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-StandardDeviation 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-StandardDeviation 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 

vii 
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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 lowincome 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 27th 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. 

xvii 
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. 

Page 4 
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. 

Page 11 
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 Definiti

Populations with Needs: The first eight indicators identify subgroups in the populations that may have specialized needs that 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 ovewho live in households with belopersons age 3 and over who live 

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 (chpopulation living in households wincomes 

POOR ELDERLY (POOROVER74) 

The elderly require different services and perhaps more citysupplied 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 livinbelow 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 hougreater than the poverty level anarea median income. Note the contain the table needed to calcthe 2005 ACS data are use for b

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 singchildren 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 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 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 entered the United States within 
Page 14 

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

Variable (Short-name Used in 

Tables) Comments Def

Housing Needs: The next five indicators identify problems with housing, housing markets, or housing finance that require city 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 (c(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 person per room is 1.01 or gavailable only for 2000 and tboth 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 occupied by a poor renter hoavailable only for 2000 and tboth 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 

POOR HOUSING APPRECIATION IN HIGH POVERTY NEIGHBORHOODS (POORAPPRECHIGHPOVNGHS) 

HUD and panel members wanted a variable that could discriminate between poor neighborhoods and poor neighborhoods with poor appreciation potential. 

Percent change between 20mortgage amount on loans i(As explained in the text, this 

Neighborhood Needs: The next three indicators identify the extent to which cities may have seriously troubled neighborhoodsHIGH POVERTY NEIGHBORHOODS (PCTPOPHIGHPOVNGHS 

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 livrates of 40 percent or higheronly for 2000 and the 2000 vand 2005. 

MODERATE POVERTY NEIGHBORHOODS (PCTPOPMODPOVNGHS) 

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 livrates greater than or equal to percent. Note this variable the 2000 values are used fo
Table 1. Needs Indicators for Developing an Index of Community Needs (continued) 

Page 15 

Variable (Short-name Used in 

Tables) Comments Def

ABANDONMENT (PCTVACMODPOVCITY) 

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 tracts with 20 percent or mocensus) to total housing unitonly one version of this variaboth 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 2004 FBI data on arrests for vehicle theft, arson, and othehttp://www.fbi.gov/ucr/cius_0for definitions of Part 1 and P

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 2004 FBI data on arrests foprostitution, drug offenses, dand other part 2 crimes - see http://www.fbi.gov/ucr/cius_0for definitions of Part 1 and P

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 pliving in households with bel

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 populaand looking for work (in labocalculated from the 2000 dec

Conditions that Complicate Dealing with Other Problems: The next four variables were added to identify conditions that might 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 whand older) have some limitat(A household is classified as household members age 14 English, and no household mwho spoke a language other "Very well.") 
Page 16 

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

Variable (Short-name Used in 

Tables) Comments Def

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 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 barea 

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 mefamily 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 populatio(without reference to bounda1990, and 2000 to current po

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 filabor force in 2000 from BLScity from the 2002 economic 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 faincomes in the bottom quintiand then take the ratio of thiand divides by the proportion 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 4311.420 -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 

 

10http://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, singleparent 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 highpoverty 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 Vi 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: Vi = ΣαjFj + Ui, where Ui are influences unique to that variable and Fj

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 
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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 

SASTM. 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 SASTM 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 SASTM-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 SASTM, 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 16th 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 Loadi

Factor 1: 

Poverty and 

Structural 

Problems 

Factor 2: 

Immigration 

and 

Housing 

Affordability 

POORPERS 0.92728 LINGISOL 0.91800 UNDEREDWORKAGE POORCHILD 0.91442 RCNTIMMIG 0.85752 UNEDUCADULTS POVCON 0.89969 OVERCROWD2000 0.82505 DENIAL SCHPOPPOOR 0.89838 LACKAFFDRENTALS 0.64700 UNEMPCEN PR70RENTPOV 0.87989 UNEDUCADULTS 0.59240 OVERCROWD2000 SGLPRNTFAM 0.85591 LWINCHHDS 0.32419 PCTPOPMODPOVNGHS MEDINCCBS2CITY 0.85455 MEDINCCBS2CITY 0.29163 CHNGEMPLBASE LWINCHHDS 0.85063 PCTPOPMODPOVNGHS 0.27201 CHGLOWINCCON MINCON 0.78498 UNDEREDWORKAGE 0.20307 POORPERS PCTVACMODPOVCITY 0.77777 POOROVER74 0.20099 SGLPRNTFAM PCTPOPMODPOVNGHS 0.77370 CHGLOWINCCON 0.18643 LACKAFFDRENTALS DENIAL 0.73175 PR70RENTPOV 0.14105 PCTVACMODPOVCITY PCTPOPHIGHPOVNGHS 0.68063 POORPERS 0.12609 POORCHILD UNEMPCEN 0.63363 POVCON 0.10734 SCHPOPPOOR EXCSINFRA 0.58278 SCHPOPPOOR 0.09173 LINGISOL UNEDUCADULTS 0.47999 POORCHILD 0.06533 MEDINCCBS2CITY POOROVER74 0.45757 UNEMPCEN 0.06183 PCTPOPHIGHPOVNGHS LACKAFFDRENTALS 0.44787 CHNGEMPLBASE 0.02851 POOROVER74 CHGLOWINCCON 0.36726 PCTPOPHIGHPOVNGHS 0.02778 EXCSINFRA UNDEREDWORKAGE 0.35296 SGLPRNTFAM 0.01532 MINCON LINGISOL 0.04718 MINCON 0.00792 LWINCHHDS OVERCROWD2000 -0.01856 EXCSINFRA -0.10178 PR70RENTPOV RCNTIMMIG -0.11242 DENIAL -0.15728 RCNTIMMIG CHNGEMPLBASE -0.16812 PCTVACMODPOVCITY -0.28525 POVCON 

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 Scores28

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 comparisonyear 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 

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 chHowever, the CDBG statute does give preceddevelopment of viable urban communities, by housing and suitable living environment and eopportunities, principally for persons of low an

[emphasis added].” Therefore, we provide a tfactor that relates to poverty. We decided on tIndices 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 Factorespectively. We added this index to see whaadd 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 AppendFactor 3 should receive three times the weight 

provides no information on how to weight Factthe same weight as Factor 1. 

5 Richardson weights 0.80 0.15 0.05 Richardson chose these weights for his unrota

rotated factors do not match well with either Ror rotated factors but they are more similar to 

6 Partial hedonic weights 0.60 0.28 0.12 

Using the hedonic analysis in Appendix B, we to obtain the closest match between the weighcoefficients for eight needs indicators to beta c

variables in the hedonic-type equation. See Sexplanation 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 56th most needy city on Index 1 and was ranked as the 16th 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 

357th most needy city on Index 1 and was ranked as the 368th 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 243rd on Index 1 and 80th on Index 2. 

Sunnyvale, CA had the biggest change in ranking between Index 1 and Index 3; it is 

ranked 284th on Index 1 and 98th on Index 3. Providence had the biggest change in 

ranking between Index 1 and Index 4; it is ranked 48th on Index 1 and 256th 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 0.0253 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 0.0868 0.0396 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 7th highest score in 2000 to the 16th highest score in 2005, 

and Richmond, which moved from the 26th highest score to the 48th 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 248th to 153rd while Cape Coral moved from 244th to 179th.

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 116th in 2000 to 196th 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 214th to 125th; Gastonia moved from 70th to 27th; and Cedar Rapids moved from 

268th to 194th. 

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

Page 60 
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

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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 po2 INCOME TAX CAPACITY INCTAXCAP Aggregate household income multiplied by the ratio of lo

2002 Census of Governments to national household inc($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 esta

occupied housing value from 2001 Residential Finance 

4 

RESIDENTIAL PROPERTY TAX CAPACITY (rental) RENTPROPTAXCAP 

Aggregate contract rent multiplied by ratio of net operatfrom 2001 RFS divided by national cap rate (from Good

of real estates paid to rental housing value from 2001 RWe do this estimate separate for central cities and non-found that cap rates vary between central cities and subFor central cities: (Estimate aggregate contract rent)*95For non-central cities: (Estimate aggregate contract ren

5 SALES TAX CAPACITY SALESTAXCAP 

Retail sales in 2002 from the Economic Census multiplirevenue from the sum of the general sale taxes and sellocalities from the 2002 Census of Governments to totaCensus of Economic Census ($61,761,893,000/$3,056,

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 provides place data times 0.05. 0.05 was derived by esestate taxes, local corporate taxes, and proportion of locto non-residents working in city; summing these items; asum 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 lostate revenue from all sources) times (the ratio of total ssources to aggregate state income) times (the ratio of cof the populations of all cities in our list in that state) div(Aggregate state income times 0.007 divided by the sum cities in our list in that state.) Note that this provides a pconceptually 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 

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 Ra

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 Georgia 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 

Page 73 
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: 

Page 74 
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 65th on the equal weight index but 182nd on the adjusted needs index; 

Atlanta is ranked 95th on the equal weight index but 211th on the adjusted needs index. 

Other cities benefit substantially by the adjustment for fiscal needs. Peoria is ranked 

190th on the equal weight index but 89th on the adjusted needs index; Henderson, NV is 

ranked 208th on the equal weight index but 108th 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 longform 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 

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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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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. 

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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 wellconceived, 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 

POP 

CHGLOWINC

CON 

CHNGEMPLB

ASE 

DENIAL 

EXCSINFRA 

LACKAFFDR

ENTALS 

LINGISOL 

LWINCHHDS 

MEDINCCBS2

CITY 

MINCON 

OVERCROW

D_2000 

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

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 

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

Factor1 Factor2 Factor3 Factor1 Factor2 

CHGLOWINCCON_2000 0.49109 0.22375 0.1822 0.38067 0.31CHNGEMPLBASE -0.09228 0.08752 0.28443 -0.14108 -0.000DENIAL 0.70361 -0.32571 0.23506 0.74345 -0.156EXCSINFRA_2000 0.5542 -0.25625 -0.07394 0.61016 -0.070LACKAFFDRENTALS_2000 0.5449 0.62309 -0.12124 0.33975 0.761LINGISOL_2000 0.28082 0.89816 -0.15684 0.00947 0.9532LWINCHHDS_2000 0.85302 0.07528 -0.23753 0.8088 0.356MEDINCCBS2CITY_2000 0.89187 0.05764 -0.17376 0.84491 0.3382MINCON_2000 0.74313 -0.22709 -0.21605 0.79404 0.037OVERCROWD2000 0.2676 0.87692 0.09703 -0.02082 0.878PCTPOPHIGHPOVNGHS_2000 0.68746 -0.21556 -0.03037 0.72008 -0.004PCTPOPMODPOVNGHS_2000 0.88635 0.09892 0.13002 0.79826 0.3145PCTVACMODPOVCITY_2000 0.69735 -0.47671 0.07489 0.79845 -0.267POORCHILD_2000 0.95021 -0.07487 0.06862 0.91725 0.1814POOROVER74_2000 0.71694 -0.09999 -0.08448 0.71821 0.1234POORPERS_2000 0.96514 0.02506 0.10894 0.89734 0.2714POVCON_2000 0.83176 -0.22906 -0.32327 0.88889 0.0822PR70RENTPOV_2000 0.87912 -0.08413 -0.2715 0.88502 0.221PT1CRIME_2000 0.29437 -0.11921 0.03159 0.31223 -0.0PT2CRIME_2000 0.27073 -0.32528 -0.04544 0.35951 -0.220RCNTIMMIG_2000 0.05266 0.87531 -0.32979 -0.18353 0.902SCHPOPPOOR_2000 0.95062 -0.03845 0.05205 0.90821 0.219SGLPRNTFAM_2000 0.90227 -0.23344 0.02561 0.92387 0.027UNDEREDWORKAGE_2000 0.57969 0.24368 0.60105 0.41879 0.2702UNEDUCADULTS_2000 0.71862 0.55254 0.26904 0.48869 0.665UNEMPCEN_2000 0.83467 0.06961 0.08606 0.76231 0.2813

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 F

Factor1 Factor2 

POORPERS 0.94261 LINGISOL 0.91454 UNEMPCEN POORCHILD 0.91804 RCNTIMMIG 0.87042 UNEDUCADULTS SCHPOPPOOR 0.90272 OVERCROWD_2000 0.85383 UNDEREDWORKAGE MEDINCCBS2CITY 0.88857 LACKAFFDRENTALS 0.6461 SGLPRNTFAM POVCON 0.88566 UNEDUCADULTS 0.58219 SCHPOPPOOR PR70RENTPOV 0.87071 LWINCHHDS 0.28366 RCNTIMMIG SGLPRNTFAM 0.86702 MEDINCCBS2CITY 0.23882 PT2CRIME LWINCHHDS 0.85881 CHGLOWINCCON 0.22623 PR70RENTPOV PCTPOPMODPOVNGHS 0.80524 UNDEREDWORKAGE 0.20319 POVCON PCTVACMODPOVCITY 0.78866 PCTPOPMODPOVNGHS 0.18899 POORPERS MINCON 0.7835 POOROVER74 0.07432 POOROVER74 DENIAL 0.76327 PR70RENTPOV 0.07252 POORCHILD PCTPOPHIGHPOVNGHS 0.69193 UNEMPCEN 0.04013 PCTVACMODPOVCITY UNEMPCEN 0.6672 POORPERS 0.0348 PCTPOPMODPOVNGHEXCSINFRA 0.59869 CHNGEMPLBASE 0.03249 PCTPOPHIGHPOVNGHVIOLCRIME 0.55578 POVCON 0.01955 OVERCROWD_2000 UNEDUCADULTS 0.54919 SCHPOPPOOR 0.0021 MINCON LACKAFFDRENTALS 0.50896 PCTPOPHIGHPOVNGHS -0.00745 MEDINCCBS2CITY UNDEREDWORKAGE 0.43965 MINCON -0.01292 LWINCHHDS POOROVER74 0.4368 POORCHILD -0.02401 LINGISOL CHGLOWINCCON 0.38837 SGLPRNTFAM -0.05827 LACKAFFDRENTALS PT2CRIME 0.18253 EXCSINFRA -0.08259 EXCSINFRA LINGISOL 0.06766 DENIAL -0.18631 DENIAL OVERCROWD_2000 0.0219 PT2CRIME -0.23332 VIOLCRIME RCNTIMMIG -0.09868 PCTVACMODPOVCITY -0.31302 CHNGEMPLBASE CHNGEMPLBASE -0.16497 VIOLCRIME -0.35229 CHGLOWINCCON 

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 

In

Ric

W

Birmingham city Alabama 0.54 1.30 -0.12 0.43 Huntsville city Alabama -0.43 -0.28 -0.71 -0.30 Mobile city Alabama 0.17 0.52 -0.35 0.36 Montgomery city Alabama -0.07 0.14 -0.54 0.21 Anchorage municipality Alaska -0.47 -0.52 -0.57 -0.31 Avondale city Arizona 0.42 0.28 0.52 0.44 Chandler city Arizona -0.46 -0.68 -0.39 -0.30 Glendale city Arizona 0.05 -0.10 -0.03 0.28 Mesa city Arizona -0.03 -0.24 0.00 0.14 Peoria city Arizona -0.44 -0.73 -0.50 -0.10 Phoenix city Arizona 0.33 0.19 0.48 0.34 Scottsdale city Arizona -0.85 -0.95 -0.81 -0.79 Tempe city Arizona -0.44 -0.54 -0.28 -0.51 Tucson city Arizona 0.17 0.21 0.15 0.15 Yuma city Arizona 0.28 0.02 0.11 0.70 Fayetteville city Arkansas -0.71 -0.43 -0.69 -1.00 Fort Smith city Arkansas 0.13 0.18 -0.21 0.43 Little Rock city Arkansas -0.37 -0.16 -0.65 -0.31 Alameda city California -0.56 -0.53 -0.29 -0.85 Alhambra city California 0.37 0.06 1.08 -0.03 Anaheim city California 0.62 0.11 1.02 0.74 Antioch city California -0.03 -0.21 0.01 0.11 Bakersfield city California 0.21 0.06 0.12 0.46 Baldwin Park city California 1.32 0.82 1.73 1.39 Bellflower city California 0.64 0.43 0.81 0.68 Berkeley city California -1.04 -0.59 -0.62 -1.91 Buena Park city California 0.21 -0.25 0.54 0.35 Burbank city California -0.25 -0.49 0.03 -0.29 

Page A-10 
Page A-11 

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Carlsbad city California -0.91 -1.07 -0.77 -0.89 Carson city California 0.26 -0.14 0.35 0.56 Chico city California -0.35 -0.06 -0.43 -0.56 Chino city California 0.18 -0.41 0.20 0.75 Chula Vista city California 0.17 -0.17 0.37 0.32 Clovis city California -0.28 -0.48 -0.34 -0.01 Concord city California -0.11 -0.32 0.18 -0.19 Corona city California -0.02 -0.45 0.06 0.33 Costa Mesa city California 0.15 -0.17 0.56 0.05 Daly City California 0.22 -0.14 0.84 -0.04 El Cajon city California 0.28 0.14 0.44 0.26 Escondido city California 0.37 -0.05 0.68 0.47 Fairfield city California -0.11 -0.28 0.01 -0.08 Fontana city California 0.62 0.13 0.82 0.91 Fremont city California -0.34 -0.64 0.30 -0.68 Fresno city California 0.63 0.58 0.66 0.65 Fullerton city California -0.16 -0.50 0.15 -0.14 Garden Grove city California 0.83 0.23 1.52 0.75 Glendale city California 0.25 -0.20 0.99 -0.05 Hawthorne city California 0.97 0.78 1.26 0.88 Hayward city California 0.26 0.02 0.56 0.19 Hemet city California 0.57 0.24 0.58 0.89 Hesperia city California 0.39 0.09 0.30 0.79 Huntington Beach city California -0.58 -0.81 -0.48 -0.46 Indio city California 1.09 0.73 1.52 1.01 Inglewood city California 0.96 0.77 1.17 0.94 Lakewood city California -0.27 -0.65 -0.18 0.02 Livermore city California -0.63 -0.80 -0.48 -0.61 Long Beach city California 0.45 0.42 0.73 0.20 
Page A-12 

