24 361 334
13 Passaic city New Jersey 68,422 1.49 1.71 0.23 5 2
14 Cedar Rapids city Iowa 119,670 -0.60 -0.37 0.23 332 276
15 Scranton city Pennsylvania 67,314 -0.02 0.21 0.23 168 119
16 Hammond city Indiana 72,507 0.36 0.57 0.21 79 47
17 Gainesville city Florida 100,879 -0.41 -0.20 0.21 288 231
18 Palmdale city California 145,800 0.44 0.64 0.21 61 36
19 Irving city Texas 212,262 0.21 0.42 0.21 109 65
20 Trenton city New Jersey 77,471 0.86 1.07 0.21 22 14
21 Bryan city Texas 56,277 0.34 0.54 0.20 85 50
22 Cleveland city Ohio 414,534 0.75 0.96 0.20 30 18
23 Baton Rouge city Louisiana 205,442 0.10 0.30 0.20 135 94
24 Dallas city Texas 1,144,946 0.60 0.80 0.20 46 28
25 Carrollton city Texas 122,699 -0.33 -0.14 0.20 273 213
26 Aurora city Colorado 291,317 -0.04 0.16 0.19 175 131
27 Wyoming city Michigan 68,960 -0.09 0.10 0.19 195 142
28 West Covina city California 116,371 0.16 0.35 0.18 118 82
29 Greensboro city N Carolina 208,552 -0.25 -0.08 0.18 257 192
30 Gastonia city N Carolina 72,183 0.23 0.40 0.17 106 72
31 Aurora city Illinois 170,490 0.07 0.24 0.17 141 110
32 Arlington city Texas 348,965 -0.11 0.06 0.17 203 153
33 Rochester city New York 189,312 0.60 0.77 0.17 45 29
34 Champaign city Illinois 65,600 -0.76 -0.59 0.17 347 324
35 High Point city N Carolina 101,852 -0.01 0.16 0.16 164 130
36 Kansas City city Kansas 142,341 0.41 0.57 0.16 68 45
37 Lawton city Oklahoma 79,486 0.02 0.18 0.16 156 122
38 Winston-Salem N Carolina 183,467 -0.09 0.07 0.16 199 152
39 Somerville city Massachusetts 74,869 -0.28 -0.13 0.16 264 208
40 Brooklyn Park Minnesota 66,408 -0.38 -0.22 0.15 281 241

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Table 22 lists the 40 cities that experienced the greatest improvement in community
needs between 2000 and 2005 as measured by the equal weight index.

Table 22. Forty Cities with the Largest Decreases in Equal Weight Index
Scores, 2000-2005

City State
2005
Population
2000
EW
Index
2005
EW
Index
Difference
2000
Rank
2005
Rank
1 Pasadena city California 129,400 0.07 -0.28 -0.34 143 254
2 San Marcos city California 77,445 0.24 -0.09 -0.33 103 197
3 Alhambra city California 76,309 0.68 0.37 -0.31 38 78
4 McKinney city Texas 92,337 -0.38 -0.67 -0.29 282 336
5 Suffolk city Virginia 77,922 -0.05 -0.32 -0.27 183 265
6 North Las Vegas Nevada 165,061 0.66 0.41 -0.25 40 68
7 Santa Ana city California 302,302 1.83 1.59 -0.24 1 3
8 Oceanside city California 162,259 0.06 -0.18 -0.24 149 225
9 Peoria city Illinois 102,136 -0.20 -0.43 -0.23 236 290
10 New York city New York 7,956,113 0.55 0.33 -0.22 48 87
11 Fort Lauderdale Florida 141,307 0.13 -0.09 -0.22 125 196
12 Miami city Florida 361,701 0.61 0.39 -0.22 44 74
13 Redding city California 89,362 -0.09 -0.31 -0.22 197 264
14 Santa Barbara city California 90,708 -0.17 -0.37 -0.20 225 274
15 Baldwin Park city California 84,812 1.52 1.32 -0.20 4 8
16 Miramar city Florida 115,444 0.04 -0.16 -0.20 151 215
17 Wilmington city N Carolina 91,207 -0.27 -0.47 -0.20 263 302
18 Buena Park city California 76,062 0.40 0.21 -0.19 70 118
19 Hawthorne city California 100,754 1.16 0.97 -0.19 12 16
20 Columbia city S Carolina 88,450 -0.12 -0.31 -0.19 209 263
21 Los Angeles city California 3,731,437 0.87 0.69 -0.19 20 33
22 Glendale city California 194,620 0.43 0.25 -0.18 63 109
23 Tustin city California 79,811 0.06 -0.13 -0.18 150 209
24 Indio city California 65,091 1.26 1.09 -0.18 7 13
25 Miami Beach city Florida 84,086 1.65 1.47 -0.18 3 4
26 Santa Monica city California 82,777 -0.74 -0.91 -0.18 344 362
27 Long Beach city California 463,956 0.62 0.45 -0.17 43 62
28 Pomona city California 161,257 1.26 1.09 -0.17 8 12
29 Alexandria city Virginia 133,479 -0.54 -0.72 -0.17 321 342
30 Burbank city California 100,053 -0.08 -0.25 -0.17 192 245
31 Asheville city N Carolina 74,889 -0.32 -0.49 -0.17 270 305
32 Stockton city California 278,515 0.78 0.61 -0.17 28 42
33 Fayetteville city Arkansas 58,839 -0.54 -0.71 -0.17 320 341
34 Roanoke city Virginia 90,074 0.12 -0.05 -0.16 129 183
35 Carson city California 92,156 0.42 0.26 -0.16 67 106
36 Des Moines city Iowa 196,917 -0.14 -0.30 -0.16 220 260
37 Cambridge city Massachusetts 81,260 -0.72 -0.88 -0.16 341 357
38 Newark city New Jersey 254,217 1.30 1.15 -0.16 6 10
39 Fullerton city California 142,064 0.00 -0.16 -0.16 162 218
40 Evanston city Illinois 62,258 -0.94 -1.10 -0.16 364 368
Page 61

The two largest cities, New York and Los Angeles, were among the biggest improvers.
Consistent with the improvement in the West region, 21 of the 40 are California cities—
11 more than would have been expected by chance. Among the biggest improvers were
cities that, in 2000, had been ranked number 1 in community needs (Santa Ana); number
3 (Miami Beach); number 4 (Baldwin Park, CA); number 6 (Newark); number 7 (Indio,
CA); and number 8 (Pomona, CA).

4.6. Summary

Factor analysis has allowed us to represent 24 needs indicators by three dimension of
need: poverty and structural problems, immigration and housing affordability, and limited
economic prospects. This chapter successfully applied factor analysis to compare
conditions in 370 cities in 2000 and 2005 on each of the dimensions of needs and an
equal weight index of community needs.

While the equal weight index offers a reliable summary statistic on community needs, the
analysis in this chapter shows that considering the individual factors separately paints a
fuller picture of what is happening in American cities. The factor-by-factor analysis
revealed the following:

• Between 2000 and 2005, cities on average became worse off with respect to
poverty and structural problems and with respect to immigration and housing
affordability problems but became better off with respect to the limited economics
prospects factor.

• Regional differences appeared on the individual factors.

o The Northeast has the highest average scores on the poverty and structural
problems factor in both 2000 and 2005 and the largest increase in average
scores between the two years. The West region has the lowest average
scores on this factor in both years and the smallest increase between the
two years.

o For the immigration and housing affordability factor, the average scores of
cities in the Northeast and West were higher than the national average in
both 2000 and 2005. Cities in the Northeast had the highest average
change between 2000 and 2005.

o Cities in the Northeast had the lowest scores on the limited economic
prospects factor in 2000 and showed the greatest improvement between
2000 and 2005.

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• Differences by class size of cities were less common.

o There appears to be a systematic relationship between the scores on the
poverty and structural problems factor and city size. The average score
declines by size class in both 2000 and 2005. The change in scores is
approximately the same for all the size classes, except for cities with
populations between 500,000 and one million, which have a slight higher
increase in average scores.

o With the exception of cities with over a million residents, there appears to
be little relationship between population size and the prevalence of
problems related to immigration and housing affordability. The largest
cities had an average score of 0.70 or more in both 2000 and 2005; the
national average was 0.00 in 2005.

• There were also some interesting patterns in the lists of cities with the biggest
increase in scores (becoming worse off) and the lists of cities with the biggest
decreases in scores (becoming better off).

o Some of the worse off cities on the poverty and structural problems factor
experienced big increases on this factor between 2000 and 2005; the cities
were Camden, Detroit, Cleveland, Rochester, Reading, and Syracuse.

o Compared with the other states, California has the most cities—95—
among the 370 scored. Still, California cities appeared in higher than
expected proportions on the lists of the 40 biggest losers and gainers. One
would expect, proportionally, 10 cities from California on each list, yet:

− Twenty-four of the 40 cities with the biggest improvements on the
poverty and structural problems factor were California cities.
− Fifteen of the 40 cities with the worse changes on the immigration
and housing affordability factor were California cities.
− The five cities with the largest improvements on the immigration
and housing affordability factor, and 18 of the top 40 were
California cities.

The equal weight index showed that, on average, community needs decreased slightly
between 2000 and 2005. According to the index, conditions were stable or got better in
202 cities. However, the chapter notes that the observed improvement appears to be
related strongly to the substantial increase in the proportion of adults with a high school
diploma between 2000 and 2005, a fact that was questioned in Section 2.3.2.
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Page 64

Page 65
5. Measuring Fiscal Capacity

The federal government, in general, and HUD in particular, are interested in developing
an index of community needs because they want to know the extent to which
communities require federal assistance. But a needs index answers only one-half of this
question; the federal government also needs to know the extent to which communities are
capable of dealing with their problems without federal assistance.

At the Orientation Meeting for this project on October 12, 2006, the question was raised
as to whether the project should attempt to construct a measure of fiscal capacity so that
needs and capacity could be looked at jointly. Opinions differed. Those opposed to
looking at capacity argued that needs are independent of the capacity of the local
government to address those needs. In general, those present seemed to favor developing
a parallel measure of capacity, because federal aid must take both needs and capacity into
account. For this reason, we attempted to create a measure of capacity and to combine
the needs and capacity measures to achieve an integrated view of local conditions.
35

This chapter explores the issues involved in estimating capacity, develops a methodology
to measure capacity, implements the methodology, and then explores how to combine a
measure of need with a measure of capacity. The capacity measure developed is a
significant advancement in assessing need at the city level. It allows one to look at cities
from two different perspectives—needs and capacity. While the work on combining the
needs and capacity measures is only exploratory, the results are reasonable and provide
the first comprehensive assessment of the relative need for federal assistance.

Section 5.1 describes the methodology we used to construct a measure of fiscal capacity.
Section 5.2 lists the variables we used to compute fiscal capacity and how we calculated
the weights applied to those variables. Section 5.3 reports the results of implementing the
measure of fiscal capacity. Section 5.4 explores how to combine a measure of fiscal
capacity with an index of community needs. Section 5.5 reports the results from
combining the equal weight index developed in Chapter 3 with the fiscal capacity
measure developed in this chapter. Section 5.6 provides a brief summary.

5.1. General Approach

We interpret capacity as “access to resources.” This approach equates capacity with the
ability to raise money through taxation—that is, we are concerned with the fiscal capacity
of cities. We measure fiscal capacity, independent of competence. If a city has an
ineffective government but the same access to resources as another city with an effective
government, then, in our opinion, the two cities should be considered to have equal
capacity. We take this perspective based on the premise that achieving effective

35
The GAO has indicated that it intends to look at capacity in its study of community needs and the CDBG
formula.

Page 66
government at the local level is the responsibility of citizens at the local level and not the
responsibility of the federal government.

A similar logic guides us to focus on the ability to raise money for community needs
rather than actual performance in raising money or using funds for community needs.
How much to tax and how to spend tax revenue are matters of choice. A measure of
fiscal capacity should be independent of choice. This orientation affects how we measure
fiscal capacity and the data sources we use.

The Census of Governments collects extensive information on expenditures by class and
on tax revenue by source, for units of government ranging from states to special districts.
Unfortunately, we cannot use this information directly, because those data record the
choices made by cities rather than the capacity of cities. Cities may differ in terms of
what they spend on parks and recreation or how much they collect from property taxes,
either because they differ in the capacity to raise revenue and address local needs or
because they choose to tax and spend differently. For this reason, we chose to estimate
what cities could raise through taxes, rather than what they do raise.

This approach requires us to estimate various tax bases separately, such as income, sales,
and property values, and to find a way to aggregate these bases. Income is a flow, while
property values are a stock, and for this reason they are taxed differently. Adding
personal income to the value of all property would not be a useful measure of fiscal
capacity. The methodological problem is to find a set of weights to combine different tax
bases; the weights should be chosen so as to represent the potential to raise taxes from
each of the bases. We selected average tax rates as weights because they represent actual
experience in translating taxable potential into tax revenue.
36

The choice of average tax rates raises two questions. First, the use of average rates may
bias the measure toward current practice instead of what could be achieved if cities taxed
to maximum capacity. The goal is not to create a measure of maximum potential tax
revenue; instead the goal is create a measure that treats each city fairly in portraying its
fiscal capacity. Actual practice seems to be the best way to achieve this fairness.

Second, since state laws prohibit some cities from taxing certain bases, is it fair to use
average rates when the actual tax rate applicable in a particular city may be zero? We do
not consider this a major limitation. First, states could eliminate any restrictions on what
can be taxed. The rationale behind creating a capacity measure is that the federal
government should not respond to a problem if the local government has the capacity to
solve the problem. Failure of a state to provide jurisdictions with the tools needed to deal
with their problems should not be a reason for federal support. Second, if a jurisdiction
cannot tax one source, it can increase the rate at which it taxes other sources. The real

36
Table 23 shows how we computed the average tax rates and applied them to estimates of income, value,
or sales as appropriate. In general, we used information from the 2002 Census of Government on revenue
raised by various taxes as the numerator for the average tax rates, and estimates of the taxable source in
2002 from other data sets. For property taxes, we used information in the 2001 Residential Finance
Survey to estimate the average tax rate.

Page 67
limit on the ability to raise taxes is the willingness of voting taxpayers to be taxed. The
willingness or ability of taxpayers to bear taxes should depend more on how much is
raised than on how the taxes are collected.

A final concern is the difference in the cost of providing services across cities. Cities
with the same taxing capacity may not be able to provide the same level of services
because it costs more in one city to provide services than it does in the other city. We
solve this problem by dividing the dollar measure of capacity by the average wage of all
government employees in the core-based statistical area in which the city is located.

5.2. Variables Used to Measure Fiscal Capacity

Table 23 explains how we constructed the dollar measure of capacity.

• Variables 2 through 6 are our estimates of the various tax bases potentially
available to cities, each weighted by an estimate of the applicable average tax
rate.

• Variable 1 sums variables 2 through 6 and puts the combined taxing capacity on a
per capita basis; it is our estimate of the capacity that cities have available from
their own resources.

• Variable 7 is our estimate of what cities can expect in funding from states.

• Variable 8 is our estimate of fiscal capacity in dollar terms. It sums the capacity
available to cities from their own resources (variable 1) and the capacity available
to them from their respective states (variable 7).

• Variable 9 is per capita income. We compare our estimate with an estimate based
strictly on per capita income.

• Variable 10 is our government wage variable.
37

• Variable 11 is our estimate of real capacity; it is variable 8 divided by variable 10.

A HUD reviewer pointed out that the use of local government wages as a cost-of-services
deflator may create a bias, because cities differ in how they contract out services. If one
city contracts out services that employ low-wage workers, its wage rate may be higher
than an identical city that provides those services directly. We acknowledge that this is a
potential bias, but we believe it is minimized by our use of local wage data at the level of

37
A HUD reviewer suggested using the federal locality pay tables instead of the BLS data on government
wages. We think that it would be complicated trying to adjust the federal tables for the local mix of white
collar and blue collar workers. The BLS data cover all local employees.

Page 68
Core-Based Statistical Areas (CBSA).
38
Contracting patterns will vary across the CBSA,
and there should be some averaging-out of practices. Another HUD reviewer noted that
we use 2005 population to transform our estimates of capacity into per capita estimates
while some of our data are from 2002. Measures 2, 3, 4, and 7 use 2005 data; measures 5
and 6 use 2002 data.
39
We think 2005 population is probably the best choice for this mix.

38
CBSAs are defined by OMB using criteria that make CBSAs coterminous with local labor markets, and
therefore all governments in a CBSA should face the same wage scale.
39
The weights we apply to measures 2, 3, 4, and 7 are based on data from 2001 and 2002, but the numbers
that vary across cities are from 2005.

Table 23. Variables Used to Measure Fiscal Capacity

Variable Short-Name Definition
1 LOCAL FISCAL CAPACITY LOCFISCAP Sum variables 2 through 6 and divide the sum by city population
2
INCOME TAX CAPACITY

INCTAXCAP
Aggregate household income multiplied by the ratio of local income taxes from
2002 Census of Governments to national household income from 2002
($17,185,681,000/$6,142,192,043,242 = 0.003)
3
RESIDENTIAL PROPERTY TAX
CAPACITY (owner-occupied)
OWNPROPTAXCAP
Aggregate property value multiplied by ratio of real estates paid to owner-
occupied housing value from 2001 Residential Finance Survey (RFS) (0.01).
4
RESIDENTIAL PROPERTY TAX
CAPACITY (rental)

RENTPROPTAXCAP
Aggregate contract rent multiplied by ratio of net operating income to rent income
from 2001 RFS divided by national cap rate (from Goodman
40
) multiplied by ratio
of real estates paid to rental housing value from 2001 Residential Finance Survey.
We do this estimate separate for central cities and non-central cities because
found that cap rates vary between central cities and suburbs.
For central cities: (Estimate aggregate contract rent)*95*0.01
For non-central cities: (Estimate aggregate contract rent)*125*0.01
5
SALES TAX CAPACITY

SALESTAXCAP
Retail sales in 2002 from the Economic Census multiplied by ratio of local
revenue from the sum of the general sale taxes and selective sales taxes in all
localities from the 2002 Census of Governments to total retail sales in 2002 from
Census of Economic Census ($61,761,893,000/$3,056,421,997,000 = 0.02)
6
BUSINESS TAX CAPACITY
(includes payroll taxes, business
property taxes, and corporate
income taxes)

BUSTAXCAP
Aggregate payrolls for the 12 sectors for which the 2002 Economic Census
provides place data times 0.05. 0.05 was derived by estimating commercial real
estate taxes, local corporate taxes, and proportion of local income taxes attributed
to non-residents working in city; summing these items; and taking the ratio of this
sum to the US total of payrolls from the 12 sectors.
7
STATE CAPACITY TO ASSIST
JURISDICTION

STATECAP
Aggregate state income times (the ratio of state aid to local governments to total
state revenue from all sources) times (the ratio of total state revenue from all
sources to aggregate state income) times (the ratio of city population to the sum
of the populations of all cities in our list in that state) divided by (city population).
(Aggregate state income times 0.007 divided by the sum of the populations of all
cities in our list in that state.) Note that this provides a per capita amount
conceptually available to all cities.
8 TOTAL FISCAL CAPACITY TOTFISCAP Sum of variables 1 and 7
9 PER CAPITA INCOME PERCAPINC City per capita income
10
AVERAGE GOVERNMENT
WAGES
CBSAGOVWAGE
The average wage rate of local government employees measured at the CBSA
level using 2004 Bureau of Labor Statistics data.
11 REAL FISCAL CAPACITY REALFISCAP Variable 8 divided by variable 10.
Page 69

40
Issues in Housing Finance: An Analysis of Data from the 2001 Residential Finance Survey, Chapter 11: Estimating Capitalization Rates for Multifamily Rental
Properties with the 2001 Residential Finance Survey, a report submitted by Econometrica, Inc. to HUD, October 30, 2006.

Page 70
We can estimate state funding for all our cities and have data on wages for all but five
cities. We were able to estimate variables 2 through 6 for 266 of the 473 cities, and
therefore could estimate real fiscal capacity only for these 266 cities. The next section
contains our findings. First, however, we note that the correlation of per capita income
with the revenue that a city itself can raise (variable 1) is 0.78. If one wanted to create
estimates of real fiscal capacity for other cities, one could do so by using per capita
income and other relevant variables to model variable 1.

Second, we also note that adjusting capacity for the cost of providing services eliminates
the need to adjust needs for differences in costs of living across jurisdictions. This does
not apply to the counts of poor persons, but does apply to dealing with the needs of
citizens, including poor persons.

5.3. Variations in Fiscal Capacity

Deflating total fiscal capacity by average local government wages translates a dollar
measure of capacity into a measure of the fraction of a year that a city could apply to a
local government worker’s time on a per capita basis to the needs of its citizens. Across
the 266 cities for which we have data, this measure ranges between 0.026 in Laredo, TX
to 0.136 in Charleston, SC. The mean is 0.059 with a standard deviation of 0.018, and
the median is 0.057. This measure implies that, on average, cities should be able to
devote the equivalent of 6 percent of a year’s work from the typical city employee to
solving the needs of a specific citizen. We say “equivalent,” because a city will devote
some of its revenue to purchases of goods and services other than labor, for example,
interest on bonds, rent on buildings, and supplies. Since our estimate sums several
different sources of tax revenue, it probably overestimates the amount available on
average since most cities do not use all the sources. This is not a problem because the
index does not try to measure absolute capacity—only relative capacity.

To provide some impression of how the fiscal capacity measure performs, we produced
three tables. Table 24 lists the 25 cities with the highest fiscal capacity scores based on
the sum of real own resources and state resources per capita; Table 25 lists the 25 cities
with the lowest fiscal capacity scores, and Table 26 shows how this measure varies across
the 25 largest cities for which we have sufficient data to estimate capacity.
41

Tables 24 and 25 show how the presence or absence of resources (income and wealth)
and the costs of providing services interact to determine rank on this measure. Table 24
contains both cities with high per capita income and cities with low government wages.
Eight of the 25 cities in Table 24 (Santa Monica, Scottsdale, Cambridge, Bellevue, Santa
Barbara, Santa Fe, Atlanta, and Boulder) are among the 25 with the highest per capita
income among the 266 cities for which we have data. At the same time, four of the 25
(Fort Smith, Boise, Salt Lake City, and North Charleston) are among the 25 with the

41
Since we have data on both needs and real capacity for only 266 cities, many of the cities discussed in
Chapter 4 do not appear in this analysis. This includes some cities with very high need scores such as
Camden and Trenton.

lowest annual wages for government employees based on data at the metropolitan-area
level.

Table 25 contains both cities with low per capita income and cities with high government
wages. Nine of the 25 cities in Table 25 (Brownsville, Laredo, Hialeah, Gary, San
Bernardino, Pomona, Inglewood, and Flint) are among the 25 with the lowest per capita
income among the 266 cities for which we have data. At the same time, two of the 25
(Mount Vernon and Yonkers) are among the 25 with the highest annual wages for
government employees based on data at the metropolitan-area level. Brownsville is an
interesting case; it has the lowest per capita income of any of the 25 cities and the
eighteenth lowest annual government wages. Despite low government wages,
Brownsville ranks as the city with the third lowest real fiscal capacity.

Table 24. Twenty-five Cities with Greatest Real Fiscal Capacity

City State Population
Real Own + State
Resources Per Cap
1 Charleston city South Carolina 109,151 0.136
2 North Charleston city South Carolina 70,001 0.121
3 Cambridge city Massachusetts 81,260 0.116
4 Bloomington city Minnesota 80,055 0.106
5 Boulder city Colorado 83,432 0.105
6 Santa Monica city California 82,777 0.104
7 Atlanta city Georgi 394,929 0.101
8 Bellevue city Washington 114,748 0.100
9 Nashua city New Hampshire 84,632 0.099
10 Fort Smith city Arkansas 81,054 0.096
11 Little Rock city Arkansas 176,924 0.095
12 Asheville city North Carolina 74,889 0.092
13 Billings city Montana 92,844 0.092
14 Fargo city North Dakota 88,809 0.090
15 Salt Lake City Utah 182,670 0.089
16 West Palm Beach city Florida 86,804 0.089
17 Fort Lauderdale city Florida 141,307 0.088
18 Scottsdale city Arizona 215,933 0.087
19 St. Cloud city Minnesota 59,624 0.086
20 Santa Barbara city California 90,708 0.085
21 Santa Fe city New Mexico 66,453 0.085
22 Norwalk city Connecticut 86,354 0.085
23 Boston city Massachusetts 520,702 0.085
24 Boise City Idaho 191,667 0.085
25 Manchester city New Hampshire 109,308 0.085

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Table 25. Twenty-five Cities with the Least Real Fiscal Capacity

City State Population
Real Own + State
Resources Per Cap
1 Laredo city Texas 207,787 0.026
2 Inglewood city California 120,204 0.028
3 Brownsville city Texas 171,528 0.028
4 Pomona city California 161,257 0.030
5 Pasadena city Texas 150,180 0.032
6 Mount Vernon city New York 65,354 0.032
7 Garland city Texas 235,750 0.032
8 San Bernardino city California 204,552 0.033
9 Gary city Indiana 97,057 0.033
10 Hemet city California 77,076 0.033
11 El Paso city Texas 583,419 0.034
12 Detroit city Michigan 836,056 0.034
13 Garden Grove city California 192,345 0.034
14 Glendale city Arizona 229,913 0.035
15 Mesquite city Texas 126,895 0.035
16 Kansas City Kansas 142,341 0.035
17 Hialeah city Florida 213,791 0.035
18 Stockton city California 278,515 0.036
19 Flint city Michigan 111,948 0.036
20 Peoria city Arizona 141,941 0.036
21 Fresno city California 477,251 0.037
22 Long Beach city California 463,956 0.037
23 Mesa city Arizona 442,445 0.037
24 Yonkers city New York 193,327 0.037
25 Arlington city Texas 348,965 0.037

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Table 26. Real Fiscal Capacity for 25 Largest Cities with Data

City State Population
Real Own + State
Resources Per Cap
1 New York city New York 7,956,113 0.041
2 Los Angeles city California 3,731,437 0.043
3 Chicago city Illinois 2,701,926 0.049
4 Houston city Texas 1,941,430 0.053
5 Philadelphia city Pennsylvania 1,406,415 0.047
6 Phoenix city Arizona 1,377,980 0.043
7 San Diego city California 1,208,331 0.065
8 San Antonio city Texas 1,202,223 0.038
9 Dallas city Texas 1,144,946 0.056
10 San Jose city California 887,330 0.051
11 Detroit city Michigan 836,056 0.034
12 Jacksonville city Florida 768,537 0.050
13 Indianapolis (balance) Indiana 765,310 0.064
14 San Francisco city California 719,077 0.064
15 Columbus city Ohio 693,983 0.057
16 Austin city Texas 678,457 0.059
17 Memphis city Tennessee 642,251 0.049
18 Baltimore city Maryland 608,481 0.066
19 Fort Worth city Texas 604,538 0.041
20 Charlotte city North Carolina 601,598 0.066
21 El Paso city Texas 583,419 0.034
22 Milwaukee city Wisconsin 556,948 0.048
23 Denver city Colorado 545,198 0.066
24 Las Vegas city Nevada 538,653 0.040
25 Seattle city Washington 536,946 0.082

In the 25 largest cities, local governments can devote between 0.03 to 0.08 percent of a
city government employee’s time to the needs of each resident. The largest cities tend to
have lower fiscal capacity, 17 of the 25 largest have a fiscal capacity score below the
average of 0.059.

5.4. Combining Need and Fiscal Capacity

5.4.1. Background

The primary reason for developing a measure of real fiscal capacity was to complete the
picture of city need for federal community development assistance. It was felt that the
need for federal help depends both on community needs and the resources available at the
community level to deal with those needs. However, an important question remains:
How does one combine information on community needs and real fiscal capacity to
obtain an accurate assessment of need? Our aims in this section are modest. We discuss
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some ideas we have about combining the needs and real fiscal capacity measures and
explore one simple way to combine them.

We experiment with combining our estimate of city needs from Chapter 3 with the
estimate of the capacity of cities to deal with their needs from this chapter. There are 234
cities for which we have both an equal weight index score and an estimate of real fiscal
capacity.

5.4.2. Simple Options for Combining the Measures

In Chapter 4, we looked at city needs in two different ways: we looked at conditions
measure by each of the three factors from the factor analysis in Chapter 3, and then we
combined the factor scores into a single-valued index of need using several different
weighing options. At this stage, we have to choose a single-valued needs index because
it would be meaningless to combine the real fiscal capacity measure with the individual
factors (“meaningless” in the sense that we would still be missing parts of the puzzle—
namely, needs measured by the omitted factors). For the purposes of this work, we will
use the equal weight index because it is the simplest. Any of the other options could have
been combined with the real fiscal capacity measure in the manner described below.

Our first thoughts involved taking a ratio of needs to capacity as measured by the two
indices. The attraction of this approach was the possibility of making statements—such
as a city’s needs exceed its capacity by 10 percent. However, the ratio approach, and all
approaches involving a multiplicative joining of the indices, falters because the two
measures have both positive and negative values. There is no way to combine series with
positive and negative values in a multiplicative way and obtain consistent results.

We define a positive needs score as meaning a city has above-average community needs
and a positive capacity score as meaning a city has above-average real fiscal capacity.
Negative scores mean being below average on both of the component indices. Using
these conventions, the following table shows how the various possibilities combine to
produce either positive or negative ratios.

Sign of the ratio of indices Above average needs Below average needs
Above average fiscal capacity Positive Negative
Below average fiscal capacity Negative Positive

In this table, the best possible situation (having below-average needs and above-average
capacity) has the same sign as the worst possible situation (having above-average needs
and below-average capacity). This result is not desirable.

One could try to avoid this problem by redefining the real fiscal capacity index so that
having above-average capacity results in a negative score and having below-average
capacity results in a positive score. If one does this, then the previous table becomes:

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Page 75
Sign of the ratio of indices Above average needs Below average needs
Above average fiscal capacity Negative Positive
Below average fiscal capacity Positive Negative

Now the best possible situation and the worst possible situation both produce positive
ratios. Again, this is not a desirable result.

The next simplest approach was an additive approach. We standardized both the scores
from the equal weight index and our estimates of real fiscal capacity. After
standardization, a need score of +1.00 indicates that the needs of a city are one standard
deviation above the needs of the average city, while a real fiscal capacity score of +1.00
means that a city’s capacity to meet the needs of its citizens is one standard deviation
above the average city. We subtract the standardized capacity score from the standard
needs score, so that a city with needs of +1.00 and capacity of +1.00 would have a
combined score of 0.00. Both the needs score and the capacity score refer to 2005.

The following table characterizes the results of combining the indices using this
approach.

Sign from the subtraction of indices Above average needs Below average needs
Above average fiscal capacity Uncertain Negative
Below average fiscal capacity Positive Uncertain

Now the best possible situation (below-average needs and above-average capacity) has a
negative sign, while the worst possible situation (above-average needs and below-average
capacity) has a positive sign. The signs of the other two alternatives depend on the
magnitude of the indices involved in the subtraction. If needs are above average by more
than capacity is above average, the score will be positive. If needs are below average by
less than fiscal capacity is below average, the score will also be positive. While the
technique of subtracting the real fiscal capacity index from the community needs index is
simple, it does produce a consistent ranking of cities.
42

The scores produced by subtracting the index of real fiscal capacity from the index of
community needs only rank cities by relative need; they do not indicate whether an
individual city needs federal assistance and, if so, how much. Consider the situation of a
city with a community needs score of 1.00 and a real fiscal capacity score of 1.00; the
combined index produces a score of 0.00. This score tells us nothing about the absolute
need for federal assistance; it does tell us only that the city needs assistance more than
cities with negative scores and less than cities with positive scores.

42
This approach assumes an implicit equivalence between the standard deviations of needs and real
capacity; in other words, it assumes that being one standard deviation away from the average has the same
implications for the two indices. A city with average needs but real capacity one standard deviation below
average will have a score of 1.00; a city with average needs one standard deviation above average but with
average real capacity will also have a score of 1.00. This approach presumes that these two scores indicate
the same level of need from the federal government.

By standardizing the two components before combining them, we produce an index
where 0.00 is the average value. Does a city where needs and capacity balance need
assistance? On average, are cities able to deal with their problems or on average do they
need assistance? None of the analysis in this chapter or anywhere in this report is capable
of answering these questions. This is perhaps the fundamental issue of fiscal federalism.

5.5. An Index of Needs Adjusted for Capacity

Combining the two indices produces an index of community needs adjusted for real fiscal
capacity. As explained in Section 5.4.2, we calculate it, first, by standardizing both the
equal weight index of community needs and the index of real fiscal capacity across the
234 cities for which we have data and then by subtracting the index of real fiscal capacity
from the equal weight index of community needs. For simplicity, we call the resulting
index, the adjusted needs index. Table A.10 in Appendix A reports the score on the
adjusted needs index for all 234 cities.

The adjusted needs index has a mean of 0.00 and a standard deviation of 1.76. Positive
values indicate adjusted needs higher than average, and negative values indicate adjusted
needs lower than average. Adjusted needs ranges from 4.29 (Santa Ana, TX) to -5.28
(Charleston, SC). The range is large (9.57), and the standard deviation is large relative to
the standard deviations of the component indices because there is moderately strong
negative correlation between community needs and fiscal capacity. The correlation
between the two component indices is -0.55. In general, the greater the needs of a city,
the less real fiscal capacity the city has. This is an important finding.

From the 234 cities for which we have data on both needs and capacity with the highest
index of community needs, Table 27 takes the 50 cities with the highest scores on the
equal weight index of community needs and shows how taking fiscal capacity into
account would affect their ranking. Because of the negative correlation between the two
component indices, adjusting needs for capacity does not produce large changes in the
rankings of these cities. Only nine of the cities ranked among the top 50 on community
needs have a rank lower than 50 on the combined index; these are Dayton (25 to 52),
Birmingham (34 to 98), Lynn (36 to 53), Irving (43 to 115), Waco (44 to 69), Jersey City
(45 to 57), Miami Beach (46 to 81), Memphis (48 to 62), and Escondido (50 to 65).

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Table 27. Impact of Combining Needs and Fiscal Capacity for 50 Cities
with the Highest Community Needs
Equal
Weight
Index
Rank
City State Population
Equal
Weight
Index
Real
Fiscal
Capacity
Adjusted
Needs
Index
Adjusted
Needs
Index
Rank
1 Passaic city New Jersey 68,422 3.28 -0.959 4.24 2
2 Santa Ana city California 302,302 3.04 -1.245 4.29 1
3 Miami city Florida 361,701 2.82 -0.116 2.93 13
4 Lawrence city Massachusetts 82,191 2.81 -1.011 3.82 3
5 Reading city Pennsylvania 81,302 2.55 -0.277 2.83 15
6 Salinas city California 156,950 2.52 -1.239 3.76 4
7 Paterson city New Jersey 148,353 2.29 -0.984 3.28 10
8 Newark city New Jersey 254,217 2.18 -0.704 2.89 14
9 San Bernardino California 204,552 2.12 -1.531 3.65 6
10 Pomona city California 161,257 2.07 -1.692 3.76 5
11 Detroit city Michigan 836,056 1.89 -1.460 3.34 9
12 Elizabeth city New Jersey 121,137 1.84 -0.726 2.56 16
13 Cleveland city Ohio 414,534 1.81 -0.394 2.21 24
14 Inglewood city California 120,204 1.81 -1.809 3.62 7
15 Oxnard city California 178,871 1.81 -1.127 2.93 12
16 Pasadena city Texas 150,180 1.79 -1.564 3.36 8
17 Garden Grove California 192,345 1.58 -1.437 3.01 11
18 Ontario city California 156,679 1.55 -0.729 2.28 21
19 Houston city Texas 1,941,430 1.54 -0.365 1.91 29
20 Springfield city Massachusetts 146,948 1.52 -0.407 1.93 28
21 Dallas city Texas 1,144,946 1.51 -0.237 1.74 35
22 Rochester city New York 189,312 1.46 -0.278 1.74 37
23 Yakima city Washington 79,517 1.37 -0.317 1.69 40
24 Los Angeles city California 3,731,437 1.29 -0.961 2.25 22
25 Dayton city Ohio 132,679 1.20 -0.240 1.44 52
26 Anaheim city California 329,483 1.17 -0.930 2.10 26
27 Stockton city California 278,515 1.13 -1.342 2.48 19
28 New Bedford Massachusetts 84,898 1.12 -0.782 1.90 30
29 Kansas City Kansas 142,341 1.07 -1.383 2.45 20
30 Hemet city California 77,076 1.06 -1.488 2.55 17
31 Providence city Rhode Island 160,264 1.04 -0.831 1.87 32
32 Buffalo city New York 256,492 1.03 -1.178 2.21 23
33 Garland city Texas 235,750 1.00 -1.548 2.55 18
34 Birmingham city Alabama 222,154 1.00 0.706 0.29 98
35 Fall River city Massachusetts 97,612 0.95 -0.783 1.74 36
36 Lynn city Massachusetts 83,419 0.90 -0.512 1.41 53
37 Pawtucket city Rhode Island 72,896 0.86 -1.119 1.98 27
38 Milwaukee city Wisconsin 556,948 0.86 -0.637 1.49 50
39 Lowell city Massachusetts 96,876 0.85 -0.699 1.54 47
40 Philadelphia city Pennsylvania 1,406,415 0.83 -0.744 1.58 44
41 Long Beach city California 463,956 0.83 -1.294 2.12 25
42 Syracuse city New York 132,495 0.82 -0.563 1.39 55
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Table 27. Impact of Combining Needs and Fiscal Capacity for 50 Cities
with the Highest Community Needs (continued)
Equal
Weight
Index
Rank
City State Population
Equal
Weight
Index
Real
Fiscal
Capacity
Adjusted
Needs
Index
Adjusted
Needs
Index
Rank
43 Irving city Texas 212,262 0.77 0.871 -0.10 115
44 Waco city Texas 107,146 0.75 -0.405 1.15 69
45 Jersey City New Jersey 246,335 0.74 -0.625 1.36 57
46 Miami Beach Florida 84,086 0.72 0.108 0.61 81
47 Oakland city California 373,910 0.72 -0.990 1.71 39
48 Memphis city Tennessee 642,251 0.71 -0.583 1.29 62
49 Fort Worth city Texas 604,538 0.68 -1.048 1.73 38
50 Escondido city California 133,017 0.67 -0.567 1.24 65

Looking at all 266 cities, some of the changes in ranking are substantial. West Palm
Beach is ranked 65
th
on the equal weight index but 182
nd
on the adjusted needs index;
Atlanta is ranked 95
th
on the equal weight index but 211
th
on the adjusted needs index.
Other cities benefit substantially by the adjustment for fiscal needs. Peoria is ranked
190
th
on the equal weight index but 89
th
on the adjusted needs index; Henderson, NV is
ranked 208
th
on the equal weight index but 108
th
on the adjusted needs index.

5.6. Summary

This chapter explains why it is important to construct an index of fiscal capacity,
develops a methodology to construct such an index, finds data to implement the index,
and suggests a reasonable approach for combining information on community needs and
real fiscal capacity to obtain an index of community needs adjusted for real fiscal
capacity.

The most important findings from the chapter are:

• It is possible to construct an index of real fiscal capacity.

• The index used is sensitive to both income and wage rates. Places with high
income or lower government wages are more likely to have high scores on the
index—that is, to have better-than-average fiscal capacity.

• The index is negatively correlated with the equal weight index of community
needs. Cities with high community needs are more likely to have low real fiscal
capacity.

• It is possible to combine a needs index and a fiscal capacity. The adjusted needs
index developed in this chapter produced different rankings than the equal weight
index of community needs. But, in general, the change in rankings was not great,
probably because of the negative correlation between the two component indices.
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6. Implications

This chapter discusses the implications of this research for future analysis in areas of
operational interest to HUD. The topics covered in successive sections are:

1. Feasibility of using ACS data to monitor community needs at the city and county
levels.

2. Feasibility of constructing a single-valued index of community needs.

3. Feasibility of comparing community needs at different points in time.

4. Feasibility of extending the factor analysis developed in this report to different
geographies when the ACS releases data for smaller places.

5. Feasibility of constructing a measure of neighborhood improvement to implement
the Administration proposal to reward communities for successful community
developments efforts.

6. Feasibility of measuring fiscal capacity.

7. The relevance of boundary changes and cost-of-living differences to an analysis
of community needs.

The last section identifies areas where more work needs to be done to improve HUD’s
ability to monitor community needs.

6.1. ACS Data and Community Needs

In previous studies, researchers at HUD used data from the long form of the decennial
census and other sources to identify and measure community needs. The Census Bureau
has replaced the long form with the American Community Survey, a monthly survey of
250,000 households that reports data using 5-year moving averages for all levels of
geography, using 3-year moving averages for places with 20,000 or more persons, and
using annual data for places with 65,000 or more persons. One objective of this research
was to test whether the ACS data would support the same type of analysis that HUD had
conducted using long-form data.

The answer to this question is “yes.” In the future, HUD can depend on the ACS to
monitor conditions in cities and counties. The report successfully uses ACS data to
construct useful measures of community needs using factor analysis. Of the 24 needs
indicators used in the final factor analysis, 16 used ACS data, one (POORPERS) used
ACS data combined with long-form data, and four used long-form data. All five
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indicators that either used long-form data or a combination of ACS and long-form data
should be available in the future from the ACS.

The study did not use as a needs indicator the proportion of persons with a disability that
limits employment. This variable is reported by both the ACS and the long form, but a
change in the way the Census Bureau collects this information created an artificial shift in
the published results. HUD should be able to use this variable as a need indicator in
future analyses.

There are some issues and open questions that HUD will have to keep in mind in future
work using the ACS:

• The reporting rules used in the ACS are similar to those used for the long form of
the decennial census; but because the ACS sample size is smaller, the rules can
result in more frequent suppression of data. Data on overcrowded housing was
missing for so many places in the 2005 ACS data that we substituted 2000 long-
form data for this need indicator. We also had to drop 36 cities from the analysis
because the ACS suppressed data on the number of minorities in the cities. HUD
should be able to work around suppression of data at the city or county level by
using 3-year or 5-year moving average data. There will be no solution to
suppression of data at the tract level.

• As a general policy, the Census Bureau plans to release for the ACS all tables
released for the 2000 long-form data. However, some special tabulations that
were made public for long-form data have not yet been released for ACS data.
The ones relevant to community needs analysis involve the intersection of
information on poverty level, age of housing, and tenure. HUD should probably
contact the Census Bureau to make sure that these tabulations are not forgotten.

• The ACS has not released data on persons in group quarters yet, so we have no
experience with the usefulness of the tabulations or the reliability of the data.

• The Census Bureau will make revisions to the ACS questionnaire. Revisions
always create the possibility of discontinuities in the data, such as the one that
occurred with the disability questions. Users will have to be aware of possible
problems.
43

43
The Census Bureau plans to restrict revisions to the ACS questionnaire to once every decade. All
changes will be incorporated into the surveys beginning in the year ending in an “8,” e.g., 2008. Using this
procedure, the first 5-year moving average using the new questionnaire for all 5 years will be centered on a
year ending in “0,” e.g., 2010. This procedure ensures that the data centered on a decennial census year
will all use the same questionnaire.

6.2. A Single-Valued Index of Community Needs

The explicit goal of this project was to develop a single-valued index of community
needs. The research achieved this objective, but the outcome was only a qualified
success. Nevertheless, the research resulted in two important insights: first, that an
equal-weighted index correlates highly with most reasonable alternatives, and second,
that changes in individual factor scores provide more useful information than do changes
in an index, for understanding how conditions in individual cities have changed. The
research also explored a new methodology that may prove useful in related future work
and incorporated fiscal capacity into the analysis.

Using data from the ACS and other sources, this report carried out a factor analysis that
identified three dimensions of community needs: needs associated with poverty and
structural problems, needs associated with immigration and lack of affordable housing,
and needs arising from limited economic prospects. The report used these factors in an
equal weight index that ranked cities on community needs.

The equal weight index was one of six indices that the report considered. The report was
unable to find any statistical, programmatic, or logical reasons that make a compelling
case for choosing one index over any of the others. We had hoped that the hedonic
analysis would provide definitive guidance in weighting the factors or even the needs
indicators. However, the prominent role of housing affordability in Factor 2 and in two
or three of the needs indicators undermined attempts to apply the hedonic results directly.
Index 4, which provides a triple weight to the limited economic prospects factor, is
derived from the hedonic analysis. Index 6 applies information from the hedonic analysis
to derive weights. This approach is interesting, but we did not have the opportunity to
examine its strengths and weaknesses. We consider the equal weight index to be only a
qualified success because of our inability to justify the choice of weights.

The efforts to develop a single-valued index did lead to two useful insights. First,
comparing the six indices indicated that the rankings of cities across the other five indices
were highly correlated with the ranking on the equal weight index. Statistically, the equal
weight index produces results that are very similar to the other indices. Statistical
closeness does not mean that the ranking of some cities are not substantially different
depending upon the index used. If HUD were to use one of these indices to allocate
funds to cities, the choice of index would be of great concern to individual cities. But, if
HUD is interested primarily in analyzing the variation in needs across cities and over
time, then the results from the equal weight index will be similar to those from any index
that applies reasonable weights to the factor scores.

Second, the analysis revealed that a single-valued index provides simplicity at the cost of
concealing interesting information. If HUD is interested in how community needs vary
across cities and over time, it should look at variation and changes in both the individual
factors and a single-valued index.

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The most important contribution of the research to the development of an index of
community needs was the development of an index of real fiscal capacity and the
development of a technique to combine an index of community needs with an index of
fiscal capacity. From the perspective of the federal government, needs cannot be
considered without reference to capacity. We discuss further the implications of the
work on fiscal capacity in Section 6.6.

6.3. Intertemporal Comparisons of Needs

This research developed a methodology for applying factor analysis to data on needs at
two points in time and successfully implemented the methodology. The methodology
allows HUD to compare factor scores from two different years to measure how
conditions have changed in individual cities. Because the factor scores are computed
from sample data, they are subject to sample variation and thus some care must be taken
in interpreting the results. But the sample variation problem is no more serious than
comparison involving sample data.

There are two keys to carrying out intertemporal comparisons correctly. First, one must
be sure that the dimensions of need identified in the base year are still relevant in the
comparison year. Second, one must measure needs relative to conditions in the year in
which the factor analysis is performed—that is, one must use the means and standard
deviations from that year to standardize the needs indicators in both years.

After reviewing the first draft of this report, HUD asked us to comment on whether we
thought the factors identified in this report would be capable of comparing conditions in
individual cities in 2005 and 2010. As noted above, one must always check to see if the
dimensions of need are the same in both the base year and comparison year. To do this,
one applies factor analysis to the needs indicators in both the base year and the
comparison and compares the results. The factor analysis and the needs indicators
should pass the Kaiser test for factor suitability in both years, and the test used to choose
the number of factors should result in the same number of factors in both years. In
addition, the factor loading should be similar in both years. To our knowledge, there is
no statistical test to determine whether the loadings are similar, so this will be a judgment
call.

There are three reasons why HUD may not be able to apply the factor analysis developed
for 2005 in this report to data from 2010. First, the underlying relationship between the
needs indicators may change between 2005 and 2010. In our opinion, a change in the
underlying relationships in only 5 years would be unusual but this is an empirical
question. If there is a change in the underlying relationships, then the only solution is to
recognize the break in pattern and try to interpret how needs have changed by comparing
the results from using factors derived from both years. In other words, one would
develop a set of factors using data from 2005 on needs indicators, apply the standardized
scoring coefficients to standardized data from 2005 and 2010, and record how conditions
changed. Then, one would repeat the steps using factors based on data from 2010. For
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each city, one would compare the answers from the two sets of factor analyses and try to
obtain a reasonable estimate of how conditions have changed in that city.

In our view, the other two reasons for a break in factor patterns are more likely. First, as
the ACS releases data on smaller cities, HUD will be applying the factor analysis to more
cities and to urban counties. The addition of more observations may result in a change in
factor pattern. Second, we believe that HUD should attempt to expand the needs
indicators to provide more information on economic conditions and to cover needs
related to education and public health. The addition of new indicators could also result in
a change in factor pattern. In both cases, there is a simple solution. One would apply the
factor pattern based on more observation or more needs indicators and derived in 2010 to
both 2005 and 2010.

6.4. Factor Analysis Involving Different Geographies

In 2008, the Census Bureau will release ACS data based on 3-year moving averages for
counties and places with populations of 20,000 or more. The 2008 release will cover data
collected in 2005, 2006, and 2007. The availability of new data raises three issues with
respect to the factor analysis developed in this research:

• Will HUD be able to use the ACS data to construct urban counties and will the
addition of urban counties change the factor pattern?

• Will the addition of cities with populations between 20,000 and 65,000 change the
factor pattern?

• Should non-urban counties be included in the same factor analysis as cities and
urban counties?

A related issue is whether to use 3-year moving average data for all places, even those
where annual data are available.

The answers to all of these questions are inherently empirical. Researchers will have to
wait for the 2008 ACS data and see how they change the analysis. The work in this
project is most relevant to the issue of whether the factor patterns will change with the
additions of observations on cities with populations between 20,000 and 65,000.

Section 3.2.3 discusses the results we obtained when we split the sample into cities with
200,000 or more residents and cities with less than 200,000 residents and ran factor
analysis on the two groups separately. (Table A.5 in Appendix A reports the results for
the rotated factors.) The most important difference between the two factor analyses
involved the Eigenvalue test that we used to determine the number of factors. The test
identified four factors for the cities with 200,000 or more residents and only three factors
for cities with less than 200,000 residents. There were some other noteworthy differences
that are discussed in Section 3.2.3. Despite these differences, we applied factor analysis
Page 83

to the combined database that includes both large and small cities. In our opinion, the
fourth factor added little to the analysis, and the other differences were minor.

Our greatest concern about combining large and small cities is that the test we applied in
Section 3.2.3 uses a boundary point, a population of 200,000, which does not adequately
distinguish large cities from small cities. The problem is that there are too few “large”
cities to run a separate factor analysis with a boundary point much higher than 200,000.
This problem will not lessen when the Census Bureau releases data on smaller cities. It
may be that adding more cities to the under-200,000 group will result in a sharper
difference in factor patterns between the two groups. However, we do not think this is
the likely outcome. The factor pattern for the under-200,000 group is very close to the
factor pattern for the entire group of cities. This leads us to believe that adding smaller
cities will not greatly change the factor pattern.

With respect to the other questions, we offer our opinions below, with the proviso that we
think reliable answers are empirical and must await the 2008 data.

• With respect to the urban counties issues, we believe that the 3-year moving
average data will allow HUD to construct reliable needs indicators for urban
counties and that adding urban counties to the factor analysis will not change
factor pattern greatly. (In this discussion, we use the HUD definition of urban
counties.)

• With respect to non-urban counties, we believe that factor analysis applied
separately to non-urban counties and to cities and urban counties combined would
result in different factor patterns. At a minimum, combining the two groups
would require eliminating important needs indicators, including
MEDINCCBS2CITY, POVCON, and MINCON, because they are defined in the
context of metropolitan areas. These needs indicators figured heavily in the
interpretation of Factor 1.

• With respect to using 3-year moving average data or annual data, we favor
consistency—that is, using the data defined for the same period for all places. At
the city and urban county level, we think 3-year average data are current enough
to represent present needs. One possible problem is that the Census Bureau may
not release some 3-year average data for smaller cities, just as it has not released
annual data on minority populations and overcrowded housing for some cities
with populations greater than 65,000. In this case, we favor using 5-year average 

data for the missing variables and relying on 3-year average data for most of the
needs indicators.

6.5. Measuring Progress at the Tract Level

HUD indicated early in the project that it was interested in the lessons that could be
drawn from this research that are applicable to measuring needs at the tract level. The
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Page 85
Administration has proposed creating a special fund within the CDBG program to award
communities for making progress in reducing neighborhood distress. Such a proposal
would require a community-needs measure at the neighborhood level. Since ACS data
will be available at the census-tract level beginning in 2010, it was hoped that the
experience gained here in constructing a city-level index using ACS data would be useful
to HUD in developing a neighborhood-level index.

In trying to apply our experience to this issue, we first considered the applicability at the
tract level of the 27 needs indicators in Table 1. We think 7 or 8 of the 27 would not be
applicable at the tract level. The two crime variables are not available because the FBI
does not collect crime data at the tract level. The three long-run decline variables,
EXCSINFRA, CHNGEMPLOYBASE, and CHGLOWINCCON, are relevant only in the
city context. Finally, there are three variables linked to poverty rate of the tract; these are
PCTPOPHIGHPOVNGHS, PCTPOPMODPOVNGHS, and PCTVACMODPOVCITY.
The first two of these variables could not be integrated into a tract-level factor analysis
because they become binary variables (100 percent or 0 percent) at the tract level.
Conceptually, one could incorporate PCTVACMODPOVCITY into a tract-level factor
analysis because this variable takes on more than two values. The other 19 needs
indicators could be computed at the tract level using 5-year moving average data from the
ACS.
44

Because of the substantial change in the number and type of indicators, a new factor
analysis would have to be performed at the tract level. This factor analysis is likely to
identify different dimensions of need than the three identified at the city level in this
report. Finding appropriate weights for the factors will continue to be a problem if a
single-value index of community needs is required.

There are important data and conceptual considerations in measuring progress at the tract
level. First, the ACS has a lower sampling rate over 5 years than the long-form survey in
the decennial censuses. For this reason, the Census Bureau is more likely to suppress
data used for some of the needs indicators at the tract level. Second, measurement error
will be relatively high so that year-to-year changes on individual indicators may not
reflect actual changes. The use of moving averages will dampen the effect of an unusual
sample in any one year, but the dampened effect will persist for 5 years. Third, the use of
5-year moving average data probably determines the time frame to be used to measure
progress. One is likely to want to compare conditions at 5-year intervals so that none of
the samples overlap in the before and after comparison.

On the conceptual side, a clear distinction needs to be made, if one plans to reward cities
for alleviating needs, between measuring a change in needs and measuring how local
government actions have reduced community needs. Even at the city level, needs can
change independent of the efforts of the city to lessen needs. In the context of the factor
analysis used in this report, a strong national economy can lower the values of important

44
The three dissimilarity indicators—MEDINCCBS2CITY, POVCON, and MINCON—could be
computed at the tract level but may have a different meaning at the tract level. We included these variables
as indicators of complicating conditions at the city level.

Page 86
needs indicators, such as the unemployment rate, the overall poverty rate, the proportion
of poor children and school-age children, and even the disparity in incomes between
cities and metropolitan areas. The same consequences of a strong national economy
apply to measured needs at the tract level. Conceptually, one would like to control for
outside influences so that cities would not benefit from favorable external conditions or
suffer from unfavorable external conditions. One possible way of doing this would be to
measure progress against some national average.

At the tract level, lower values for the needs indicators may or may not correspond to
what HUD would consider improved conditions. Consider two different scenarios that
result in a lower poverty rate for a single tract. In the first scenario, the city provides
training to low-skilled workers, provides day care for working families, and encourages
small business startups in the tract. These changes result in lower unemployment and
higher incomes. In the second scenario, the city undertakes rigorous code enforcement
resulting in demolition of low-rent structures, increases police protection, and improves
neighborhood schools. These changes result in gentrification and, therefore, a lower
poverty rate for the tract. The values of many of the needs indicators that can be
computed at the tract level are sensitive to movement of households into and out of a
tract.
45
The ACS tables on migration offer limited help in controlling for the impact of
gentrification. The ACS provides tables on age, race, household type, education
attainment, individual income, and poverty status separately for persons who lived in the
same house one year ago and persons who moved into the house within the past year.

All of these considerations suggest that, despite some useful insights gained from factor
analysis at the city level, HUD will need to do a lot of conceptual and empirical work to
develop a measure capable of implementing the Administration’s proposal.

6.6. Measuring Fiscal Capacity

The report developed and implemented a measure of real fiscal capacity. This is a
substantial advance in understanding the relative need for federal assistance among cities.
In our opinion, capacity should be considered along with needs in determining how much
federal assistance cities should receive.

With respect to the measure of real fiscal capacity, more work needs to be done in two
areas.

First, because of data limitations, we were able to estimate relative real fiscal capacity for
only 266 of our 473 cities. The data failure that had the largest impact on the analysis
was the absence of data from the 2002 Economic Census for a large number of cities.
We used the economic census data to estimate sales tax capacity and business tax

45
As noted earlier in the discussion of this topic, 19 of the 27 need indicators can be computed at the tract
level. Fifteen of these depend on the characteristics of persons residing in the tract, and therefore are
affected by movement of households into and out of the tract. Examples of these are: POORPERS,
POORCHILD, LWINCHHDS, SGLPRNTFAM, RCNTIMMIG, and UNEDUCADULTS.

capacity. There are three possible strategies for increasing the number of cities for which
real fiscal capacity can be computed. First, HUD could work with the Census Bureau to
obtain data on more places from the economic census. Perhaps, HUD could construct the
index at the Census Bureau using data not released to the public. Second, HUD could try
to find alternative ways to estimate sales tax capacity and business tax capacity. Third,
HUD could use the data from the 266 cities to develop a model of real fiscal capacity
applicable to all cities.

We think the modeling approach is imminently doable. The correlation between per
capita income and fiscal capacity was 0.78, indicating the per capita income explains half
of the variation in fiscal capacity. The addition of other variables such as region, city
size, principal city/suburb status, and proportion of working-age population should
produce a model that fits the data well.

Second, more thought needs to be given to how to combine an index of real fiscal
capacity with an index of community needs. The approach we developed provides a
consistent linking of the two indices and works reasonably well. Its main weakness is the
assumption that the extent to which a city is below average on real fiscal capacity
(measured in standard deviations) indicates the same level of need for federal assistance
as an equal distance above average on community needs.

6.7. Changing City Boundaries and Cost-of-Living Differences

In the Statement of Work, HUD asked us to consider the implications for an index of
community needs of changing city boundaries and cost-of-living differences between
cities.

After selecting the 27 needs indicators in Table 1, we considered whether changes in city
boundaries could affect the relevance or interpretation of any of the indicators. This
question is particularly relevant to this research because an explicit goal was to measure,
city-by-city, changes in community needs between 2000 and 2005. The indicators are
defined so that they record conditions in a city using the boundaries in effect on the date
the data were collected. Some indicators, such as the change in the concentration of low
income families (CHGLOWINCCON), compare conditions at two points in time—for
example, 1970 and 2005. These multiyear indicators are defined using the city
boundaries in effect at each point in time.