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Los Angeles city California 0.69 0.56 1.16 0.33 Merced city California 0.76 0.68 0.77 0.84 Mission Viejo city California -0.82 -1.10 -0.78 -0.58 Modesto city California 0.15 -0.07 0.07 0.45 Mountain View city California -0.60 -0.72 -0.02 -1.08 Napa city California -0.14 -0.31 0.11 -0.22 Norwalk city California 0.72 0.09 1.05 1.01 Oakland city California 0.39 0.59 0.73 -0.14 Oceanside city California -0.18 -0.50 0.02 -0.06 Ontario city California 0.82 0.36 1.07 1.03 Orange city California -0.10 -0.52 0.09 0.14 Oxnard city California 0.95 0.63 1.45 0.77 Palmdale city California 0.64 0.35 0.65 0.93 Pasadena city California -0.28 -0.25 -0.03 -0.55 Pleasanton city California -0.93 -1.05 -0.72 -1.03 Pomona city California 1.09 0.55 1.56 1.14 Redding city California -0.31 -0.39 -0.48 -0.06 Redlands city California -0.36 -0.42 -0.41 -0.26 Redwood City California 0.12 -0.03 0.61 -0.22 Rialto city California 0.66 0.13 0.84 1.02 Richmond city California 0.34 0.36 0.56 0.09 Riverside city California 0.23 -0.06 0.39 0.36 Roseville city California -0.58 -0.81 -0.53 -0.39 Sacramento city California 0.26 0.37 0.36 0.06 Salinas city California 1.32 0.78 1.82 1.37 San Bernardino city California 1.11 1.18 1.29 0.87 San Buenaventura (Ventura) city California -0.44 -0.44 -0.38 -0.50 San Diego city California -0.25 -0.27 0.05 -0.53 
Page A-13 

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San Francisco city California -0.30 -0.29 0.20 -0.81 San Jose city California 0.02 -0.21 0.46 -0.19 San Leandro city California 0.08 -0.24 0.36 0.13 San Marcos city California -0.09 -0.43 0.08 0.08 San Mateo city California -0.39 -0.61 0.01 -0.56 Santa Ana city California 1.59 0.84 2.31 1.62 Santa Barbara city California -0.37 -0.40 -0.17 -0.54 Santa Clara city California -0.31 -0.44 0.31 -0.78 Santa Maria city California 0.93 0.62 1.37 0.82 Santa Monica city California -0.91 -0.95 -0.65 -1.13 Santa Rosa city California -0.16 -0.29 -0.06 -0.13 Simi Valley city California -0.54 -0.84 -0.54 -0.23 Stockton city California 0.61 0.52 0.70 0.61 Sunnyvale city California -0.40 -0.54 0.26 -0.94 Thousand Oaks city California -0.74 -0.87 -0.60 -0.74 Torrance city California -0.53 -0.78 -0.19 -0.62 Tracy city California -0.07 -0.50 0.03 0.27 Turlock city California 0.34 -0.13 0.49 0.66 Tustin city California -0.13 -0.52 0.38 -0.23 Union City California 0.29 -0.15 0.87 0.16 Upland city California -0.28 -0.29 -0.28 -0.28 Vacaville city California -0.41 -0.69 -0.45 -0.09 Vallejo city California -0.01 -0.08 0.07 -0.03 Victorville city California 0.52 0.29 0.43 0.84 Visalia city California 0.23 0.06 0.10 0.53 Vista city California 0.42 0.22 0.70 0.33 West Covina city California 0.35 0.08 0.50 0.46 Westminster city California 0.57 -0.09 1.11 0.69 Arvada city Colorado -0.51 -0.71 -0.66 -0.15 
Page A-14 

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Aurora city Colorado 0.16 0.03 0.19 0.25 Boulder city Colorado -1.03 -0.70 -0.65 -1.73 Colorado Springs city Colorado -0.45 -0.40 -0.54 -0.39 Denver city Colorado -0.04 0.10 0.09 -0.32 Fort Collins city Colorado -0.81 -0.57 -0.75 -1.11 Greeley city Colorado -0.04 -0.07 -0.08 0.02 Lakewood city Colorado -0.33 -0.37 -0.47 -0.14 Longmont city Colorado -0.16 -0.31 -0.07 -0.10 Pueblo city Colorado 0.31 0.53 -0.14 0.54 Thornton city Colorado -0.20 -0.51 -0.28 0.20 Westminster city Colorado -0.48 -0.69 -0.58 -0.17 Washington city 

District of Columbia -0.25 0.42 -0.10 -1.06 

Boca Raton city Florida -0.62 -0.75 -0.51 -0.59 Cape Coral city Florida -0.17 -0.44 -0.17 0.09 Clearwater city Florida -0.18 -0.29 -0.21 -0.05 Davie town Florida -0.33 -0.50 -0.22 -0.28 Deerfield Beach city Florida 0.16 -0.04 0.54 -0.01 Deltona city Florida -0.10 -0.48 -0.25 0.42 Fort Lauderdale city Florida -0.09 0.01 -0.16 -0.11 Gainesville city Florida -0.20 0.25 -0.23 -0.62 Hollywood city Florida 0.06 -0.14 0.31 0.01 Jacksonville city Florida -0.29 -0.25 -0.45 -0.17 Largo city Florida -0.18 -0.44 -0.31 0.23 Melbourne city Florida -0.25 -0.38 -0.36 -0.02 Miami city Florida 1.47 1.48 2.34 0.60 Miami Beach city Florida 0.39 0.28 1.22 -0.32 Miramar city Florida -0.16 -0.45 0.04 -0.06 Orlando city Florida 0.10 0.11 0.18 0.01 
Page A-15 

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Palm Bay city Florida -0.11 -0.31 -0.16 0.14 Pembroke Pines city Florida -0.39 -0.63 -0.21 -0.33 Plantation city Florida -0.40 -0.65 -0.32 -0.24 Pompano Beach city Florida 0.25 0.04 0.37 0.35 St. Petersburg city Florida -0.18 -0.14 -0.36 -0.03 Sunrise city Florida 0.04 -0.29 0.22 0.20 Tallahassee city Florida -0.65 -0.37 -0.66 -0.93 Tampa city Florida 0.02 0.16 -0.04 -0.05 West Palm Beach city Florida 0.27 0.35 0.41 0.06 Atlanta city Georgia 0.09 0.86 -0.19 -0.39 Roswell city Georgia -0.85 -1.04 -0.58 -0.92 Savannah city Georgia 0.18 0.54 -0.11 0.12 Honolulu CDP Hawaii -0.31 -0.38 0.05 -0.58 Boise City Idaho -0.53 -0.54 -0.64 -0.42 Nampa city Idaho 0.23 0.26 0.05 0.38 Aurora city Illinois 0.24 0.00 0.53 0.21 Champaign city Illinois -0.59 -0.30 -0.53 -0.96 Chicago city Illinois 0.33 0.65 0.37 -0.01 Elgin city Illinois 0.29 -0.05 0.49 0.45 Evanston city Illinois -1.10 -0.91 -0.92 -1.47 Joliet city Illinois -0.05 -0.22 -0.18 0.25 Naperville city Illinois -1.15 -1.26 -0.94 -1.23 Peoria city Illinois -0.43 0.01 -0.80 -0.51 Rockford city Illinois 0.24 0.41 -0.07 0.38 Schaumburg village Illinois -0.57 -0.79 -0.33 -0.58 Springfield city Illinois -0.42 -0.18 -0.75 -0.33 Waukegan city Illinois 0.68 0.40 0.93 0.70 Bloomington city Indiana -0.66 -0.24 -0.46 -1.28 Evansville city Indiana 0.04 0.19 -0.44 0.35 
Page A-16 

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Fort Wayne city Indiana -0.01 0.14 -0.46 0.30 Hammond city Indiana 0.57 0.73 0.17 0.80 Indianapolis city (balance) Indiana -0.07 0.11 -0.41 0.09 South Bend city Indiana 0.29 0.61 -0.08 0.34 Cedar Rapids city Iowa -0.37 -0.24 -0.65 -0.23 Davenport city Iowa -0.26 -0.07 -0.55 -0.17 Des Moines city Iowa -0.30 -0.24 -0.45 -0.23 Sioux City Iowa 0.05 0.03 -0.26 0.39 Kansas City Kansas 0.57 0.83 0.24 0.65 Lawrence city Kansas -0.81 -0.59 -0.63 -1.21 Olathe city Kansas -0.80 -0.84 -0.83 -0.72 Overland Park city Kansas -0.94 -1.05 -0.89 -0.87 Topeka city Kansas -0.17 -0.03 -0.56 0.08 Wichita city Kansas -0.10 -0.05 -0.35 0.10 Lexington-Fayette Kentucky -0.51 -0.38 -0.61 -0.53 Baton Rouge city Louisiana 0.30 0.75 -0.13 0.27 Lafayette city Louisiana -0.35 -0.25 -0.56 -0.25 Shreveport city Louisiana 0.26 0.62 -0.35 0.51 Baltimore city Maryland 0.30 0.91 -0.02 0.01 Boston city Massachusetts 0.02 0.52 0.43 -0.90 Brockton city Massachusetts 0.37 0.38 0.38 0.34 Cambridge city Massachusetts -0.88 -0.51 -0.28 -1.84 Fall River city Massachusetts 0.51 0.55 0.44 0.54 Lawrence city Massachusetts 1.47 2.04 1.85 0.53 Lowell city Massachusetts 0.46 0.71 0.72 -0.06 Lynn city Massachusetts 0.49 0.73 0.71 0.02 New Bedford city Massachusetts 0.60 0.76 0.40 0.63 Newton city Massachusetts -1.21 -1.20 -0.96 -1.49 
Page A-17 

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Quincy city Massachusetts -0.40 -0.58 -0.19 -0.44 Somerville city Massachusetts -0.13 0.12 0.34 -0.84 Springfield city Massachusetts 0.81 1.27 0.50 0.65 Worcester city Massachusetts 0.04 0.34 0.21 -0.43 Ann Arbor city Michigan -1.00 -0.63 -0.52 -1.84 Detroit city Michigan 0.99 1.85 0.24 0.89 Grand Rapids city Michigan 0.17 0.43 -0.02 0.11 Lansing city Michigan 0.19 0.54 -0.11 0.13 Livonia city Michigan -0.64 -0.96 -0.75 -0.23 Pontiac city Michigan 0.87 1.18 0.44 0.98 Southfield city Michigan -0.27 -0.39 -0.51 0.09 Sterling Heights city Michigan -0.20 -0.54 -0.24 0.17 Troy city Michigan -0.84 -0.96 -0.69 -0.86 Warren city Michigan 0.08 -0.14 -0.20 0.58 Westland city Michigan -0.10 -0.29 -0.39 0.37 Wyoming city Michigan 0.10 -0.10 -0.02 0.44 Bloomington city Minnesota -0.60 -0.70 -0.62 -0.49 Brooklyn Park city Minnesota -0.22 -0.30 -0.27 -0.10 Duluth city Minnesota -0.63 -0.37 -0.77 -0.75 Minneapolis city Minnesota -0.27 0.22 -0.11 -0.93 Plymouth city Minnesota -0.90 -1.00 -0.84 -0.87 Rochester city Minnesota -0.82 -0.78 -0.73 -0.94 St. Paul city Minnesota -0.21 0.17 -0.16 -0.65 Columbia city Missouri -0.76 -0.54 -0.69 -1.05 Independence city Missouri -0.04 -0.14 -0.33 0.36 Kansas City Missouri -0.13 0.15 -0.39 -0.14 Lee's Summit city Missouri -0.86 -0.95 -0.94 -0.70 St. Louis city Missouri 0.35 1.24 -0.20 0.01 Springfield city Missouri -0.15 0.01 -0.47 0.03 
Page A-18 

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Lincoln city Nebraska -0.57 -0.49 -0.59 -0.62 Omaha city Nebraska -0.18 -0.02 -0.33 -0.20 Henderson city Nevada -0.53 -0.77 -0.64 -0.19 Las Vegas city Nevada 0.12 -0.16 0.19 0.34 North Las Vegas city Nevada 0.41 0.05 0.57 0.59 Reno city Nevada -0.11 -0.16 -0.01 -0.17 Sparks city Nevada -0.13 -0.46 -0.08 0.15 Manchester city New Hampshire -0.24 -0.10 -0.21 -0.41 Nashua city New Hampshire -0.52 -0.59 -0.41 -0.56 Camden city New Jersey 2.03 3.02 1.76 1.31 Clifton city New Jersey -0.08 -0.40 0.16 0.01 Elizabeth city New Jersey 0.97 0.91 1.35 0.64 Jersey City New Jersey 0.40 0.60 0.80 -0.20 Newark city New Jersey 1.15 1.50 1.35 0.59 Passaic city New Jersey 1.71 1.73 2.45 0.96 Paterson city New Jersey 1.20 1.28 1.56 0.77 Trenton city New Jersey 1.07 1.46 0.92 0.83 Albuquerque city New Mexico -0.17 -0.20 -0.27 -0.03 Santa Fe city New Mexico -0.34 -0.25 -0.27 -0.50 Albany city New York -0.07 0.80 -0.38 -0.62 Buffalo city New York 0.55 1.45 -0.21 0.42 New Rochelle city New York -0.21 -0.30 0.05 -0.38 New York city New York 0.33 0.52 0.71 -0.23 Rochester city New York 0.77 1.62 0.30 0.40 Syracuse city New York 0.44 1.29 0.00 0.04 Yonkers city New York 0.14 0.14 0.22 0.04 Asheville city North Carolina -0.49 -0.29 -0.62 -0.55 Cary town North Carolina -0.90 -1.01 -0.69 -1.00 Charlotte city North Carolina -0.25 -0.22 -0.28 -0.27 
Page A-19 

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Durham city North Carolina -0.21 -0.08 -0.15 -0.39 Fayetteville city North Carolina 0.02 0.19 -0.45 0.34 Gastonia city North Carolina 0.40 0.42 0.08 0.69 Greensboro city North Carolina -0.08 -0.01 -0.21 0.00 High Point city North Carolina 0.16 0.20 -0.12 0.40 Raleigh city North Carolina -0.51 -0.32 -0.43 -0.78 Wilmington city North Carolina -0.47 -0.30 -0.55 -0.56 Winston-Salem city North Carolina 0.07 0.24 -0.08 0.04 Fargo city North Dakota -0.65 -0.53 -0.76 -0.68 Akron city Ohio 0.12 0.52 -0.33 0.18 Cincinnati city Ohio 0.13 0.89 -0.36 -0.14 Cleveland city Ohio 0.96 1.83 0.17 0.87 Columbus city Ohio -0.08 0.19 -0.27 -0.16 Dayton city Ohio 0.64 1.41 -0.13 0.64 Lorain city Ohio 0.46 0.58 -0.09 0.89 Parma city Ohio -0.25 -0.52 -0.47 0.23 Toledo city Ohio 0.31 0.64 -0.30 0.60 Broken Arrow city Oklahoma -0.51 -0.63 -0.75 -0.16 Edmond city Oklahoma -0.90 -0.95 -0.93 -0.82 Lawton city Oklahoma 0.18 0.30 -0.32 0.57 Norman city Oklahoma -0.64 -0.49 -0.73 -0.68 Oklahoma City Oklahoma 0.04 0.17 -0.26 0.21 Tulsa city Oklahoma -0.01 0.14 -0.27 0.10 Beaverton city Oregon -0.47 -0.61 -0.26 -0.54 Eugene city Oregon -0.54 -0.38 -0.50 -0.75 Gresham city Oregon 0.32 0.24 0.33 0.39 Hillsboro city Oregon 0.09 0.13 0.20 -0.07 Medford city Oregon -0.21 -0.25 -0.37 0.00 Portland city Oregon -0.30 -0.10 -0.27 -0.53 
Page A-20 

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Salem city Oregon 0.15 0.10 0.09 0.25 Allentown city Pennsylvania 0.40 0.82 0.31 0.07 Bethlehem city Pennsylvania -0.25 -0.17 -0.35 -0.24 Erie city Pennsylvania 0.12 0.48 -0.40 0.28 Philadelphia city Pennsylvania 0.45 0.96 0.15 0.23 Pittsburgh city Pennsylvania -0.08 0.58 -0.48 -0.35 Reading city Pennsylvania 1.34 1.96 1.05 1.00 Scranton city Pennsylvania 0.21 0.55 -0.14 0.20 Cranston city Rhode Island -0.38 -0.59 -0.39 -0.16 Pawtucket city Rhode Island 0.47 0.65 0.42 0.33 Providence city Rhode Island 0.56 1.18 0.74 -0.25 Warwick city Rhode Island -0.52 -0.75 -0.64 -0.17 Charleston city South Carolina -0.46 -0.12 -0.59 -0.67 Columbia city South Carolina -0.31 0.10 -0.48 -0.54 Sioux Falls city South Dakota -0.45 -0.39 -0.57 -0.39 Chattanooga city Tennessee -0.06 0.29 -0.53 0.06 Clarksville city Tennessee -0.35 -0.40 -0.60 -0.04 Knoxville city Tennessee -0.06 0.33 -0.42 -0.10 Memphis city Tennessee 0.39 0.68 -0.11 0.59 Murfreesboro city Tennessee -0.45 -0.31 -0.51 -0.53 Nashville-Davidson (balance) Tennessee -0.19 -0.05 -0.29 -0.24 Abilene city Texas 0.07 0.12 -0.28 0.36 Amarillo city Texas 0.09 0.05 -0.16 0.38 Arlington city Texas 0.06 -0.12 0.01 0.29 Austin city Texas -0.17 -0.08 -0.03 -0.39 Baytown city Texas 0.50 0.28 0.26 0.96 Beaumont city Texas 0.29 0.56 -0.20 0.50 Bryan city Texas 0.54 0.56 0.43 0.63 
Page A-21 

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Carrollton city Texas -0.14 -0.52 0.02 0.08 College Station city Texas -0.70 -0.45 -0.39 -1.27 Corpus Christi city Texas 0.25 0.18 -0.02 0.59 Dallas city Texas 0.80 0.78 0.97 0.64 Denton city Texas -0.41 -0.39 -0.33 -0.50 Fort Worth city Texas 0.37 0.38 0.30 0.44 Garland city Texas 0.54 0.21 0.61 0.79 Grand Prairie city Texas 0.33 0.13 0.20 0.65 Houston city Texas 0.82 0.75 0.94 0.75 Irving city Texas 0.42 0.09 0.71 0.46 Killeen city Texas -0.07 -0.03 -0.41 0.24 Lewisville city Texas -0.21 -0.48 -0.16 -0.01 Lubbock city Texas 0.00 0.01 -0.30 0.28 McKinney city Texas -0.67 -0.83 -0.64 -0.53 Mesquite city Texas 0.28 -0.16 0.17 0.81 Midland city Texas 0.10 -0.05 -0.16 0.50 Odessa city Texas 0.45 0.30 0.01 1.03 Pasadena city Texas 0.95 0.62 0.99 1.23 Plano city Texas -0.71 -0.88 -0.55 -0.70 Richardson city Texas -0.58 -0.82 -0.41 -0.50 Round Rock city Texas -0.40 -0.54 -0.50 -0.17 San Angelo city Texas 0.08 0.04 -0.28 0.49 San Antonio city Texas 0.32 0.26 0.20 0.50 Tyler city Texas 0.19 0.31 -0.13 0.39 Waco city Texas 0.41 0.52 0.19 0.51 Wichita Falls city Texas -0.05 -0.01 -0.30 0.16 Ogden city Utah 0.21 0.53 0.15 -0.03 Orem city Utah -0.46 -0.56 -0.53 -0.31 Provo city Utah -0.41 -0.09 -0.17 -0.96 
Page A-22 

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Salt Lake City Utah -0.18 0.05 -0.07 -0.54 Sandy city Utah -0.72 -0.89 -0.86 -0.42 West Jordan city Utah -0.40 -0.70 -0.48 -0.03 Alexandria city Virginia -0.72 -0.77 -0.18 -1.20 Chesapeake city Virginia -0.46 -0.68 -0.65 -0.05 Hampton city Virginia -0.21 -0.15 -0.48 -0.02 Newport News city Virginia -0.21 -0.12 -0.40 -0.11 Norfolk city Virginia -0.01 0.30 -0.18 -0.15 Portsmouth city Virginia 0.03 0.23 -0.27 0.14 Richmond city Virginia -0.02 0.38 -0.21 -0.23 Roanoke city Virginia -0.05 0.14 -0.45 0.16 Suffolk city Virginia -0.32 -0.35 -0.59 -0.01 Virginia Beach city Virginia -0.53 -0.66 -0.62 -0.30 Bellevue city Washington -0.75 -0.84 -0.30 -1.10 Bellingham city Washington -0.66 -0.44 -0.58 -0.97 Everett city Washington 0.02 0.03 0.06 -0.03 Kent city Washington 0.05 -0.05 0.20 -0.01 Seattle city Washington -0.71 -0.55 -0.44 -1.13 Spokane city Washington -0.22 -0.03 -0.45 -0.16 Tacoma city Washington -0.03 0.16 -0.09 -0.17 Vancouver city Washington -0.01 0.06 -0.05 -0.04 Yakima city Washington 0.73 0.71 0.54 0.93 Green Bay city Wisconsin -0.09 -0.02 -0.23 -0.03 Kenosha city Wisconsin -0.10 -0.21 -0.30 0.19 Madison city Wisconsin -0.79 -0.49 -0.61 -1.28 Milwaukee city Wisconsin 0.46 0.96 0.19 0.23 Waukesha city Wisconsin -0.68 -0.83 -0.77 -0.43 
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 

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

Burbank city California 100,053 -0.84 0.45 -0.35 -0.25 -0.78 0.47 Carlsbad city California 92,998 -1.30 -0.56 -0.86 -0.91 -1.21 -0.54 Carson city California 92,156 -0.74 0.49 1.02 0.26 -0.96 0.66 Chico city California 71,298 0.37 -0.55 -0.87 -0.35 -0.01 -0.36 Chino city California 69,732 -1.29 0.23 1.59 0.18 -1.16 0.19 Chula Vista city California 212,954 -0.69 0.67 0.53 0.17 -0.61 0.52 Clovis city California 80,529 -0.79 -0.43 0.38 -0.28 -0.92 -0.65 Concord city California 116,782 -0.64 0.62 -0.30 -0.11 -0.78 0.42 Corona city California 162,410 -1.09 0.17 0.86 -0.02 -1.08 0.08 Costa Mesa city California 105,333 -0.65 1.19 -0.10 0.15 -0.72 0.88 Daly City California 93,513 -0.67 1.78 -0.44 0.22 -0.85 1.50 El Cajon city California 92,507 -0.06 0.67 0.23 0.28 0.06 0.49 Escondido city California 133,017 -0.69 1.16 0.63 0.37 -0.46 0.98 Fairfield city California 102,642 -0.52 0.21 -0.03 -0.11 -0.56 -0.13 Fontana city California 158,235 -0.60 1.12 1.35 0.62 -0.54 0.92 Fremont city California 210,387 -1.08 1.26 -1.20 -0.34 -1.15 0.86 Fresno city California 477,251 0.51 0.70 0.68 0.63 0.57 0.55 Fullerton city California 142,064 -1.00 0.62 -0.10 -0.16 -0.84 0.65 Garden Grove city California 192,345 -0.66 2.55 0.62 0.83 -0.70 2.28 Glendale city California 194,620 -0.88 2.11 -0.49 0.25 -0.38 2.29 Hawthorne city California 100,754 0.49 1.70 0.74 0.97 0.28 1.80 Hayward city California 135,474 -0.33 1.00 0.10 0.26 -0.49 1.31 Hemet city California 77,076 -0.26 0.60 1.37 0.57 0.01 0.11 Hesperia city California 79,714 -0.36 0.15 1.39 0.39 -0.47 -0.19 Huntington Beach city California 189,451 -1.16 -0.32 -0.27 -0.58 -1.23 -0.35 Indio city California 65,091 0.19 2.18 0.89 1.09 0.15 2.00 Inglewood city California 120,204 0.48 1.48 0.91 0.96 0.63 1.47 Lakewood city California 88,253 -1.23 -0.04 0.46 -0.27 -1.18 -0.14 
City State 2005 Population 2005 Factor 1 2005 Factor 2 2005 Factor 3 

2005 

Equal 

Weight 

Index 

Page A-25 

2000 

Factor 1 

2000 

Factor 2 

Livermore city California 87,054 -1.06 -0.26 -0.58 -0.63 -1.12 -0.43 Long Beach city California 463,956 0.38 1.15 -0.18 0.45 0.62 1.15 Los Angeles city California 3,731,437 0.38 1.88 -0.20 0.69 0.43 2.01 Merced city California 65,391 0.55 0.78 0.95 0.76 0.68 0.79 Mission Viejo city California 90,136 -1.53 -0.72 -0.21 -0.82 -1.55 -0.63 Modesto city California 202,971 -0.39 -0.06 0.90 0.15 -0.29 -0.07 Mountain View city California 69,427 -0.88 0.87 -1.79 -0.60 -0.89 1.11 Napa city California 73,085 -0.56 0.49 -0.33 -0.14 -0.71 0.34 Norwalk city California 103,844 -0.84 1.54 1.46 0.72 -0.78 1.49 Oakland city California 373,910 0.88 1.24 -0.95 0.39 0.91 1.22 Oceanside city California 162,259 -0.98 0.32 0.11 -0.18 -0.58 0.29 Ontario city California 156,679 -0.32 1.43 1.35 0.82 -0.39 1.44 Orange city California 137,994 -1.14 0.37 0.48 -0.10 -1.00 0.37 Oxnard city California 178,871 0.15 2.21 0.50 0.95 -0.15 2.10 Palmdale city California 145,800 -0.09 0.66 1.36 0.64 -0.38 0.22 Pasadena city California 129,400 -0.21 0.34 -0.97 -0.28 -0.20 0.92 Pleasanton city California 67,018 -1.22 -0.40 -1.19 -0.93 -1.45 -0.53 Pomona city California 161,257 -0.24 2.28 1.22 1.09 0.16 2.11 Redding city California 89,362 -0.51 -0.73 0.31 -0.31 -0.19 -0.73 Redlands city California 73,548 -0.51 -0.47 -0.10 -0.36 -0.70 -0.38 Redwood City California 81,195 -0.25 1.34 -0.73 0.12 -1.00 0.82 Rialto city California 93,284 -0.67 1.11 1.55 0.66 -0.26 0.65 Richmond city California 96,648 0.38 0.90 -0.27 0.34 0.45 1.01 Riverside city California 294,059 -0.49 0.63 0.55 0.23 -0.37 0.34 Roseville city California 108,848 -1.15 -0.46 -0.12 -0.58 -1.21 -0.63 Sacramento city California 445,287 0.52 0.50 -0.24 0.26 0.53 0.42 Salinas city California 156,950 -0.04 2.56 1.44 1.32 -0.31 2.14 San Bernardino city California 204,552 1.28 1.56 0.50 1.11 1.05 1.00 
City State 2005 Population 2005 Factor 1 2005 Factor 2 2005 Factor 3 

2005 

Equal 

Weight 

Index 

Page A-26 

2000 

Factor 1 

2000 

Factor 2 

San Buenaventura (Ventura) city California 100,154 -0.44 -0.29 -0.59 -0.44 -0.59 -0.19 San Diego city California 1,208,331 -0.30 0.51 -0.95 -0.25 -0.28 0.48 San Francisco city California 719,077 -0.26 0.95 -1.59 -0.30 -0.32 1.12 San Jose city California 887,330 -0.56 1.12 -0.50 0.02 -0.72 1.31 San Leandro city California 77,631 -0.73 0.77 0.21 0.08 -0.82 0.58 San Marcos city California 77,445 -0.94 0.33 0.34 -0.09 -0.71 0.79 San Mateo city California 93,481 -0.94 0.59 -0.82 -0.39 -1.00 0.52 Santa Ana city California 302,302 -0.28 3.39 1.66 1.59 -0.28 3.81 Santa Barbara city California 90,708 -0.45 0.14 -0.79 -0.37 -0.49 0.63 Santa Clara city California 102,204 -0.65 1.23 -1.49 -0.31 -0.85 1.14 Santa Maria city California 88,817 0.14 2.02 0.65 0.93 0.09 1.91 Santa Monica city California 82,777 -1.00 -0.26 -1.47 -0.91 -0.74 -0.25 Santa Rosa city California 146,500 -0.49 0.08 -0.08 -0.16 -0.70 0.03 Simi Valley city California 116,722 -1.30 -0.54 0.23 -0.54 -1.18 -0.38 Stockton city California 278,515 0.38 0.83 0.61 0.61 0.52 0.79 Sunnyvale city California 132,725 -0.75 1.26 -1.73 -0.40 -1.03 1.26 Thousand Oaks city California 127,895 -1.07 -0.39 -0.75 -0.74 -1.25 -0.42 Torrance city California 138,618 -1.16 0.32 -0.76 -0.53 -1.20 0.25 Tracy city California 82,218 -1.15 0.18 0.78 -0.07 -1.21 -0.20 Turlock city California 74,883 -0.84 0.71 1.14 0.34 -0.38 0.32 Tustin city California 79,811 -1.11 1.13 -0.40 -0.13 -1.08 0.93 Union City California 65,239 -0.82 1.74 -0.04 0.29 -1.06 1.16 Upland city California 74,420 -0.29 -0.29 -0.27 -0.28 -0.70 -0.11 Vacaville city California 81,117 -1.10 -0.50 0.38 -0.41 -1.05 -0.49 Vallejo city California 115,657 -0.18 0.19 -0.05 -0.01 -0.41 0.08 Victorville city California 93,042 -0.04 0.29 1.31 0.52 0.01 -0.04 Visalia city California 108,467 -0.18 -0.10 0.97 0.23 -0.40 -0.16 Vista city California 83,228 -0.06 1.12 0.19 0.42 -0.33 0.94 
City State 2005 Population 2005 Factor 1 2005 Factor 2 2005 Factor 3 

2005 

Equal 

Weight 

Index 

Page A-27 

2000 

Factor 1 

2000 

Factor 2 

West Covina city California 116,371 -0.33 0.74 0.63 0.35 -0.97 0.60 Westminster city California 97,946 -1.08 1.92 0.88 0.57 -0.77 1.85 Arvada city Colorado 104,766 -1.02 -0.88 0.38 -0.51 -1.04 -0.85 Aurora city Colorado 291,317 -0.16 0.23 0.40 0.16 -0.44 -0.05 Boulder city Colorado 83,432 -0.21 -0.08 -2.79 -1.03 -0.33 -0.15 Colorado Springs city Colorado 376,985 -0.34 -0.69 -0.31 -0.45 -0.56 -0.72 Denver city Colorado 545,198 0.31 0.28 -0.73 -0.04 0.25 0.26 Fort Collins city Colorado 122,297 -0.20 -0.67 -1.56 -0.81 -0.52 -0.63 Greeley city Colorado 82,836 -0.11 -0.14 0.11 -0.04 -0.09 -0.27 Lakewood city Colorado 142,434 -0.43 -0.69 0.14 -0.33 -0.74 -0.57 Longmont city Colorado 76,181 -0.54 0.08 -0.01 -0.16 -0.59 -0.14 Pueblo city Colorado 101,302 0.86 -0.81 0.88 0.31 0.35 -0.69 Thornton city Colorado 102,331 -0.98 -0.41 0.80 -0.20 -1.00 -0.61 Westminster city Colorado 99,305 -1.01 -0.72 0.29 -0.48 -1.08 -0.54 Washington city 

District of Columbia 515,118 1.41 0.12 -2.28 -0.25 1.31 0.10 

Boca Raton city Florida 74,361 -0.95 -0.36 -0.55 -0.62 -1.26 -0.47 Cape Coral city Florida 134,388 -0.84 -0.16 0.48 -0.17 -1.01 -0.58 Clearwater city Florida 108,382 -0.46 -0.26 0.16 -0.18 -0.35 -0.34 Davie town Florida 88,683 -0.75 -0.05 -0.20 -0.33 -0.98 -0.26 Deerfield Beach city Florida 71,599 -0.34 1.11 -0.27 0.16 -0.57 0.27 Deltona city Florida 85,979 -1.04 -0.47 1.21 -0.10 -0.86 -0.61 Fort Lauderdale city Florida 141,307 0.15 -0.26 -0.15 -0.09 0.14 0.14 Gainesville city Florida 100,879 0.93 -0.27 -1.26 -0.20 0.24 -0.56 Hollywood city Florida 138,412 -0.44 0.68 -0.07 0.06 -0.42 0.29 Jacksonville city Florida 768,537 -0.19 -0.69 0.01 -0.29 -0.23 -0.73 Largo city Florida 71,269 -0.84 -0.52 0.83 -0.18 -0.75 -0.52 Melbourne city Florida 76,373 -0.58 -0.51 0.33 -0.25 -0.45 -0.64 Miami city Florida 361,701 1.48 3.64 -0.70 1.47 1.42 3.61 
City State 2005 Population 2005 Factor 1 2005 Factor 2 2005 Factor 3 