If a principal city annexed a high-income suburb in 2003, our changing-boundaries
definition would result in the city having a lower measured poverty rate in 2005 than in
2000 even if it had the same number of poor persons in both years. In the case of the
poverty rate variable (POORPERS), this result is completely consistent with the role of
that need indicator in the analysis. We looked at each of the needs indicators in this
way; in every case, we concluded that any change in city boundary between 2000 and
2005 should be incorporated into the measurement of that indicator.

Page 87

Page 88
This conclusion applies to the three multiyear variables—EXCSINFRA,
CHNGEMPLOYBASE, and CHGLOWINCCON—as well. However, we acknowledge
that boundary changes could confound EXCSINFRA, but we believe that this possibility
is remote. EXCSINFRA, as calculated in 2005, compares the maximum number of
households recorded in 1970, 1980, 1990, 2000, and 2005 to the number of households in
the city in 2005. If a city is steadily growing, then the maximum number of households
would have occurred in 2005 and EXCSINFRA = 1. If a city is declining, then the
maximum number of households would have occurred earlier than 2005 and
EXCSINFRA > 1. The possibility exists that the original part of a city declined between
1970 and 2005, but the city annexed surrounding areas so that the number of households
stayed constant or increased. In such a case, EXCSINFRA = 1, but the city would have
excess infrastructure in the original part. We think that this possibility is remote and
that, if a case like this did exist, holding original boundaries constant could produce an
undesirable result as well.
46

Cost-of-living differences enter into the analysis of community needs in two ways. First,
the poverty rate (POORPERS) and related variables are based on counts of persons living
in households with incomes below poverty-level incomes. The assumption is that these
persons have needs that cities have to provide for either because they have insufficient
incomes or because they have other characteristics, such as an elderly age, that combines
with insufficient income to create needs. Insufficient income is a function of both
income level and the cost of necessities. Being below the national poverty level in
Wichita poses fewer problems for a family than being below the national poverty level in
San Francisco because of the cost of housing and other necessities.
47
Need indicators
based on cost-adjusted poverty levels would be desirable.
48

Cost-of-living differences also enter the analysis in terms of the cost of responding to
needs. A poor elderly person may need services such as meals on wheels, adult day care,
and visiting nurse care. The costs of providing such services will differ across cities. We
believe that the real fiscal capacity index adjusts for these differences directly and,
therefore, no corresponding adjustments need to be made in the needs indicators used in
constructing measures of community needs.

6.8. Areas for Future Work

In our opinion, the most important area for future work is to expand and improve upon
the list of needs indicators. We believe that the 27 variables in Table 1 are a well-
conceived, broad-based, and carefully defined set of needs indicators. We believe these

46
For example, if the original part of the city was small in population relative to its size in 2005, then the
decline in households measured using constant boundaries would be out of proportion to the problem.
47
The Census Bureau bases its poverty counts in the 48 contiguous states on the same income levels by
household size; it uses higher income levels in Alaska and Hawaii.
48
The Government Accountability Office (GAO) has asked the Census Bureau to produce counts of poor
persons using a poverty level adjusted for cost-of-living differences. The GAO used HUD fair market rents
to adjust the poverty level. HUD should experiment with the special counts prepared for GAO.

indicators provided the basis for a useful factor analysis, and we would recommend the
inclusion of all of these indicators in future work. However, we think the greatest payoff
for understanding community needs is to improve these indicators and fill in some
missing gaps. Here are our suggestions:

• Carry out further analysis of the crime variables to determine whether there were
any data errors, on our part or the part of the FBI, which caused the low
correlations between the part 1 arrest data and the violent crime statistics. Since
these data have many missing observations, the gains from solving this mystery
may be limited. Future work may avoid crime statistics to keep from losing cities
because of missing data.

• Test whether there are any significant changes in the factor analysis results if one
substitutes for POORPERS the counts that the Census Bureau is preparing for
GAO using fair market rent (FMR) data to adjust for cost-of-living differences.

• Test whether the employment disability counts can be used in the future.

• Test whether the “poor housing appreciation in high poverty neighborhood”
indicator based on HMDA data provides useful information. We agreed that this
was a reasonable variable in concept but were not able to compute it within the
resources available.

• Explore with the Census Bureau the possibility of obtaining better information
from the economic censuses to gauge the change in economic conditions in cities.
In our opinion, this may be the single most important improvement to the set of
needs indicators. It may be necessary to conduct some of the analysis at the
Census Bureau to take advantage of data not released to the public. However, it is
possible that HUD could obtain public release of useful data. For example, the
Census Bureau does not release the count of total jobs in a city. HUD may need
to know only the change in the number of total jobs. The Census Bureau may be
willing to release the change in jobs but not the total number of jobs.

• Investigate whether there are other useful measures of long-term trends that could
be constructed.

• Investigate whether HUD could work with the Department of Education to
construct useful measures of education needs defined at the city level, such as the
number of children who are not fluent in English.

• Investigate whether HUD could compile from HHS useful information at the city
level on public health needs and conditions such as infant mortality rates.

Page 89

Page 90
While we believe better data would produce the best payoff for measuring community
needs, we also recommend some future conceptual work in the following areas:

• Further testing of the regression approach.

• Further analysis of the relationship between the regression approach and factor
analysis. We believe that looking at the relationship between principal
components analysis and the regression approach may be a useful way to proceed
in this area.

• Development of a model to estimate real fiscal capacity for the cities for which
the needed data are not available.

• Further consideration of how to combine a measure of real fiscal capacity with a
measure of community needs.

Appendix A—Supplemental Tables

Page A-1

Table A.1. Correlation Matrix for Needs Indicators
PO
P
CHGLOWI
NC
CON CHN
GEMPLB
ASE DENIAL EXCSINFRA LACK
AFF
DR
ENTALS LING
ISO
L

LWINCHH
DS
MEDINCCBS
2
CITY MINCON OVERCR
OW
D_2000 PCTPO
P
HIGH
PO
VNG
H
S
PCTPO
P
M
O
D
PO
VNG
H
S
PCTVACM
OD
PO
VCITY
POP 1.00 0.03 -0.06 0.07 0.03 0.08 0.11 0.12 0.10 0.06 0.10 0.17 0.14 0.06
CHGLOWINCCON 0.03 1.00 0.15 0.44 0.26 0.28 0.24 0.42 0.47 0.34 0.18 0.12 0.29 0.24
CHNGEMPLBASE -0.06 0.15 1.00 0.08 -0.06 0.00 0.10 0.02 -0.01 -0.05 0.12 -0.01 0.04 0.01
DENIAL 0.07 0.44 0.08 1.00 0.52 0.28 -0.01 0.58 0.62 0.65 -0.03 0.51 0.60 0.76
EXCSINFRA 0.03 0.26 -0.06 0.52 1.00 0.21 -0.06 0.41 0.44 0.44 -0.07 0.33 0.38 0.64
LACKAFFDRENTALS 0.08 0.28 0.00 0.28 0.21 1.00 0.57 0.60 0.66 0.35 0.58 0.34 0.54 0.15
LINGISOL 0.11 0.24 0.10 -0.01 -0.06 0.57 1.00 0.32 0.30 0.09 0.77 0.10 0.34 -0.17
LWINCHHDS 0.12 0.42 0.02 0.58 0.41 0.60 0.32 1.00 0.90 0.70 0.29 0.51 0.74 0.52
MEDINCCBS2CITY 0.10 0.47 -0.01 0.62 0.44 0.66 0.30 0.90 1.00 0.73 0.23 0.46 0.71 0.52
MINCON 0.06 0.34 -0.05 0.65 0.44 0.35 0.09 0.70 0.73 1.00 0.00 0.45 0.57 0.61
OVERCROWD_2000 0.10 0.18 0.12 -0.03 -0.07 0.58 0.77 0.29 0.23 0.00 1.00 0.11 0.39 -0.15
PCTPOPHIGHPOVNGHS 0.17 0.12 -0.01 0.51 0.33 0.34 0.10 0.51 0.46 0.45 0.11 1.00 0.52 0.52
PCTPOPMODPOVNGHS 0.14 0.29 0.04 0.60 0.38 0.54 0.34 0.74 0.71 0.57 0.39 0.52 1.00 0.63
PCTVACMODPOVCITY 0.06 0.24 0.01 0.76 0.64 0.15 -0.17 0.52 0.52 0.61 -0.15 0.52 0.63 1.00
POORCHILD 0.12 0.36 0.05 0.71 0.39 0.45 0.14 0.80 0.72 0.68 0.14 0.62 0.79 0.69
POOROVER74 0.13 0.13 0.02 0.38 0.20 0.35 0.29 0.42 0.42 0.35 0.19 0.37 0.49 0.31
POORPERS 0.12 0.38 0.07 0.73 0.42 0.51 0.22 0.84 0.76 0.67 0.22 0.68 0.83 0.68
POVCON 0.11 0.34 -0.03 0.56 0.43 0.45 0.13 0.88 0.86 0.79 0.06 0.47 0.66 0.58
PR70RENTPOV 0.17 0.32 -0.05 0.59 0.51 0.49 0.20 0.82 0.80 0.71 0.16 0.57 0.77 0.63
VIOLCRIME 0.05 0.06 0.03 0.51 0.22 0.05 -0.16 0.42 0.36 0.39 -0.15 0.39 0.44 0.53
PT1CRIME -0.02 -0.04 0.06 0.04 -0.03 -0.03 -0.08 0.03 0.05 0.06 -0.05 0.08 0.02 0.09
PT2CRIME -0.03 -0.02 0.04 0.20 0.03 0.01 -0.12 0.12 0.20 0.20 -0.15 0.18 0.13 0.21
RCNTIMMIG 0.09 0.24 0.04 -0.17 -0.07 0.44 0.81 0.21 0.15 -0.02 0.60 -0.09 0.12 -0.27
SCHPOPPOOR 0.13 0.35 0.02 0.67 0.38 0.47 0.15 0.78 0.69 0.66 0.15 0.62 0.77 0.66
SGLPRNTFAM 0.07 0.35 0.01 0.68 0.44 0.47 0.02 0.74 0.76 0.69 0.05 0.55 0.69 0.64
UNDEREDWORKAGE 0.00 0.29 0.18 0.57 0.20 0.49 0.27 0.45 0.54 0.33 0.37 0.25 0.54 0.31
UNEDUCADULTS 0.10 0.35 0.13 0.48 0.20 0.66 0.65 0.65 0.64 0.41 0.69 0.39 0.71 0.28
UNEMPCEN 0.08 0.34 0.02 0.67 0.46 0.36 0.12 0.63 0.60 0.53 0.15 0.48 0.59 0.58
Page A-2

Page A-3

Table A.1. Correlation Matrix for Needs Indicators (continued)
POORCHILD POOROVER74 POORPERS POVCON PR70RENTPOV VIOLCRIME PT1CRIME PT2CRIME RCNTIMMIG SCHPOPPOOR SGLPRNTFAM UNDERE
DWOR
KAGE UNEDU
CAD
U
L
TS UNEMPCEN
POP 0.12 0.13 0.12 0.11 0.17 0.05 -0.02 -0.03 0.09 0.13 0.07 0.00 0.10 0.08
CHGLOWINCCON 0.36 0.13 0.38 0.34 0.32 0.06 -0.04 -0.02 0.24 0.35 0.35 0.29 0.35 0.34
CHNGEMPLBASE 0.05 0.02 0.07 -0.03 -0.05 0.03 0.06 0.04 0.04 0.02 0.01 0.18 0.13 0.02
DENIAL 0.71 0.38 0.73 0.56 0.59 0.51 0.04 0.20 -0.17 0.67 0.68 0.57 0.48 0.67
EXCSINFRA 0.39 0.20 0.42 0.43 0.51 0.22 -0.03 0.03 -0.07 0.38 0.44 0.20 0.20 0.46
LACKAFFDRENTALS 0.45 0.35 0.51 0.45 0.49 0.05 -0.03 0.01 0.44 0.47 0.47 0.49 0.66 0.36
LINGISOL 0.14 0.29 0.22 0.13 0.20 -0.16 -0.08 -0.12 0.81 0.15 0.02 0.27 0.65 0.12
LWINCHHDS 0.80 0.42 0.84 0.88 0.82 0.42 0.03 0.12 0.21 0.78 0.74 0.45 0.65 0.63
MEDINCCBS2CITY 0.72 0.42 0.76 0.86 0.80 0.36 0.05 0.20 0.15 0.69 0.76 0.54 0.64 0.60
MINCON 0.68 0.35 0.67 0.79 0.71 0.39 0.06 0.20 -0.02 0.66 0.69 0.33 0.41 0.53
OVERCROWD_2000 0.14 0.19 0.22 0.06 0.16 -0.15 -0.05 -0.15 0.60 0.15 0.05 0.37 0.69 0.15
PCTPOPHIGHPOVNGHS 0.62 0.37 0.68 0.47 0.57 0.39 0.08 0.18 -0.09 0.62 0.55 0.25 0.39 0.48
PCTPOPMODPOVNGHS 0.79 0.49 0.83 0.66 0.77 0.44 0.02 0.13 0.12 0.77 0.69 0.54 0.71 0.59
PCTVACMODPOVCITY 0.69 0.31 0.68 0.58 0.63 0.53 0.09 0.21 -0.27 0.66 0.64 0.31 0.28 0.58
POORCHILD 1.00 0.41 0.96 0.80 0.76 0.56 0.05 0.22 -0.05 0.98 0.79 0.49 0.57 0.65
POOROVER74 0.41 1.00 0.49 0.37 0.46 0.23 0.06 0.16 0.19 0.42 0.39 0.25 0.42 0.34
POORPERS 0.96 0.49 1.00 0.80 0.78 0.55 0.06 0.22 0.03 0.94 0.80 0.52 0.64 0.68
POVCON 0.80 0.37 0.80 1.00 0.82 0.47 0.07 0.23 0.04 0.77 0.74 0.32 0.45 0.55
PR70RENTPOV 0.76 0.46 0.78 0.82 1.00 0.40 0.03 0.17 0.06 0.73 0.73 0.30 0.51 0.59
VIOLCRIME 0.56 0.23 0.55 0.47 0.40 1.00 0.20 0.23 -0.24 0.53 0.50 0.27 0.19 0.40
PT1CRIME 0.05 0.06 0.06 0.07 0.03 0.20 1.00 0.53 -0.06 0.04 0.08 0.01 0.00 0.06
PT2CRIME 0.22 0.16 0.22 0.23 0.17 0.23 0.53 1.00 -0.19 0.19 0.27 0.09 0.04 0.17
RCNTIMMIG -0.05 0.19 0.03 0.04 0.06 -0.24 -0.06 -0.19 1.00 -0.01 -0.12 -0.02 0.36 0.03
SCHPOPPOOR 0.98 0.42 0.94 0.77 0.73 0.53 0.04 0.19 -0.01 1.00 0.76 0.45 0.55 0.63
SGLPRNTFAM 0.79 0.39 0.80 0.74 0.73 0.50 0.08 0.27 -0.12 0.76 1.00 0.49 0.49 0.63
UNDEREDWORKAGE 0.49 0.25 0.52 0.32 0.30 0.27 0.01 0.09 -0.02 0.45 0.49 1.00 0.74 0.50
UNEDUCADULTS 0.57 0.42 0.64 0.45 0.51 0.19 0.00 0.04 0.36 0.55 0.49 0.74 1.00 0.51
UNEMPCEN 0.65 0.34 0.68 0.55 0.59 0.40 0.06 0.17 0.03 0.63 0.63 0.50 0.51 1.00

Table A.2. Initial Factor Analysis, 2005 Data: Factor Loading for Unrotated
Factors
Factor1 Factor2 Factor3
CHGLOWINCCON 0.42991 0.16552 0.07619
CHNGEMPLBASE -0.10283 0.116 0.27252
DENIAL 0.7626 -0.23761 0.2932
EXCSINFRA 0.56431 -0.22377 -0.11208
LACKAFFDRENTALS 0.60586 0.55234 -0.07049
LINGISOL 0.21712 0.89532 -0.09646
LWINCHHDS 0.86956 0.11171 -0.25189
MEDINCCBS2CITY 0.90339 0.07241 -0.12628
MINCON 0.75356 -0.16559 -0.14176
OVERCROWD2000 0.17479 0.87566 0.05896
PCTPOPHIGHPOVNGHS 0.67931 -0.11091 -0.0474
PCTPOPMODPOVNGHS 0.84469 0.10956 0.08503
PCTVACMODPOVCITY 0.73709 -0.41366 0.05368
POORCHILD 0.91385 -0.1337 0.01959
POOROVER74 0.45471 0.0221 0.00709
POORPERS 0.94876 -0.07822 0.03881
POVCON 0.8429 -0.1709 -0.31292
PR70RENTPOV 0.8403 -0.11492 -0.28779
PT1CRIME 0.02003 -0.15022 0.09945
PT2CRIME 0.15692 -0.28168 0.11429
RCNTIMMIG 0.00871 0.82496 -0.32501
SCHPOPPOOR 0.89979 -0.11129 -0.00439
SGLPRNTFAM 0.85943 -0.1672 0.05814
UNDEREDWORKAGE 0.54942 0.27346 0.62907
UNEDUCADULTS 0.68537 0.57306 0.29624
UNEMPCEN 0.68953 -0.02331 0.1231

Page A-4

Page A-5
Table A.3. Initial Factor Analysis, 2005 Data: Factor Loading for Varimax
Orthogonal Rotated Factors
Factor1 Factor2 Factor3
CHGLOWINCCON 0.39005 0.15917 0.20137
CHNGEMPLBASE -0.15496 0.00839 0.27243
DENIAL 0.73597 -0.26953 0.33119
EXCSINFRA 0.59824 -0.13193 -0.07606
LACKAFFDRENTALS 0.53521 0.58412 0.2224
LINGISOL 0.11422 0.88724 0.24041
LWINCHHDS 0.87577 0.25161 -0.04226
MEDINCCBS2CITY 0.89575 0.17422 0.06764
MINCON 0.78093 -0.05402 -0.0509
OVERCROWD2000 0.05267 0.81275 0.37078
PCTPOPHIGHPOVNGHS 0.68753 -0.04021 0.04124
PCTPOPMODPOVNGHS 0.8029 0.13272 0.26546
PCTVACMODPOVCITY 0.76776 -0.35445 0.04696
POORCHILD 0.91089 -0.06806 0.13793
POOROVER74 0.44246 0.05011 0.09498
POORPERS 0.93538 -0.02019 0.18005
POVCON 0.89404 0.00574 -0.19561
PR70RENTPOV 0.8808 0.04943 -0.15453
PT1CRIME 0.02421 -0.17337 0.04706
PT2CRIME 0.17296 -0.29207 0.04254
RCNTIMMIG -0.04839 0.88482 -0.03193
SCHPOPPOOR 0.89774 -0.03985 0.12043
SGLPRNTFAM 0.85613 -0.11644 0.15311
UNDEREDWORKAGE 0.41377 0.07976 0.77123
UNEDUCADULTS 0.55759 0.48379 0.58389
UNEMPCEN 0.66185 -0.01566 0.22989

Table A.4. Factor Analysis Using 2000 Data: Unrotated and Rotated Factor Loading
Unrotated Factors Orthogonal Rotated Factors
Factor1 Factor2 Factor3 Factor1 Factor2 Factor3
CHGLOWINCCON_2000 0.49109 0.22375 0.1822 0.38067 0.3108 0.28796
CHNGEMPLBASE -0.09228 0.08752 0.28443 -0.14108 -0.00066 0.27779
DENIAL 0.70361 -0.32571 0.23506 0.74345 -0.15634 0.28151
EXCSINFRA_2000 0.5542 -0.25625 -0.07394 0.61016 -0.0709 -0.03083
LACKAFFDRENTALS_2000 0.5449 0.62309 -0.12124 0.33975 0.76166 0.0655
LINGISOL_2000 0.28082 0.89816 -0.15684 0.00947 0.95329 0.03615
LWINCHHDS_2000 0.85302 0.07528 -0.23753 0.8088 0.35672 -0.09126
MEDINCCBS2CITY_2000 0.89187 0.05764 -0.17376 0.84491 0.33822 -0.02614
MINCON_2000 0.74313 -0.22709 -0.21605 0.79404 0.03778 -0.13627
OVERCROWD2000 0.2676 0.87692 0.09703 -0.02082 0.87873 0.27822
PCTPOPHIGHPOVNGHS_2000 0.68746 -0.21556 -0.03037 0.72008 -0.00419 0.0383
PCTPOPMODPOVNGHS_2000 0.88635 0.09892 0.13002 0.79826 0.31452 0.27599
PCTVACMODPOVCITY_2000 0.69735 -0.47671 0.07489 0.79845 -0.26775 0.0997
POORCHILD_2000 0.95021 -0.07487 0.06862 0.91725 0.18149 0.19727
POOROVER74_200 0.71694 -0.09999 -0.08448 0.71821 0.12342 0.00887
POORPERS_2000 0.96514 0.02506 0.10894 0.89734 0.27141 0.25517
POVCON_2000 0.83176 -0.22906 -0.32327 0.88889 0.08221 -0.22784
PR70RENTPOV_2000 0.87912 -0.08413 -0.2715 0.88502 0.22114 -0.14654
PT1CRIME_2000 0.29437 -0.11921 0.03159 0.31223 -0.036 0.05548
PT2CRIME_2000 0.27073 -0.32528 -0.04544 0.35951 -0.22065 -0.05689
RCNTIMMIG_2000 0.05266 0.87531 -0.32979 -0.18353 0.90275 -0.17047
SCHPOPPOOR_2000 0.95062 -0.03845 0.05205 0.90821 0.21913 0.18714
SGLPRNTFAM_2000 0.90227 -0.23344 0.02561 0.92387 0.02783 0.1222
UNDEREDWORKAGE_2000 0.57969 0.24368 0.60105 0.41879 0.27028 0.71292
UNEDUCADULTS_2000 0.71862 0.55254 0.26904 0.48869 0.6657 0.46057
UNEMPCEN_2000 0.83467 0.06961 0.08606 0.76231 0.28137 0.22057
Page A-6

Table A.5. Factor Analysis Using 2005 Data, Rotated Factor Loadings for
Large Cities and Small Cities
Cities of 200,000 or More Cities of Less Than 200,000
Rotated Factor1 Factor2 Factor3 Factor4 Factor1 Factor2 Factor3
CHGLOWINCCON 0.32092 0.22554 0.09338 0.10486 0.25905 0.21509 0.10375
CHNGEMPLBASE -0.06154 0.13943 0.10658
-
0.06794
-0.11825 0.00499 0.08009
DENIAL 0.60478 -0.2097 0.32227 0.2362 0.53746 -0.14706 0.38518
EXCSINFRA 0.5062 -0.10094 0.12355 0.70666 0.36096 -0.06999 0.04443
LACKAFFDRENTAL
S
0.60231 0.53932 0.35559 0.16933 0.37883 0.64542 0.29564
LINGISOL -0.04102 0.96264 0.04793
-
0.04789
0.0736 0.91129 0.06674
LWINCHHDS 0.87853 0.1837 0.03086 0.18513 0.82297 0.27014 0.08334
MEDINCCBS2CITY 0.83051 0.07524 0.16249 0.2488 0.73633 0.24373 0.21856
MINCON 0.62726 -0.15293 0.02349 0.46406 0.70944 0.02424 0.03982
OVERCROWD2000 0.02875 0.89844 0.23333
-
0.05285
0.00614 0.8519 0.28414
PCTPOPHIGHPOVN
GHS
0.77314 0.07007 0.18185 0.03701 0.57861 0.01207 0.13945
PCTPOPMODPOVN
GHS
0.85335 0.22304 0.30456 0.1798 0.72246 0.22209 0.32766
PCTVACMODPOVCI
TY
0.68697 -0.3124 0.13567 0.42289 0.62676 -0.23827 0.14662
POORCHILD 0.9602 -0.05175 0.1048
-
0.01914
0.93392 0.0226 0.22342
POOROVER74 0.56129 0.14416 0.08524 0.20817 0.31713 0.09699 0.03095
POORPERS 0.93887 0.04466 0.20104 0.09484 0.91952 0.07197 0.24322
POVCON 0.86501 -0.07434 -0.07634 0.19655 0.89469 0.02452 -0.05989
PR70RENTPOV 0.82284 0.05715 0.05072 0.37423 0.80899 0.08035 -0.03507
PT1CRIME 0.07619 -0.00352 0.00198 0.06547 -0.01406 -0.04468 -0.02989
PT2CRIME 0.05836 -0.2747 0.01233 0.00065 0.16651 -0.13149 0.03528
RCNTIMMIG -0.16047 0.89867 -0.22456
-
0.06925
-0.0769 0.87511 -0.2177
SCHPOPPOOR 0.96533 -0.03595 0.05385
-
0.00219
0.90835 0.05846 0.20762
SGLPRNTFAM 0.82503 -0.13778 0.34831 0.14783 0.75812 -0.02687 0.29724
UNDEREDWORKAG
E
0.28883 0.15058 0.85108 0.10433 0.30405 0.19232 0.7888
UNEDUCADULTS 0.45417 0.67608 0.51839 0.01181 0.442 0.5655 0.56173
UNEMPCEN 0.68525 -0.06257 0.21859 0.43727 0.46274 0.09936 0.34128

Page A-7

Table A.6. 2005 Factor Analysis Using VIOLCRIME Instead of PT1CRIME, VARIMAX Rotated Factors

Factor1 Factor2 Factor3
POORPERS 0.94261 LINGISOL 0.91454 UNEMPCEN 0.19566
POORCHILD 0.91804 RCNTIMMIG 0.87042 UNEDUCADULTS 0.49499
SCHPOPPOOR 0.90272 OVERCROWD_2000 0.85383 UNDEREDWORKAGE 0.72952
MEDINCCBS2CITY 0.88857 LACKAFFDRENTALS 0.6461 SGLPRNTFAM 0.12659
POVCON 0.88566 UNEDUCADULTS 0.58219 SCHPOPPOOR 0.10997
PR70RENTPOV 0.87071 LWINCHHDS 0.28366 RCNTIMMIG -0.13826
SGLPRNTFAM 0.86702 MEDINCCBS2CITY 0.23882 PT2CRIME 0.01392
LWINCHHDS 0.85881 CHGLOWINCCON 0.22623 PR70RENTPOV -0.20693
PCTPOPMODPOVNGHS 0.80524 UNDEREDWORKAGE 0.20319 POVCON -0.23032
PCTVACMODPOVCITY 0.78866 PCTPOPMODPOVNGHS 0.18899 POORPERS 0.1599
MINCON 0.7835 POOROVER74 0.07432 POOROVER74 0.07732
DENIAL 0.76327 PR70RENTPOV 0.07252 POORCHILD 0.13235
PCTPOPHIGHPOVNGHS 0.69193 UNEMPCEN 0.04013 PCTVACMODPOVCITY 0.05213
UNEMPCEN 0.6672 POORPERS 0.0348 PCTPOPMODPOVNGHS 0.23272
EXCSINFRA 0.59869 CHNGEMPLBASE 0.03249 PCTPOPHIGHPOVNGHS 0.02412
VIOLCRIME 0.55578 POVCON 0.01955 OVERCROWD_2000 0.27431
UNEDUCADULTS 0.54919 SCHPOPPOOR 0.0021 MINCON -0.09542
LACKAFFDRENTALS 0.50896 PCTPOPHIGHPOVNGHS -0.00745 MEDINCCBS2CITY -0.01553
UNDEREDWORKAGE 0.43965 MINCON -0.01292 LWINCHHDS -0.10573
POOROVER74 0.4368 POORCHILD -0.02401 LINGISOL 0.12639
CHGLOWINCCON 0.38837 SGLPRNTFAM -0.05827 LACKAFFDRENTALS 0.11336
PT2CRIME 0.18253 EXCSINFRA -0.08259 EXCSINFRA -0.12857
LINGISOL 0.06766 DENIAL -0.18631 DENIAL 0.31377
OVERCROWD_2000 0.0219 PT2CRIME -0.23332 VIOLCRIME 0.13291
RCNTIMMIG -0.09868 PCTVACMODPOVCITY -0.31302 CHNGEMPLBASE 0.29818
CHNGEMPLBASE -0.16497 VIOLCRIME -0.35229 CHGLOWINCCON 0.14697
Page A-8