2005 

Equal 

Weight 

Index 

Page A-28 

2000 

Factor 1 

2000 

Factor 2 

Miami Beach city Florida 84,086 0.10 2.46 -1.38 0.39 0.84 2.31 Miramar city Florida 115,444 -0.90 0.35 0.09 -0.16 -0.93 0.41 Orlando city Florida 221,299 0.14 0.29 -0.13 0.10 0.18 0.07 Palm Bay city Florida 90,102 -0.60 -0.25 0.51 -0.11 -0.65 -0.56 Pembroke Pines city Florida 159,422 -0.99 0.07 -0.24 -0.39 -1.25 -0.08 Plantation city Florida 88,859 -1.02 -0.19 0.00 -0.40 -1.24 -0.32 Pompano Beach city Florida 94,892 -0.28 0.54 0.49 0.25 -0.02 0.18 St. Petersburg city Florida 232,960 -0.08 -0.63 0.18 -0.18 -0.11 -0.62 Sunrise city Florida 86,586 -0.80 0.48 0.44 0.04 -0.83 0.22 Tallahassee city Florida 141,148 0.06 -0.67 -1.36 -0.65 0.07 -0.82 Tampa city Florida 312,855 0.36 -0.14 -0.15 0.02 0.40 -0.17 West Palm Beach city Florida 86,804 0.46 0.62 -0.25 0.27 0.29 0.44 Atlanta city Georgia 394,929 2.02 -0.63 -1.11 0.09 1.82 -0.58 Roswell city Georgia 98,137 -1.33 -0.18 -1.03 -0.85 -1.24 -0.19 Savannah city Georgia 117,478 1.08 -0.55 0.01 0.18 0.92 -0.57 Honolulu CDP Hawaii 362,252 -0.50 0.57 -0.99 -0.31 -0.54 0.71 Boise City Idaho 191,667 -0.54 -0.80 -0.26 -0.53 -0.75 -0.79 Nampa city Idaho 67,112 0.31 -0.22 0.60 0.23 -0.27 -0.28 Aurora city Illinois 170,490 -0.38 0.96 0.15 0.24 -0.75 0.46 Champaign city Illinois 65,600 0.15 -0.43 -1.51 -0.59 -0.18 -0.47 Chicago city Illinois 2,701,926 1.12 0.42 -0.54 0.33 0.97 0.47 Elgin city Illinois 93,412 -0.58 0.78 0.68 0.29 -0.80 0.63 Evanston city Illinois 62,258 -0.63 -0.65 -2.02 -1.10 -0.58 -0.47 Joliet city Illinois 128,090 -0.47 -0.37 0.69 -0.05 -0.41 -0.39 Naperville city Illinois 147,779 -1.44 -0.64 -1.35 -1.15 -1.48 -0.74 Peoria city Illinois 102,136 0.68 -1.35 -0.63 -0.43 0.86 -1.21 Rockford city Illinois 139,173 0.67 -0.54 0.59 0.24 0.26 -0.69 Schaumburg village Illinois 77,817 -1.13 0.03 -0.60 -0.57 -1.37 -0.23 
City State 2005 Population 2005 Factor 1 2005 Factor 2 2005 Factor 3 

2005 

Equal 

Weight 

Index 

Page A-29 

2000 

Factor 1 

2000 

Factor 2 

Springfield city Illinois 110,262 0.17 -1.24 -0.20 -0.42 -0.02 -1.13 Waukegan city Illinois 82,355 -0.02 1.30 0.75 0.68 -0.08 1.12 Bloomington city Indiana 55,406 0.38 -0.16 -2.21 -0.66 0.09 -0.47 Evansville city Indiana 110,708 0.43 -1.16 0.83 0.04 0.19 -1.19 Fort Wayne city Indiana 219,346 0.36 -1.14 0.76 -0.01 0.18 -1.14 Hammond city Indiana 72,507 0.97 -0.43 1.15 0.57 0.35 -0.61 Indianapolis city (balance) Indiana 765,310 0.38 -0.91 0.32 -0.07 0.14 -0.99 South Bend city Indiana 97,070 1.08 -0.63 0.41 0.29 0.65 -0.85 Cedar Rapids city Iowa 119,670 -0.04 -1.07 -0.01 -0.37 -0.62 -0.95 Davenport city Iowa 95,382 0.21 -0.99 -0.02 -0.26 0.03 -0.99 Des Moines city Iowa 196,917 -0.14 -0.66 -0.11 -0.30 -0.10 -0.61 Sioux City Iowa 78,395 0.00 -0.74 0.90 0.05 -0.31 -0.58 Kansas City Kansas 142,341 1.22 -0.26 0.76 0.57 0.83 -0.46 Lawrence city Kansas 74,951 -0.27 -0.35 -1.81 -0.81 -0.37 -0.61 Olathe city Kansas 107,710 -0.90 -0.88 -0.61 -0.80 -1.18 -0.82 Overland Park city Kansas 161,901 -1.22 -0.82 -0.77 -0.94 -1.35 -0.79 Topeka city Kansas 117,326 0.19 -1.15 0.45 -0.17 0.03 -1.02 Wichita city Kansas 354,582 0.04 -0.73 0.39 -0.10 -0.18 -0.75 Lexington-Fayette Kentucky 255,389 -0.20 -0.76 -0.56 -0.51 -0.29 -0.81 Baton Rouge city Louisiana 205,442 1.44 -0.77 0.23 0.30 0.79 -0.85 Lafayette city Louisiana 108,175 -0.10 -0.88 -0.08 -0.35 -0.22 -0.82 Shreveport city Louisiana 192,531 1.16 -1.26 0.89 0.26 0.91 -1.24 Baltimore city Maryland 608,481 1.82 -0.50 -0.41 0.30 1.63 -0.52 Boston city Massachusetts 520,702 1.27 1.04 -2.27 0.02 0.98 0.88 Brockton city Massachusetts 91,938 0.40 0.41 0.29 0.37 0.40 0.16 Cambridge city Massachusetts 81,260 0.04 0.61 -3.28 -0.88 -0.03 0.55 Fall River city Massachusetts 97,612 0.61 0.34 0.58 0.51 0.52 0.30 Lawrence city Massachusetts 82,191 2.89 2.41 -0.88 1.47 1.88 2.05 
Page A-30 

City State 2005 Population 2005 Factor 1 2005 Factor 2 2005 Factor 3 

2005 

Equal 

Weight 

Index 

2000 

Factor 2 

2000 

Factor 1 

Lowell city Massachusetts 96,876 1.10 1.10 -0.83 0.46 0.65 0.64 Lynn city Massachusetts 83,419 1.09 1.04 -0.67 0.49 0.66 0.78 New Bedford city Massachusetts 84,898 1.01 0.10 0.68 0.60 0.97 0.30 Newton city Massachusetts 82,383 -1.18 -0.57 -1.89 -1.21 -1.25 -0.43 Quincy city Massachusetts 84,080 -0.85 0.12 -0.48 -0.40 -0.69 0.08 Somerville city Massachusetts 74,869 0.48 1.05 -1.91 -0.13 -0.12 0.87 Springfield city Massachusetts 146,948 1.96 0.04 0.42 0.81 1.32 -0.18 Worcester city Massachusetts 154,398 0.80 0.47 -1.14 0.04 0.67 0.12 Ann Arbor city Michigan 98,743 -0.08 0.19 -3.10 -1.00 -0.33 0.01 Detroit city Michigan 836,056 3.13 -0.88 0.73 0.99 2.59 -1.00 Grand Rapids city Michigan 193,568 0.81 -0.32 0.03 0.17 0.42 -0.38 Lansing city Michigan 119,675 1.06 -0.55 0.04 0.19 0.57 -0.61 Livonia city Michigan 103,497 -1.42 -0.91 0.40 -0.64 -1.44 -0.95 Pontiac city Michigan 59,472 1.64 -0.20 1.16 0.87 1.41 -0.33 Southfield city Michigan 75,053 -0.56 -0.87 0.62 -0.27 -0.71 -0.67 Sterling Heights city Michigan 123,368 -1.05 -0.29 0.73 -0.20 -1.27 -0.47 Troy city Michigan 83,958 -1.15 -0.47 -0.89 -0.84 -1.48 -0.44 Warren city Michigan 134,901 -0.48 -0.62 1.33 0.08 -0.85 -0.55 Westland city Michigan 80,284 -0.57 -0.82 1.08 -0.10 -0.90 -0.68 Wyoming city Michigan 68,960 -0.41 -0.21 0.93 0.10 -0.75 -0.52 Bloomington city Minnesota 80,055 -0.84 -0.64 -0.32 -0.60 -1.12 -0.62 Brooklyn Park city Minnesota 66,408 -0.42 -0.34 0.08 -0.22 -0.79 -0.44 Duluth city Minnesota 76,918 0.02 -0.98 -0.93 -0.63 -0.04 -1.00 Minneapolis city Minnesota 350,260 0.95 0.14 -1.91 -0.27 0.75 0.07 Plymouth city Minnesota 68,978 -1.15 -0.74 -0.81 -0.90 -1.38 -0.78 Rochester city Minnesota 88,338 -0.72 -0.61 -1.12 -0.82 -0.62 -0.57 St. Paul city Minnesota 261,559 0.75 -0.09 -1.30 -0.21 0.61 0.01 Columbia city Missouri 82,103 -0.20 -0.59 -1.49 -0.76 -0.17 -0.69 
Page A-31 

City State 2005 Population 2005 Factor 1 2005 Factor 3 

2005 

Equal 

Weight 

Index 

2000 

Factor 1 

2000 

Factor 2 

2005 

Factor 2 

Independence city Missouri 111,842 -0.30 -0.76 0.95 -0.04 -0.55 -0.85 Kansas City Missouri 440,885 0.57 -0.79 -0.16 -0.13 0.39 -0.79 Lee's Summit city Missouri 86,357 -1.08 -1.05 -0.46 -0.86 -1.20 -1.07 St. Louis city Missouri 333,730 2.57 -1.03 -0.50 0.35 2.43 -1.02 Springfield city Missouri 139,600 0.24 -0.96 0.28 -0.15 0.01 -0.98 Lincoln city Nebraska 226,062 -0.38 -0.62 -0.70 -0.57 -0.55 -0.70 Omaha city Nebraska 373,215 0.23 -0.55 -0.23 -0.18 -0.15 -0.65 Henderson city Nevada 223,776 -1.13 -0.79 0.31 -0.53 -1.20 -0.72 Las Vegas city Nevada 538,653 -0.58 0.29 0.66 0.12 -0.57 0.18 North Las Vegas city Nevada 165,061 -0.47 0.82 0.87 0.41 -0.35 0.99 Reno city Nevada 204,478 -0.22 0.15 -0.26 -0.11 -0.39 0.11 Sparks city Nevada 76,405 -0.96 0.00 0.57 -0.13 -0.90 0.01 Manchester city 

New Hampshire 109,308 0.12 -0.17 -0.67 -0.24 -0.12 -0.22 

Nashua city 

New Hampshire 84,632 -0.69 -0.25 -0.62 -0.52 -0.69 -0.48 

Camden city New Jersey 73,305 4.51 1.36 0.23 2.03 3.62 0.77 Clifton city New Jersey 72,667 -0.89 0.51 0.14 -0.08 -1.02 0.46 Elizabeth city New Jersey 121,137 0.83 1.93 0.14 0.97 0.45 2.32 Jersey City New Jersey 246,335 0.91 1.40 -1.11 0.40 0.71 1.32 Newark city New Jersey 254,217 2.02 1.65 -0.24 1.15 2.21 1.27 Passaic city New Jersey 68,422 1.76 3.56 -0.18 1.71 0.93 3.35 Paterson city New Jersey 148,353 1.40 2.09 0.13 1.20 1.17 1.92 Trenton city New Jersey 77,471 2.04 0.69 0.47 1.07 1.84 0.15 Albuquerque city New Mexico 488,133 -0.26 -0.42 0.18 -0.17 -0.31 -0.53 Santa Fe city New Mexico 66,453 -0.12 -0.15 -0.75 -0.34 -0.40 -0.14 Albany city New York 78,402 2.10 -0.84 -1.45 -0.07 1.53 -0.82 Buffalo city New York 256,492 2.79 -1.35 0.23 0.55 2.66 -1.36 New Rochelle city New York 75,961 -0.43 0.44 -0.64 -0.21 -0.65 0.29 
Page A-32 

City State 2005 Population 2005 Factor 1 2005 Factor 3 

2005 

Equal 

Weight 

Index 

2000 

Factor 1 

2000 

Factor 2 

2005 

Factor 2 

New York city New York 7,956,113 0.79 1.28 -1.08 0.33 0.88 1.38 Rochester city New York 189,312 2.90 -0.41 -0.17 0.77 2.42 -0.70 Syracuse city New York 132,495 2.55 -0.66 -0.56 0.44 2.02 -0.74 Yonkers city New York 193,327 0.15 0.35 -0.10 0.14 0.17 0.43 Asheville city North Carolina 74,889 0.01 -0.81 -0.65 -0.49 0.07 -0.75 Cary town North Carolina 107,446 -1.17 -0.37 -1.16 -0.90 -1.33 -0.49 Charlotte city North Carolina 601,598 -0.16 -0.32 -0.28 -0.25 -0.38 -0.36 Durham city North Carolina 191,731 0.11 -0.06 -0.67 -0.21 0.03 -0.33 Fayetteville city North Carolina 128,777 0.43 -1.17 0.82 0.02 0.17 -1.10 Gastonia city North Carolina 72,183 0.46 -0.40 1.14 0.40 0.25 -0.49 Greensboro city North Carolina 208,552 0.08 -0.42 0.11 -0.08 -0.24 -0.63 High Point city North Carolina 101,852 0.26 -0.55 0.76 0.16 -0.15 -0.51 Raleigh city North Carolina 315,249 -0.04 -0.31 -1.19 -0.51 -0.36 -0.30 Wilmington city North Carolina 91,207 -0.05 -0.67 -0.69 -0.47 0.30 -0.67 Winston-Salem city North Carolina 183,467 0.50 -0.30 0.00 0.07 0.17 -0.54 Fargo city North Dakota 88,809 -0.33 -0.92 -0.71 -0.65 -0.60 -0.79 Akron city Ohio 200,181 1.12 -1.00 0.26 0.12 0.84 -1.03 Cincinnati city Ohio 287,540 2.03 -1.09 -0.54 0.13 1.75 -1.02 Cleveland city Ohio 414,534 3.14 -1.00 0.73 0.96 2.52 -0.97 Columbus city Ohio 693,983 0.59 -0.56 -0.27 -0.08 0.23 -0.70 Dayton city Ohio 132,679 2.56 -1.28 0.64 0.64 1.92 -1.41 Lorain city Ohio 65,476 0.76 -0.91 1.53 0.46 0.62 -0.76 Parma city Ohio 79,708 -0.93 -0.80 0.96 -0.25 -1.10 -0.76 Toledo city Ohio 285,937 1.14 -1.23 1.03 0.31 0.72 -1.18 Broken Arrow city Oklahoma 85,039 -0.80 -1.11 0.37 -0.51 -1.19 -1.02 Edmond city Oklahoma 71,658 -1.02 -0.98 -0.70 -0.90 -1.12 -0.94 Lawton city Oklahoma 79,486 0.48 -1.09 1.15 0.18 0.31 -1.21 Norman city Oklahoma 97,484 -0.27 -0.88 -0.76 -0.64 -0.56 -0.75 
Page A-33 

City State 2005 Population 2005 Factor 1 2005 Factor 3 

2005 

Equal 

Weight 

Index 

2000 

Factor 1 

2000 

Factor 2 

2005 

Factor 2 

Oklahoma City Oklahoma 515,751 0.37 -0.71 0.46 0.04 0.20 -0.75 Tulsa city Oklahoma 370,447 0.36 -0.66 0.27 -0.01 -0.04 -0.80 Beaverton city Oregon 83,447 -0.81 0.05 -0.65 -0.47 -0.85 -0.04 Eugene city Oregon 142,716 -0.13 -0.44 -1.06 -0.54 -0.28 -0.61 Gresham city Oregon 95,334 0.11 0.35 0.50 0.32 -0.43 -0.09 Hillsboro city Oregon 82,732 0.19 0.37 -0.30 0.09 -0.74 0.32 Medford city Oregon 73,782 -0.31 -0.62 0.31 -0.21 -0.27 -0.59 Portland city Oregon 513,627 0.20 -0.22 -0.88 -0.30 -0.14 -0.18 Salem city Oregon 142,006 0.02 0.00 0.42 0.15 -0.17 -0.32 Allentown city Pennsylvania 105,231 1.46 0.17 -0.44 0.40 0.89 -0.06 Bethlehem city Pennsylvania 68,144 -0.04 -0.49 -0.23 -0.25 0.20 -0.43 Erie city Pennsylvania 91,423 1.02 -1.18 0.53 0.12 0.93 -1.13 Philadelphia city Pennsylvania 1,406,415 1.74 -0.29 -0.09 0.45 1.49 -0.37 Pittsburgh city Pennsylvania 284,366 1.58 -1.07 -0.76 -0.08 1.26 -1.14 Reading city Pennsylvania 81,302 2.89 0.62 0.50 1.34 2.04 0.12 Scranton city Pennsylvania 67,314 1.07 -0.65 0.20 0.21 0.28 -0.95 Cranston city Rhode Island 77,025 -0.91 -0.40 0.17 -0.38 -0.90 -0.62 Pawtucket city Rhode Island 72,896 0.92 0.34 0.13 0.47 0.50 0.49 Providence city Rhode Island 160,264 2.12 1.01 -1.46 0.56 2.04 0.86 Warwick city Rhode Island 85,804 -1.09 -0.83 0.36 -0.52 -1.10 -0.81 Charleston city South Carolina 109,151 0.38 -0.78 -0.98 -0.46 0.24 -0.85 Columbia city South Carolina 88,450 0.72 -0.75 -0.89 -0.31 0.93 -0.85 Sioux Falls city South Dakota 132,358 -0.29 -0.75 -0.30 -0.45 -0.61 -0.73 Chattanooga city Tennessee 139,158 0.81 -1.24 0.24 -0.06 0.57 -1.07 Clarksville city Tennessee 107,130 -0.48 -0.98 0.43 -0.35 -0.51 -0.99 Knoxville city Tennessee 168,744 0.91 -0.96 -0.15 -0.06 0.66 -0.90 Memphis city Tennessee 642,251 1.12 -0.86 0.90 0.39 0.89 -0.93 Murfreesboro city Tennessee 83,822 -0.10 -0.59 -0.65 -0.45 -0.42 -0.65 
Page A-34 

City State 2005 Population 2005 Factor 1 2005 Factor 3 

2005 

Equal 

Weight 

Index 

2000 

Factor 1 

2000 

Factor 2 

2005 

Factor 2 

Nashville-Davidson (balance) Tennessee 522,662 0.17 -0.45 -0.30 -0.19 -0.04 -0.58 Abilene city Texas 105,165 0.19 -0.79 0.80 0.07 -0.11 -0.90 Amarillo city Texas 176,999 -0.01 -0.54 0.82 0.09 -0.11 -0.63 Arlington city Texas 348,965 -0.39 -0.06 0.63 0.06 -0.62 -0.18 Austin city Texas 678,457 0.05 0.17 -0.73 -0.17 -0.25 0.24 Baytown city Texas 61,504 -0.05 -0.09 1.65 0.50 -0.12 -0.03 Beaumont city Texas 107,876 0.97 -0.92 0.81 0.29 0.64 -1.01 Bryan city Texas 56,277 0.59 0.27 0.76 0.54 0.09 -0.02 Carrollton city Texas 122,699 -1.10 0.26 0.42 -0.14 -1.11 -0.01 College Station city Texas 65,370 -0.08 0.08 -2.12 -0.70 -0.40 -0.14 Corpus Christi city Texas 280,002 0.09 -0.43 1.09 0.25 0.00 -0.43 Dallas city Texas 1,144,946 0.76 1.22 0.41 0.80 0.38 0.97 Denton city Texas 87,766 -0.37 -0.22 -0.63 -0.41 -0.32 -0.17 Fort Worth city Texas 604,538 0.38 0.20 0.54 0.37 0.20 0.17 Garland city Texas 235,750 -0.27 0.72 1.17 0.54 -0.73 0.26 Grand Prairie city Texas 148,677 -0.16 0.02 1.13 0.33 -0.42 -0.04 Houston city Texas 1,941,430 0.65 1.13 0.66 0.82 0.34 0.91 Irving city Texas 212,262 -0.41 1.16 0.51 0.42 -0.57 0.85 Killeen city Texas 98,434 0.03 -0.91 0.69 -0.07 -0.13 -0.65 Lewisville city Texas 81,484 -0.87 -0.08 0.30 -0.21 -1.08 -0.37 Lubbock city Texas 199,789 0.03 -0.74 0.70 0.00 -0.11 -0.69 McKinney city Texas 92,337 -1.07 -0.60 -0.33 -0.67 -0.95 -0.27 Mesquite city Texas 126,895 -0.82 0.02 1.62 0.28 -0.93 -0.58 Midland city Texas 100,799 -0.27 -0.54 1.11 0.10 -0.38 -0.68 Odessa city Texas 94,329 0.08 -0.65 1.91 0.45 0.05 -0.62 Pasadena city Texas 150,180 0.14 1.05 1.66 0.95 -0.13 0.70 Plano city Texas 251,648 -1.13 -0.31 -0.68 -0.71 -1.30 -0.49 Richardson city Texas 107,892 -1.19 -0.17 -0.39 -0.58 -1.20 -0.18 
Page A-35 

City State 2005 Population 2005 Factor 1 2005 Factor 3 

2005 

Equal 

Weight 

Index 

2000 

Factor 1 

2000 

Factor 2 

2005 

Factor 2 

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

Tacoma city Washington 191,934 0.45 -0.17 -0.38 -0.03 0.35 -0.24 Vancouver city Washington 155,488 0.17 -0.11 -0.08 -0.01 -0.31 -0.09 Yakima city Washington 79,517 0.68 0.26 1.24 0.73 0.61 0.15 Green Bay city Wisconsin 94,242 0.09 -0.44 0.06 -0.09 -0.29 -0.55 Kenosha city Wisconsin 95,440 -0.36 -0.58 0.63 -0.10 -0.47 -0.64 Madison city Wisconsin 203,704 -0.04 -0.32 -2.02 -0.79 -0.27 -0.36 Milwaukee city Wisconsin 556,948 1.72 -0.21 -0.12 0.46 1.29 -0.34 Waukesha city Wisconsin 62,690 -1.05 -0.91 -0.06 -0.68 -1.09 -0.72 
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

NashvilleDavidson 

(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 singlevalue 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 Specification51

The house prices (Pijk) 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, Ci includes housing characteristics, Cj includes municipal tax 

rate, and Ck includes climate and housing supply elasticity. Symbolically: 

[1] Pijk = f(Ci, Cj, Ck) 

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] Cj = g(Ci) 

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

can be written: 

[3] Pijk = f(Ci, [Cj*, g(Ci)] , Ck) 

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, 

Ck could be measured by a dummy variable for each metropolitan area. [Cj*, g(Ci)] 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 [Cj*, g(Ci)] 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 (3rd quartile value – 1st 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. 
Table B.2. Parameter Estimates for Tract-Level Variables 

Variable Name Parameter Estimate 

Probability Estimate Different 

from zero 

Characteristics of owner-occupied housing in the tract 

ROOMS 0.06354 <.0001 

BUILTB50 -0.07358 <.0001 

BUILT50S -0.17592 <.0001 

BUILT60S 0.04551 0.0629 

BUILT80S 0.0719 0.0038 

BUILTA89 0.38942 <.0001 

Characteristics of the tract 

ORATE -0.11829 <.0001 

PCTSFDETACHED -0.01155 0.5314 

PCT5PLUSUNITS 0.26393 <.0001 

OVERCROWD2000_TR -0.01706 0.6733 

HISPAN -0.92027 <.0001 

NHBLACK -0.71908 <.0001 

NHOTHER -0.61469 <.0001 

POV10_19 -0.02347 0.0011 

POV20_29 -0.03605 0.0002 

POV30_39 -0.05207 <.0001 

POV40PLUS -0.03562 0.0187 

SGLPRNTFAM_TR -0.27932 <.0001 

RCNTIMMIG_TR -0.00535 0.0001 

LINGISOL_TR 0.1206 0.0078 

Only two of the four variables that describe the housing in the tract are significant. The 

signs of these two variables are different than anticipated and suggest that they are acting 

as proxies for other factors. According to the coefficient of PCT5PLUSUNITS, 

neighborhoods with more multifamily housing have higher median house values. Higher 

density development is usually the response of the housing market to high land prices. 

One can suppose that the median value of owner-occupied housing in tracts where land 

prices are high would also be higher. The negative coefficient for the homeownership 

rate is more puzzling; neighborhoods with higher homeownership rates appear to have 

lower median house values. One possibility is that we are not taking into account the 

relationship between the homeownership rate and the type of housing in a tract. 

Homeownership rates vary systematically by type of structure; they are highest for 

single-family detached structures, lower for single-family attached structures, lower still 

for two-to-four unit structures, and lowest for five-plus unit structures. The omitted 

group in this analysis consists of the combination of single-family attached units and 

units in two-to-four unit structures. The equation does not take the relationship between 

tenure and structure type into account and the failure to do so may have biased the 

coefficient of the homeownership rate variable. 

All but one of the variables that describe the demographic composition of the tract have 

statistically significant coefficients. The race and ethnicity coefficients indicate that the 

larger the percentage of persons other than non-Hispanic Whites in the tract, the lower 

the median house value. This negative relationship may result from non-Hispanic Whites 

having higher incomes on average and, therefore, being able to afford more expensive 

housing, or it may represent a premium on neighborhoods with a higher proportion of 

non-Hispanic White residents. The coefficients of all the poverty variables are negative, 

Page B-7 
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 citylevel 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 
Table B.3. Estimated Coefficients for City-Level Needs Indicators 

Variable Name 

Parameter 

Estimate 

Probability Estimate 

Different from Zero 

Variables that describe market conditions 

LACKAFFDRENTALS_2000 3.60592 <.0001 

PR70RENTPOV_2000 3.98406 <.0001 

OVERCROWD_2000 0.97017 <.0001 

Variables not statistically significant at the 0.05 level 

SGLPRNTFAM_2000 -0.19016 0.5071 

POORPERS_2000 -0.71551 0.4368 

LINGISOL_2000 0.23691 0.3735 

SCHPOPPOOR_2000 -0.91598 0.3324 

PCTVACMODPOVCITY_2000 1.05775 0.2383 

EXCSINFRA_2000 -0.18106 0.1234 

CHNGEMPLBASE 0.04516 0.1221 

POORCHILD_2000 1.75478 0.0944 

UNEMPCEN_2000 -0.89899 0.0873 

CHGLOWINCCON_2000 0.03215 0.0507 

Statistical Significant Need Variables with Unanticipated Signs

DENIAL 0.99676 0.0008 

POOROVER74_2000 1.03706 <.0001 

UNEDUCADULTS_2000 1.05456 <.0001 

MINCON_2000 0.097 <.0001 

Statistical Significant Need Variables with Anticipated Signs

RCNTIMMIG_2000 -0.42276 0.0479 

POVCON_2000 -0.08581 0.0211 

PCTPOPHIGHPOVNGHS_2000 -0.52367 0.0145 

LWINCHHDS_2000 -0.47981 0.0016 

PCTPOPMODPOVNGHS_2000 -0.41167 <.0001 

MEDINCCBS2CITY_2000 -0.57655 <.0001 

UNDEREDWORKAGE_2000 -1.61828 <.0001 

The first group contains three variables that describe conditions in the housing market: 

lack of affordable housing, poor households renting older units, and overcrowded units. 

Each variable has a statistically significant positive sign, indicating that—other things 

being equal—cities with higher values on these variables are associated with higher 

median house prices. Care must be taken in interpreting these results. These variables 

are problematic for use in a hedonic model because they do not measure attributes of 

communities that can independently be priced by the housing market. Rather, they are 

variables that are the result of housing market conditions themselves; causation is 

reversed from the typical hedonic model specification. Thus, in this case, we believe that 

all three variables probably reflect higher housing costs and therefore higher housing 

values, instead of signaling that consumers “value” these conditions more highly. 

The second group contains 10 needs indicators that have coefficients that are not 

statistically significant. Three would be statistically significant at the 0.10 level, and one 

of these (CHGLOWNINCCON) is almost significant at the 0.05 level. 

Page B-9 
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 lowincome 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.4. Parameter Estimates for Tract-Level Variables with Modified 

 Specification 

Variable Name Parameter Estimate 

Probability Estimate Different 

from zero 

Characteristics of owner-occupied housing in the tract 

ROOMS 0.06484 <.0001 

BUILTB50 -0.06862 0.0001 

BUILT50S -0.16982 <.0001 

BUILT60S 0.04634 0.0599 

BUILT80S 0.07176 0.0041 

BUILTA89 0.38898 <.0001 

Characteristics of the tract 

ORATE -0.10577 <.0001 

PCTSFDETACHED -0.03583 0.0526 

PCT5PLUSUNITS 0.26388 <.0001 

OVERCROWD2000_TR 0.07004 0.0789 

HISPAN -0.92366 <.0001 

NHBLACK -0.71292 <.0001 

NHOTHER -0.61038 <.0001 

POV10_19 -0.02522 0.0005 

POV20_29 -0.03892 <.0001 

POV30_39 -0.05484 <.0001 

POV40PLUS -0.03883 0.0109 

SGLPRNTFAM_TR -0.30528 <.0001 

RCNTIMMIG_TR -0.00544 0.0001 

LINGISOL_TR 0.06017 0.1834 

Table B.5 presents the result for the city-level variables, using the same categories as 

Table B.3. The first panel in Table B.3 is dropped in Table B.5 because the modified 

specification eliminates these variables from the regression. The second panel in Table 

B.3 included 10 variables that were not statistically significant in the initial regression. 

Nine of the 10 are still insignificant; only LINGISOL_2000 is significant in the modified 

regression. We anticipated a negative coefficient for the percentage of households that 

are linguistically isolated (LINGISOL) because we thought the extra services required by 

such households would increase the tax burden. In the modified regression, 

LINGISOL_2000 has a positive coefficient; this unexpected result may be a result of the 

correlation between LINGISOL_2000 and OVERCROWD2000 or because immigrants 

may be attracted to cities that are more economically vibrant. 

The third panel in Table B.3 included four variables whose coefficients were statistically 

significant and, contrary to our expectations, had positive signs. The results in Table B.5 

for these four variables are substantively the same as those in B.3. The discussion of B.3 

explained that the correct interpretation is to combine the coefficient of 

UNEDUCADULTS_2000 with the coefficient of UNDEREDWORKAGE_2000. In the 

initial specification, the combined value of these coefficients was -0.56; in the modified 

specification, the combined value is -0.42. In both equations, the combined effect of a 

low level of education level is to lower median house prices; this effect is stronger in the 

initial specification. 

Page B-11 
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 0.76863 0.002 

SCHPOPPOOR_2000 1.11468 0.2344 

PCTVACMODPOVCITY_2000 0.41418 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 <.0001 

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-standarddeviation 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 Indicator57

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-standarddeviation 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-standarddeviation 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 
Table B.10. Regressions Using All Tracts with Median House Values 

THE HEDONIC MODEL USING 20 TRACT VARIABLES, 24 CITY VARIABLES 

AND 191 (203-12) metro DUMMIES 

 DEPENDENT VARIABLE Natural log of median housing price: ln (H076001) 