Table A.7. Standardized Scoring Coefficients, Based on 2005 Data Without
Crime Variables
Factor1 Factor2 Factor3
CHGLOWINCCON -0.02266 -0.00619 0.06911
CHNGEMPLBASE -0.00733 0.00465 0.05042
DENIAL 0.05521 -0.1065 0.2061
EXCSINFRA 0.04954 -0.02226 -0.00265
LACKAFFDRENTALS -0.01665 0.11404 -0.01053
LINGISOL -0.03114 0.36138 -0.06993
LWINCHHDS 0.02825 0.09883 -0.10354
MEDINCCBS2CITY 0.20368 0.10156 -0.01419
MINCON 0.04264 -0.02485 -0.00641
OVERCROWD_2000 -0.01755 0.16406 0.05127
PCTPOPHIGHPOVNGHS 0.01425 -0.01001 -0.042
PCTPOPMODPOVNGHS 0.00878 0.04533 0.02058
PCTVACMODPOVCITY 0.09204 -0.13162 0.03791
POORCHILD 0.0883 -0.1326 0.12027
POOROVER74 0.00719 -0.00089 -0.02458
POORPERS 0.18723 0.00824 0.2283
POVCON 0.09751 0.00589 -0.429
PR70RENTPOV 0.15118 0.00913 -0.24679
RCNTIMMIG -0.00757 0.24664 -0.1858
SCHPOPPOOR 0.12067 0.08762 -0.1873
SGLPRNTFAM 0.06006 -0.02148 0.01251
UNDEREDWORKAGE -0.05684 -0.04957 0.41739
UNEDUCADULTS -0.04105 0.13016 0.41726
UNEMPCEN 0.01966 -0.02896 0.04813

Page A-9

Table A.8. Alternative Index Scores

City State
Index 1:
Equal
Weight
Index 2: Triple Weight
to Poverty and
Structural Problems
Index 3: Triple Weight to
Immigration and Housing
Affordability Factor
Index 4:
Hedonic
Weights
Index 5:
Richardson
Weights
Index 6: Partial
Hedonic
Weights
Birmingham city Alabama 0.54 1.30 -0.12 0.43 1.81 1.19
Huntsville city Alabama -0.43 -0.28 -0.71 -0.30 -0.21 -0.36
Mobile city Alabama 0.17 0.52 -0.35 0.36 0.68 0.37
Montgomery city Alabama -0.07 0.14 -0.54 0.21 0.20 -0.01
Anchorage municipality Alaska -0.47 -0.52 -0.57 -0.31 -0.60 -0.58
Avondale city Arizona 0.42 0.28 0.52 0.44 0.19 0.30
Chandler city Arizona -0.46 -0.68 -0.39 -0.30 -0.85 -0.70
Glendale city Arizona 0.05 -0.10 -0.03 0.28 -0.25 -0.16
Mesa city Arizona -0.03 -0.24 0.00 0.14 -0.42 -0.27
Peoria city Arizona -0.44 -0.73 -0.50 -0.10 -0.99 -0.81
Phoenix city Arizona 0.33 0.19 0.48 0.34 0.09 0.21
Scottsdale city Arizona -0.85 -0.95 -0.81 -0.79 -1.03 -0.95
Tempe city Arizona -0.44 -0.54 -0.28 -0.51 -0.59 -0.50
Tucson city Arizona 0.17 0.21 0.15 0.15 0.23 0.20
Yuma city Arizona 0.28 0.02 0.11 0.70 -0.25 -0.10
Fayetteville city Arkansas -0.71 -0.43 -0.69 -1.00 -0.18 -0.37
Fort Smith city Arkansas 0.13 0.18 -0.21 0.43 0.14 0.05
Little Rock city Arkansas -0.37 -0.16 -0.65 -0.31 -0.04 -0.23
Alameda city California -0.56 -0.53 -0.29 -0.85 -0.43 -0.41
Alhambra city California 0.37 0.06 1.08 -0.03 -0.03 0.29
Anaheim city California 0.62 0.11 1.02 0.74 -0.24 0.16
Antioch city California -0.03 -0.21 0.01 0.11 -0.36 -0.23
Bakersfield city California 0.21 0.06 0.12 0.46 -0.10 -0.01
Baldwin Park city California 1.32 0.82 1.73 1.39 0.50 0.89
Bellflower city California 0.64 0.43 0.81 0.68 0.29 0.46
Berkeley city California -1.04 -0.59 -0.62 -1.91 -0.09 -0.33
Buena Park city California 0.21 -0.25 0.54 0.35 -0.57 -0.21
Burbank city California -0.25 -0.49 0.03 -0.29 -0.62 -0.42
Page A-10

Page A-11

City State
Index 1:
Equal
Weight
Index 2: Triple Weight
to Poverty and
Structural Problems
Index 3: Triple Weight to
Immigration and Housing
Affordability Factor
Index 4:
Hedonic
Weights
Index 5: Index 6: Partial
Richardson Hedonic
Weights Weights
Carlsbad city California -0.91 -1.07 -0.77 -0.89 -1.17 -1.04
Carson city California 0.26 -0.14 0.35 0.56 -0.46 -0.18
Chico city California -0.35 -0.06 -0.43 -0.56 0.17 -0.04
Chino city California 0.18 -0.41 0.20 0.75 -0.91 -0.52
Chula Vista city California 0.17 -0.17 0.37 0.32 -0.42 -0.16
Clovis city California -0.28 -0.48 -0.34 -0.01 -0.68 -0.55
Concord city California -0.11 -0.32 0.18 -0.19 -0.44 -0.25
Corona city California -0.02 -0.45 0.06 0.33 -0.80 -0.50
Costa Mesa city California 0.15 -0.17 0.56 0.05 -0.35 -0.07
Daly City California 0.22 -0.14 0.84 -0.04 -0.29 0.04
El Cajon city California 0.28 0.14 0.44 0.26 0.06 0.18
Escondido city California 0.37 -0.05 0.68 0.47 -0.34 -0.01
Fairfield city California -0.11 -0.28 0.01 -0.08 -0.39 -0.26
Fontana city California 0.62 0.13 0.82 0.91 -0.24 0.12
Fremont city California -0.34 -0.64 0.30 -0.68 -0.73 -0.44
Fresno city California 0.63 0.58 0.66 0.65 0.54 0.58
Fullerton city California -0.16 -0.50 0.15 -0.14 -0.71 -0.44
Garden Grove city California 0.83 0.23 1.52 0.75 -0.12 0.39
Glendale city California 0.25 -0.20 0.99 -0.05 -0.41 0.00
Hawthorne city California 0.97 0.78 1.26 0.88 0.68 0.86
Hayward city California 0.26 0.02 0.56 0.19 -0.11 0.10
Hemet city California 0.57 0.24 0.58 0.89 -0.05 0.18
Hesperia city California 0.39 0.09 0.30 0.79 -0.20 -0.01
Huntington Beach city California -0.58 -0.81 -0.48 -0.46 -0.99 -0.82
Indio city California 1.09 0.73 1.52 1.01 0.52 0.83
Inglewood city California 0.96 0.77 1.17 0.94 0.65 0.81
Lakewood city California -0.27 -0.65 -0.18 0.02 -0.97 -0.69
Livermore city California -0.63 -0.80 -0.48 -0.61 -0.92 -0.78
Long Beach city California 0.45 0.42 0.73 0.20 0.46 0.53

Page A-12

City State
Index 1:
Equal
Weight
Index 2: Triple Weight
to Poverty and
Structural Problems
Index 3: Triple Weight to
Immigration and Housing
Affordability Factor
Index 4:
Hedonic
Weights
Index 5: Index 6: Partial
Richardson Hedonic
Weights Weights
Los Angeles city California 0.69 0.56 1.16 0.33 0.57 0.73
Merced city California 0.76 0.68 0.77 0.84 0.61 0.67
Mission Viejo city California -0.82 -1.10 -0.78 -0.58 -1.34 -1.15
Modesto city California 0.15 -0.07 0.07 0.45 -0.28 -0.14
Mountain View city California -0.60 -0.72 -0.02 -1.08 -0.67 -0.50
Napa city California -0.14 -0.31 0.11 -0.22 -0.39 -0.24
Norwalk city California 0.72 0.09 1.05 1.01 -0.37 0.10
Oakland city California 0.39 0.59 0.73 -0.14 0.84 0.76
Oceanside city California -0.18 -0.50 0.02 -0.06 -0.73 -0.49
Ontario city California 0.82 0.36 1.07 1.03 0.03 0.37
Orange city California -0.10 -0.52 0.09 0.14 -0.84 -0.53
Oxnard city California 0.95 0.63 1.45 0.77 0.48 0.77
Palmdale city California 0.64 0.35 0.65 0.93 0.10 0.29
Pasadena city California -0.28 -0.25 -0.03 -0.55 -0.16 -0.14
Pleasanton city California -0.93 -1.05 -0.72 -1.03 -1.09 -0.98
Pomona city California 1.09 0.55 1.56 1.14 0.21 0.64
Redding city California -0.31 -0.39 -0.48 -0.06 -0.50 -0.47
Redlands city California -0.36 -0.42 -0.41 -0.26 -0.48 -0.45
Redwood City California 0.12 -0.03 0.61 -0.22 -0.04 0.14
Rialto city California 0.66 0.13 0.84 1.02 -0.29 0.09
Richmond city California 0.34 0.36 0.56 0.09 0.43 0.45
Riverside city California 0.23 -0.06 0.39 0.36 -0.27 -0.05
Roseville city California -0.58 -0.81 -0.53 -0.39 -0.99 -0.83
Sacramento city California 0.26 0.37 0.36 0.06 0.48 0.43
Salinas city California 1.32 0.78 1.82 1.37 0.42 0.87
San Bernardino city California 1.11 1.18 1.29 0.87 1.29 1.27
San Buenaventura
(Ventura) city California -0.44 -0.44 -0.38 -0.50 -0.43 -0.42
San Diego city California -0.25 -0.27 0.05 -0.53 -0.21 -0.15

Page A-13

City State
Index 1:
Equal
Weight
Index 2: Triple Weight
to Poverty and
Structural Problems
Index 3: Triple Weight to
Immigration and Housing
Affordability Factor
Index 4:
Hedonic
Weights
Index 5:
Richardson
Weights
Index 6: Partial
Hedonic
Weights
San Francisco city California -0.30 -0.29 0.20 -0.81 -0.15 -0.08
San Jose city California 0.02 -0.21 0.46 -0.19 -0.31 -0.08
San Leandro city California 0.08 -0.24 0.36 0.13 -0.46 -0.20
San Marcos city California -0.09 -0.43 0.08 0.08 -0.68 -0.43
San Mateo city California -0.39 -0.61 0.01 -0.56 -0.70 -0.49
Santa Ana city California 1.59 0.84 2.31 1.62 0.37 0.98
Santa Barbara city California -0.37 -0.40 -0.17 -0.54 -0.38 -0.33
Santa Clara city California -0.31 -0.44 0.31 -0.78 -0.41 -0.23
Santa Maria city California 0.93 0.62 1.37 0.82 0.45 0.73
Santa Monica city California -0.91 -0.95 -0.65 -1.13 -0.92 -0.85
Santa Rosa city California -0.16 -0.29 -0.06 -0.13 -0.38 -0.28
Simi Valley city California -0.54 -0.84 -0.54 -0.23 -1.11 -0.90
Stockton city California 0.61 0.52 0.70 0.61 0.46 0.53
Sunnyvale city California -0.40 -0.54 0.26 -0.94 -0.49 -0.30
Thousand Oaks city California -0.74 -0.87 -0.60 -0.74 -0.96 -0.84
Torrance city California -0.53 -0.78 -0.19 -0.62 -0.92 -0.70
Tracy city California -0.07 -0.50 0.03 0.27 -0.86 -0.55
Turlock city California 0.34 -0.13 0.49 0.66 -0.51 -0.17
Tustin city California -0.13 -0.52 0.38 -0.23 -0.74 -0.40
Union City California 0.29 -0.15 0.87 0.16 -0.40 -0.01
Upland city California -0.28 -0.29 -0.28 -0.28 -0.29 -0.29
Vacaville city California -0.41 -0.69 -0.45 -0.09 -0.94 -0.76
Vallejo city California -0.01 -0.08 0.07 -0.03 -0.12 -0.06
Victorville city California 0.52 0.29 0.43 0.84 0.07 0.21
Visalia city California 0.23 0.06 0.10 0.53 -0.11 -0.02
Vista city California 0.42 0.22 0.70 0.33 0.13 0.30
West Covina city California 0.35 0.08 0.50 0.46 -0.12 0.08
Westminster city California 0.57 -0.09 1.11 0.69 -0.53 -0.01
Arvada city Colorado -0.51 -0.71 -0.66 -0.15 -0.93 -0.81

Page A-14

City State
Index 1:
Equal
Weight
Index 2: Triple Weight
to Poverty and
Structural Problems
Index 3: Triple Weight to
Immigration and Housing
Affordability Factor
Index 4:
Hedonic
Weights
Index 5:
Richardson
Weights
Index 6: Partial
Hedonic
Weights
Aurora city Colorado 0.16 0.03 0.19 0.25 -0.07 0.02
Boulder city Colorado -1.03 -0.70 -0.65 -1.73 -0.32 -0.48
Colorado Springs city Colorado -0.45 -0.40 -0.54 -0.39 -0.39 -0.44
Denver city Colorado -0.04 0.10 0.09 -0.32 0.26 0.18
Fort Collins city Colorado -0.81 -0.57 -0.75 -1.11 -0.34 -0.50
Greeley city Colorado -0.04 -0.07 -0.08 0.02 -0.10 -0.09
Lakewood city Colorado -0.33 -0.37 -0.47 -0.14 -0.44 -0.43
Longmont city Colorado -0.16 -0.31 -0.07 -0.10 -0.42 -0.30
Pueblo city Colorado 0.31 0.53 -0.14 0.54 0.61 0.39
Thornton city Colorado -0.20 -0.51 -0.28 0.20 -0.81 -0.61
Westminster city Colorado -0.48 -0.69 -0.58 -0.17 -0.90 -0.77
Washington city
District of
Columbia -0.25 0.42 -0.10 -1.06 1.04 0.61
Boca Raton city Florida -0.62 -0.75 -0.51 -0.59 -0.84 -0.73
Cape Coral city Florida -0.17 -0.44 -0.17 0.09 -0.67 -0.49
Clearwater city Florida -0.18 -0.29 -0.21 -0.05 -0.40 -0.33
Davie town Florida -0.33 -0.50 -0.22 -0.28 -0.62 -0.49
Deerfield Beach city Florida 0.16 -0.04 0.54 -0.01 -0.12 0.07
Deltona city Florida -0.10 -0.48 -0.25 0.42 -0.84 -0.61
Fort Lauderdale city Florida -0.09 0.01 -0.16 -0.11 0.08 0.00
Gainesville city Florida -0.20 0.25 -0.23 -0.62 0.64 0.33
Hollywood city Florida 0.06 -0.14 0.31 0.01 -0.26 -0.08
Jacksonville city Florida -0.29 -0.25 -0.45 -0.17 -0.26 -0.31
Largo city Florida -0.18 -0.44 -0.31 0.23 -0.71 -0.55
Melbourne city Florida -0.25 -0.38 -0.36 -0.02 -0.52 -0.45
Miami city Florida 1.47 1.48 2.34 0.60 1.70 1.83
Miami Beach city Florida 0.39 0.28 1.22 -0.32 0.38 0.59
Miramar city Florida -0.16 -0.45 0.04 -0.06 -0.66 -0.43
Orlando city Florida 0.10 0.11 0.18 0.01 0.15 0.15

Page A-15

City State
Index 1:
Equal
Weight
Index 2: Triple Weight
to Poverty and
Structural Problems
Index 3: Triple Weight to
Immigration and Housing
Affordability Factor
Index 4:
Hedonic
Weights
Index 5:
Richardson
Weights
Index 6: Partial
Hedonic
Weights
Palm Bay city Florida -0.11 -0.31 -0.16 0.14 -0.49 -0.37
Pembroke Pines city Florida -0.39 -0.63 -0.21 -0.33 -0.79 -0.60
Plantation city Florida -0.40 -0.65 -0.32 -0.24 -0.85 -0.67
Pompano Beach city Florida 0.25 0.04 0.37 0.35 -0.12 0.04
St. Petersburg city Florida -0.18 -0.14 -0.36 -0.03 -0.15 -0.20
Sunrise city Florida 0.04 -0.29 0.22 0.20 -0.54 -0.29
Tallahassee city Florida -0.65 -0.37 -0.66 -0.93 -0.12 -0.31
Tampa city Florida 0.02 0.16 -0.04 -0.05 0.26 0.16
West Palm Beach city Florida 0.27 0.35 0.41 0.06 0.45 0.42
Atlanta city Georgia 0.09 0.86 -0.19 -0.39 1.47 0.90
Roswell city Georgia -0.85 -1.04 -0.58 -0.92 -1.15 -0.97
Savannah city Georgia 0.18 0.54 -0.11 0.12 0.79 0.50
Honolulu CDP Hawaii -0.31 -0.38 0.05 -0.58 -0.37 -0.26
Boise City Idaho -0.53 -0.54 -0.64 -0.42 -0.57 -0.58
Nampa city Idaho 0.23 0.26 0.05 0.38 0.24 0.20
Aurora city Illinois 0.24 0.00 0.53 0.21 -0.15 0.06
Champaign city Illinois -0.59 -0.30 -0.53 -0.96 -0.02 -0.21
Chicago city Illinois 0.33 0.65 0.37 -0.01 0.93 0.72
Elgin city Illinois 0.29 -0.05 0.49 0.45 -0.31 -0.04
Evanston city Illinois -1.10 -0.91 -0.92 -1.47 -0.70 -0.80
Joliet city Illinois -0.05 -0.22 -0.18 0.25 -0.40 -0.30
Naperville city Illinois -1.15 -1.26 -0.94 -1.23 -1.32 -1.21
Peoria city Illinois -0.43 0.01 -0.80 -0.51 0.31 -0.05
Rockford city Illinois 0.24 0.41 -0.07 0.38 0.48 0.32
Schaumburg village Illinois -0.57 -0.79 -0.33 -0.58 -0.93 -0.74
Springfield city Illinois -0.42 -0.18 -0.75 -0.33 -0.06 -0.27
Waukegan city Illinois 0.68 0.40 0.93 0.70 0.22 0.44
Bloomington city Indiana -0.66 -0.24 -0.46 -1.28 0.17 -0.08
Evansville city Indiana 0.04 0.19 -0.44 0.35 0.21 0.04

Page A-16

City State
Index 1:
Equal
Weight
Index 2: Triple Weight
to Poverty and
Structural Problems
Index 3: Triple Weight to
Immigration and Housing
Affordability Factor
Index 4:
Hedonic
Weights
Index 5:
Richardson
Weights
Index 6: Partial
Hedonic
Weights
Fort Wayne city Indiana -0.01 0.14 -0.46 0.30 0.16 -0.01
Hammond city Indiana 0.57 0.73 0.17 0.80 0.77 0.60
Indianapolis city
(balance) Indiana -0.07 0.11 -0.41 0.09 0.18 0.01
South Bend city Indiana 0.29 0.61 -0.08 0.34 0.79 0.52
Cedar Rapids city Iowa -0.37 -0.24 -0.65 -0.23 -0.20 -0.33
Davenport city Iowa -0.26 -0.07 -0.55 -0.17 0.02 -0.15
Des Moines city Iowa -0.30 -0.24 -0.45 -0.23 -0.22 -0.28
Sioux City Iowa 0.05 0.03 -0.26 0.39 -0.07 -0.10
Kansas City Kansas 0.57 0.83 0.24 0.65 0.98 0.75
Lawrence city Kansas -0.81 -0.59 -0.63 -1.21 -0.36 -0.48
Olathe city Kansas -0.80 -0.84 -0.83 -0.72 -0.88 -0.86
Overland Park city Kansas -0.94 -1.05 -0.89 -0.87 -1.14 -1.06
Topeka city Kansas -0.17 -0.03 -0.56 0.08 0.00 -0.16
Wichita city Kansas -0.10 -0.05 -0.35 0.10 -0.06 -0.14
Lexington-Fayette Kentucky -0.51 -0.38 -0.61 -0.53 -0.30 -0.40
Baton Rouge city Louisiana 0.30 0.75 -0.13 0.27 1.04 0.67
Lafayette city Louisiana -0.35 -0.25 -0.56 -0.25 -0.22 -0.32
Shreveport city Louisiana 0.26 0.62 -0.35 0.51 0.78 0.45
Baltimore city Maryland 0.30 0.91 -0.02 0.01 1.36 0.90
Boston city Massachusetts 0.02 0.52 0.43 -0.90 1.06 0.78
Brockton city Massachusetts 0.37 0.38 0.38 0.34 0.39 0.39
Cambridge city Massachusetts -0.88 -0.51 -0.28 -1.84 -0.04 -0.20
Fall River city Massachusetts 0.51 0.55 0.44 0.54 0.57 0.53
Lawrence city Massachusetts 1.47 2.04 1.85 0.53 2.63 2.30
Lowell city Massachusetts 0.46 0.71 0.72 -0.06 1.00 0.87
Lynn city Massachusetts 0.49 0.73 0.71 0.02 0.99 0.86
New Bedford city Massachusetts 0.60 0.76 0.40 0.63 0.86 0.72
Newton city Massachusetts -1.21 -1.20 -0.96 -1.49 -1.12 -1.09

Page A-17

City State
Index 1:
Equal
Weight
Index 2: Triple Weight
to Poverty and
Structural Problems
Index 3: Triple Weight to
Immigration and Housing
Affordability Factor
Index 4:
Hedonic
Weights
Index 5:
Richardson
Weights
Index 6: Partial
Hedonic
Weights
Quincy city Massachusetts -0.40 -0.58 -0.19 -0.44 -0.68 -0.53
Somerville city Massachusetts -0.13 0.12 0.34 -0.84 0.45 0.36
Springfield city Massachusetts 0.81 1.27 0.50 0.65 1.59 1.24
Worcester city Massachusetts 0.04 0.34 0.21 -0.43 0.65 0.47
Ann Arbor city Michigan -1.00 -0.63 -0.52 -1.84 -0.19 -0.37
Detroit city Michigan 0.99 1.85 0.24 0.89 2.41 1.72
Grand Rapids city Michigan 0.17 0.43 -0.02 0.11 0.60 0.40
Lansing city Michigan 0.19 0.54 -0.11 0.13 0.77 0.49
Livonia city Michigan -0.64 -0.96 -0.75 -0.23 -1.26 -1.06
Pontiac city Michigan 0.87 1.18 0.44 0.98 1.34 1.07
Southfield city Michigan -0.27 -0.39 -0.51 0.09 -0.55 -0.51
Sterling Heights city Michigan -0.20 -0.54 -0.24 0.17 -0.85 -0.62
Troy city Michigan -0.84 -0.96 -0.69 -0.86 -1.04 -0.93
Warren city Michigan 0.08 -0.14 -0.20 0.58 -0.41 -0.30
Westland city Michigan -0.10 -0.29 -0.39 0.37 -0.52 -0.44
Wyoming city Michigan 0.10 -0.10 -0.02 0.44 -0.32 -0.20
Bloomington city Minnesota -0.60 -0.70 -0.62 -0.49 -0.78 -0.72
Brooklyn Park city Minnesota -0.22 -0.30 -0.27 -0.10 -0.38 -0.34
Duluth city Minnesota -0.63 -0.37 -0.77 -0.75 -0.18 -0.37
Minneapolis city Minnesota -0.27 0.22 -0.11 -0.93 0.69 0.38
Plymouth city Minnesota -0.90 -1.00 -0.84 -0.87 -1.07 -1.00
Rochester city Minnesota -0.82 -0.78 -0.73 -0.94 -0.72 -0.73
St. Paul city Minnesota -0.21 0.17 -0.16 -0.65 0.52 0.27
Columbia city Missouri -0.76 -0.54 -0.69 -1.05 -0.32 -0.46
Independence city Missouri -0.04 -0.14 -0.33 0.36 -0.31 -0.28
Kansas City Missouri -0.13 0.15 -0.39 -0.14 0.33 0.10
Lee's Summit city Missouri -0.86 -0.95 -0.94 -0.70 -1.04 -1.00
St. Louis city Missouri 0.35 1.24 -0.20 0.01 1.88 1.20
Springfield city Missouri -0.15 0.01 -0.47 0.03 0.06 -0.09

Page A-18

City State
Index 1:
Equal
Weight
Index 2: Triple Weight
to Poverty and
Structural Problems
Index 3: Triple Weight to
Immigration and Housing
Affordability Factor
Index 4:
Hedonic
Weights
Index 5:
Richardson
Weights
Index 6: Partial
Hedonic
Weights
Lincoln city Nebraska -0.57 -0.49 -0.59 -0.62 -0.43 -0.49
Omaha city Nebraska -0.18 -0.02 -0.33 -0.20 0.09 -0.05
Henderson city Nevada -0.53 -0.77 -0.64 -0.19 -1.01 -0.86
Las Vegas city Nevada 0.12 -0.16 0.19 0.34 -0.38 -0.19
North Las Vegas city Nevada 0.41 0.05 0.57 0.59 -0.21 0.05
Reno city Nevada -0.11 -0.16 -0.01 -0.17 -0.17 -0.12
Sparks city Nevada -0.13 -0.46 -0.08 0.15 -0.74 -0.51
Manchester city New Hampshire -0.24 -0.10 -0.21 -0.41 0.04 -0.06
Nashua city New Hampshire -0.52 -0.59 -0.41 -0.56 -0.62 -0.56
Camden city New Jersey 2.03 3.02 1.76 1.31 3.82 3.11
Clifton city New Jersey -0.08 -0.40 0.16 0.01 -0.63 -0.37
Elizabeth city New Jersey 0.97 0.91 1.35 0.64 0.96 1.06
Jersey City New Jersey 0.40 0.60 0.80 -0.20 0.88 0.80
Newark city New Jersey 1.15 1.50 1.35 0.59 1.86 1.65
Passaic city New Jersey 1.71 1.73 2.45 0.96 1.93 2.03
Paterson city New Jersey 1.20 1.28 1.56 0.77 1.44 1.44
Trenton city New Jersey 1.07 1.46 0.92 0.83 1.76 1.47
Albuquerque city New Mexico -0.17 -0.20 -0.27 -0.03 -0.26 -0.25
Santa Fe city New Mexico -0.34 -0.25 -0.27 -0.50 -0.16 -0.21
Albany city New York -0.07 0.80 -0.38 -0.62 1.48 0.85
Buffalo city New York 0.55 1.45 -0.21 0.42 2.04 1.32
New Rochelle city New York -0.21 -0.30 0.05 -0.38 -0.31 -0.21
New York city New York 0.33 0.52 0.71 -0.23 0.77 0.71
Rochester city New York 0.77 1.62 0.30 0.40 2.25 1.60
Syracuse city New York 0.44 1.29 0.00 0.04 1.91 1.28
Yonkers city New York 0.14 0.14 0.22 0.04 0.17 0.18
Asheville city North Carolina -0.49 -0.29 -0.62 -0.55 -0.15 -0.30
Cary town North Carolina -0.90 -1.01 -0.69 -1.00 -1.05 -0.94
Charlotte city North Carolina -0.25 -0.22 -0.28 -0.27 -0.19 -0.22

Page A-19

City State
Index 1:
Equal
Weight
Index 2: Triple Weight
to Poverty and
Structural Problems
Index 3: Triple Weight to
Immigration and Housing
Affordability Factor
Index 4:
Hedonic
Weights
Index 5:
Richardson
Weights
Index 6: Partial
Hedonic
Weights
Durham city North Carolina -0.21 -0.08 -0.15 -0.39 0.04 -0.03
Fayetteville city North Carolina 0.02 0.19 -0.45 0.34 0.21 0.03
Gastonia city North Carolina 0.40 0.42 0.08 0.69 0.36 0.30
Greensboro city North Carolina -0.08 -0.01 -0.21 0.00 0.01 -0.05
High Point city North Carolina 0.16 0.20 -0.12 0.40 0.17 0.10
Raleigh city North Carolina -0.51 -0.32 -0.43 -0.78 -0.14 -0.25
Wilmington city North Carolina -0.47 -0.30 -0.55 -0.56 -0.17 -0.30
Winston-Salem city North Carolina 0.07 0.24 -0.08 0.04 0.36 0.22
Fargo city North Dakota -0.65 -0.53 -0.76 -0.68 -0.44 -0.54
Akron city Ohio 0.12 0.52 -0.33 0.18 0.76 0.42
Cincinnati city Ohio 0.13 0.89 -0.36 -0.14 1.43 0.85
Cleveland city Ohio 0.96 1.83 0.17 0.87 2.40 1.69
Columbus city Ohio -0.08 0.19 -0.27 -0.16 0.38 0.17
Dayton city Ohio 0.64 1.41 -0.13 0.64 1.89 1.25
Lorain city Ohio 0.46 0.58 -0.09 0.89 0.55 0.39
Parma city Ohio -0.25 -0.52 -0.47 0.23 -0.81 -0.66
Toledo city Ohio 0.31 0.64 -0.30 0.60 0.78 0.46
Broken Arrow city Oklahoma -0.51 -0.63 -0.75 -0.16 -0.79 -0.75
Edmond city Oklahoma -0.90 -0.95 -0.93 -0.82 -1.00 -0.97
Lawton city Oklahoma 0.18 0.30 -0.32 0.57 0.28 0.12
Norman city Oklahoma -0.64 -0.49 -0.73 -0.68 -0.38 -0.50
Oklahoma City Oklahoma 0.04 0.17 -0.26 0.21 0.21 0.08
Tulsa city Oklahoma -0.01 0.14 -0.27 0.10 0.20 0.06
Beaverton city Oregon -0.47 -0.61 -0.26 -0.54 -0.68 -0.55
Eugene city Oregon -0.54 -0.38 -0.50 -0.75 -0.22 -0.33
Gresham city Oregon 0.32 0.24 0.33 0.39 0.17 0.22
Hillsboro city Oregon 0.09 0.13 0.20 -0.07 0.19 0.18
Medford city Oregon -0.21 -0.25 -0.37 0.00 -0.33 -0.32
Portland city Oregon -0.30 -0.10 -0.27 -0.53 0.08 -0.05

Page A-20

City State
Index 1:
Equal
Weight
Index 2: Triple Weight
to Poverty and
Structural Problems
Index 3: Triple Weight to
Immigration and Housing
Affordability Factor
Index 4:
Hedonic
Weights
Index 5:
Richardson
Weights
Index 6: Partial
Hedonic
Weights
Salem city Oregon 0.15 0.10 0.09 0.25 0.04 0.06
Allentown city Pennsylvania 0.40 0.82 0.31 0.07 1.17 0.87
Bethlehem city Pennsylvania -0.25 -0.17 -0.35 -0.24 -0.12 -0.19
Erie city Pennsylvania 0.12 0.48 -0.40 0.28 0.66 0.34
Philadelphia city Pennsylvania 0.45 0.96 0.15 0.23 1.34 0.95
Pittsburgh city Pennsylvania -0.08 0.58 -0.48 -0.35 1.07 0.56
Reading city Pennsylvania 1.34 1.96 1.05 1.00 2.43 1.97
Scranton city Pennsylvania 0.21 0.55 -0.14 0.20 0.77 0.49
Cranston city Rhode Island -0.38 -0.59 -0.39 -0.16 -0.78 -0.63
Pawtucket city Rhode Island 0.47 0.65 0.42 0.33 0.79 0.66
Providence city Rhode Island 0.56 1.18 0.74 -0.25 1.77 1.38
Warwick city Rhode Island -0.52 -0.75 -0.64 -0.17 -0.98 -0.84
Charleston city South Carolina -0.46 -0.12 -0.59 -0.67 0.14 -0.11
Columbia city South Carolina -0.31 0.10 -0.48 -0.54 0.42 0.11
Sioux Falls city South Dakota -0.45 -0.39 -0.57 -0.39 -0.36 -0.42
Chattanooga city Tennessee -0.06 0.29 -0.53 0.06 0.48 0.17
Clarksville city Tennessee -0.35 -0.40 -0.60 -0.04 -0.51 -0.51
Knoxville city Tennessee -0.06 0.33 -0.42 -0.10 0.58 0.26
Memphis city Tennessee 0.39 0.68 -0.11 0.59 0.81 0.54
Murfreesboro city Tennessee -0.45 -0.31 -0.51 -0.53 -0.21 -0.31
Nashville-Davidson
(balance) Tennessee -0.19 -0.05 -0.29 -0.24 0.05 -0.06
Abilene city Texas 0.07 0.12 -0.28 0.36 0.07 -0.01
Amarillo city Texas 0.09 0.05 -0.16 0.38 -0.05 -0.06
Arlington city Texas 0.06 -0.12 0.01 0.29 -0.29 -0.17
Austin city Texas -0.17 -0.08 -0.03 -0.39 0.03 -0.01
Baytown city Texas 0.50 0.28 0.26 0.96 0.03 0.14
Beaumont city Texas 0.29 0.56 -0.20 0.50 0.68 0.42
Bryan city Texas 0.54 0.56 0.43 0.63 0.55 0.52

Page A-21

City State
Index 1:
Equal
Weight
Index 2: Triple Weight
to Poverty and
Structural Problems
Index 3: Triple Weight to
Immigration and Housing
Affordability Factor
Index 4:
Hedonic
Weights
Index 5:
Richardson
Weights
Index 6: Partial
Hedonic
Weights
Carrollton city Texas -0.14 -0.52 0.02 0.08 -0.82 -0.54
College Station city Texas -0.70 -0.45 -0.39 -1.27 -0.16 -0.28
Corpus Christi city Texas 0.25 0.18 -0.02 0.59 0.06 0.06
Dallas city Texas 0.80 0.78 0.97 0.64 0.81 0.85
Denton city Texas -0.41 -0.39 -0.33 -0.50 -0.36 -0.36
Fort Worth city Texas 0.37 0.38 0.30 0.44 0.36 0.35
Garland city Texas 0.54 0.21 0.61 0.79 -0.05 0.18
Grand Prairie city Texas 0.33 0.13 0.20 0.65 -0.07 0.04
Houston city Texas 0.82 0.75 0.94 0.75 0.73 0.79
Irving city Texas 0.42 0.09 0.71 0.46 -0.13 0.14
Killeen city Texas -0.07 -0.03 -0.41 0.24 -0.08 -0.16
Lewisville city Texas -0.21 -0.48 -0.16 -0.01 -0.69 -0.51
Lubbock city Texas 0.00 0.01 -0.30 0.28 -0.05 -0.11
McKinney city Texas -0.67 -0.83 -0.64 -0.53 -0.96 -0.85
Mesquite city Texas 0.28 -0.16 0.17 0.81 -0.57 -0.29
Midland city Texas 0.10 -0.05 -0.16 0.50 -0.25 -0.18
Odessa city Texas 0.45 0.30 0.01 1.03 0.06 0.09
Pasadena city Texas 0.95 0.62 0.99 1.23 0.35 0.57
Plano city Texas -0.71 -0.88 -0.55 -0.70 -0.98 -0.85
Richardson city Texas -0.58 -0.82 -0.41 -0.50 -0.99 -0.80
Round Rock city Texas -0.40 -0.54 -0.50 -0.17 -0.68 -0.60
San Angelo city Texas 0.08 0.04 -0.28 0.49 -0.09 -0.12
San Antonio city Texas 0.32 0.26 0.20 0.50 0.17 0.20
Tyler city Texas 0.19 0.31 -0.13 0.39 0.34 0.21
Waco city Texas 0.41 0.52 0.19 0.51 0.57 0.46
Wichita Falls city Texas -0.05 -0.01 -0.30 0.16 -0.04 -0.10
Ogden city Utah 0.21 0.53 0.15 -0.03 0.78 0.56
Orem city Utah -0.46 -0.56 -0.53 -0.31 -0.65 -0.60
Provo city Utah -0.41 -0.09 -0.17 -0.96 0.25 0.07

Page A-22

City State
Index 1:
Equal
Weight
Index 2: Triple Weight
to Poverty and
Structural Problems
Index 3: Triple Weight to
Immigration and Housing
Affordability Factor
Index 4:
Hedonic
Weights
Index 5:
Richardson
Weights
Index 6: Partial
Hedonic
Weights
Salt Lake City Utah -0.18 0.05 -0.07 -0.54 0.29 0.15
Sandy city Utah -0.72 -0.89 -0.86 -0.42 -1.06 -0.97
West Jordan city Utah -0.40 -0.70 -0.48 -0.03 -0.98 -0.79
Alexandria city Virginia -0.72 -0.77 -0.18 -1.20 -0.68 -0.57
Chesapeake city Virginia -0.46 -0.68 -0.65 -0.05 -0.91 -0.80
Hampton city Virginia -0.21 -0.15 -0.48 -0.02 -0.16 -0.24
Newport News city Virginia -0.21 -0.12 -0.40 -0.11 -0.09 -0.18
Norfolk city Virginia -0.01 0.30 -0.18 -0.15 0.53 0.29
Portsmouth city Virginia 0.03 0.23 -0.27 0.14 0.33 0.15
Richmond city Virginia -0.02 0.38 -0.21 -0.23 0.68 0.39
Roanoke city Virginia -0.05 0.14 -0.45 0.16 0.21 0.02
Suffolk city Virginia -0.32 -0.35 -0.59 -0.01 -0.44 -0.46
Virginia Beach city Virginia -0.53 -0.66 -0.62 -0.30 -0.80 -0.72
Bellevue city Washington -0.75 -0.84 -0.30 -1.10 -0.81 -0.68
Bellingham city Washington -0.66 -0.44 -0.58 -0.97 -0.23 -0.36
Everett city Washington 0.02 0.03 0.06 -0.03 0.05 0.05
Kent city Washington 0.05 -0.05 0.20 -0.01 -0.10 -0.01
Seattle city Washington -0.71 -0.55 -0.44 -1.13 -0.34 -0.41
Spokane city Washington -0.22 -0.03 -0.45 -0.16 0.07 -0.09
Tacoma city Washington -0.03 0.16 -0.09 -0.17 0.32 0.18
Vancouver city Washington -0.01 0.06 -0.05 -0.04 0.11 0.06
Yakima city Washington 0.73 0.71 0.54 0.93 0.65 0.63
Green Bay city Wisconsin -0.09 -0.02 -0.23 -0.03 0.01 -0.06
Kenosha city Wisconsin -0.10 -0.21 -0.30 0.19 -0.34 -0.30
Madison city Wisconsin -0.79 -0.49 -0.61 -1.28 -0.18 -0.36
Milwaukee city Wisconsin 0.46 0.96 0.19 0.23 1.34 0.96
Waukesha city Wisconsin -0.68 -0.83 -0.77 -0.43 -0.98 -0.89

Table A.9. Factor and Equal Weight Index Scores in 2005 and 2000 for 370 Cities

Page A-23
City State
2005
Population
2005
Factor 1
2005
Factor 2
2005
Factor 3
2005
Equal
Weight
Index
2000
Factor 1
2000
Factor 2
2000
Factor 3
2000
Equal
Weight
Index
Birmingham city Alabama 222,154 2.46 -1.11 0.26 0.54 1.82 -1.26 0.82 0.46
Huntsville city Alabama 158,618 -0.05 -1.13 -0.11 -0.43 -0.10 -1.09 0.16 -0.34
Mobile city Alabama 193,332 1.03 -1.14 0.63 0.17 0.71 -1.17 0.83 0.12
Montgomery city Alabama 193,042 0.45 -1.26 0.61 -0.07 0.48 -1.25 0.75 -0.01
Anchorage municipality Alaska 266,281 -0.61 -0.73 -0.06 -0.47 -0.78 -0.66 0.04 -0.47
Avondale city Arizona 61,666 0.08 0.68 0.48 0.42 -0.42 0.45 0.83 0.29
Chandler city Arizona 225,725 -1.01 -0.30 -0.07 -0.46 -1.08 -0.28 0.11 -0.42
Glendale city Arizona 229,913 -0.33 -0.15 0.63 0.05 -0.54 -0.20 0.72 0.00
Mesa city Arizona 442,445 -0.56 0.05 0.41 -0.03 -0.83 -0.17 0.63 -0.12
Peoria city Arizona 141,941 -1.16 -0.58 0.41 -0.44 -1.19 -0.65 0.56 -0.43
Phoenix city Arizona 1,377,980 -0.04 0.70 0.34 0.33 -0.15 0.52 0.52 0.30
Scottsdale city Arizona 215,933 -1.10 -0.75 -0.70 -0.85 -1.26 -0.71 -0.48 -0.81
Tempe city Arizona 166,171 -0.69 -0.04 -0.61 -0.44 -0.60 -0.20 -0.64 -0.48
Tucson city Arizona 507,362 0.26 0.12 0.13 0.17 0.09 0.01 0.37 0.16
Yuma city Arizona 91,433 -0.37 -0.14 1.33 0.28 -0.52 -0.04 1.55 0.33
Fayetteville city Arkansas 58,839 -0.02 -0.67 -1.44 -0.71 -0.12 -0.62 -0.89 -0.54
Fort Smith city Arkansas 81,054 0.26 -0.73 0.87 0.13 -0.04 -0.72 1.20 0.15
Little Rock city Arkansas 176,924 0.16 -1.06 -0.21 -0.37 0.07 -0.99 0.13 -0.27
Alameda city California 77,058 -0.48 0.11 -1.30 -0.56 -0.62 0.31 -1.08 -0.46
Alhambra city California 76,309 -0.40 2.15 -0.64 0.37 -0.45 2.50 -0.03 0.68
Anaheim city California 329,483 -0.67 1.62 0.92 0.62 -0.61 1.73 0.99 0.70
Antioch city California 103,339 -0.48 0.07 0.31 -0.03 -0.64 -0.28 0.49 -0.14
Bakersfield city California 286,316 -0.18 -0.01 0.83 0.21 -0.22 -0.16 1.07 0.23
Baldwin Park city California 84,812 0.09 2.36 1.50 1.32 -0.25 2.82 1.99 1.52
Bellflower city California 78,198 0.12 1.07 0.73 0.64 -0.15 1.15 1.12 0.70
Berkeley city California 90,432 0.08 0.02 -3.22 -1.04 0.19 -0.01 -3.26 -1.03
Buena Park city California 76,062 -0.94 1.03 0.55 0.21 -0.82 1.16 0.88 0.40

City State
2005
Population
2005
Factor 1
2005
Factor 2
2005
Factor 3
2005
Equal
Weight
Index

Page A-24

2000
Factor 1
2000
Factor 2
2000
Factor 3
2000
Equal
Weight
Index
Burbank city California 100,053 -0.84 0.45 -0.35 -0.25 -0.78 0.47 0.08 -0.08
Carlsbad city California 92,998 -1.30 -0.56 -0.86 -0.91 -1.21 -0.54 -0.56 -0.77
Carson city California 92,156 -0.74 0.49 1.02 0.26 -0.96 0.66 1.56 0.42
Chico city California 71,298 0.37 -0.55 -0.87 -0.35 -0.01 -0.36 -0.93 -0.43
Chino city California 69,732 -1.29 0.23 1.59 0.18 -1.16 0.19 1.85 0.29
Chula Vista city California 212,954 -0.69 0.67 0.53 0.17 -0.61 0.52 0.65 0.19
Clovis city California 80,529 -0.79 -0.43 0.38 -0.28 -0.92 -0.65 0.85 -0.24
Concord city California 116,782 -0.64 0.62 -0.30 -0.11 -0.78 0.42 0.02 -0.12
Corona city California 162,410 -1.09 0.17 0.86 -0.02 -1.08 0.08 0.92 -0.02
Costa Mesa city California 105,333 -0.65 1.19 -0.10 0.15 -0.72 0.88 0.22 0.13
Daly City California 93,513 -0.67 1.78 -0.44 0.22 -0.85 1.50 -0.35 0.10
El Cajon city California 92,507 -0.06 0.67 0.23 0.28 0.06 0.49 0.58 0.38
Escondido city California 133,017 -0.69 1.16 0.63 0.37 -0.46 0.98 0.75 0.42
Fairfield city California 102,642 -0.52 0.21 -0.03 -0.11 -0.56 -0.13 0.27 -0.14
Fontana city California 158,235 -0.60 1.12 1.35 0.62 -0.54 0.92 1.79 0.72
Fremont city California 210,387 -1.08 1.26 -1.20 -0.34 -1.15 0.86 -0.86 -0.38
Fresno city California 477,251 0.51 0.70 0.68 0.63 0.57 0.55 0.97 0.70
Fullerton city California 142,064 -1.00 0.62 -0.10 -0.16 -0.84 0.65 0.18 0.00
Garden Grove city California 192,345 -0.66 2.55 0.62 0.83 -0.70 2.28 0.89 0.82
Glendale city California 194,620 -0.88 2.11 -0.49 0.25 -0.38 2.29 -0.62 0.43
Hawthorne city California 100,754 0.49 1.70 0.74 0.97 0.28 1.80 1.41 1.16
Hayward city California 135,474 -0.33 1.00 0.10 0.26 -0.49 1.31 0.35 0.39
Hemet city California 77,076 -0.26 0.60 1.37 0.57 0.01 0.11 1.34 0.48
Hesperia city California 79,714 -0.36 0.15 1.39 0.39 -0.47 -0.19 1.80 0.38
Huntington Beach city California 189,451 -1.16 -0.32 -0.27 -0.58 -1.23 -0.35 0.07 -0.50
Indio city California 65,091 0.19 2.18 0.89 1.09 0.15 2.00 1.65 1.26
Inglewood city California 120,204 0.48 1.48 0.91 0.96 0.63 1.47 1.17 1.09
Lakewood city California 88,253 -1.23 -0.04 0.46 -0.27 -1.18 -0.14 0.87 -0.15

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Livermore city California 87,054 -1.06 -0.26 -0.58 -0.63 -1.12 -0.43 -0.09 -0.55
Long Beach city California 463,956 0.38 1.15 -0.18 0.45 0.62 1.15 0.10 0.62
Los Angeles city California 3,731,437 0.38 1.88 -0.20 0.69 0.43 2.01 0.18 0.87
Merced city California 65,391 0.55 0.78 0.95 0.76 0.68 0.79 1.19 0.89
Mission Viejo city California 90,136 -1.53 -0.72 -0.21 -0.82 -1.55 -0.63 -0.07 -0.75
Modesto city California 202,971 -0.39 -0.06 0.90 0.15 -0.29 -0.07 1.18 0.27
Mountain View city California 69,427 -0.88 0.87 -1.79 -0.60 -0.89 1.11 -1.74 -0.51
Napa city California 73,085 -0.56 0.49 -0.33 -0.14 -0.71 0.34 0.21 -0.05
Norwalk city California 103,844 -0.84 1.54 1.46 0.72 -0.78 1.49 1.80 0.83
Oakland city California 373,910 0.88 1.24 -0.95 0.39 0.91 1.22 -0.79 0.45
Oceanside city California 162,259 -0.98 0.32 0.11 -0.18 -0.58 0.29 0.46 0.06
Ontario city California 156,679 -0.32 1.43 1.35 0.82 -0.39 1.44 1.57 0.87
Orange city California 137,994 -1.14 0.37 0.48 -0.10 -1.00 0.37 0.49 -0.05
Oxnard city California 178,871 0.15 2.21 0.50 0.95 -0.15 2.10 0.82 0.92
Palmdale city California 145,800 -0.09 0.66 1.36 0.64 -0.38 0.22 1.47 0.44
Pasadena city California 129,400 -0.21 0.34 -0.97 -0.28 -0.20 0.92 -0.53 0.07
Pleasanton city California 67,018 -1.22 -0.40 -1.19 -0.93 -1.45 -0.53 -0.68 -0.89
Pomona city California 161,257 -0.24 2.28 1.22 1.09 0.16 2.11 1.50 1.26
Redding city California 89,362 -0.51 -0.73 0.31 -0.31 -0.19 -0.73 0.65 -0.09
Redlands city California 73,548 -0.51 -0.47 -0.10 -0.36 -0.70 -0.38 0.04 -0.35
Redwood City California 81,195 -0.25 1.34 -0.73 0.12 -1.00 0.82 -0.36 -0.18
Rialto city California 93,284 -0.67 1.11 1.55 0.66 -0.26 0.65 1.99 0.79
Richmond city California 96,648 0.38 0.90 -0.27 0.34 0.45 1.01 -0.19 0.42
Riverside city California 294,059 -0.49 0.63 0.55 0.23 -0.37 0.34 0.88 0.28
Roseville city California 108,848 -1.15 -0.46 -0.12 -0.58 -1.21 -0.63 0.13 -0.57
Sacramento city California 445,287 0.52 0.50 -0.24 0.26 0.53 0.42 0.11 0.35
Salinas city California 156,950 -0.04 2.56 1.44 1.32 -0.31 2.14 1.71 1.18
San Bernardino city California 204,552 1.28 1.56 0.50 1.11 1.05 1.00 0.90 0.98

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San Buenaventura (Ventura)
city California 100,154 -0.44 -0.29 -0.59 -0.44 -0.59 -0.19 -0.27 -0.35
San Diego city California 1,208,331 -0.30 0.51 -0.95 -0.25 -0.28 0.48 -0.52 -0.11
San Francisco city California 719,077 -0.26 0.95 -1.59 -0.30 -0.32 1.12 -1.44 -0.21
San Jose city California 887,330 -0.56 1.12 -0.50 0.02 -0.72 1.31 -0.35 0.08
San Leandro city California 77,631 -0.73 0.77 0.21 0.08 -0.82 0.58 0.27 0.01
San Marcos city California 77,445 -0.94 0.33 0.34 -0.09 -0.71 0.79 0.64 0.24
San Mateo city California 93,481 -0.94 0.59 -0.82 -0.39 -1.00 0.52 -0.57 -0.35
Santa Ana city California 302,302 -0.28 3.39 1.66 1.59 -0.28 3.81 1.96 1.83
Santa Barbara city California 90,708 -0.45 0.14 -0.79 -0.37 -0.49 0.63 -0.64 -0.17
Santa Clara city California 102,204 -0.65 1.23 -1.49 -0.31 -0.85 1.14 -1.33 -0.35
Santa Maria city California 88,817 0.14 2.02 0.65 0.93 0.09 1.91 1.19 1.06
Santa Monica city California 82,777 -1.00 -0.26 -1.47 -0.91 -0.74 -0.25 -1.22 -0.74
Santa Rosa city California 146,500 -0.49 0.08 -0.08 -0.16 -0.70 0.03 -0.04 -0.24
Simi Valley city California 116,722 -1.30 -0.54 0.23 -0.54 -1.18 -0.38 0.38 -0.39
Stockton city California 278,515 0.38 0.83 0.61 0.61 0.52 0.79 1.01 0.78
Sunnyvale city California 132,725 -0.75 1.26 -1.73 -0.40 -1.03 1.26 -1.49 -0.42
Thousand Oaks city California 127,895 -1.07 -0.39 -0.75 -0.74 -1.25 -0.42 -0.40 -0.69
Torrance city California 138,618 -1.16 0.32 -0.76 -0.53 -1.20 0.25 -0.36 -0.43
Tracy city California 82,218 -1.15 0.18 0.78 -0.07 -1.21 -0.20 1.14 -0.09
Turlock city California 74,883 -0.84 0.71 1.14 0.34 -0.38 0.32 1.04 0.33
Tustin city California 79,811 -1.11 1.13 -0.40 -0.13 -1.08 0.93 0.31 0.06
Union City California 65,239 -0.82 1.74 -0.04 0.29 -1.06 1.16 0.00 0.03
Upland city California 74,420 -0.29 -0.29 -0.27 -0.28 -0.70 -0.11 0.40 -0.14
Vacaville city California 81,117 -1.10 -0.50 0.38 -0.41 -1.05 -0.49 0.74 -0.27
Vallejo city California 115,657 -0.18 0.19 -0.05 -0.01 -0.41 0.08 0.15 -0.06
Victorville city California 93,042 -0.04 0.29 1.31 0.52 0.01 -0.04 1.33 0.43
Visalia city California 108,467 -0.18 -0.10 0.97 0.23 -0.40 -0.16 1.18 0.21
Vista city California 83,228 -0.06 1.12 0.19 0.42 -0.33 0.94 0.52 0.38

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West Covina city California 116,371 -0.33 0.74 0.63 0.35 -0.97 0.60 0.86 0.16
Westminster city California 97,946 -1.08 1.92 0.88 0.57 -0.77 1.85 0.71 0.60
Arvada city Colorado 104,766 -1.02 -0.88 0.38 -0.51 -1.04 -0.85 0.37 -0.51
Aurora city Colorado 291,317 -0.16 0.23 0.40 0.16 -0.44 -0.05 0.37 -0.04
Boulder city Colorado 83,432 -0.21 -0.08 -2.79 -1.03 -0.33 -0.15 -2.67 -1.05
Colorado Springs city Colorado 376,985 -0.34 -0.69 -0.31 -0.45 -0.56 -0.72 -0.32 -0.53
Denver city Colorado 545,198 0.31 0.28 -0.73 -0.04 0.25 0.26 -0.63 -0.04
Fort Collins city Colorado 122,297 -0.20 -0.67 -1.56 -0.81 -0.52 -0.63 -1.49 -0.88
Greeley city Colorado 82,836 -0.11 -0.14 0.11 -0.04 -0.09 -0.27 0.29 -0.03
Lakewood city Colorado 142,434 -0.43 -0.69 0.14 -0.33 -0.74 -0.57 0.05 -0.42
Longmont city Colorado 76,181 -0.54 0.08 -0.01 -0.16 -0.59 -0.14 0.03 -0.24
Pueblo city Colorado 101,302 0.86 -0.81 0.88 0.31 0.35 -0.69 1.06 0.24
Thornton city Colorado 102,331 -0.98 -0.41 0.80 -0.20 -1.00 -0.61 1.09 -0.17
Westminster city Colorado 99,305 -1.01 -0.72 0.29 -0.48 -1.08 -0.54 0.29 -0.44
Washington city
District of
Columbia 515,118 1.41 0.12 -2.28 -0.25 1.31 0.10 -1.74 -0.11
Boca Raton city Florida 74,361 -0.95 -0.36 -0.55 -0.62 -1.26 -0.47 -0.48 -0.73
Cape Coral city Florida 134,388 -0.84 -0.16 0.48 -0.17 -1.01 -0.58 0.79 -0.27
Clearwater city Florida 108,382 -0.46 -0.26 0.16 -0.18 -0.35 -0.34 0.14 -0.18
Davie town Florida 88,683 -0.75 -0.05 -0.20 -0.33 -0.98 -0.26 0.56 -0.23
Deerfield Beach city Florida 71,599 -0.34 1.11 -0.27 0.16 -0.57 0.27 0.67 0.12
Deltona city Florida 85,979 -1.04 -0.47 1.21 -0.10 -0.86 -0.61 1.27 -0.07
Fort Lauderdale city Florida 141,307 0.15 -0.26 -0.15 -0.09 0.14 0.14 0.12 0.13
Gainesville city Florida 100,879 0.93 -0.27 -1.26 -0.20 0.24 -0.56 -0.90 -0.41
Hollywood city Florida 138,412 -0.44 0.68 -0.07 0.06 -0.42 0.29 0.46 0.11
Jacksonville city Florida 768,537 -0.19 -0.69 0.01 -0.29 -0.23 -0.73 0.38 -0.19
Largo city Florida 71,269 -0.84 -0.52 0.83 -0.18 -0.75 -0.52 0.83 -0.15
Melbourne city Florida 76,373 -0.58 -0.51 0.33 -0.25 -0.45 -0.64 0.34 -0.25
Miami city Florida 361,701 1.48 3.64 -0.70 1.47 1.42 3.61 -0.07 1.65

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Miami Beach city Florida 84,086 0.10 2.46 -1.38 0.39 0.84 2.31 -1.32 0.61
Miramar city Florida 115,444 -0.90 0.35 0.09 -0.16 -0.93 0.41 0.66 0.04
Orlando city Florida 221,299 0.14 0.29 -0.13 0.10 0.18 0.07 -0.21 0.01
Palm Bay city Florida 90,102 -0.60 -0.25 0.51 -0.11 -0.65 -0.56 0.92 -0.09
Pembroke Pines city Florida 159,422 -0.99 0.07 -0.24 -0.39 -1.25 -0.08 0.18 -0.39
Plantation city Florida 88,859 -1.02 -0.19 0.00 -0.40 -1.24 -0.32 0.13 -0.48
Pompano Beach city Florida 94,892 -0.28 0.54 0.49 0.25 -0.02 0.18 0.65 0.27
St. Petersburg city Florida 232,960 -0.08 -0.63 0.18 -0.18 -0.11 -0.62 0.40 -0.11
Sunrise city Florida 86,586 -0.80 0.48 0.44 0.04 -0.83 0.22 0.52 -0.03
Tallahassee city Florida 141,148 0.06 -0.67 -1.36 -0.65 0.07 -0.82 -0.97 -0.57
Tampa city Florida 312,855 0.36 -0.14 -0.15 0.02 0.40 -0.17 0.20 0.15
West Palm Beach city Florida 86,804 0.46 0.62 -0.25 0.27 0.29 0.44 0.11 0.28
Atlanta city Georgia 394,929 2.02 -0.63 -1.11 0.09 1.82 -0.58 -0.69 0.18
Roswell city Georgia 98,137 -1.33 -0.18 -1.03 -0.85 -1.24 -0.19 -0.93 -0.79
Savannah city Georgia 117,478 1.08 -0.55 0.01 0.18 0.92 -0.57 0.49 0.28
Honolulu CDP Hawaii 362,252 -0.50 0.57 -0.99 -0.31 -0.54 0.71 -0.65 -0.16
Boise City Idaho 191,667 -0.54 -0.80 -0.26 -0.53 -0.75 -0.79 -0.16 -0.57
Nampa city Idaho 67,112 0.31 -0.22 0.60 0.23 -0.27 -0.28 0.88 0.11
Aurora city Illinois 170,490 -0.38 0.96 0.15 0.24 -0.75 0.46 0.51 0.07
Champaign city Illinois 65,600 0.15 -0.43 -1.51 -0.59 -0.18 -0.47 -1.62 -0.76
Chicago city Illinois 2,701,926 1.12 0.42 -0.54 0.33 0.97 0.47 -0.24 0.40
Elgin city Illinois 93,412 -0.58 0.78 0.68 0.29 -0.80 0.63 0.78 0.20
Evanston city Illinois 62,258 -0.63 -0.65 -2.02 -1.10 -0.58 -0.47 -1.79 -0.94
Joliet city Illinois 128,090 -0.47 -0.37 0.69 -0.05 -0.41 -0.39 0.91 0.04
Naperville city Illinois 147,779 -1.44 -0.64 -1.35 -1.15 -1.48 -0.74 -1.06 -1.10
Peoria city Illinois 102,136 0.68 -1.35 -0.63 -0.43 0.86 -1.21 -0.25 -0.20
Rockford city Illinois 139,173 0.67 -0.54 0.59 0.24 0.26 -0.69 0.72 0.10
Schaumburg village Illinois 77,817 -1.13 0.03 -0.60 -0.57 -1.37 -0.23 -0.18 -0.60