Dependent Variable: LNVALUE 

Number of Observations Read 26287 

Number of Observations Used 21375 

Number of Observations with Missing Values 4912 

Analysis of Variance 

Sum of Mean 

Source DF Squares Square F 

Value Pr > F 

Model 234 7943.464 33.94643 284.57 <.0001 

Error 21140 2521.795 0.11929 

Corrected Total 21374 10465

Root MSE 0.34538 R-Square 0.759 

Dependent Mean 11.70984 Adj R-Sq 0.7564 

Coeff Var 2.94952 

 Parameter Estimates 

Parameter Standard 

Variable DF Estimate Error t Value Pr > |t| 

Intercept 1 12.35706 0.11572 106.78 <.0001 

ROOMS 1 0.06753 0.00427 15.83 <.0001 

ORATE 1 -0.09851 0.02486 -3.96 <.0001 

BUILTB50 1 -0.03393 0.01807 -1.88 0.0604

BUILT50S 1 -0.13859 0.02043 -6.78 <.0001 

BUILT60S 1 0.08409 0.02597 3.24 0.0012

BUILT80S 1 0.11951 0.02606 4.59 <.0001 

BUILTA89 1 0.42183 0.01957 21.56 <.0001 

PCTSFDETACHED 1 -0.0036 0.01823 -0.2 0.8433

PCT5PLUSUNITS 1 0.36086 0.01788 20.19 <.0001 

HISPAN 1 -1.08989 0.01675 -65.07 <.0001 

NHBLACK 1 -0.87194 0.01141 -76.42 <.0001 

NHOTHER 1 -0.73878 0.03094 -23.88 <.0001 

POV10_19 1 -0.0174 0.00724 -2.41 0.0162

POV20_29 1 -0.02829 0.00965 -2.93 0.0034

POV30_39 1 -0.06768 0.01205 -5.62 <.0001 

POV40PLUS 1 -0.08843 0.01423 -6.22 <.0001 

Page B-19 
SGLPRNTFAM_TR 1 -0.33302 0.02935 -11.35 <.0001 

RCNTIMMIG_TR 1 -0.00842 0.00153 -5.5 <.0001 

OVERCROWD2000_TR 1 0.06022 0.04117 1.46 0.1435

LINGISOL_TR 1 0.06294 0.04544 1.39 0.166

LACKAFFDRENTALS_2000 1 4.16489 0.44378 9.38 <.0001 

CHGLOWINCCON_2000 1 0.05786 0.01723 3.36 0.0008

chngemplbase 1 0.06896 0.03064 2.25 0.0244

DENIAL 1 0.52985 0.30034 1.76 0.0777

EXCSINFRA_2000 1 -0.38307 0.1105 -3.47 0.0005

LINGISOL_2000 1 0.19548 0.2776 0.7 0.4813

LWINCHHDS_2000 1 -0.51596 0.1594 -3.24 0.0012

MEDINCCBS2CITY_2000 1 -0.69046 0.10478 -6.59 <.0001 

MINCON_2000 1 0.13255 0.02107 6.29 <.0001 

OVERCROWD_2000 1 0.86862 0.18575 4.68 <.0001 

PCTPOPHIGHPOVNGHS_2000 1 -0.30971 0.21464 -1.44 0.1491

PCTPOPMODPOVNGHS_2000 1 -0.32628 0.09248 -3.53 0.0004

PCTVACMODPOVCITY_2000 1 -0.73593 0.79317 -0.93 0.3535

POORCHILD_2000 1 1.4214 1.06529 1.33 0.1821

POOROVER74_2000 1 0.81763 0.24188 3.38 0.0007

POORPERS_2000 1 -1.45744 0.93481 -1.56 0.119

POVCON_2000 1 -0.02125 0.03774 -0.56 0.5735

PR70RENTPOV_2000 1 4.52297 0.45901 9.85 <.0001 

RCNTIMMIG_2000 1 -0.60178 0.22422 -2.68 0.0073

SCHPOPPOOR_2000 1 -0.3664 0.96535 -0.38 0.7043

SGLPRNTFAM_2000 1 -0.46251 0.29514 -1.57 0.1171

UNDEREDWORKAGE_2000 1 -1.59802 0.10246 -15.6 <.0001 

UNEDUCADULTS_2000 1 1.32086 0.2002 6.6 <.0001 

UNEMPCEN_2000 1 -0.06319 0.52636 -0.12 0.9044

Abilene_TX 1 -0.54106 0.08515 -6.35 <.0001 

Akron_OH 1 -0.09629 0.06819 -1.41 0.158

Albany_GA 1 -0.2584 0.08975 -2.88 0.004

Albany_Schenectady_Troy_NY 1 -0.37119 0.09301 -3.99 <.0001 

Albuquerque_NM 1 0.48704 0.06404 7.61 <.0001 

Allentown_Bethlehem_Easton_PA_NJ 1 -0.19006 0.08373 -2.27 0.0232

Amarillo_TX 1 -0.18867 0.07065 -2.67 0.0076

Anchorage_AK 1 0.57395 0.07652 7.5 <.0001 

Ann_Arbor_MI 1 0.11825 0.09327 1.27 0.2049

Appleton_WI 1 0.05857 0.10447 0.56 0.5751

Asheville_NC 1 -0.23004 0.09522 -2.42 0.0157

Atlanta_SandySprings_Marietta_GA 1 0.31578 0.06903 4.57 <.0001 

Austin_Round_Rock_TX 1 0.23569 0.06559 3.59 0.0003

Bakersfield_CA 1 0.17576 0.07616 2.31 0.021

Baltimore_Towson_MD 1 0.09904 0.06271 1.58 0.1143

Baton_Rouge_LA 1 0.05705 0.06476 0.88 0.3783

Beaumont_Port_Arthur_TX 1 -0.29618 0.08018 -3.69 0.0002

Page B-20 
Bellingham_WA 1 0.15348 0.12011 1.28 0.2014

Bend_OR 1 0.19986 0.12051 1.66 0.0972

Billings_MT 1 -0.0984 0.09731 -1.01 0.3119

Bloomington_IN 1 -0.6786 0.11334 -5.99 <.0001 

Bloomington_Normal_IL 1 -0.08398 0.09711 -0.86 0.3871

Boise_City_Nampa_ID 1 0.02208 0.07683 0.29 0.7738

Boston_Cambridge_Quincy_MA_NH 1 0.50352 0.06472 7.78 <.0001 

Boulder_CO 1 0.58623 0.08795 6.67 <.0001 

Buffalo_Niagara_Falls_NY 1 -0.52216 0.07037 -7.42 <.0001 

Canton_Massillon_OH 1 -0.1872 0.09272 -2.02 0.0435

Cape_Coral_Fort_Myers_FL 1 0.09997 0.09294 1.08 0.2821

Cedar_Rapids_IA 1 -0.02369 0.08729 -0.27 0.7861

Champaign_Urbana_IL 1 -0.38321 0.1055 -3.63 0.0003

Charleston_North_Charleston_SC 1 0.13113 0.0763 1.72 0.0857

Charlotte_Gastonia_Concord_NC_SC 1 0.15606 0.06018 2.59 0.0095

Chattanooga_TN_GA 1 -0.31599 0.07269 -4.35 <.0001 

Chicago_Naperville_Joliet_IL_IN 1 0.56731 0.05433 10.44 <.0001 

Chico_CA 1 0.04538 0.10799 0.42 0.6743

Cincinnati_Middletown_OH_KY_IN 1 -0.16788 0.06997 -2.4 0.0164

Clarksville_TN_KY 1 -0.21985 0.091 -2.42 0.0157

Cleveland_Elyria_Mentor_OH 1 0.1117 0.05777 1.93 0.0532

College_Station_Bryan_TX 1 -0.19731 0.08957 -2.2 0.0276

Colorado_Springs_CO 1 0.35995 0.0692 5.2 <.0001 

Columbia_MO 1 -0.32383 0.10692 -3.03 0.0025

Columbia_SC 1 0.07747 0.07144 1.08 0.2782

Columbus_OH 1 -0.01512 0.06097 -0.25 0.8041

Corpus_Christi_TX 1 -0.20687 0.07398 -2.8 0.0052

Dallas_Fort_Worth_Arlington_TX 1 0.07344 0.05658 1.3 0.1943

Davenport_Moline_Rock_Island_IA 1 -0.14088 0.08413 -1.67 0.094

Dayton_OH 1 -0.00538 0.06692 -0.08 0.9359

Decatur_IL 1 -0.21275 0.08125 -2.62 0.0088

Deltona_Daytona_Beach_Ormond_FL 1 -0.21663 0.13239 -1.64 0.1018

Denver_Aurora_CO 1 0.62122 0.06167 10.07 <.0001 

Des_Moines_IA 1 0.01039 0.07395 0.14 0.8883

Detroit_Warren_Livonia_MI 1 0.38657 0.0573 6.75 <.0001 

Duluth_MN_WI 1 -0.35563 0.08159 -4.36 <.0001 

Durham_NC 1 0.03869 0.07338 0.53 0.598

Erie_PA 1 -0.57339 0.0859 -6.67 <.0001 

Eugene_Springfield_OR 1 0.05661 0.08547 0.66 0.5077

Evansville_IN_KY 1 -0.23815 0.07782 -3.06 0.0022

Fargo_ND_MN 1 -0.13151 0.10541 -1.25 0.2122

Fayetteville_NC 1 -0.07988 0.08068 -0.99 0.3221

Fayetteville_Springdale_AR_MO 1 -0.30134 0.11117 -2.71 0.0067

Flint_MI 1 -0.19732 0.07821 -2.52 0.0116

Fort_Collins_Loveland_CO 1 0.19266 0.08875 2.17 0.03

Fort_Smith_AR_OK 1 -0.37366 0.09635 -3.88 0.0001

Fort_Wayne_IN 1 -0.15878 0.06927 -2.29 0.0219

Page B-21 
Fresno_CA 1 0.13313 0.07248 1.84 0.0663

Gainesville_FL 1 -0.21756 0.10479 -2.08 0.0379

Grand_Rapids_Wyoming_MI 1 0.04878 0.06954 0.7 0.4831

Greeley_CO 1 0.2582 0.09463 2.73 0.0064

Green_Bay_WI 1 0.19492 0.09105 2.14 0.0323

Greensboro_High_Point_NC 1 -0.02609 0.06558 -0.4 0.6908

Greenville_NC 1 -0.14274 0.10525 -1.36 0.175

Gulfport_Biloxi_MS 1 -0.26297 0.09169 -2.87 0.0041

Honolulu_HI 1 1.29919 0.0778 16.7 <.0001 

Houston_Baytown_Sugar_Land_TX 1 0.06153 0.05769 1.07 0.2862

Huntsville_AL 1 -0.049 0.07258 -0.68 0.4996

Indianapolis_IN 1 0.08265 0.06263 1.32 0.1869

Jackson_MS 1 -0.08067 0.0674 -1.2 0.2314

Jacksonville_FL 1 0.05102 0.06316 0.81 0.4193

Kalamazoo_Portage_MI 1 -0.18026 0.09742 -1.85 0.0643

Kansas_City_MO_KS 1 -0.06304 0.05605 -1.12 0.2607

Killeen_Temple_Fort_Hood_TX 1 0.11341 0.10281 1.1 0.27

Knoxville_TN 1 -0.3554 0.07256 -4.9 <.0001 

Lafayette_LA 1 -0.02434 0.07794 -0.31 0.7548

Lake_Charles_LA 1 -0.05051 0.08145 -0.62 0.5352

Lakeland_FL 1 -0.18879 0.08932 -2.11 0.0346

Lansing_East_Lansing_MI 1 0.01996 0.07837 0.25 0.799

Las_Vegas_Paradise_NV 1 0.302 0.06613 4.57 <.0001 

Lawrence_KS 1 -0.13828 0.10461 -1.32 0.1862

Lawton_OK 1 -0.13014 0.08532 -1.53 0.1272

Lexington_Fayette_KY 1 -0.06153 0.07146 -0.86 0.3892

Lincoln_NE 1 -0.01104 0.07917 -0.14 0.8891

Little_Rock_North_Little_Rock_AR 1 -0.18258 0.07296 -2.5 0.0123

Longview_TX 1 -0.23432 0.09384 -2.5 0.0125

LosAngeles_LongBeach_SantaAna_CA 1 0.84023 0.06159 13.64 <.0001 

Lubbock_TX 1 -0.42982 0.07447 -5.77 <.0001 

Lynchburg_VA 1 -0.23863 0.09395 -2.54 0.0111

Macon_GA 1 0.03729 0.07826 0.48 0.6337

Madison_WI 1 0.04788 0.086 0.56 0.5777

Manchester_Nashua_NH 1 0.04756 0.08254 0.58 0.5645

Medford_OR 1 0.0847 0.11063 0.77 0.4439

Memphis_TN_MS_AR 1 0.00882 0.0582 0.15 0.8796

Merced_CA 1 0.46135 0.11125 4.15 <.0001 

Miami_Fort_Lauderdale_Miami_FL 1 0.2799 0.06114 4.58 <.0001 

Midland_TX 1 -0.21241 0.08664 -2.45 0.0142

Milwaukee_Waukesha_West_Allis_WI 1 0.00562 0.06053 0.09 0.926

Minneapolis_St_Paul_MN_WI 1 0.23074 0.07258 3.18 0.0015

Mobile_AL 1 -0.1817 0.06659 -2.73 0.0064

Modesto_CA 1 0.34971 0.07943 4.4 <.0001 

Montgomery_AL 1 -0.11058 0.06795 -1.63 0.1037

Muncie_IN 1 -0.5804 0.09347 -6.21 <.0001 

Napa_CA 1 1.02033 0.10587 9.64 <.0001 

Page B-22 
Nashville_Murfreesboro_TN 1 0.08022 0.06283 1.28 0.2017

NewYork_N_NJ_LongIsland_NY_NJ_PA 1 0.68255 0.05813 11.74 <.0001 

Odessa_TX 1 -0.42231 0.09065 -4.66 <.0001 

Ogden_Clearfield_UT 1 0.2624 0.1032 2.54 0.011

Oklahoma_City_OK 1 -0.17471 0.05442 -3.21 0.0013

Omaha_Council_Bluffs_NE_IA 1 -0.05705 0.06455 -0.88 0.3768

Orlando_FL 1 0.0181 0.08436 0.21 0.8301

Oxnard_Thousand_Oaks_Ventura_CA 1 0.98926 0.06827 14.49 <.0001 

Palm_Bay_Melbourne_Titusville_FL 1 -0.10723 0.09252 -1.16 0.2464

Peoria_IL 1 -0.38694 0.08138 -4.75 <.0001 

Philadelphia_Camden_PA_NJ_DE_MD 1 -0.08523 0.05325 -1.6 0.1095

Phoenix_Mesa_Scottsdale_AZ 1 0.31493 0.05874 5.36 <.0001 

Pittsburgh_PA 1 -0.72159 0.0755 -9.56 <.0001 

Portland_Vancouver_OR_WA 1 0.5228 0.06117 8.55 <.0001 

Providence_Fall_River_RI_MA 1 0.06255 0.06455 0.97 0.3325

Provo_Orem_UT 1 0.16217 0.09287 1.75 0.0808

Pueblo_CO 1 0.27169 0.07909 3.43 0.0006

Racine_WI 1 0.14412 0.09573 1.51 0.1322

Raleigh_Cary_NC 1 0.25525 0.06982 3.66 0.0003

Reading_PA 1 -0.53845 0.09639 -5.59 <.0001 

Redding_CA 1 0.17502 0.10098 1.73 0.0831

Reno_Sparks_NV 1 0.45325 0.08174 5.55 <.0001 

Richmond_VA 1 0.09209 0.07529 1.22 0.2213

Riverside_San_Bernardino_Ont_CA 1 0.56192 0.06272 8.96 <.0001 

Roanoke_VA 1 -0.11941 0.09221 -1.29 0.1954

Rochester_MN 1 0.01463 0.09015 0.16 0.8711

Rochester_NY 1 -0.39128 0.07145 -5.48 <.0001 

Rockford_IL 1 -0.08064 0.07245 -1.11 0.2657

Sacramento_Arden_Roseville_CA 1 0.56968 0.06617 8.61 <.0001 

Salem_OR 1 0.29188 0.08495 3.44 0.0006

Salinas_CA 1 0.9546 0.09748 9.79 <.0001 

Salt_Lake_City_UT 1 0.44892 0.0699 6.42 <.0001 

San_Angelo_TX 1 -0.28251 0.09283 -3.04 0.0023

San_Antonio_TX 1 0.02422 0.06214 0.39 0.6967

San_Diego_Carlsbad_San_Marcos_CA 1 0.85281 0.06142 13.89 <.0001 

San_Francisco_Oakland_Fremont_CA 1 1.23172 0.06179 19.93 <.0001 

San_Jose_Sunnyvale_St_Clara_CA 1 1.4657 0.06869 21.34 <.0001 

Santa_Barbara_Maria_Goleta_CA 1 1.09522 0.08308 13.18 <.0001 

Santa_Fe_NM 1 0.75498 0.09667 7.81 <.0001 

Santa_Rosa_Petaluma_CA 1 0.96654 0.08605 11.23 <.0001 

Savannah_GA 1 0.17328 0.07248 2.39 0.0168

Scranton_Wilkes_Barre_PA 1 -0.37611 0.09229 -4.08 <.0001 

Seattle_Tacoma_Bellevue_WA 1 0.67053 0.06243 10.74 <.0001 

Shreveport_Bossier_City_LA 1 -0.13435 0.06443 -2.09 0.0371

Sioux_City_IA_NE_SD 1 -0.27551 0.09366 -2.94 0.0033

Sioux_Falls_SD 1 -0.00363 0.09272 -0.04 0.9687

South_Bend_Mishawaka_IN_MI 1 -0.27853 0.07137 -3.9 <.0001 

Page B-23 
Spokane_WA 1 0.1193 0.07122 1.68 0.0939

Springfield_IL 1 -0.10948 0.0752 -1.46 0.1455

Springfield_MA 1 0.21599 0.08513 2.54 0.0112

Springfield_MO 1 -0.34164 0.07552 -4.52 <.0001 

St_Cloud_MN 1 -0.1274 0.12609 -1.01 0.3123

St_Joseph_MO_KS 1 -0.41145 0.08561 -4.81 <.0001 

St_Louis_MO_IL 1 0.08682 0.07546 1.15 0.2499

Stockton_CA 1 0.51968 0.07494 6.93 <.0001 

Syracuse_NY 1 -0.72491 0.07931 -9.14 <.0001 

Tallahassee_FL 1 -0.07902 0.0839 -0.94 0.3463

Tampa_St_Petersburg_Clwater_FL 1 -0.01435 0.05897 -0.24 0.8078

Toledo_OH 1 -0.14222 0.06239 -2.28 0.0227

Topeka_KS 1 -0.31523 0.0799 -3.95 <.0001 

Trenton_Ewing_NJ 1 0.33843 0.09898 3.42 0.0006

Tucson_AZ 1 0.30822 0.06522 4.73 <.0001 

Tulsa_OK 1 -0.13173 0.05985 -2.2 0.0278

Tuscaloosa_AL 1 -0.10535 0.08617 -1.22 0.2215

Tyler_TX 1 -0.21119 0.08591 -2.46 0.014

Vallejo_Fairfield_CA 1 0.87619 0.07443 11.77 <.0001 

Virginia_Beach_Norfolk_VA_NC 1 0.23105 0.05983 3.86 0.0001

Visalia_Porterville_CA 1 0.20604 0.10185 2.02 0.0431

Waco_TX 1 -0.45084 0.08453 -5.33 <.0001 

Washington_Arl_Alex_DC_VA_MD_WV 1 0.85627 0.08215 10.42 <.0001 

Waterloo_Cedar_Falls_IA 1 -0.34838 0.08709 -4 <.0001 

Wichita_Falls_TX 1 -0.52884 0.08086 -6.54 <.0001 

Wichita_KS 1 -0.20276 0.06304 -3.22 0.0013

Wilmington_NC 1 -0.05655 0.09838 -0.57 0.5654

Winston_Salem_NC 1 0.02539 0.07235 0.35 0.7256

Worcester_MA 1 0.03909 0.08083 0.48 0.6286

Yakima_WA 1 0.13032 0.11493 1.13 0.2568

Youngstown_Warren_Boardman_OH_PA 1 -0.68743 0.09237 -7.44 <.0001 

Yuma_AZ 1 -0.00583 0.10301 -0.06 0.9549

Page B-24 
Table B.11. Regressions on Tracts with Less Dispersion in Median Prices 

THE HEDONIC MODEL USING 20 TRACT VARIABLES, 24 CITY VARIABLES 

AND 191 (203-12) metro dUMMIES 

 DEPENDENT VARIABLE Natural log of median housing price: ln (H076001) 

where SPECIAL LE .56 

Dependent Variable: LNVALUE 

Number of Observations Read 20485 

Number of Observations Used 16096 

Number of Observations with Missing Values 4389 

 Analysis of Variance 

Sum of Mean 

Source DF Squares Square F Value Pr > F 

Model 234 4846.302 20.71069 225.36 <.0001 

Error 15861 1457.622 0.0919 

Corrected Total 16095 6303.924 

Root MSE 0.30315 R-Square 0.7688 

Dependent Mean 11.7506 Adj R-Sq 0.7654 

Coeff Var 2.57987 

 Parameter Estimates 

 