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Springfield city Illinois 110,262 0.17 -1.24 -0.20 -0.42 -0.02 -1.13 -0.07 -0.41
Waukegan city Illinois 82,355 -0.02 1.30 0.75 0.68 -0.08 1.12 1.04 0.69
Bloomington city Indiana 55,406 0.38 -0.16 -2.21 -0.66 0.09 -0.47 -2.34 -0.90
Evansville city Indiana 110,708 0.43 -1.16 0.83 0.04 0.19 -1.19 0.88 -0.04
Fort Wayne city Indiana 219,346 0.36 -1.14 0.76 -0.01 0.18 -1.14 0.82 -0.05
Hammond city Indiana 72,507 0.97 -0.43 1.15 0.57 0.35 -0.61 1.33 0.36
Indianapolis city (balance) Indiana 765,310 0.38 -0.91 0.32 -0.07 0.14 -0.99 0.39 -0.15
South Bend city Indiana 97,070 1.08 -0.63 0.41 0.29 0.65 -0.85 0.74 0.18
Cedar Rapids city Iowa 119,670 -0.04 -1.07 -0.01 -0.37 -0.62 -0.95 -0.23 -0.60
Davenport city Iowa 95,382 0.21 -0.99 -0.02 -0.26 0.03 -0.99 0.35 -0.20
Des Moines city Iowa 196,917 -0.14 -0.66 -0.11 -0.30 -0.10 -0.61 0.28 -0.14
Sioux City Iowa 78,395 0.00 -0.74 0.90 0.05 -0.31 -0.58 0.77 -0.04
Kansas City Kansas 142,341 1.22 -0.26 0.76 0.57 0.83 -0.46 0.85 0.41
Lawrence city Kansas 74,951 -0.27 -0.35 -1.81 -0.81 -0.37 -0.61 -1.44 -0.81
Olathe city Kansas 107,710 -0.90 -0.88 -0.61 -0.80 -1.18 -0.82 -0.19 -0.73
Overland Park city Kansas 161,901 -1.22 -0.82 -0.77 -0.94 -1.35 -0.79 -0.68 -0.94
Topeka city Kansas 117,326 0.19 -1.15 0.45 -0.17 0.03 -1.02 0.31 -0.23
Wichita city Kansas 354,582 0.04 -0.73 0.39 -0.10 -0.18 -0.75 0.36 -0.19
Lexington-Fayette Kentucky 255,389 -0.20 -0.76 -0.56 -0.51 -0.29 -0.81 -0.48 -0.53
Baton Rouge city Louisiana 205,442 1.44 -0.77 0.23 0.30 0.79 -0.85 0.35 0.10
Lafayette city Louisiana 108,175 -0.10 -0.88 -0.08 -0.35 -0.22 -0.82 0.39 -0.22
Shreveport city Louisiana 192,531 1.16 -1.26 0.89 0.26 0.91 -1.24 1.10 0.26
Baltimore city Maryland 608,481 1.82 -0.50 -0.41 0.30 1.63 -0.52 0.20 0.44
Boston city Massachusetts 520,702 1.27 1.04 -2.27 0.02 0.98 0.88 -1.86 0.00
Brockton city Massachusetts 91,938 0.40 0.41 0.29 0.37 0.40 0.16 0.28 0.28
Cambridge city Massachusetts 81,260 0.04 0.61 -3.28 -0.88 -0.03 0.55 -2.67 -0.72
Fall River city Massachusetts 97,612 0.61 0.34 0.58 0.51 0.52 0.30 1.10 0.64
Lawrence city Massachusetts 82,191 2.89 2.41 -0.88 1.47 1.88 2.05 -0.24 1.23

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2005
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2005
Factor 2
2005
Factor 3
2005
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Weight
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2000
Factor 2
2000
Factor 3
2000
Equal
Weight
Index
2000
Factor 1
Lowell city Massachusetts 96,876 1.10 1.10 -0.83 0.46 0.65 0.64 -0.33 0.32
Lynn city Massachusetts 83,419 1.09 1.04 -0.67 0.49 0.66 0.78 -0.33 0.37
New Bedford city Massachusetts 84,898 1.01 0.10 0.68 0.60 0.97 0.30 0.98 0.75
Newton city Massachusetts 82,383 -1.18 -0.57 -1.89 -1.21 -1.25 -0.43 -1.68 -1.12
Quincy city Massachusetts 84,080 -0.85 0.12 -0.48 -0.40 -0.69 0.08 -0.49 -0.37
Somerville city Massachusetts 74,869 0.48 1.05 -1.91 -0.13 -0.12 0.87 -1.61 -0.28
Springfield city Massachusetts 146,948 1.96 0.04 0.42 0.81 1.32 -0.18 0.42 0.52
Worcester city Massachusetts 154,398 0.80 0.47 -1.14 0.04 0.67 0.12 -0.50 0.09
Ann Arbor city Michigan 98,743 -0.08 0.19 -3.10 -1.00 -0.33 0.01 -2.99 -1.10
Detroit city Michigan 836,056 3.13 -0.88 0.73 0.99 2.59 -1.00 0.95 0.85
Grand Rapids city Michigan 193,568 0.81 -0.32 0.03 0.17 0.42 -0.38 0.13 0.06
Lansing city Michigan 119,675 1.06 -0.55 0.04 0.19 0.57 -0.61 0.25 0.07
Livonia city Michigan 103,497 -1.42 -0.91 0.40 -0.64 -1.44 -0.95 0.62 -0.59
Pontiac city Michigan 59,472 1.64 -0.20 1.16 0.87 1.41 -0.33 1.13 0.74
Southfield city Michigan 75,053 -0.56 -0.87 0.62 -0.27 -0.71 -0.67 0.43 -0.32
Sterling Heights city Michigan 123,368 -1.05 -0.29 0.73 -0.20 -1.27 -0.47 0.83 -0.30
Troy city Michigan 83,958 -1.15 -0.47 -0.89 -0.84 -1.48 -0.44 -0.68 -0.87
Warren city Michigan 134,901 -0.48 -0.62 1.33 0.08 -0.85 -0.55 1.64 0.08
Westland city Michigan 80,284 -0.57 -0.82 1.08 -0.10 -0.90 -0.68 1.33 -0.08
Wyoming city Michigan 68,960 -0.41 -0.21 0.93 0.10 -0.75 -0.52 1.00 -0.09
Bloomington city Minnesota 80,055 -0.84 -0.64 -0.32 -0.60 -1.12 -0.62 -0.08 -0.61
Brooklyn Park city Minnesota 66,408 -0.42 -0.34 0.08 -0.22 -0.79 -0.44 0.10 -0.38
Duluth city Minnesota 76,918 0.02 -0.98 -0.93 -0.63 -0.04 -1.00 -0.57 -0.54
Minneapolis city Minnesota 350,260 0.95 0.14 -1.91 -0.27 0.75 0.07 -1.75 -0.31
Plymouth city Minnesota 68,978 -1.15 -0.74 -0.81 -0.90 -1.38 -0.78 -0.67 -0.94
Rochester city Minnesota 88,338 -0.72 -0.61 -1.12 -0.82 -0.62 -0.57 -0.92 -0.70
St. Paul city Minnesota 261,559 0.75 -0.09 -1.30 -0.21 0.61 0.01 -1.24 -0.21
Columbia city Missouri 82,103 -0.20 -0.59 -1.49 -0.76 -0.17 -0.69 -1.43 -0.76

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2005
Factor 3
2005
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Factor 2
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2005
Factor 2
Independence city Missouri 111,842 -0.30 -0.76 0.95 -0.04 -0.55 -0.85 1.02 -0.12
Kansas City Missouri 440,885 0.57 -0.79 -0.16 -0.13 0.39 -0.79 0.04 -0.12
Lee's Summit city Missouri 86,357 -1.08 -1.05 -0.46 -0.86 -1.20 -1.07 -0.13 -0.80
St. Louis city Missouri 333,730 2.57 -1.03 -0.50 0.35 2.43 -1.02 -0.09 0.44
Springfield city Missouri 139,600 0.24 -0.96 0.28 -0.15 0.01 -0.98 0.43 -0.18
Lincoln city Nebraska 226,062 -0.38 -0.62 -0.70 -0.57 -0.55 -0.70 -0.51 -0.59
Omaha city Nebraska 373,215 0.23 -0.55 -0.23 -0.18 -0.15 -0.65 -0.18 -0.33
Henderson city Nevada 223,776 -1.13 -0.79 0.31 -0.53 -1.20 -0.72 0.55 -0.46
Las Vegas city Nevada 538,653 -0.58 0.29 0.66 0.12 -0.57 0.18 0.83 0.15
North Las Vegas city Nevada 165,061 -0.47 0.82 0.87 0.41 -0.35 0.99 1.33 0.66
Reno city Nevada 204,478 -0.22 0.15 -0.26 -0.11 -0.39 0.11 0.02 -0.09
Sparks city Nevada 76,405 -0.96 0.00 0.57 -0.13 -0.90 0.01 0.77 -0.04
Manchester city
New
Hampshire 109,308 0.12 -0.17 -0.67 -0.24 -0.12 -0.22 -0.29 -0.21
Nashua city
New
Hampshire 84,632 -0.69 -0.25 -0.62 -0.52 -0.69 -0.48 -0.22 -0.46
Camden city New Jersey 73,305 4.51 1.36 0.23 2.03 3.62 0.77 0.77 1.72
Clifton city New Jersey 72,667 -0.89 0.51 0.14 -0.08 -1.02 0.46 0.44 -0.04
Elizabeth city New Jersey 121,137 0.83 1.93 0.14 0.97 0.45 2.32 0.59 1.12
Jersey City New Jersey 246,335 0.91 1.40 -1.11 0.40 0.71 1.32 -0.49 0.52
Newark city New Jersey 254,217 2.02 1.65 -0.24 1.15 2.21 1.27 0.44 1.30
Passaic city New Jersey 68,422 1.76 3.56 -0.18 1.71 0.93 3.35 0.18 1.49
Paterson city New Jersey 148,353 1.40 2.09 0.13 1.20 1.17 1.92 0.61 1.23
Trenton city New Jersey 77,471 2.04 0.69 0.47 1.07 1.84 0.15 0.59 0.86
Albuquerque city New Mexico 488,133 -0.26 -0.42 0.18 -0.17 -0.31 -0.53 0.26 -0.19
Santa Fe city New Mexico 66,453 -0.12 -0.15 -0.75 -0.34 -0.40 -0.14 -0.22 -0.25
Albany city New York 78,402 2.10 -0.84 -1.45 -0.07 1.53 -0.82 -1.07 -0.12
Buffalo city New York 256,492 2.79 -1.35 0.23 0.55 2.66 -1.36 0.33 0.55
New Rochelle city New York 75,961 -0.43 0.44 -0.64 -0.21 -0.65 0.29 -0.30 -0.22

Page A-32

City State
2005
Population
2005
Factor 1
2005
Factor 3
2005
Equal
Weight
Index
2000
Factor 1
2000
Factor 2
2000
Factor 3
2000
Equal
Weight
Index
2005
Factor 2
New York city New York 7,956,113 0.79 1.28 -1.08 0.33 0.88 1.38 -0.60 0.55
Rochester city New York 189,312 2.90 -0.41 -0.17 0.77 2.42 -0.70 0.09 0.60
Syracuse city New York 132,495 2.55 -0.66 -0.56 0.44 2.02 -0.74 -0.33 0.32
Yonkers city New York 193,327 0.15 0.35 -0.10 0.14 0.17 0.43 0.08 0.23
Asheville city North Carolina 74,889 0.01 -0.81 -0.65 -0.49 0.07 -0.75 -0.27 -0.32
Cary town North Carolina 107,446 -1.17 -0.37 -1.16 -0.90 -1.33 -0.49 -1.26 -1.03
Charlotte city North Carolina 601,598 -0.16 -0.32 -0.28 -0.25 -0.38 -0.36 -0.15 -0.30
Durham city North Carolina 191,731 0.11 -0.06 -0.67 -0.21 0.03 -0.33 -0.40 -0.23
Fayetteville city North Carolina 128,777 0.43 -1.17 0.82 0.02 0.17 -1.10 0.72 -0.07
Gastonia city North Carolina 72,183 0.46 -0.40 1.14 0.40 0.25 -0.49 0.91 0.23
Greensboro city North Carolina 208,552 0.08 -0.42 0.11 -0.08 -0.24 -0.63 0.11 -0.25
High Point city North Carolina 101,852 0.26 -0.55 0.76 0.16 -0.15 -0.51 0.65 -0.01
Raleigh city North Carolina 315,249 -0.04 -0.31 -1.19 -0.51 -0.36 -0.30 -0.87 -0.51
Wilmington city North Carolina 91,207 -0.05 -0.67 -0.69 -0.47 0.30 -0.67 -0.45 -0.27
Winston-Salem city North Carolina 183,467 0.50 -0.30 0.00 0.07 0.17 -0.54 0.08 -0.09
Fargo city North Dakota 88,809 -0.33 -0.92 -0.71 -0.65 -0.60 -0.79 -0.61 -0.67
Akron city Ohio 200,181 1.12 -1.00 0.26 0.12 0.84 -1.03 0.51 0.11
Cincinnati city Ohio 287,540 2.03 -1.09 -0.54 0.13 1.75 -1.02 -0.49 0.08
Cleveland city Ohio 414,534 3.14 -1.00 0.73 0.96 2.52 -0.97 0.72 0.75
Columbus city Ohio 693,983 0.59 -0.56 -0.27 -0.08 0.23 -0.70 -0.14 -0.20
Dayton city Ohio 132,679 2.56 -1.28 0.64 0.64 1.92 -1.41 0.55 0.35
Lorain city Ohio 65,476 0.76 -0.91 1.53 0.46 0.62 -0.76 1.42 0.43
Parma city Ohio 79,708 -0.93 -0.80 0.96 -0.25 -1.10 -0.76 1.25 -0.21
Toledo city Ohio 285,937 1.14 -1.23 1.03 0.31 0.72 -1.18 1.00 0.18
Broken Arrow city Oklahoma 85,039 -0.80 -1.11 0.37 -0.51 -1.19 -1.02 0.46 -0.58
Edmond city Oklahoma 71,658 -1.02 -0.98 -0.70 -0.90 -1.12 -0.94 -0.56 -0.87
Lawton city Oklahoma 79,486 0.48 -1.09 1.15 0.18 0.31 -1.21 0.96 0.02
Norman city Oklahoma 97,484 -0.27 -0.88 -0.76 -0.64 -0.56 -0.75 -0.73 -0.68

Page A-33

City State
2005
Population
2005
Factor 1
2005
Factor 3
2005
Equal
Weight
Index
2000
Factor 1
2000
Factor 2
2000
Factor 3
2000
Equal
Weight
Index
2005
Factor 2
Oklahoma City Oklahoma 515,751 0.37 -0.71 0.46 0.04 0.20 -0.75 0.59 0.01
Tulsa city Oklahoma 370,447 0.36 -0.66 0.27 -0.01 -0.04 -0.80 0.46 -0.13
Beaverton city Oregon 83,447 -0.81 0.05 -0.65 -0.47 -0.85 -0.04 -0.44 -0.44
Eugene city Oregon 142,716 -0.13 -0.44 -1.06 -0.54 -0.28 -0.61 -0.92 -0.60
Gresham city Oregon 95,334 0.11 0.35 0.50 0.32 -0.43 -0.09 0.45 -0.02
Hillsboro city Oregon 82,732 0.19 0.37 -0.30 0.09 -0.74 0.32 -0.10 -0.17
Medford city Oregon 73,782 -0.31 -0.62 0.31 -0.21 -0.27 -0.59 0.56 -0.10
Portland city Oregon 513,627 0.20 -0.22 -0.88 -0.30 -0.14 -0.18 -0.63 -0.31
Salem city Oregon 142,006 0.02 0.00 0.42 0.15 -0.17 -0.32 0.66 0.06
Allentown city Pennsylvania 105,231 1.46 0.17 -0.44 0.40 0.89 -0.06 0.21 0.35
Bethlehem city Pennsylvania 68,144 -0.04 -0.49 -0.23 -0.25 0.20 -0.43 -0.17 -0.13
Erie city Pennsylvania 91,423 1.02 -1.18 0.53 0.12 0.93 -1.13 0.79 0.20
Philadelphia city Pennsylvania 1,406,415 1.74 -0.29 -0.09 0.45 1.49 -0.37 0.33 0.48
Pittsburgh city Pennsylvania 284,366 1.58 -1.07 -0.76 -0.08 1.26 -1.14 -0.22 -0.03
Reading city Pennsylvania 81,302 2.89 0.62 0.50 1.34 2.04 0.12 0.63 0.93
Scranton city Pennsylvania 67,314 1.07 -0.65 0.20 0.21 0.28 -0.95 0.62 -0.02
Cranston city Rhode Island 77,025 -0.91 -0.40 0.17 -0.38 -0.90 -0.62 0.75 -0.26
Pawtucket city Rhode Island 72,896 0.92 0.34 0.13 0.47 0.50 0.49 0.53 0.50
Providence city Rhode Island 160,264 2.12 1.01 -1.46 0.56 2.04 0.86 -1.00 0.63
Warwick city Rhode Island 85,804 -1.09 -0.83 0.36 -0.52 -1.10 -0.81 0.62 -0.43
Charleston city South Carolina 109,151 0.38 -0.78 -0.98 -0.46 0.24 -0.85 -0.65 -0.42
Columbia city South Carolina 88,450 0.72 -0.75 -0.89 -0.31 0.93 -0.85 -0.43 -0.12
Sioux Falls city South Dakota 132,358 -0.29 -0.75 -0.30 -0.45 -0.61 -0.73 -0.24 -0.52
Chattanooga city Tennessee 139,158 0.81 -1.24 0.24 -0.06 0.57 -1.07 0.56 0.02
Clarksville city Tennessee 107,130 -0.48 -0.98 0.43 -0.35 -0.51 -0.99 0.86 -0.21
Knoxville city Tennessee 168,744 0.91 -0.96 -0.15 -0.06 0.66 -0.90 -0.04 -0.09
Memphis city Tennessee 642,251 1.12 -0.86 0.90 0.39 0.89 -0.93 1.17 0.38
Murfreesboro city Tennessee 83,822 -0.10 -0.59 -0.65 -0.45 -0.42 -0.65 -0.23 -0.44

Page A-34

City State
2005
Population
2005
Factor 1
2005
Factor 3
2005
Equal
Weight
Index
2000
Factor 1
2000
Factor 2
2000
Factor 3
2000
Equal
Weight
Index
2005
Factor 2
Nashville-Davidson (balance) Tennessee 522,662 0.17 -0.45 -0.30 -0.19 -0.04 -0.58 -0.09 -0.24
Abilene city Texas 105,165 0.19 -0.79 0.80 0.07 -0.11 -0.90 1.01 0.00
Amarillo city Texas 176,999 -0.01 -0.54 0.82 0.09 -0.11 -0.63 0.93 0.06
Arlington city Texas 348,965 -0.39 -0.06 0.63 0.06 -0.62 -0.18 0.48 -0.11
Austin city Texas 678,457 0.05 0.17 -0.73 -0.17 -0.25 0.24 -0.58 -0.20
Baytown city Texas 61,504 -0.05 -0.09 1.65 0.50 -0.12 -0.03 1.69 0.51
Beaumont city Texas 107,876 0.97 -0.92 0.81 0.29 0.64 -1.01 1.04 0.22
Bryan city Texas 56,277 0.59 0.27 0.76 0.54 0.09 -0.02 0.95 0.34
Carrollton city Texas 122,699 -1.10 0.26 0.42 -0.14 -1.11 -0.01 0.12 -0.33
College Station city Texas 65,370 -0.08 0.08 -2.12 -0.70 -0.40 -0.14 -1.97 -0.84
Corpus Christi city Texas 280,002 0.09 -0.43 1.09 0.25 0.00 -0.43 1.38 0.32
Dallas city Texas 1,144,946 0.76 1.22 0.41 0.80 0.38 0.97 0.44 0.60
Denton city Texas 87,766 -0.37 -0.22 -0.63 -0.41 -0.32 -0.17 -0.43 -0.31
Fort Worth city Texas 604,538 0.38 0.20 0.54 0.37 0.20 0.17 0.78 0.38
Garland city Texas 235,750 -0.27 0.72 1.17 0.54 -0.73 0.26 1.14 0.22
Grand Prairie city Texas 148,677 -0.16 0.02 1.13 0.33 -0.42 -0.04 1.27 0.27
Houston city Texas 1,941,430 0.65 1.13 0.66 0.82 0.34 0.91 0.77 0.67
Irving city Texas 212,262 -0.41 1.16 0.51 0.42 -0.57 0.85 0.35 0.21
Killeen city Texas 98,434 0.03 -0.91 0.69 -0.07 -0.13 -0.65 0.62 -0.05
Lewisville city Texas 81,484 -0.87 -0.08 0.30 -0.21 -1.08 -0.37 0.36 -0.36
Lubbock city Texas 199,789 0.03 -0.74 0.70 0.00 -0.11 -0.69 0.68 -0.04
McKinney city Texas 92,337 -1.07 -0.60 -0.33 -0.67 -0.95 -0.27 0.07 -0.38
Mesquite city Texas 126,895 -0.82 0.02 1.62 0.28 -0.93 -0.58 1.47 -0.02
Midland city Texas 100,799 -0.27 -0.54 1.11 0.10 -0.38 -0.68 1.02 -0.01
Odessa city Texas 94,329 0.08 -0.65 1.91 0.45 0.05 -0.62 2.13 0.52
Pasadena city Texas 150,180 0.14 1.05 1.66 0.95 -0.13 0.70 1.85 0.81
Plano city Texas 251,648 -1.13 -0.31 -0.68 -0.71 -1.30 -0.49 -0.72 -0.84
Richardson city Texas 107,892 -1.19 -0.17 -0.39 -0.58 -1.20 -0.18 -0.38 -0.59

Page A-35

City State
2005
Population
2005
Factor 1
2005
Factor 3
2005
Equal
Weight
Index
2000
Factor 1
2000
Factor 2
2000
Factor 3
2000
Equal
Weight
Index
2005
Factor 2
Round Rock city Texas 81,639 -0.74 -0.65 0.18 -0.40 -1.14 -0.51 0.33 -0.44
San Angelo city Texas 82,293 -0.03 -0.83 1.11 0.08 -0.04 -0.71 1.23 0.16
San Antonio city Texas 1,202,223 0.17 0.02 0.77 0.32 0.05 -0.04 1.05 0.35
Tyler city Texas 87,687 0.50 -0.62 0.70 0.19 0.09 -0.55 0.71 0.08
Waco city Texas 107,146 0.69 -0.14 0.67 0.41 0.69 -0.35 0.89 0.41
Wichita Falls city Texas 88,861 0.04 -0.67 0.48 -0.05 -0.12 -0.84 0.88 -0.03
Ogden city Utah 79,171 1.00 0.04 -0.40 0.21 0.64 -0.20 0.06 0.17
Orem city Utah 85,616 -0.69 -0.63 -0.07 -0.46 -0.85 -0.58 0.07 -0.46
Provo city Utah 101,164 0.39 0.18 -1.79 -0.41 0.24 -0.04 -1.62 -0.47
Salt Lake City Utah 182,670 0.41 0.10 -1.06 -0.18 0.23 0.14 -0.97 -0.20
Sandy city Utah 88,189 -1.13 -1.07 0.04 -0.72 -1.29 -0.96 0.10 -0.71
West Jordan city Utah 101,626 -1.14 -0.59 0.52 -0.40 -1.10 -0.77 0.72 -0.38
Alexandria city Virginia 133,479 -0.85 0.64 -1.93 -0.72 -0.51 0.60 -1.72 -0.54
Chesapeake city Virginia 214,835 -1.00 -0.94 0.56 -0.46 -0.92 -0.91 0.63 -0.40
Hampton city Virginia 133,584 -0.05 -0.88 0.28 -0.21 -0.31 -0.88 0.59 -0.20
Newport News city Virginia 176,591 0.01 -0.68 0.05 -0.21 -0.01 -0.73 0.32 -0.14
Norfolk city Virginia 206,172 0.76 -0.44 -0.35 -0.01 0.70 -0.49 -0.01 0.07
Portsmouth city Virginia 95,183 0.53 -0.73 0.30 0.03 0.35 -0.73 0.80 0.14
Richmond city Virginia 180,757 0.98 -0.49 -0.55 -0.02 1.22 -0.52 -0.51 0.06
Roanoke city Virginia 90,074 0.43 -1.06 0.48 -0.05 0.50 -0.91 0.75 0.12
Suffolk city Virginia 77,922 -0.39 -1.00 0.45 -0.32 -0.26 -0.84 0.96 -0.05
Virginia Beach city Virginia 430,856 -0.86 -0.75 0.04 -0.53 -0.98 -0.74 0.27 -0.48
Bellevue city Washington 114,748 -0.98 0.37 -1.64 -0.75 -1.12 0.12 -1.27 -0.76
Bellingham city Washington 69,057 -0.11 -0.44 -1.44 -0.66 -0.19 -0.50 -0.94 -0.54
Everett city Washington 88,850 0.04 0.13 -0.11 0.02 -0.07 -0.01 0.15 0.03
Kent city Washington 84,979 -0.20 0.43 -0.09 0.05 -0.32 0.09 0.08 -0.05
Seattle city Washington 536,946 -0.31 -0.04 -1.77 -0.71 -0.32 -0.11 -1.55 -0.66
Spokane city Washington 192,777 0.25 -0.81 -0.09 -0.22 0.20 -0.74 -0.11 -0.22

Page A-36

City State
2005
Population
2005
Factor 1
2005
Factor 2
2005
Factor 3
2005
Equal
Weight
Index
2000
Factor 1
2000
Factor 2
2000
Factor 3
2000
Equal
Weight
Index
Tacoma city Washington 191,934 0.45 -0.17 -0.38 -0.03 0.35 -0.24 -0.23 -0.04
Vancouver city Washington 155,488 0.17 -0.11 -0.08 -0.01 -0.31 -0.09 0.06 -0.11
Yakima city Washington 79,517 0.68 0.26 1.24 0.73 0.61 0.15 1.55 0.77
Green Bay city Wisconsin 94,242 0.09 -0.44 0.06 -0.09 -0.29 -0.55 0.10 -0.24
Kenosha city Wisconsin 95,440 -0.36 -0.58 0.63 -0.10 -0.47 -0.64 0.80 -0.10
Madison city Wisconsin 203,704 -0.04 -0.32 -2.02 -0.79 -0.27 -0.36 -2.04 -0.89
Milwaukee city Wisconsin 556,948 1.72 -0.21 -0.12 0.46 1.29 -0.34 0.12 0.36
Waukesha city Wisconsin 62,690 -1.05 -0.91 -0.06 -0.68 -1.09 -0.72 0.21 -0.53

Table A.10. Adjusted Needs Index for 234 Cities
City State Population
Standardized
Equal
Weight Index
Standardized
Real Fiscal
Capacity
Index
Adjusted
Needs
Index
Equal
Weight
Index
Rank
Adjusted
Needs
Index
Rank
Birmingham city Alabama 222,154 1.00 0.71 0.29 34 98
Huntsville city Alabama 158,618 -0.87 1.40 -2.27 188 214
Mobile city Alabama 193,332 0.30 0.60 -0.30 80 130
Anchorage
municipality Alaska 266,281 -0.95 -0.85 -0.10 198 114
Chandler city Arizona 225,725 -0.93 -0.98 0.05 194 109
Mesa city Arizona 442,445 -0.10 -1.29 1.18 117 67
Peoria city Arizona 141,941 -0.90 -1.32 0.43 190 89
Phoenix city Arizona 1,377,980 0.61 -0.96 1.57 55 45
Scottsdale city Arizona 215,933 -1.69 1.54 -3.23 227 227
Tempe city Arizona 166,171 -0.90 -0.05 -0.85 191 162
Tucson city Arizona 507,362 0.29 -1.07 1.36 83 56
Fort Smith city Arkansas 81,054 0.22 2.04 -1.82 88 200
Little Rock city Arkansas 176,924 -0.76 2.02 -2.78 182 221
Anaheim city California 329,483 1.17 -0.93 2.10 26 26
Bakersfield city California 286,316 0.37 -1.10 1.48 76 51
Berkeley city California 90,432 -2.05 0.07 -2.12 233 207
Burbank city California 100,053 -0.52 0.90 -1.42 165 193
Chula Vista city California 212,954 0.29 -0.75 1.05 82 72
Concord city California 116,782 -0.25 -0.66 0.40 135 92
Corona city California 162,410 -0.08 -0.48 0.40 116 93
El Cajon city California 92,507 0.50 -0.85 1.35 63 58
Escondido city California 133,017 0.67 -0.57 1.24 50 65
Fairfield city California 102,642 -0.26 -1.31 1.05 137 71
Fullerton city California 142,064 -0.35 -0.65 0.30 143 96
Garden Grove city California 192,345 1.58 -1.44 3.01 17 11
Glendale city California 194,620 0.44 -0.61 1.05 71 70
Hayward city California 135,474 0.46 -0.82 1.28 68 63
Hemet city California 77,076 1.06 -1.49 2.55 30 17
Inglewood city California 120,204 1.81 -1.81 3.62 14 7
Long Beach city California 463,956 0.83 -1.29 2.12 41 25
Los Angeles city California 3,731,437 1.29 -0.96 2.25 24 22
Modesto city California 202,971 0.25 -0.93 1.18 85 68
Oakland city California 373,910 0.72 -0.99 1.71 47 39
Oceanside city California 162,259 -0.39 -0.70 0.31 150 95
Ontario city California 156,679 1.55 -0.73 2.28 18 21
Oxnard city California 178,871 1.81 -1.13 2.93 15 12
Pasadena city California 129,400 -0.58 0.34 -0.92 171 168
Pomona city California 161,257 2.07 -1.69 3.76 10 5
Redwood City California 81,195 0.19 1.02 -0.83 93 159
Richmond city California 96,648 0.61 -1.04 1.65 53 42
Riverside city California 294,059 0.41 -0.99 1.40 74 54
Roseville city California 108,848 -1.16 0.29 -1.45 212 195
Sacramento city California 445,287 0.47 -0.80 1.26 66 64
Page A-37