Parameter Standard 

Variable Estimate Error t Value Pr > |t| 

Intercept 12.0885 0.12086 100.02 <.0001 

ROOMS 0.06354 0.00435 14.6 <.0001 

ORATE -0.11829 0.02562 -4.62 <.0001 

BUILTB50 -0.07358 0.01775 -4.15 <.0001 

BUILT50S -0.17592 0.01944 -9.05 <.0001 

BUILT60S 0.04551 0.02447 1.86 0.0629

BUILT80S 0.0719 0.02482 2.9 0.0038

BUILTA89 0.38942 0.0186 20.93 <.0001 

PCTSFDETACHED -0.01155 0.01845 -0.63 0.5314

PCT5PLUSUNITS 0.26393 0.01844 14.31 <.0001 

HISPAN -0.92027 0.01703 -54.05 <.0001 

NHBLACK -0.71908 0.01232 -58.37 <.0001 

NHOTHER -0.61469 0.03014 -20.39 <.0001 

POV10_19 -0.02347 0.00718 -3.27 0.0011

POV20_29 -0.03605 0.00984 -3.66 0.0002

POV30_39 -0.05207 0.01272 -4.09 <.0001 

POV40PLUS -0.03562 0.01515 -2.35 0.0187

Page B-25 
SGLPRNTFAM_TR -0.27932 0.03077 -9.08 <.0001 

RCNTIMMIG_TR -0.00535 0.0014 -3.83 0.0001

OVERCROWD2000_TR -0.01706 0.04048 -0.42 0.6733

LINGISOL_TR 0.1206 0.04535 2.66 0.0078

LACKAFFDRENTALS_2000 3.60592 0.42479 8.49 <.0001 

CHGLOWINCCON_2000 0.03215 0.01645 1.95 0.0507

chngemplbase 0.04516 0.02921 1.55 0.1221

DENIAL 0.99676 0.2974 3.35 0.0008

EXCSINFRA_2000 -0.18106 0.11751 -1.54 0.1234

LINGISOL_2000 0.23691 0.26618 0.89 0.3735

LWINCHHDS_2000 -0.47981 0.15164 -3.16 0.0016

MEDINCCBS2CITY_2000 -0.57655 0.10209 -5.65 <.0001 

MINCON_2000 0.097 0.02082 4.66 <.0001 

OVERCROWD_2000 0.97017 0.17949 5.41 <.0001 

PCTPOPHIGHPOVNGHS_2000 -0.52367 0.21423 -2.44 0.0145

PCTPOPMODPOVNGHS_2000 -0.41167 0.09011 -4.57 <.0001 

PCTVACMODPOVCITY_2000 1.05775 0.89695 1.18 0.2383

POORCHILD_2000 1.75478 1.04911 1.67 0.0944

POOROVER74_2000 1.03706 0.23313 4.45 <.0001 

POORPERS_2000 -0.71551 0.92003 -0.78 0.4368

POVCON_2000 -0.08581 0.03719 -2.31 0.0211

PR70RENTPOV_2000 3.98406 0.44457 8.96 <.0001 

RCNTIMMIG_2000 -0.42276 0.21372 -1.98 0.0479

SCHPOPPOOR_2000 -0.91598 0.94506 -0.97 0.3324

SGLPRNTFAM_2000 -0.19016 0.28668 -0.66 0.5071

UNDEREDWORKAGE_2000 -1.61828 0.10227 -15.82 <.0001 

UNEDUCADULTS_2000 1.05456 0.19703 5.35 <.0001 

UNEMPCEN_2000 -0.89899 0.52569 -1.71 0.0873

Abilene_TX -0.30477 0.10261 -2.97 0.003

Akron_OH 0.05554 0.07178 0.77 0.4391

Albany_GA -0.0643 0.09395 -0.68 0.4938

Albany_Schenectady_Troy_NY -0.20011 0.09666 -2.07 0.0384

Albuquerque_NM 0.52279 0.06762 7.73 <.0001 

Allentown_Bethlehem_Easton_PA_NJ 0.03917 0.08886 0.44 0.6593

Amarillo_TX -0.04372 0.07695 -0.57 0.5699

Anchorage_AK 0.69935 0.07848 8.91 <.0001 

Ann_Arbor_MI 0.26431 0.09835 2.69 0.0072

Appleton_WI 0.23745 0.10111 2.35 0.0189

Asheville_NC -0.08851 0.10509 -0.84 0.3997

Atlanta_SandySprings_Marietta_GA 0.3062 0.07333 4.18 <.0001 

Austin_Round_Rock_TX 0.25751 0.06942 3.71 0.0002

Bakersfield_CA 0.28697 0.08076 3.55 0.0004

Baltimore_Towson_MD 0.23928 0.06857 3.49 0.0005

Baton_Rouge_LA 0.2176 0.07261 3 0.0027

Beaumont_Port_Arthur_TX -0.22713 0.11959 -1.9 0.0575

Page B-26 
Bellingham_WA 0.40534 0.12107 3.35 0.0008

Bend_OR 0.35234 0.13063 2.7 0.007

Billings_MT 0.04631 0.09517 0.49 0.6265

Bloomington_IN -0.46117 0.11835 -3.9 <.0001 

Bloomington_Normal_IL 0.0495 0.09619 0.51 0.6068

Boise_City_Nampa_ID 0.11606 0.07946 1.46 0.1441

Boston_Cambridge_Quincy_MA_NH 0.68356 0.07154 9.56 <.0001 

Boulder_CO 0.68413 0.0933 7.33 <.0001 

Buffalo_Niagara_Falls_NY -0.54614 0.07501 -7.28 <.0001 

Canton_Massillon_OH 0.00683 0.10198 0.07 0.9466

Cape_Coral_Fort_Myers_FL 0.19777 0.09531 2.08 0.038

Cedar_Rapids_IA 0.11871 0.09005 1.32 0.1874

Champaign_Urbana_IL -0.31142 0.11197 -2.78 0.0054

Charleston_North_Charleston_SC 0.00627 0.09606 0.07 0.948

Charlotte_Gastonia_Concord_NC_SC 0.19505 0.06555 2.98 0.0029

Chattanooga_TN_GA -0.10972 0.08426 -1.3 0.1929

Chicago_Naperville_Joliet_IL_IN 0.6131 0.06015 10.19 <.0001 

Chico_CA 0.2417 0.11044 2.19 0.0287

Cincinnati_Middletown_OH_KY_IN -0.08625 0.07443 -1.16 0.2465

Clarksville_TN_KY -0.09387 0.10787 -0.87 0.3842

Cleveland_Elyria_Mentor_OH 0.18059 0.06136 2.94 0.0033

College_Station_Bryan_TX -0.10283 0.09718 -1.06 0.29

Colorado_Springs_CO 0.44595 0.0722 6.18 <.0001 

Columbia_MO -0.30199 0.12027 -2.51 0.0121

Columbia_SC 0.04587 0.07733 0.59 0.5531

Columbus_OH 0.06535 0.06502 1.01 0.3149

Corpus_Christi_TX -0.12055 0.08034 -1.5 0.1335

Dallas_Fort_Worth_Arlington_TX 0.10698 0.06123 1.75 0.0806

Davenport_Moline_Rock_Island_IA 0.00994 0.0873 0.11 0.9094

Dayton_OH -0.0444 0.07122 -0.62 0.533

Decatur_IL -0.02509 0.10243 -0.24 0.8065

Deltona_Daytona_Beach_Ormond_FL -0.08684 0.12865 -0.68 0.4997

Denver_Aurora_CO 0.7208 0.06575 10.96 <.0001 

Des_Moines_IA 0.14221 0.07788 1.83 0.0679

Detroit_Warren_Livonia_MI 0.56204 0.06209 9.05 <.0001 

Duluth_MN_WI -0.19807 0.08927 -2.22 0.0265

Durham_NC 0.04939 0.07589 0.65 0.5152

Erie_PA -0.45312 0.09016 -5.03 <.0001 

Eugene_Springfield_OR 0.22649 0.08911 2.54 0.011

Evansville_IN_KY -0.09678 0.08067 -1.2 0.2303

Fargo_ND_MN 0.03814 0.10127 0.38 0.7065

Fayetteville_NC -0.03441 0.08239 -0.42 0.6762

Fayetteville_Springdale_AR_MO -0.11734 0.13145 -0.89 0.3721

Flint_MI -0.18226 0.09183 -1.98 0.0472

Fort_Collins_Loveland_CO 0.35723 0.09064 3.94 <.0001 

Fort_Smith_AR_OK -0.35488 0.11031 -3.22 0.0013

Fort_Wayne_IN -0.02872 0.07269 -0.4 0.6928

Page B-27 
Fresno_CA 0.26975 0.0767 3.52 0.0004

Gainesville_FL -0.07339 0.11235 -0.65 0.5136

Grand_Rapids_Wyoming_MI 0.1963 0.07336 2.68 0.0075

Greeley_CO 0.34292 0.0962 3.56 0.0004

Green_Bay_WI 0.34747 0.09238 3.76 0.0002

Greensboro_High_Point_NC 0.00421 0.07067 0.06 0.9525

Greenville_NC -0.09324 0.11938 -0.78 0.4348

Gulfport_Biloxi_MS -0.27602 0.10039 -2.75 0.006

Honolulu_HI 1.44719 0.08262 17.52 <.0001 

Houston_Baytown_Sugar_Land_TX 0.08725 0.06189 1.41 0.1586

Huntsville_AL -0.06043 0.07621 -0.79 0.4279

Indianapolis_IN 0.09643 0.06516 1.48 0.1389

Jackson_MS -0.12784 0.07433 -1.72 0.0855

Jacksonville_FL 0.07758 0.06953 1.12 0.2646

Kalamazoo_Portage_MI -0.04987 0.10961 -0.45 0.6491

Kansas_City_MO_KS 0.10913 0.06134 1.78 0.0752

Killeen_Temple_Fort_Hood_TX 0.18234 0.0994 1.83 0.0666

Knoxville_TN -0.24437 0.07805 -3.13 0.0017

Lafayette_LA 0.02529 0.10951 0.23 0.8174

Lake_Charles_LA 0.06006 0.0883 0.68 0.4964

Lakeland_FL -0.14718 0.10156 -1.45 0.1473

Lansing_East_Lansing_MI 0.11677 0.08112 1.44 0.15

Las_Vegas_Paradise_NV 0.43187 0.07142 6.05 <.0001 

Lawrence_KS -0.04195 0.10577 -0.4 0.6916

Lawton_OK -0.11731 0.08713 -1.35 0.1782

Lexington_Fayette_KY -0.02488 0.07597 -0.33 0.7433

Lincoln_NE 0.10606 0.08116 1.31 0.1913

Little_Rock_North_Little_Rock_AR -0.16585 0.08341 -1.99 0.0468

Longview_TX -0.19355 0.1128 -1.72 0.0862

LosAngeles_LongBeach_SantaAna_CA 0.9646 0.06762 14.27 <.0001 

Lubbock_TX -0.38731 0.08284 -4.68 <.0001 

Lynchburg_VA -0.12824 0.11561 -1.11 0.2673

Macon_GA -0.08535 0.08768 -0.97 0.3304

Madison_WI 0.24536 0.08952 2.74 0.0061

Manchester_Nashua_NH 0.24318 0.08496 2.86 0.0042

Medford_OR 0.2525 0.10837 2.33 0.0198

Memphis_TN_MS_AR -0.03273 0.06069 -0.54 0.5897

Merced_CA 0.59994 0.1106 5.42 <.0001 

Miami_Fort_Lauderdale_Miami_FL 0.31523 0.06693 4.71 <.0001 

Midland_TX -0.16258 0.09676 -1.68 0.0929

Milwaukee_Waukesha_West_Allis_WI 0.17229 0.0661 2.61 0.0092

Minneapolis_St_Paul_MN_WI 0.36836 0.07676 4.8 <.0001 

Mobile_AL -0.17661 0.07466 -2.37 0.018

Modesto_CA 0.48982 0.08281 5.92 <.0001 

Montgomery_AL -0.13255 0.07297 -1.82 0.0693

Muncie_IN -0.41326 0.09843 -4.2 <.0001 

Napa_CA 1.15046 0.11065 10.4 <.0001 

Page B-28 
Nashville_Murfreesboro_TN 0.15974 0.06806 2.35 0.0189

NewYork_N_NJ_LongIsland_NY_NJ_PA 0.82946 0.06492 12.78 <.0001 

Odessa_TX -0.36904 0.10077 -3.66 0.0003

Ogden_Clearfield_UT 0.46515 0.10267 4.53 <.0001 

Oklahoma_City_OK -0.1149 0.05958 -1.93 0.0538

Omaha_Council_Bluffs_NE_IA 0.0937 0.07012 1.34 0.1815

Orlando_FL 0.05323 0.08878 0.6 0.5488

Oxnard_Thousand_Oaks_Ventura_CA 1.10356 0.0723 15.26 <.0001 

Palm_Bay_Melbourne_Titusville_FL -0.00867 0.09612 -0.09 0.9282

Peoria_IL -0.13402 0.09559 -1.4 0.1609

Philadelphia_Camden_PA_NJ_DE_MD 0.02546 0.0587 0.43 0.6645

Phoenix_Mesa_Scottsdale_AZ 0.37127 0.06439 5.77 <.0001 

Pittsburgh_PA -0.67064 0.08602 -7.8 <.0001 

Portland_Vancouver_OR_WA 0.63741 0.06709 9.5 <.0001 

Providence_Fall_River_RI_MA 0.21488 0.07027 3.06 0.0022

Provo_Orem_UT 0.29563 0.09358 3.16 0.0016

Pueblo_CO 0.37537 0.08093 4.64 <.0001 

Racine_WI 0.29251 0.09763 3 0.0027

Raleigh_Cary_NC 0.29144 0.07387 3.95 <.0001 

Reading_PA -0.40604 0.09936 -4.09 <.0001 

Redding_CA 0.31348 0.10307 3.04 0.0024

Reno_Sparks_NV 0.56874 0.08476 6.71 <.0001 

Richmond_VA 0.15943 0.08159 1.95 0.0507

Riverside_San_Bernardino_Ont_CA 0.68707 0.06879 9.99 <.0001 

Roanoke_VA -0.01715 0.0949 -0.18 0.8566

Rochester_MN 0.13019 0.0932 1.4 0.1625

Rochester_NY -0.33743 0.07386 -4.57 <.0001 

Rockford_IL 0.05556 0.0758 0.73 0.4636

Sacramento_Arden_Roseville_CA 0.66794 0.07085 9.43 <.0001 

Salem_OR 0.42147 0.08564 4.92 <.0001 

Salinas_CA 1.11718 0.09549 11.7 <.0001 

Salt_Lake_City_UT 0.55884 0.07269 7.69 <.0001 

San_Angelo_TX -0.21813 0.11518 -1.89 0.0583

San_Antonio_TX 0.03624 0.06605 0.55 0.5833

San_Diego_Carlsbad_San_Marcos_CA 0.97065 0.06767 14.34 <.0001 

San_Francisco_Oakland_Fremont_CA 1.33513 0.06656 20.06 <.0001 

San_Jose_Sunnyvale_St_Clara_CA 1.54372 0.07215 21.4 <.0001 

Santa_Barbara_Maria_Goleta_CA 1.19215 0.08946 13.33 <.0001 

Santa_Fe_NM 0.75551 0.09826 7.69 <.0001 

Santa_Rosa_Petaluma_CA 1.06617 0.08803 12.11 <.0001 

Savannah_GA 0.07082 0.08416 0.84 0.4001

Scranton_Wilkes_Barre_PA -0.2391 0.09563 -2.5 0.0124

Seattle_Tacoma_Bellevue_WA 0.78121 0.0682 11.45 <.0001 

Shreveport_Bossier_City_LA -0.2115 0.07289 -2.9 0.0037

Sioux_City_IA_NE_SD -0.18902 0.09907 -1.91 0.0564

Sioux_Falls_SD 0.11364 0.0934 1.22 0.2238

South_Bend_Mishawaka_IN_MI -0.18462 0.07426 -2.49 0.0129

Page B-29 
Spokane_WA 0.22543 0.07511 3 0.0027

Springfield_IL -0.00835 0.07811 -0.11 0.9149

Springfield_MA 0.25335 0.08736 2.9 0.0037

Springfield_MO -0.21719 0.07843 -2.77 0.0056

St_Cloud_MN 0.00619 0.12643 0.05 0.9609

St_Joseph_MO_KS -0.14809 0.11089 -1.34 0.1817

St_Louis_MO_IL 0.02149 0.07938 0.27 0.7866

Stockton_CA 0.65459 0.07877 8.31 <.0001 

Syracuse_NY -0.53325 0.08275 -6.44 <.0001 

Tallahassee_FL -0.00834 0.08939 -0.09 0.9257

Tampa_St_Petersburg_Clwater_FL 0.01739 0.06615 0.26 0.7927

Toledo_OH 0.00582 0.0667 0.09 0.9305

Topeka_KS -0.19351 0.08402 -2.3 0.0213

Trenton_Ewing_NJ 0.2694 0.09981 2.7 0.007

Tucson_AZ 0.3522 0.0693 5.08 <.0001 

Tulsa_OK -0.10254 0.06416 -1.6 0.11

Tuscaloosa_AL -0.11362 0.09421 -1.21 0.2278

Tyler_TX -0.13077 0.12686 -1.03 0.3026

Vallejo_Fairfield_CA 0.96565 0.07669 12.59 <.0001 

Virginia_Beach_Norfolk_VA_NC 0.31641 0.06683 4.73 <.0001 

Visalia_Porterville_CA 0.30273 0.11435 2.65 0.0081

Waco_TX -0.34872 0.10122 -3.45 0.0006

Washington_Arl_Alex_DC_VA_MD_WV 0.95056 0.08615 11.03 <.0001 

Waterloo_Cedar_Falls_IA -0.19408 0.09743 -1.99 0.0464

Wichita_Falls_TX -0.4187 0.09302 -4.5 <.0001 

Wichita_KS -0.10938 0.06676 -1.64 0.1014

Wilmington_NC -0.04251 0.11594 -0.37 0.7139

Winston_Salem_NC 0.06889 0.08158 0.84 0.3984

Worcester_MA 0.16624 0.08752 1.9 0.0575

Yakima_WA 0.23051 0.12054 1.91 0.0559

Youngstown_Warren_Boardman_OH_PA -0.48621 0.11728 -4.15 <.0001 

Yuma_AZ -0.01662 0.10992 -0.15 0.8798

Page B-30 
Table B.12. Regressions on Tracts with Less Dispersion in Median Prices 