City State Population
Standardized
Equal
Weight Index
Standardized
Real Fiscal
Capacity
Index
Adjusted
Needs
Index
Equal
Weight
Index
Rank
Adjusted
Needs
Index
Rank
Salinas city California 156,950 2.52 -1.24 3.76 6 4
San Bernardino
city California 204,552 2.12 -1.53 3.65 9 6
San Buenaventura
(Ventura) city California 100,154 -0.90 0.18 -1.08 189 179
San Diego city California 1,208,331 -0.52 0.32 -0.84 164 161
San Francisco city California 719,077 -0.62 0.26 -0.88 173 164
San Jose city California 887,330 0.00 -0.47 0.46 107 87
Santa Ana city California 302,302 3.04 -1.24 4.29 2 1
Santa Barbara city California 90,708 -0.76 1.45 -2.20 181 212
Santa Clara city California 102,204 -0.63 -0.63 -0.01 177 110
Santa Monica city California 82,777 -1.81 2.52 -4.33 229 231
Santa Rosa city California 146,500 -0.35 0.48 -0.83 142 160
Stockton city California 278,515 1.13 -1.34 2.48 27 19
Vallejo city California 115,657 -0.07 -0.86 0.79 114 77
Arvada city Colorado 104,766 -1.02 -0.92 -0.10 203 116
Aurora city Colorado 291,317 0.26 -1.07 1.33 84 60
Boulder city Colorado 83,432 -2.03 2.54 -4.57 232 232
Colorado Springs
city Colorado 376,985 -0.90 -0.31 -0.59 192 146
Denver city Colorado 545,198 -0.13 0.35 -0.48 120 139
Fort Collins city Colorado 122,297 -1.61 -0.41 -1.20 225 185
Greeley city Colorado 82,836 -0.12 -0.54 0.42 119 91
Lakewood city Colorado 142,434 -0.67 -0.23 -0.44 178 137
Longmont city Colorado 76,181 -0.35 -0.11 -0.24 141 129
Westminster city Colorado 99,305 -0.97 -0.78 -0.19 200 123
Washington city
District of
Columbia 515,118 -0.52 0.72 -1.24 163 186
Cape Coral city Florida 134,388 -0.37 -0.50 0.12 147 107
Clearwater city Florida 108,382 -0.40 1.24 -1.64 153 197
Fort Lauderdale
city Florida 141,307 -0.21 1.58 -1.79 130 199
Gainesville city Florida 100,879 -0.42 -0.07 -0.36 155 134
Hollywood city Florida 138,412 0.07 -0.48 0.55 99 84
Jacksonville city Florida 768,537 -0.61 -0.56 -0.05 172 112
Largo city Florida 71,269 -0.38 -0.22 -0.16 148 119
Melbourne city Florida 76,373 -0.53 -0.34 -0.19 166 124
Miami Beach city Florida 84,086 0.72 0.11 0.61 46 81
Miami city Florida 361,701 2.82 -0.12 2.93 3 13
Orlando city Florida 221,299 0.15 1.28 -1.13 94 181
Pompano Beach
city Florida 94,892 0.45 0.16 0.28 69 99
St. Petersburg city Florida 232,960 -0.38 0.42 -0.80 149 155
Tallahassee city Florida 141,148 -1.31 0.08 -1.38 215 191
Tampa city Florida 312,855 0.01 1.04 -1.03 104 176
Page A-38

City State Population
Standardized
Equal
Weight Index
Standardized
Real Fiscal
Capacity
Index
Adjusted
Needs
Index
Equal
Weight
Index
Rank
Adjusted
Needs
Index
Rank
West Palm Beach
city Florida 86,804 0.49 1.64 -1.15 65 182
Atlanta city Georgia 394,929 0.14 2.32 -2.18 95 211
Savannah city Georgia 117,478 0.31 1.34 -1.03 79 175
Honolulu CDP Hawaii 362,252 -0.63 0.29 -0.92 176 169
Boise City Idaho 191,667 -1.08 1.41 -2.49 209 218
Aurora city Illinois 170,490 0.43 -1.12 1.56 72 46
Chicago city Illinois 2,701,926 0.61 -0.63 1.24 54 66
Evanston city Illinois 62,258 -2.17 0.92 -3.09 234 226
Joliet city Illinois 128,090 -0.14 -1.08 0.94 121 75
Rockford city Illinois 139,173 0.42 0.18 0.24 73 102
Springfield city Illinois 110,262 -0.86 0.19 -1.05 187 177
Bloomington city Indiana 55,406 -1.32 0.83 -2.15 217 210
Evansville city Indiana 110,708 0.03 0.63 -0.60 103 147
Fort Wayne city Indiana 219,346 -0.05 -0.25 0.19 110 104
Indianapolis city
(balance) Indiana 765,310 -0.18 0.26 -0.44 125 136
South Bend city Indiana 97,070 0.52 0.26 0.26 62 100
Cedar Rapids city Iowa 119,670 -0.76 1.19 -1.95 183 204
Davenport city Iowa 95,382 -0.55 0.77 -1.32 169 189
Des Moines city Iowa 196,917 -0.63 0.26 -0.89 175 165
Kansas City Kansas 142,341 1.07 -1.38 2.45 29 20
Overland Park city Kansas 161,901 -1.86 1.17 -3.03 230 224
Topeka city Kansas 117,326 -0.37 0.44 -0.82 146 157
Wichita city Kansas 354,582 -0.23 -0.02 -0.21 132 127
Lexington-Fayette Kentucky 255,389 -1.02 0.63 -1.65 202 198
Baton Rouge city Louisiana 205,442 0.54 0.40 0.14 61 106
Lafayette city Louisiana 108,175 -0.73 0.54 -1.27 180 187
Shreveport city Louisiana 192,531 0.46 -0.27 0.73 67 79
Baltimore city Maryland 608,481 0.54 0.37 0.17 60 105
Boston city Massachusetts 520,702 -0.01 1.42 -1.43 108 194
Brockton city Massachusetts 91,938 0.67 -0.35 1.02 51 74
Cambridge city Massachusetts 81,260 -1.74 3.17 -4.91 228 233
Fall River city Massachusetts 97,612 0.95 -0.78 1.74 35 36
Lawrence city Massachusetts 82,191 2.81 -1.01 3.82 4 3
Lowell city Massachusetts 96,876 0.85 -0.70 1.54 39 47
Lynn city Massachusetts 83,419 0.90 -0.51 1.41 36 53
New Bedford city Massachusetts 84,898 1.12 -0.78 1.90 28 30
Quincy city Massachusetts 84,080 -0.82 0.14 -0.97 185 171
Somerville city Massachusetts 74,869 -0.28 -0.24 -0.04 138 111
Springfield city Massachusetts 146,948 1.52 -0.41 1.93 20 28
Worcester city Massachusetts 154,398 0.04 0.25 -0.21 101 126
Ann Arbor city Michigan 98,743 -1.98 0.84 -2.82 231 222
Detroit city Michigan 836,056 1.89 -1.46 3.34 11 9
Grand Rapids city Michigan 193,568 0.29 -0.17 0.46 81 86
Page A-39

City State Population
Standardized
Equal
Weight Index
Standardized
Real Fiscal
Capacity
Index
Adjusted
Needs
Index
Equal
Weight
Index
Rank
Adjusted
Needs
Index
Rank
Sterling Heights
city Michigan 123,368 -0.43 -0.08 -0.35 156 133
Warren city Michigan 134,901 0.11 -0.45 0.56 96 83
Westland city Michigan 80,284 -0.24 -1.11 0.87 133 76
Bloomington city Minnesota 80,055 -1.21 2.60 -3.81 213 230
Duluth city Minnesota 76,918 -1.26 0.78 -2.05 214 205
Minneapolis city Minnesota 350,260 -0.57 0.85 -1.42 170 192
Rochester city Minnesota 88,338 -1.62 0.87 -2.49 226 217
St. Paul city Minnesota 261,559 -0.45 0.16 -0.62 159 148
Columbia city Missouri 82,103 -1.52 0.85 -2.37 223 215
Independence city Missouri 111,842 -0.12 -0.41 0.29 118 97
Kansas City Missouri 440,885 -0.29 0.50 -0.79 139 153
Springfield city Missouri 139,600 -0.32 1.28 -1.61 140 196
St. Louis city Missouri 333,730 0.63 0.19 0.44 52 88
Lincoln city Nebraska 226,062 -1.14 -0.50 -0.64 211 149
Omaha city Nebraska 373,215 -0.40 0.17 -0.57 151 144
Henderson city Nevada 223,776 -1.08 -0.96 -0.12 208 118
Las Vegas city Nevada 538,653 0.20 -1.11 1.31 91 61
Reno city Nevada 204,478 -0.26 -0.07 -0.19 136 122
Manchester city
New
Hampshire 109,308 -0.50 1.41 -1.92 162 202
Nashua city
New
Hampshire 84,632 -1.05 2.25 -3.30 206 228
Clifton city New Jersey 72,667 -0.20 0.31 -0.50 128 140
Elizabeth city New Jersey 121,137 1.84 -0.73 2.56 12 16
Jersey City New Jersey 246,335 0.74 -0.62 1.36 45 57
Newark city New Jersey 254,217 2.18 -0.70 2.89 8 14
Passaic city New Jersey 68,422 3.28 -0.96 4.24 1 2
Paterson city New Jersey 148,353 2.29 -0.98 3.28 7 10
Albuquerque city New Mexico 488,133 -0.36 -0.20 -0.17 144 120
Santa Fe city New Mexico 66,453 -0.70 1.44 -2.14 179 208
Albany city New York 78,402 -0.17 0.63 -0.80 124 154
Buffalo city New York 256,492 1.03 -1.18 2.21 32 23
New York city New York 7,956,113 0.60 -1.03 1.64 56 43
Rochester city New York 189,312 1.46 -0.28 1.74 22 37
Syracuse city New York 132,495 0.82 -0.56 1.39 42 55
Yonkers city New York 193,327 0.22 -1.29 1.51 87 48
Asheville city North Carolina 74,889 -0.98 1.85 -2.84 201 223
Charlotte city North Carolina 601,598 -0.53 0.36 -0.89 168 166
Durham city North Carolina 191,731 -0.45 0.13 -0.58 157 145
Fayetteville city North Carolina 128,777 0.01 -0.34 0.34 105 94
Greensboro city North Carolina 208,552 -0.19 0.84 -1.03 126 174
Raleigh city North Carolina 315,249 -1.04 0.32 -1.36 204 190
Wilmington city North Carolina 91,207 -0.95 0.99 -1.94 199 203
Winston-Salem
city North Carolina 183,467 0.09 0.20 -0.11 97 117
Page A-40

City State Population
Standardized
Equal
Weight Index
Standardized
Real Fiscal
Capacity
Index
Adjusted
Needs
Index
Equal
Weight
Index
Rank
Adjusted
Needs
Index
Rank
Fargo city North Dakota 88,809 -1.31 1.72 -3.03 216 225
Akron city Ohio 200,181 0.20 -0.45 0.65 90 80
Cincinnati city Ohio 287,540 0.21 0.72 -0.50 89 141
Cleveland city Ohio 414,534 1.81 -0.39 2.21 13 24
Columbus city Ohio 693,983 -0.19 -0.14 -0.05 127 113
Dayton city Ohio 132,679 1.20 -0.24 1.44 25 52
Toledo city Ohio 285,937 0.57 -0.46 1.03 59 73
Oklahoma City Oklahoma 515,751 0.03 -0.22 0.26 102 101
Tulsa city Oklahoma 370,447 -0.06 0.49 -0.55 113 143
Eugene city Oregon 142,716 -1.09 -0.13 -0.96 210 170
Gresham city Oregon 95,334 0.58 -1.09 1.67 57 41
Portland city Oregon 513,627 -0.62 0.12 -0.74 174 152
Salem city Oregon 142,006 0.24 -0.52 0.77 86 78
Bethlehem city Pennsylvania 68,144 -0.53 0.15 -0.69 167 150
Erie city Pennsylvania 91,423 0.19 -0.01 0.20 92 103
Philadelphia city Pennsylvania 1,406,415 0.83 -0.74 1.58 40 44
Pittsburgh city Pennsylvania 284,366 -0.20 0.99 -1.20 129 183
Reading city Pennsylvania 81,302 2.55 -0.28 2.83 5 15
Scranton city Pennsylvania 67,314 0.36 0.30 0.06 77 108
Cranston city Rhode Island 77,025 -0.77 -0.47 -0.31 184 131
Pawtucket city Rhode Island 72,896 0.86 -1.12 1.98 37 27
Providence city Rhode Island 160,264 1.04 -0.83 1.87 31 32
Warwick city Rhode Island 85,804 -1.05 -0.14 -0.90 205 167
Charleston city South Carolina 109,151 -0.93 4.35 -5.28 195 234
Sioux Falls city South Dakota 132,358 -0.91 1.36 -2.27 193 213
Chattanooga city Tennessee 139,158 -0.16 0.91 -1.07 122 178
Knoxville city Tennessee 168,744 -0.17 1.03 -1.20 123 184
Memphis city Tennessee 642,251 0.71 -0.58 1.29 48 62
Nashville-
Davidson
(balance) Tennessee 522,662 -0.42 0.12 -0.53 154 142
Arlington city Texas 348,965 0.08 -1.27 1.35 98 59
Austin city Texas 678,457 -0.36 -0.04 -0.33 145 132
Corpus Christi city Texas 280,002 0.44 -1.06 1.50 70 49
Dallas city Texas 1,144,946 1.51 -0.24 1.74 21 35
Fort Worth city Texas 604,538 0.68 -1.05 1.73 49 38
Garland city Texas 235,750 1.00 -1.55 2.55 33 18
Houston city Texas 1,941,430 1.54 -0.36 1.91 19 29
Irving city Texas 212,262 0.77 0.87 -0.10 43 115
Laredo Texas 199,789 -0.05 -1.88 1.84 109 33
Mesquite city Texas 126,895 0.49 -1.39 1.88 64 31
Pasadena city Texas 150,180 1.79 -1.56 3.36 16 8
San Antonio city Texas 1,202,223 0.58 -1.24 1.82 58 34
Tyler city Texas 87,687 0.33 0.51 -0.18 78 121
Waco city Texas 107,146 0.75 -0.40 1.15 44 69
Page A-41

Page A-42
City State Population
Standardized
Equal
Weight Index
Standardized
Real Fiscal
Capacity
Index
Adjusted
Needs
Index
Equal
Weight
Index
Rank
Adjusted
Needs
Index
Rank
Ogden city Utah 79,171 0.38 -0.09 0.47 75 85
Orem city Utah 85,616 -0.94 -0.12 -0.83 197 158
Provo city Utah 101,164 -0.83 -0.01 -0.82 186 156
Salt Lake City Utah 182,670 -0.40 1.66 -2.06 152 206
Alexandria city Virginia 133,479 -1.43 1.20 -2.63 221 219
Chesapeake city Virginia 214,835 -0.94 0.07 -1.01 196 173
Hampton city Virginia 133,584 -0.46 -0.23 -0.23 160 128
Newport News city Virginia 176,591 -0.45 0.24 -0.69 158 151
Norfolk city Virginia 206,172 -0.06 0.41 -0.47 111 138
Richmond city Virginia 180,757 -0.08 1.02 -1.10 115 180
Virginia Beach city Virginia 430,856 -1.06 0.24 -1.30 207 188
Bellevue city Washington 114,748 -1.49 2.29 -3.77 222 229
Bellingham city Washington 69,057 -1.33 0.81 -2.14 218 209
Everett city Washington 88,850 0.00 0.85 -0.85 106 163
Kent city Washington 84,979 0.05 0.26 -0.21 100 125
Seattle city Washington 536,946 -1.41 1.28 -2.69 220 220
Spokane city Washington 192,777 -0.46 -0.09 -0.37 161 135
Vancouver city Washington 155,488 -0.06 -0.48 0.42 112 90
Yakima city Washington 79,517 1.37 -0.32 1.69 23 40
Green Bay city Wisconsin 94,242 -0.22 0.76 -0.98 131 172
Kenosha city Wisconsin 95,440 -0.24 -0.81 0.57 134 82
Madison city Wisconsin 203,704 -1.58 0.86 -2.44 224 216
Milwaukee city Wisconsin 556,948 0.86 -0.64 1.49 38 50
Waukesha city Wisconsin 62,690 -1.35 0.49 -1.84 219 201

Appendix B—Using Regression Analysis to
Develop a Community Needs Index

This report uses factor analysis to identify common themes in the needs of cities and to
measure the needs represented by those common themes. Chapter 3 points out that factor
analysis has several conceptual weaknesses. The main flaws of factor analysis are:

• It assumes the existence of unobservable factors that cause the observed needs.
The existence of these factors cannot be proved.

• There are no definite ways to determine how many factors are at work or to select
among all the possible “rotations” of the factors. We resorted to some common
rules of thumb and judgment to select the factors used to analyze cities needs in
Chapter 4.

• Judgment must be used to choose weights to combine the factors into a single-
value index of need.

• The individual factor scores and any index created by combining them are unitless
measures of need—that is, a factor score of 1.0 says that that city is one standard
deviation above the average of all cities on that factor; it does not provide any
information on how serious a 1.0 score is.

At the project’s Orientation Meeting on October 12, 2006, George Galster suggested an
alternative to factor analysis that would avoid all of these weaknesses. This appendix
describes this alternative approach, explains how we implemented it, and presents the
results. The alternative approach produced some interesting findings, but we were unable
to translate the results into a comprehensive assessment of community needs. The
problem was that some community needs, such as the lack of affordable housing, cannot
be measured using the alternative approach. For this reason, we used factor analysis to
measure needs and relegated our experience with the alternative approach to this
appendix. The concluding section of the appendix contains a summary of the insights we
gained from the alternative approach.

B.1. General Concept, Model Specification, and Difficulties

B.1.1. General Concept

The alternative approach is derived from, but is not the same as, two methods employed
in economics. The first is a methodology used to explain price differences across similar
products with different characteristics. This “hedonic” model was originally developed to
explain the variation in prices of cars that have different features. The model has since
Page B-1

Page B-2
been applied frequently to explain differences in the price of individual houses based on
such features as house size; the condition of the house; lot size; location with respect to
employment centers, good schools, and shopping; amenities such as air conditioning,
garage, and decks; and the quality of the neighborhood. These applications use data on
the price and the features of specific houses.

The second method uses variations in median house prices across metropolitan areas to
measure the market valuation of amenities associated with living in these various places,
such as climate, culture, and sports teams. This method uses housing prices and amenities
measured at the metropolitan scale. The proposed methodology draws from the intuitions
and conclusions of both prior strands of work, but at an intermediate spatial scale. It
attempts to explain variation in the median price of houses across census tracts in various
cities. It uses census information on various characteristics of the census tracts and the
information produced by this study on the characteristics of the cities in which the tracts
are located. Drawing upon the central insight of both prior strands of work—that the
housing market effectively capitalizes the value placed by consumers on characteristics
of the geography surrounding the dwelling—this approach seeks to find how aspects of
the political jurisdiction that have been associated traditionally with “distress” are
capitalized, controlling for characteristics of the dwellings, neighborhoods, and larger
metropolitan area.

The “hedonic” model starts from recognizing two facts: (1) house prices vary
significantly across the country, and (2) the value of a house depends upon the
characteristics of the house, the characteristics of the immediate neighborhood in which
the house is located, and the characteristics of the political jurisdiction and the broader
metropolitan market in which the house is located.
49
To the extent that a city has
problems, the value of houses in that city should be lower than the value of comparable
housing in comparable neighborhoods in a city without such problems. For example,
consider two identical neighborhoods in two different cities in the same metropolitan
area. Because the neighborhoods have similar housing and are located in the same
housing market, one would expect that the median price of houses in the two
neighborhoods would be equal unless one city was a more desirable place to live than the
other city. The goal is to use regression analysis to determine how much effect particular
city-level problems have on the value of homes in those cities.

If one can successfully isolate these effects, then one has a direct measure of the impact
of the problems we have been considering. The measure is a “dollars and cents”
measure, and therefore the effects of different problems can be added together. In this
way, one could determine the combined effects of a number of city-level problems and
could also assess which problems have the greatest negative impact. The key advantage
here is that the respective coefficients measuring these effects provide guidance about
how the market evaluates the various problematic characteristics of the city, without the
imposition of value judgments from the researcher.

49
While it is a misnomer to call the techniques employed in this appendix hedonic models, we shall use the
“hedonic” label for convenience and because the reasoning behind the model draws on the hedonic
literature.

Page B-3

From the 2000 decennial census, we have information on owners’ assessments of the
value of their homes.
50
In particular, we know the median value (as reported by owners)
of the owner-occupied homes in 26,287 census tracts in the 370 cities for which we have
data on our 24 needs indicators.

B.1.2. Model Specification
51

The house prices (P
ijk
) in the ith neighborhood located in the jth political jurisdiction in
the kth metro area is a function of the myriad characteristics (C) of these various scales of
geography. For example, C
i
includes housing characteristics, C
j
includes municipal tax
rate, and C
k
includes climate and housing supply elasticity. Symbolically:

[1] P
ijk
= f(C
i
, C
j
, C
k
)

But because many (not all) of the characteristics of jurisdictions (demographic,
economic, social, etc.) are mathematical summations of the corresponding characteristics
of the constituent neighborhoods, we can write:

[2] C
j
= g(C
i
)

Other characteristics of the jurisdiction (like tax rate) can be symbolized C
j*
. Thus, [1]
can be written:

[3] P
ijk
= f(C
i
, [Cj
*
, g(C
i
)] , C
k
)

From the perspective of a community needs index, the only level of geography that is
directly relevant is the jurisdictional j level. Certainly, variations among neighborhoods
in j may matter for needs, but such variation should be captured with variables measured
at the community j scale to be operational, e.g., percentage of tracts in a community
exceeding 40 percent poverty.

If we were to run a regression using median census tract housing prices as the dependent
variable for a large set of tracts across j jurisdictions and k different metropolitan areas,
C
k
could be measured by a dummy variable for each metropolitan area. [C
j*
, g(C
i
)] will
be measured with the variables used for input into the factor analysis; coefficients of
these variables would be the key item of interest that weight the various attributes of the
jurisdiction. The Ci term is trickiest. We certainly can measure many tract
characteristics using census data, but others of interest (like crime rates) we cannot for
the entire nation. So, there will be some omitted variables of interest. It is likely,
however, that many of these will be highly correlated with others that we can measure.

50
The ACS will provide same information at the census-tract level beginning in 2010.
51
George Galster drafted this formal presentation of the hedonic-like model.

Page B-4
The measure of community needs would be the sum of the product of [C
j*
, g(C
i
)] and its
coefficients. Its interpretation is straightforward. Community conditions in city A reduce
the median price of houses in city A by X dollars.

B.1.3. Difficulties

The statistical problem is how to relate the variation in median home values to the
information we have about the tract and the local market.
52
Because city-level problems
are likely to have less impact on the values of the houses in a tract than the characteristics
of the houses themselves and their neighborhood, it is very important to have good
information on the houses and the tract. This is the first problem that must be overcome.

A second problem is the possibility that not all city-level problems will be associated
with lower house values. An example of such a situation is housing affordability. The
needs indicators in Table 1 include several measures of possible housing market
problems. While the lack of affordable rental housing (LACKAFFDRENTALS) given
the city’s income level is definitely a legitimate city-level problem, it is likely to be
associated with high housing prices, not low housing prices.

There are also situations where the functioning of private markets may run counter to
public perception of what is desirable and what is not. For example, city-level diversity
in terms of income and racial or ethnic composition is considered desirable from the
perspective of public policy, but some homebuyers may pay a premium to live in cities
that are homogenous in terms of income or race or ethnicity. If enough homebuyers value
racial and income homogeneity, then the implicit prices derived from the hedonic model
will represent the market value but not the social value of diversity.

B.2. Implementing the “Hedonic” Approach for City-Level Needs
Indicators

B.2.1. Choice of Database

As noted above, we have data on median housing values for 26,287 census tracts. After
eliminating tracts with missing values for various variables, this left 21,375 for our first
regression. In reviewing earlier versions of this model, some HUD staff members
expressed concern about using median values in the regression because housing values
can vary significantly within a tract. At HUD’s suggestion, we experimented with
eliminating tracts where housing values vary greatly within the tract. We dropped
roughly 25 percent of the tracts—those with the greatest variation relative to the

52
The Final Work Plan for this project contains the formal presentation of this model.

Page B-5
median.
53
This resulted in a database of 20,485 tracts, of which we used 16,096 in the
second regression.

The two regressions produced similar results, and neither set of results was clearly
superior to the other.
54
Table B.10 at the end of this appendix contains the complete
results from the regression using the full set of tracts, while Table B.11 contains the
complete results from the regression using the tracts with less dispersion in house values.
The discussion in this chapter will focus on the regression based on the tracts with less
dispersion—that is, the regression reported in Table B.11. Both models fit the data well.
The regression using the restricted data set explains 77 percent of the variation in median
house prices, and the probability that the reduction in variance is due to chance is less
than 1 in 10,000.

B.2.2. The Tract-Level Variables

To isolate the effects of city-level needs indicators on median house prices, we used 20
control variables defined at the tract level. Table B.1 lists the tract-level variables.

In choosing these variables, we looked first for information that might describe the
characteristics of the houses whose median value we are trying to explain. The decennial
census has two tables that contain relevant information: a distribution of owner-occupied
homes by the number of rooms in the home and a distribution of owner-occupied housing
by the year the unit was built. We use the median number of rooms and create five
variables to characterize the age of the owner-occupied housing in the tract.
55

Next, we use the decennial census to provide information about the housing and people in
the tract that might relate to the value of owner-occupied housing. Table B.1 contains
variables that describe the tenure pattern and the type of structures in the tract and a
variable that measures the proportion of overcrowded units in the tract. The decennial
census provides information on the poverty rate in the tract and the racial and ethnic
composition of the tract. It also provides information at the tract level on populations that
we include in the city-level needs indicators—namely, single-parent families, recent
immigrants, and linguistically isolated households.

53
We calculated the ratio of (3
rd
quartile value – 1
st
quartile value)/median and eliminated all tracts where
this ratio equaled or exceeded 0.56. The distribution of this ratio had a first quartile of 0.30, a median of
0.41, and a third quartile of 0.56.
54
Both regressions use the natural log of the median house value as the dependent variable. This means
that independent variables have a multiplicative effect on median house price—that is, if a variable has a
value of 1.0 and its coefficient is 1.04, then the impact of that variable is to increase the median value of the
homes in that tract by 4 percent. Those with positive coefficients increase median house price, while those
with negative coefficients decrease median house price.
55
Our age-of-housing variables segment age into six periods. We use only five of them, because in
regression analysis if there is a group of mutually exclusive categories, one must category must serve as the
point of comparison. With a set of categorical variables, the coefficients tell how much impact each of the
included variables has compared to the excluded variable, for example, by what percentage the median
value of a units built after 1989 is greater or less the median value of a units built in the 1970s, the omitted
age category.

Page B-6
Table B.1. Tract-Level Variables Used in Regressions
Variable Name Explanation
Characteristics of owner-occupied housing in the tract
ROOMS Median number of rooms in owner-occupied houses.
BUILTB50 Percent of owner-occupied was built before 1950
BUILT50S Percent of owner-occupied was built the 1950s
BUILT60S Percent of owner-occupied was built the 1960s
BUILT80S Percent of owner-occupied was built the 1970s
BUILTA89 Percent of owner-occupied was built after 1989
Characteristics of the tract
ORATE Percent of the occupied units that are owner-occupied
PCTSFDETACHED Percent of the units that are in single-family detached structures
PCT5PLUSUNITS Percent of the units that are in structures containing 5 or more units
OVERCROWD2000_TR
Percent of households in the tract living in units where the number of
person per room is 1.01 or greater.
HISPAN Percent of the population that is Hispanic
NHBLACK Percent of the population that is non-Hispanic Black
NHOTHER
Percent of the population that is non-Hispanic and not White alone or
Black alone
POV10_19
The poverty rate in the tract is greater than or equal to 10 percent and
less than 20 percent.
POV20_29
The poverty rate in the tract is greater than or equal to 20 percent and
less than 30 percent.
POV30_39
The poverty rate in the tract is greater than or equal to 10 percent and
less than 40 percent.
POV40PLUS The poverty rate in the tract is greater than or equal to 40 percent.
SGLPRNTFAM_TR
Percent of families in the tract that are single parent-headed with own
children under 18.
RCNTIMMIG_TR
Percent of household population in the tract that is foreign born and
entered the United States within the last 15 years.
LINGISOL_TR
Percent of households in the tract that are “linguistically isolated”
according to the definition in Table 1.

Table B.2 shows that the tract-level variables perform very well. Median housing value is
positively and significantly related to the median number of rooms. All the year-built
coefficients are statistically significant, except the coefficient for houses built in the
1960s. The sign and size of the coefficients suggest that older housing is less valuable
than more recently built housing, with the exception that houses built before 1950 are
more valuable than those built in the 1950s.
56

56
One possible explanation for this result is that “being built before 1950” proxies for other features of a
housing unit that are not picked up by the other variables, for example, being located close to the central
business district where land prices are high.

indicating that poverty is associated with lower house values. In general, where the
poverty rate is higher, the value of houses is disproportionately lower. Finally, housing
values are lower in neighborhoods with a higher percentage of single-parent households
or recent immigrants.

B.2.3. Variation Across Core-Based Statistical Areas

Our sample includes tracts in 191 core-based statistical areas. We included 190 dummy
variables in the equation to account for unspecified, metro-wide amenity, housing supply
elasticity, and speculative dynamic features unique to each of those housing markets.
There was no dummy variable included for Birmingham-Hoover, so all the effects are
measured relative to this CBSA.

These variables also performed according to prior expectations. Housing values are
highest in the San Jose, Honolulu, San Francisco, and Santa Barbara metropolitan areas
and lowest in the Pittsburgh, Buffalo, Syracuse, and Youngstown metropolitan areas.

B.2.4. City-Level Needs Indicators

The model fits well, and both the tract-level variables and metropolitan-area dummy
variables are controlling for housing and tract characteristics and broader market
characteristics. Thus now we can feel comfortable in looking at the impact of the city-
level needs indicators on median house values.

As expected, the effect of the city-level indicators is small relative to the other variables.
The adjusted R-square is 0.73 without the city-level needs indicators, and increases to
0.77 with these variables. The F statistic increases from 220.05 to 225.36; the 5-percent
gain in explained variance is partially offset by the increase in the number of variables.

Table B.3 provides the estimated coefficients for the 24 city-level needs indicators. The
results are organized into four subgroups.

Page B-8

CHGLOWNINCCON is a trend variable that compares the proportion of families in the
bottom 20 percent of the national family income distribution in 2000 to the proportion in
1970. The sign of the coefficient of CHGLOWNINCCON suggests that a growing low-
income population is associated with higher housing prices—an unexpected result.

The third group contains four needs indicators that have statistically significant
coefficients with unexpected positive signs. These are the mortgage denial rate
(DENIAL), the percent of the population over 74 who are poor (POOROVER74), the
percent of the working-age adults without a high school diploma (UNEDUCADULTS),
and the minority share of the city population relative to the minority share of the
metropolitan population (MINCON). We defined UNDEREDWORKAGE in such a way
that it includes working-age persons without a high school diploma. While the base
groups are different (all adults verses working-age adults), it makes sense to consider
these two variables together. Their coefficients sum to -0.56, which has the appropriate
sign. We have no ready explanations for the signs of the other variables. The sign of the
coefficient of the MINCON variable is inconsistent with the sign on the ethnic and racial
tract-level variables.

The last group contains seven needs indicators that have statistically significant
coefficients, and the coefficients have the expected negative sign. This group includes
two of the city/suburb disparity variables, POVCON and MEDINCCBS2CITY. It also
includes the two variables (PCTPOPHIGHPOVNGHS and PCTPOPMODPOVNGHS)
that measure the extent to which poverty is concentrated in certain neighborhoods. The
last three variables measure the relative importance of certain populations: recent
immigrants (RCNTIMMIG), households that are low income but above the poverty level
(LWINCHHDS), and working-age adults without a college degree
(UNDEREDWORKAGE).

B.3. Modified Specification of the Hedonic Model

From the initial analysis, we concluded that some variables, namely those in the top panel
of Table B.3, are not appropriate for the hedonic model because they are the result of
high housing prices. With this in mind, we estimated a new regression using all the same
variables except for LACKAFFDRENTALS_2000, PR70RENTPOV_2000, and
OVERCROWD_2000.

Table B.4 presents the results for the tract-level variables. The elimination of the three
housing-market related variables had very little effect on the results for the tract-level
variables. There are only two noteworthy changes. Now the coefficient of
OVERCROWD2000_TR is positive and nearly significant; previously, it had a negative
coefficient of very low significance. After dropping the city-level overcrowding variable,
the tract-level variable now appears to be picking the impact of housing market
conditions. The other noteworthy change involves LINGISOL_TR. Previously this
variable had a positive significant coefficient; now the coefficient is still positive but
insignificant.
Page B-10

Table B.5. Estimated Coefficients for City-Level Needs Indicators with
Modified Specification
Variable Name
Parameter
Estimate
Probability Estimate
Different from Zero
Variables not statistically significant at the 0.05 level in initial specification
SGLPRNTFAM_2000
0.11269 0.6934
POORPERS_2000
-0.3083 0.7371
LINGISOL_2000
0.7686 0.002
SCHPOPPOOR_2000
1.11468 0.2344
PCTVACMODPOVCITY_2000
0.4141 0.639
EXCSINFRA_2000
-0.13529 0.2453
CHNGEMPLBASE
0.00307 0.9166
POORCHILD_2000
-0.43336 0.6741
UNEMPCEN_2000 -0.60749 0.242
CHGLOWINCCON_2000
0.01802 0.2741
Statistical significant need variables with unanticipated signs in initial specification
DENIAL
1.03135 0.0004
POOROVER74_2000
1.2255 <.0001
UNEDUCADULTS_2000
1.55449 <.000
MINCON_2000
0.1155 <.0001
Statistical significant need variables with anticipated signs in initial specification
RCNTIMMIG_2000
-0.08441 0.6648
POVCON_2000
-0.058 0.0955
PCTPOPHIGHPOVNGHS_2000
0.000347 0.9987
LWINCHHDS_2000
-0.39888 0.0089
PCTPOPMODPOVNGHS_2000
-0.39901 <.0001
MEDINCCBS2CITY_2000
-0.12208 0.1368
UNDEREDWORKAGE_2000
-1.97809 <.0001

The final panel contains seven variables whose coefficients were statistically significant
in the initial specification and had the anticipated negative signs. In the modified
specification, only two of the seven have statistically significant signs.

We conclude that, while we cannot give the three housing market-related variables a
“community needs” interpretation in the hedonic model, their inclusion is important in
the specification of the model. The three variables appear to account for the effect of
important housing market conditions.

B.4. Interpreting the Coefficients of City-Level Needs Indicators

For this analysis, we use the results from the initial specification reported in Table B.3.
But instead of using estimates of the regular coefficient, we use estimates of the beta
coefficients. The beta coefficient of variable x measures the impact of a one-standard-
deviation change in variable x on the dependent variable—in this case, on the log of
median house prices. Table B.6 reports the beta coefficients and transforms them into the
percentage effect that a one-standard-deviation change will have on the median price.

Page B-12

Page B-13
Table B.6. Percentage Decline in Median House Prices Caused by a One-
Standard-Deviation Worsening in a Need Indicator
57

Need Indicator Beta Coefficient Percentage Impact
RCNTIMMIG_2000 -0.051 -0.050
POVCON_2000 -0.068 -0.065
PCTPOPHIGHPOVNGHS_2000 -0.043 -0.042
LWINCHHDS_2000 -0.072 -0.069
PCTPOPMODPOVNGHS_2000 -0.098 -0.093
MEDINCCBS2CITY_2000 -0.222 -0.199
UNDEREDWORKAGE_2000 -0.263 -0.231

The percentage impacts range from a 4-percent decline to a 23-percent decline in median
house value. The first five needs indicators have approximately the same impact: a 4- to
7-percent decline. This is to be expected, given that several indicators measure similar
aspects of disadvantaged communities. The relatively high impact of
MEDINCCBS2CITY suggests that housing prices vary substantially inside of
metropolitan areas where income inequality is significant across jurisdictions. The strong
impact of UNDEREDWORKAGE suggests that we should group it with
UNEDUADULTS as discussed above. If we were to use the combined coefficients, the
estimated impact of a one standard deviation worsening on both indicators would be an 8-
percent decline, an impact in line with the earlier variables.

B.5. Use of the “Hedonic” Approach for Assessing Importance
of Factors

In Chapter 3, we used factor analysis to identify three factors underlying the 24 needs
indicators. In Chapter 4, we reported a score on each factor for 370 cities. In this
section, we substitute the factor scores for the 24 need variables and use the hedonic
approach to measure the impact of the factors on median house value. In particular, we
are interested in using the measured impact to inform our choice of weights for
combining the factors into a single index of community need.

We used the factor scores in regressions using both all the census tracts and only the
census tracts with limited dispersion of home values. The two regressions produced
similar results. The discussion in this section will focus on the regression based on the
tracts with less dispersion; Table B.12 at the end of this appendix contains the complete
results from that regression. The model fit the data well, explaining 75 percent of the
variation in median house prices, and the probability that the reduction in variance is due
to chance is less than 1 in 10,000. The effect of adding the factor score variables is small
relative to the other variables. The adjusted R-square is 0.73 without the city-level needs
indicators, and increases to 0.75 with these variables. The F statistic increases from
220.05 to 232.98.

57
The formula used to estimate the impact was: exp(beta coefficient*one standard deviation) – 1, which
equals the change in median value over the original median value.

Page B-14
Table B.7 presents the parameter estimates for the three factor scores. All three
coefficients are highly significant, and two of the three have the expected negative sign.
The coefficient for Factor 2 has a positive sign. This factor had high factor loadings for
overcrowded housing (OVERCROWD2000) and the lack of affordable rental housing
(LACKAFFDRENTALS)—two needs indicators that had significant positive coefficients
in the regression with the city-level need variables. Therefore, it is not surprising that
this factor also has a positive coefficient.

Table B.7. Estimated Coefficients for the Three Factors
Variable Parameter Estimate
Probability Estimate Different from
Zero
Factor 1 -0.05551 <.0001
Factor 2 0.09500 <.0001
Factor 3 -0.19129 <.0001

As explained above, we believe that OVERCROWD2000 and LACKAFFDRENTALS
had positive coefficients because they were produced by higher housing prices, not
because markets positively value overcrowding or lack of affordable housing. For this
reason, we did not think it appropriate to use the estimated impact of these variables on
median prices in Table B.3 to measure the “social costs” of these conditions. Similarly,
we will not try to use the hedonic model to estimate the social costs of the conditions
measured by Factor 2. We will, however, explore what the hedonic model tells us about
the relative social costs of Factors 1 and 3.

Table B.8 calculates the impact on median home values of a one-standard-deviation
increase in the scores for Factors 1 and 3. A one-standard-deviation increase in the score
for Factor 1 lowers the median house price by 5 percent, while a one-standard-deviation
increase in the score for Factor 3 lowers the median house price by 16 percent. Higher
values of Factors 1 and 3 are associated with lower median home values; the impact of a
one-standard-deviation increase is approximately three times worse for Factor 3. This
suggests that elements of community need associated with economic prospects (Factor 3)
are approximately three times more important to consumers in the market than elements
of community need associated with poverty and structural problems (Factor 1). We used
this information to create Index 4, which weights the three factors 0.20, 0.20, and 0.60.
58

Table B.8. Percentage Change in Median House Prices Caused by a One-
Standard-Deviation Worsening in a Factor Score
Standard Deviation Percentage Impact
Factor 1 0.84 -0.05
Factor 3 0.88 -0.16

58
We experimented with a regression containing only scores for Factors 1 and 3. (This is parallel to the
analysis in Section 4.3 where we modified the regression containing the city-level needs indicators by
dropping the three indicators associated with high-priced housing markets.) The coefficients from this
regression suggest that the market values a one-standard-deviation change in Factor 3 seven times more
than it values a one-standard-deviation change in Factor 1.

Page B-15
B.6. Use of Information on City-Level Variables to Create Factor
Weights

B.6.1. The Relationship between Beta Coefficients and Standardized
Scoring Coefficients

Table B.6 made use of beta coefficients. There is a parallel interpretation that can be
given to beta coefficients and the standardized scoring coefficients derived in factor
analysis.
59

• Beta coefficients measure the impact of a one-standard-deviation change in a need
indicator on the log of median housing prices. In the hedonic analysis, the change
in the log of median housing prices is the measuring stick, or metric, used to
gauge the impact of a variable.

• Standardized scoring coefficients measure the impact of a one-standard-deviation
change in a need indicator on the estimated factor score. In factor analysis, the
change in a factor score is the metric used to gauge the impact of a variable.

When we create a single-value index, we take a linear combination of the standardized
scoring coefficients and convert them into a measure of the impact of a one-standard-
deviation change in a variable on the index score, the metric used to gauge the overall
impact of a variable.

Table B.9 has five columns. The second column contains the beta coefficients for the
seven variables that had statistically significant coefficients of the correct sign. The only
difference is that the last row contains the sum of the beta coefficients for
UNDEREDWORKAGE and UNEDUCADULTS because we concluded that these
coefficients should be considered jointly. Columns three, four, and five contain the
standardized scoring coefficients for these variables.

We can combine Factors 1, 2, and 3 into a single-valued index by selecting weights for
the factors. What if we selected the weights to make the sum of the standardized
coefficients in columns three, four, and five as close as possible to the beta coefficients in
column two? The result would be a single-valued index that approximated the impact of
these eight variables on median housing prices.

59
George Galster pointed out the relationship between beta coefficients and the standardized scoring
coefficients. However, he is not responsible for any erroneous use of this relationship by the principal
investigator.

Table B.9. Beta Coefficients and Standardized Scoring Coefficients for
Needs Indicators with Valid Hedonic Coefficients

Standardized Scoring
Coefficients
Variable beta Coefficient Factor 1 Factor 2 Factor 3
RCNTIMMIG -0.051 -0.008 0.247 -0.186
POVCON -0.068 0.098 0.006 -0.429
PCTPOPHIGHPOVNGHS -0.043 0.014 -0.010 -0.042
LWINCHHDS -0.072 0.028 0.099 -0.104
PCTPOPMODPOVNGHS -0.098 0.009 0.045 0.021
MEDINCCBS2CITY -0.222 0.204 0.102 -0.014
UNDEREDWORKAGE + UNEDUCADULTS -0.122 -0.098 0.081 0.835

We used ordinary least squares regression to estimate raw weights that would make the
weighted sum of the standardized scoring coefficients as close as possible to the beta
coefficients. The raw coefficients were: -0.98, -0.45, and -0.19. When we adjust the raw
weights so that the coefficients sum to 1.00, the weights become 0.60, 0.28, and 0.12.
These are the weights we used in Index 6, the partial hedonic weights index.

This approach was purely experimental. It ignores all the needs indicators for which the
hedonic-type equation failed to find valid coefficients. Yet it generated reasonable
results. Index 6 is a blend of Index 2 and Index 5.

B.6.2. Extension of Experimental Use of Beta Coefficients to Guide
Selection of Weights

In the last stages of writing the second draft report, the principal investigator had an idea
on how to extend the analysis in Section B.4.1 that produced Index 6. Here is the
extension of the rationale used in B.4.1.

Table 3 in section 3.2.5 contains the factor loadings (based on an orthogonal rotation) for
the factors used in this report. In the case of each factor, some of the factor loadings are
negative. This is not surprising. In factor analysis, the first unrotated factor will have all
positive factor loading; the other unrotated factors will always have some negative factor
loadings. After rotation, all factors will have some negative factor loadings. The
existence of some negative factor loading results from the mathematics used to identify
the factors.

In Table A.7 in Appendix A, there are negative standardized scoring coefficients for each
factor. Table 9 in section 3.4.3 converts the standard scoring coefficients for the
individual factors into the weights applied to the needs indicators to generate four of the
six single-valued indices. Each of the single-value indices in Table 9 employs some
negative weights.

The preceding section described a parallelism between the beta coefficients from the
hedonic analysis and the weights derived from the standardized scoring coefficients.
Page B-16

Page B-17
Both the beta coefficients and the weights estimate the impact of a one-standard-
deviation change in a need indicator on a metric. In the case of the regression analysis,
the metric is the log of median housing value; in the case of the single-value index, the
metric is the weighted sum of the factor scores.

In B.4.1, we limited our attention to the seven needs indicators that had significant
coefficients with the correct sign in the regression analysis. In the regression, sign was
important; but for the standardized scoring coefficients, it appears to be less important.

With this in mind, we used an OLS regression to fit the standardized scoring coefficients
to the beta coefficients for all the variables except LACKAFFDRENTALS and
OVERCROWD, which are variables that clearly do not fit the rationale of the regression
model.
60
The raw coefficients were: -0.11, -0.40, and -0.10. When we adjust the raw
weights so that the coefficients sum to 1.00, the weights become 0.18, 0.66, and 0.16.
These are similar to the weights used in Index 3, the triple weight to the immigration and
housing affordability factor.

We were unable to work through the implications of this analysis or to investigate its
weaknesses. Therefore, we cannot claim that this analysis provides a justification for
choosing Index 3. We present the results to call attention to the relationship between the
regression analysis and the factor analysis and to the need to explore this relationship in
greater depth.

B.7. Insights Gained from the Hedonic-type Analysis

Initially the project focused solely on factor analysis as the technique to be used in
constructing a community needs index. At the Orientation Meeting, George Galster
suggested using a hedonic analysis in order to resolve some of the uncertainties in factor
analysis.

As reported in this appendix, the hedonic approach cannot put a price on community
needs related to affordability housing. This limitation prevents the hedonic approach
from being a substitute for factor analysis. In addition, because housing affordability is a
core element in the dimension of need represented by Factor 2, the hedonic approach
cannot determine the relative weight of Factor 2.

The work described in this appendix did provide some valuable insights related to
community needs. The two most important were:

• Consistent with previous literature, the regression model shows that city-level
characteristics are reflected in median housing values at the tract level. This
provides proof that, at least, some of the conditions that we include among
community needs have a detrimental impact on city residents.

60
Again, we summed the beta coefficients and the standardized scoring coefficients for
UNDEREDWORKAGE and UNEDUCADULTS before running the OLS regression.

• The model was able to provide reasonable estimates of how large an impact the
following conditions may have on median house values:

o Having a high proportion of low-income households.
o Having a high proportion of residents living in neighborhoods with
poverty rates over 40 percent.
o Having a high proportion of residents living in neighborhoods with
poverty rates over 20 percent but less than 40 percent.
o Having a high proportion of working-age residents without a college
degree.
o Having a city poverty rate that is high relative to the metropolitan poverty
rate.
o Having city median income that is low relative to the metropolitan median
income.

The estimated impacts were sizable, particularly the impact of differences in
median income.

The discussion in this appendix has also shown that the regression methodology and the
factor analysis methodology are not unrelated. There are parallels in both the objectives
of the two methodologies and in the statistical techniques employed. Understanding how
the two methodologies are related may provide insights that will guide future research
into community needs. One possible avenue of research would be to consider principal
components as a bridge between the two methodologies.

Tables B.10, B.11, and B.12 contain the full regression results. Table B.10 contains the
results for the regression over all tracts using the need indicators as independent
variables. Table B.11 contains the results for the regression over the tracts with less
house value dispersion using the need indicators as independent variables. Tables B.10
and B.11 are discussed in section B.2.1. Table B.12 contains the regression results for
the regression over the tracts with less house value dispersion using the factor scores as
independent variables instead of the need indicators. Table B.12 is discussed in section
B.5.
Page B-18

Appendix C—Comparison of Factor Analysis for
2000 with Richardson Factor Analysis for 2000

Originally we had hoped to construct community needs indices using both factor analysis
and the regression method. One advantage of having two different methodologies would
have been the ability to compare the results and use one approach to check for
weaknesses in the other approach. This mutual checking against each other would have
been valuable, because there is no external standard to compare a community needs index
against.

We were unable, however, to use the regression results to build a needs index, so we
must identify another way to test the index that we derived from factor analysis. HUD
suggested trying to compare our index to the index constructed by Richardson for the
2000 decennial census data in the study published in 2005.

This appendix makes that comparison. There is one important difference between the
results used in this appendix and the results reported elsewhere in the report. In Chapter
4, we used factors derived using 2005 data and obtained standardized scoring coefficients
based on those factors. We then estimated factor scores for 2000 using 2000 data for the
needs indicators but standardizing those data using means and standard deviations from
2005. In this appendix, we used factors derived from 2000 data and obtain standardized
scoring coefficients based of those factors. We then estimated factor scores for 2000
using 2000 data for the needs indicators, but standardized those data using means and
standard deviations from 2000. In other words, we conducted a new factor analysis using
2000 as the base. These results are more comparable to Richardson (2005) in approach
than the factor analysis reported in Chapter 3 and should produce a clearer comparison.

While there are important similarities between the needs indicators used by Richardson
(2005) and those used in this study, there are important differences. As a result, the
factors identified by Richardson (2005) and those identified by us are only partially
similar. Despite the differences the scores on the two indices correlate highly, but the
rankings of a number of cities change substantially. This comparison provided little
useful information for benchmarking purposes.

C.1. Comparison of Index Structure

There are several similarities and some differences in how Richardson (2005) constructed
his needs index and how the equal weight index used in this study was constructed:

• Richardson (2005) based his selection of indicators on needs eligible for
assistance under the Community Development Block Grant (CDBG) program;
this study uses a slightly broader concept of need focusing on HUD’s overall
community development mission.
Page C-1

Page C-2
• Richardson (2005) used 17 needs indicators; this study uses 24 indicators.

• There are significant overlaps in 11 indicators used in both studies (see Table
C.1):

o Six indicators are identical in terms of definition and data source.

o Five indicators are essentially identical in concept but are implemented
somewhat differently in terms of definition and data.

• This research also attempted to use a crime variable similar to the one used in
Richardson (2005), but the crime variable was dropped because of missing values
for a large number of cities.

• Both Richardson (2005) and this study used factor analysis to extract factors from
a larger number of indicators. There were differences in how the factors were
extracted, but these differences are minor.
61

• Because of differences in the needs indicators used, the factor analyses produced
significantly different results. Richardson (2005) found four factors which he
characterized as: poverty/age/decline, overcrowding/immigration, poverty
concentration, and income growth. We characterized our three factors as: poverty
and structural problems, immigration and housing affordability, and limited
economic prospects.

• Richardson (2005) used 0.80, 0.15, 0.05, and 0.00 respectively, as weights for his
four factors; this study used an equal weight index to combine its three factors.

61
Richardson uses the principal components method to extract initial factors; we used principal factors.
Richardson used unrotated factors; we used a varimax rotation. Richardson used factor loading to create
factor scores; we used standardized scoring coefficients. The use of factor loadings is consistent with the
use of principal components whereas the use of standardized scoring coefficients is consistent with the use
of principal factors.

Table C.1. Identical or Similar Indicators in Richardson (2005) and This
Study
Description of Indicator Name Used in This Study
Used same data
Percent of housing units with more than 1.01 persons per room. OVERCROWD2000
Occupied housing units that are pre-1970 and occupied by a poverty
renter.
PR70RENTPOV
Percent of city population living in census tracts with poverty rates of
40 percent or higher.
PCTPOPHIGHPOVNGHS
Persons in poverty (excluding students living off campus) –
Richardson (2005) used 2000 data, this study used 2005 data on all
poor but subtracted Richardson’s 2000 count of students.
POORPERS
Percent of adult population without a high school diploma –
Richardson (2005) used 2000 data; this study used 2005 data.
UNEDUCADULTS
Percent of persons age 16 years or older in the labor force that was
unemployed.
UNEMPCEN
Used similar concept
Fiscal disparity: Richardson (2005) used the ratio of city per capita
income to metro per capita income; this study used the ratio of median
metro family income to median city family income.
MEDINCCBS2CITY
Children in one parent households: Richardson (2005) used female
headed families; this study used single-parent households.
SGLPRNTFAM
Lack of ethnic and racial diversity: Richardson (2005) used a
segregation index weighted by the proportion of minorities in
population; this study used the minority proportion of the population in
the city divided by the proportion in the metropolitan area.
MINCON
Long term population decline: Richardson (2005) used population loss
between 1960 and 2000; this study uses the ratio of households in
1970 to maximum count of households in the years 1980, 1990, 2000,
and 2005.
EXCSINFRA
Growth in low-income households: Richardson (2005) used the
percentage point change in the poverty rate between 1990 and 2000;
this study used the ratio of the proportion of families in the bottom
quintile nationally in 2005 to the proportion in 1970.
CHGLOWINCCON

C.2. Comparison of Need Scores

Richardson (2005) was able to estimate need scores for 416 of our 473 cities; we were
able to estimate scores for 415 of the 473 in 2000. Richardson (2005) was unable to
calculate factor scores and therefore index scores for some cities, mainly because of
missing crime data; this study was unable to calculate factor scores for some cities mainly
because of missing data on the change in employment base indicator. There are 379 cities
that have index scores in 2000 from both Richardson (2005) and this study.

Table C.2 provides a statistical overview of the index scores from Richardson (2005) and
this study for the 379 cities.

Page C-3

Page C-4
Table C.2. Comparison of Richardson (2005) Index Scores and the Equal
Weight Index Scores

Richardson (2005) Index
Score
Equal weight Index
Score
Mean 0.16 -0.02
Standard Deviation 0.81 0.52
Range 4.98 2.92
Highest Score 3.39 1.78
Median -0.05 -0.09
Lowest score -1.59 -1.14
Number of cities within ½ standard deviation of
mean
168 165
Number of cities within 1 standard deviation from
mean
260 276
Number of cities within 1 ½ standard deviations from
mean
327 339
Number of cities within 2 standard deviations from
mean
358 361
Number of cities within 2 ½ standard deviations from
mean
373 368

The scores from both indices are distributed nearly normally.
62
Richardson (2005)’s
index scores have a higher standard deviation and therefore a greater range. The
correlation between the two need scores is 0.87. On the basis of these statistics, the two
indices appear to perform very similarly.

We next examined the ranking of cities under both indices, where a higher rank (a low
number) means a city has serious problems, and a low rank (a high number) means a city
is well off. Camden had the number-one ranking under the Richardson (2005) index and
was number 9 under the equal weight index, whereas El Monte, CA had the number-one
ranking under the equal weight index and was number 12 under the Richardson (2005)
index. Newport Beach, CA was number 379 in the Richardson (2005) ranking and
number 375 in the equal weight ranking. Ann Arbor was ranked number 379 under the
equal weight index and 297 under the Richardson (2005) index. Of the 379 cities, 307
places changed rank by more than 50 places.

We were surprised by the number of large shifts in rank despite the high correlation
between the two indices. To see how the weighting affected the ranking, we substituted
index 5, which uses weights based on the Richardson (2005) index, for the equal weight
index. The change in weights had little impact on the correlation between an index built
on our factors and an index built on Richardson’s (2005) factors but did affect the change
in rankings. The correlation increased from 0.87 to 0.89; but now, of the 379 cities, only
97 change rank by more than 50 places. As noted earlier in the report, the index scores

62
We compared the percentage of cases within each of the standard deviation ranges with a normal
distribution, and the percentages match the percentages from a normal distribution almost perfectly. Each
index score is a linear combination of the needs indicators. If every need indicator were normally
distributed, then the index scores would have to be normally distributed. However, we have no a priori
reason to believe that all the needs indicators are normally distributed and the indicators are certainly not
independently distributed, so this result was not preordained. The result is obviously an example of the law
of large numbers at work even when all the conditions are not satisfied.

Page C-5
are approximately normally distributed. This means that the distribution is tightly packed
in the middle; therefore, small changes in scores can result in large changes in rankings.

C.3. Conclusion

The selection of need indices is the most important step in developing measures to track
changes in community needs. Despite an apparent overlap in 11 needs indicators, the
factor analysis conducted in this report and that conducted by Richardson resulted in sets
of factors that differ significantly. As a result, it is difficult to use either result to
benchmark the other. Nevertheless, despite the differences, the two approaches produced
index scores that were highly correlated. Within this high correlation, a large number of
cities had substantially different ranks. The number of cities with substantially
differences declined when we substituted Richardson (2005)’s weights for our equal
weights. This confirms the observation in section 3.4.3 that the choice of weights is more
important to the ranking of individual cities than it is to a general understanding of
community needs.

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Bunce, Harold L. and Robert L. Goldberg. 1979. City Need and Community
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Bunce, Harold L., Sue G. Neal, and John L. Gardner. 1983. Effects of the 1980
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Federal Bureau of Investigation. 2005. “Crime in the United States 2005, Offenses
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Kim, Jae-On and Charles W. Mueller. 1978. Introduction to Factor Analysis: What It
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———. 1978. Factor Analysis: Statistical Methods and Practical Issues. Sage
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———. 2007. “Analyzing a Community Development Needs Index,” Cityscape: A
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U.S. Department of Housing and Urban Developme