Using Factor Scores 

THE HEDONIC MODEL USING 20 TRACT VARIABLES, 3 2000 

FACTOR SCORES AND (203) metro dUMMIES 

 DEPENDENT VARIABLE Natural log of median housing price 

where SPECIAL LE .56: ln (H076001) 

Dependent Variable: LNVALUE 

Number of Observations Read 20485

Number of Observations Used 16096

Number of Observations with Missing Values 4389

 Analysis of Variance 

 

Sum of Mean 

Source DF Squares Square F 

Value Pr > F 

Model 213 4775.534 22.42035 232.98 <.0001 

Error 15882 1528.39 0.09623 

Corrected Total 16095 6303.924 

Root MSE 0.31022 R-Square 0.7575 

Dependent Mean 11.7506 Adj R-Sq 0.7543 

Coeff Var 2.64 

 Parameter Estimates 

 

Parameter Standard 

Variable DF Estimate Error t Value Pr > |t| 

Intercept 1 11.67077 0.05571 209.49 <.0001 

ROOMS 1 0.07169 0.00439 16.33 <.0001 

ORATE 1 -0.11065 0.02605 -4.25 <.0001 

BUILTB50 1 -0.06047 0.01801 -3.36 0.0008

BUILT50S 1 -0.16268 0.01978 -8.22 <.0001 

BUILT60S 1 0.0556 0.02495 2.23 0.0258

BUILT80S 1 0.07399 0.02534 2.92 0.0035

BUILTA89 1 0.38087 0.01895 20.1 <.0001 

PCTSFDETACHED 1 -0.03267 0.01868 -1.75 0.0804

PCT5PLUSUNITS 1 0.28452 0.01872 15.2 <.0001 

HISPAN 1 -0.89505 0.01714 -52.23 <.0001 

NHBLACK 1 -0.68054 0.01236 -55.04 <.0001 

NHOTHER 1 -0.58534 0.02984 -19.61 <.0001 

POV10_19 1 -0.02889 0.0073 -3.96 <.0001 

POV20_29 1 -0.04846 0.00999 -4.85 <.0001 

Page B-31 
POV30_39 1 -0.06432 0.01294 -4.97 <.0001 

POV40PLUS 1 -0.05452 0.01537 -3.55 0.0004

SGLPRNTFAM_TR 1 -0.31284 0.03124 -10.01 <.0001 

RCNTIMMIG_TR 1 -0.00518 0.00143 -3.62 0.0003

OVERCROWD2000_TR 1 0.12258 0.03984 3.08 0.0021

LINGISOL_TR 1 0.0492 0.04508 1.09 0.2752

Factor 1 1 -0.05551 0.00505 -11 <.0001 

Factor 2 1 0.095 0.00614 15.46 <.0001 

Factor 3 1 -0.19129 0.00589 -32.5 <.0001 

Abilene_TX 1 -0.4806 0.09694 -4.96 <.0001 

Akron_OH 1 -0.23545 0.06134 -3.84 0.0001

Albany_GA 1 0.07687 0.08704 0.88 0.3772

Albany_Schenectady_Troy_NY 1 -0.35326 0.08446 -4.18 <.0001 

Albuquerque_NM 1 0.20864 0.05471 3.81 0.0001

Allentown_Bethlehem_Easton_PA_NJ 1 -0.37837 0.06882 -5.5 <.0001 

Amarillo_TX 1 -0.32767 0.0701 -4.67 <.0001 

Anchorage_AK 1 0.07285 0.06345 1.15 0.2509

Ann_Arbor_MI 1 -0.33176 0.08274 -4.01 <.0001 

Appleton_WI 1 -0.49689 0.0834 -5.96 <.0001 

Asheville_NC 1 -0.2553 0.08974 -2.84 0.0044

Atlanta_SandySprings_Marietta_GA 1 -0.02446 0.05767 -0.42 0.6714

Austin_Round_Rock_TX 1 -0.23524 0.05398 -4.36 <.0001 

Bakersfield_CA 1 0.07171 0.06059 1.18 0.2367

Baltimore_Towson_MD 1 -0.18325 0.05287 -3.47 0.0005

Baton_Rouge_LA 1 0.10633 0.06522 1.63 0.103

Beaumont_Port_Arthur_TX 1 -0.2749 0.11806 -2.33 0.0199

Bellingham_WA 1 -0.1745 0.10855 -1.61 0.108

Bend_OR 1 -0.15741 0.11899 -1.32 0.1859

Billings_MT 1 -0.3319 0.08261 -4.02 <.0001 

Bloomington_IN 1 -0.80301 0.09892 -8.12 <.0001 

Bloomington_Normal_IL 1 -0.49504 0.08337 -5.94 <.0001 

Boise_City_Nampa_ID 1 -0.34716 0.06709 -5.17 <.0001 

Boston_Cambridge_Quincy_MA_NH 1 0.1686 0.05252 3.21 0.0013

Boulder_CO 1 0.06597 0.07846 0.84 0.4005

Buffalo_Niagara_Falls_NY 1 -0.31702 0.06226 -5.09 <.0001 

Canton_Massillon_OH 1 -0.18331 0.08898 -2.06 0.0394

Cape_Coral_Fort_Myers_FL 1 -0.34935 0.08341 -4.19 <.0001 

Cedar_Rapids_IA 1 -0.51652 0.07615 -6.78 <.0001 

Champaign_Urbana_IL 1 -0.91988 0.10141 -9.07 <.0001 

Charleston_North_Charleston_SC 1 -0.12862 0.08971 -1.43 0.1517

Charlotte_Gastonia_Concord_NC_SC 1 -0.16388 0.05441 -3.01 0.0026

Chattanooga_TN_GA 1 -0.14825 0.07809 -1.9 0.0577

Chicago_Naperville_Joliet_IL_IN 1 0.22152 0.04716 4.7 <.0001 

Chico_CA 1 -0.21522 0.09515 -2.26 0.0237

Cincinnati_Middletown_OH_KY_IN 1 -0.19744 0.05727 -3.45 0.0006

Page B-32 
Clarksville_TN_KY 1 -0.3777 0.10025 -3.77 0.0002

Cleveland_Elyria_Mentor_OH 1 0.00653 0.04944 0.13 0.895

College_Station_Bryan_TX 1 -0.45403 0.08656 -5.25 <.0001 

Colorado_Springs_CO 1 -0.0938 0.05858 -1.6 0.1094

Columbia_MO 1 -0.7767 0.1136 -6.84 <.0001 

Columbia_SC 1 -0.15849 0.07348 -2.16 0.031

Columbus_OH 1 -0.29074 0.05074 -5.73 <.0001 

Corpus_Christi_TX 1 -0.03904 0.06863 -0.57 0.5694

Dallas_Fort_Worth_Arlington_TX 1 -0.23624 0.04836 -4.88 <.0001 

Davenport_Moline_Rock_Island_IA 1 -0.3788 0.07741 -4.89 <.0001 

Dayton_OH 1 -0.19617 0.06538 -3 0.0027

Decatur_IL 1 -0.31459 0.09965 -3.16 0.0016

Deltona_Daytona_Beach_Ormond_FL 1 -0.36133 0.11902 -3.04 0.0024

Denver_Aurora_CO 1 0.20352 0.04961 4.1 <.0001 

Des_Moines_IA 1 -0.43792 0.0626 -7 <.0001 

Detroit_Warren_Livonia_MI 1 0.20343 0.04747 4.29 <.0001 

Duluth_MN_WI 1 -0.73214 0.07605 -9.63 <.0001 

Durham_NC 1 -0.10269 0.06757 -1.52 0.1286

Erie_PA 1 -0.52718 0.08074 -6.53 <.0001 

Eugene_Springfield_OR 1 -0.12875 0.07177 -1.79 0.0729

Evansville_IN_KY 1 -0.38298 0.07115 -5.38 <.0001 

Fargo_ND_MN 1 -0.60924 0.08639 -7.05 <.0001 

Fayetteville_NC 1 -0.14937 0.07048 -2.12 0.0341

Fayetteville_Springdale_AR_MO 1 -0.67793 0.1191 -5.69 <.0001 

Flint_MI 1 -0.07169 0.08524 -0.84 0.4003

Fort_Collins_Loveland_CO 1 -0.29184 0.0735 -3.97 <.0001 

Fort_Smith_AR_OK 1 -0.41433 0.10362 -4 <.0001 

Fort_Wayne_IN 1 -0.3621 0.06358 -5.7 <.0001 

Fresno_CA 1 0.09294 0.05454 1.7 0.0884

Gainesville_FL 1 -0.38594 0.10411 -3.71 0.0002

Grand_Rapids_Wyoming_MI 1 -0.24632 0.06163 -4 <.0001 

Greeley_CO 1 -0.01088 0.08992 -0.12 0.9037

Green_Bay_WI 1 -0.32992 0.07593 -4.35 <.0001 

Greensboro_High_Point_NC 1 -0.21785 0.05695 -3.83 0.0001

Greenville_NC 1 -0.33542 0.10818 -3.1 0.0019

Gulfport_Biloxi_MS 1 -0.19985 0.09386 -2.13 0.0332

Honolulu_HI 1 0.9186 0.06373 14.41 <.0001 

Houston_Baytown_Sugar_Land_TX 1 -0.16051 0.04957 -3.24 0.0012

Huntsville_AL 1 -0.30784 0.06663 -4.62 <.0001 

Indianapolis_IN 1 -0.24477 0.05083 -4.82 <.0001 

Jackson_MS 1 -0.01233 0.06926 -0.18 0.8587

Jacksonville_FL 1 -0.38143 0.05539 -6.89 <.0001 

Kalamazoo_Portage_MI 1 -0.48395 0.10018 -4.83 <.0001 

Kansas_City_MO_KS 1 -0.33362 0.04931 -6.77 <.0001 

Killeen_Temple_Fort_Hood_TX 1 -0.3401 0.08783 -3.87 0.0001

Knoxville_TN 1 -0.45555 0.06642 -6.86 <.0001 

Lafayette_LA 1 -0.17428 0.10378 -1.68 0.0931

Page B-33 
Lake_Charles_LA 1 0.02718 0.07939 0.34 0.7321

Lakeland_FL 1 -0.31726 0.08943 -3.55 0.0004

Lansing_East_Lansing_MI 1 -0.33757 0.06973 -4.84 <.0001 

Las_Vegas_Paradise_NV 1 -0.04667 0.05312 -0.88 0.3797

Lawrence_KS 1 -0.58682 0.09552 -6.14 <.0001 

Lawton_OK 1 -0.2432 0.08217 -2.96 0.0031

Lexington_Fayette_KY 1 -0.50416 0.06533 -7.72 <.0001 

Lincoln_NE 1 -0.54824 0.06611 -8.29 <.0001 

Little_Rock_North_Little_Rock_AR 1 -0.30143 0.07736 -3.9 <.0001 

Longview_TX 1 -0.38551 0.10776 -3.58 0.0003

LosAngeles_LongBeach_SantaAna_CA 1 0.75216 0.04942 15.22 <.0001 

Lubbock_TX 1 -0.47904 0.07148 -6.7 <.0001 

Lynchburg_VA 1 -0.31301 0.11239 -2.79 0.0054

Macon_GA 1 -0.01637 0.08335 -0.2 0.8443

Madison_WI 1 -0.45961 0.06727 -6.83 <.0001 

Manchester_Nashua_NH 1 -0.34424 0.0662 -5.2 <.0001 

Medford_OR 1 -0.0672 0.09441 -0.71 0.4766

Memphis_TN_MS_AR 1 0.01004 0.05175 0.19 0.8462

Merced_CA 1 0.2562 0.09024 2.84 0.0045

Miami_Fort_Lauderdale_Miami_FL 1 -0.02714 0.05235 -0.52 0.6042

Midland_TX 1 -0.4143 0.09181 -4.51 <.0001 

Milwaukee_Waukesha_West_Allis_WI 1 -0.23531 0.05015 -4.69 <.0001 

Minneapolis_St_Paul_MN_WI 1 -0.38009 0.05199 -7.31 <.0001 

Mobile_AL 1 -0.04918 0.06811 -0.72 0.4702

Modesto_CA 1 0.23241 0.06327 3.67 0.0002

Montgomery_AL 1 -0.08913 0.06945 -1.28 0.1994

Muncie_IN 1 -0.43734 0.09135 -4.79 <.0001 

Napa_CA 1 0.56122 0.10129 5.54 <.0001 

Nashville_Murfreesboro_TN 1 -0.20111 0.05306 -3.79 0.0002

NewYork_N_NJ_LongIsland_NY_NJ_PA 1 0.5207 0.04854 10.73 <.0001 

Odessa_TX 1 -0.24229 0.0944 -2.57 0.0103

Ogden_Clearfield_UT 1 -0.14999 0.08585 -1.75 0.0806

Oklahoma_City_OK 1 -0.40658 0.05201 -7.82 <.0001 

Omaha_Council_Bluffs_NE_IA 1 -0.42278 0.05562 -7.6 <.0001 

Orlando_FL 1 -0.26063 0.06586 -3.96 <.0001 

Oxnard_Thousand_Oaks_Ventura_CA 1 0.58537 0.05707 10.26 <.0001 

Palm_Bay_Melbourne_Titusville_FL 1 -0.4792 0.08438 -5.68 <.0001 

Peoria_IL 1 -0.37497 0.08716 -4.3 <.0001 

Philadelphia_Camden_PA_NJ_DE_MD 1 -0.32447 0.04933 -6.58 <.0001 

Phoenix_Mesa_Scottsdale_AZ 1 -0.11279 0.04856 -2.32 0.0202

Pittsburgh_PA 1 -0.63708 0.06186 -10.3 <.0001 

Portland_Vancouver_OR_WA 1 0.04363 0.05076 0.86 0.39

Providence_Fall_River_RI_MA 1 -0.10821 0.05307 -2.04 0.0415

Provo_Orem_UT 1 -0.30503 0.07427 -4.11 <.0001 

Pueblo_CO 1 0.22105 0.07143 3.09 0.002

Racine_WI 1 -0.15952 0.08935 -1.79 0.0742

Raleigh_Cary_NC 1 -0.2096 0.06049 -3.47 0.0005

Page B-34 
Reading_PA 1 -0.66826 0.0804 -8.31 <.0001 

Redding_CA 1 -0.02051 0.08972 -0.23 0.8191

Reno_Sparks_NV 1 0.03366 0.06624 0.51 0.6113

Richmond_VA 1 -0.21911 0.06499 -3.37 0.0008

Riverside_San_Bernardino_Ont_CA 1 0.33481 0.05015 6.68 <.0001 

Roanoke_VA 1 -0.24505 0.0853 -2.87 0.0041

Rochester_MN 1 -0.54847 0.08006 -6.85 <.0001 

Rochester_NY 1 -0.3149 0.05905 -5.33 <.0001 

Rockford_IL 1 -0.31468 0.0661 -4.76 <.0001 

Sacramento_Arden_Roseville_CA 1 0.18116 0.05566 3.25 0.0011

Salem_OR 1 0.03054 0.07247 0.42 0.6734

Salinas_CA 1 0.80124 0.07889 10.16 <.0001 

Salt_Lake_City_UT 1 -0.03296 0.05773 -0.57 0.568

San_Angelo_TX 1 -0.3801 0.11261 -3.38 0.0007

San_Antonio_TX 1 -0.14877 0.05101 -2.92 0.0035

San_Diego_Carlsbad_San_Marcos_CA 1 0.48229 0.04986 9.67 <.0001 

San_Francisco_Oakland_Fremont_CA 1 0.76054 0.05118 14.86 <.0001 

San_Jose_Sunnyvale_St_Clara_CA 1 0.92824 0.05476 16.95 <.0001 

Santa_Barbara_Maria_Goleta_CA 1 0.79819 0.07584 10.52 <.0001 

Santa_Fe_NM 1 0.52152 0.0851 6.13 <.0001 

Santa_Rosa_Petaluma_CA 1 0.45127 0.07438 6.07 <.0001 

Savannah_GA 1 -0.12671 0.07709 -1.64 0.1003

Scranton_Wilkes_Barre_PA 1 -0.45236 0.07883 -5.74 <.0001 

Seattle_Tacoma_Bellevue_WA 1 0.16125 0.05218 3.09 0.002

Shreveport_Bossier_City_LA 1 -0.14616 0.06807 -2.15 0.0318

Sioux_City_IA_NE_SD 1 -0.53363 0.0921 -5.79 <.0001 

Sioux_Falls_SD 1 -0.5515 0.07932 -6.95 <.0001 

South_Bend_Mishawaka_IN_MI 1 -0.27955 0.06902 -4.05 <.0001 

Spokane_WA 1 -0.30505 0.0634 -4.81 <.0001 

Springfield_IL 1 -0.44426 0.07006 -6.34 <.0001 

Springfield_MA 1 0.06333 0.07186 0.88 0.3782

Springfield_MO 1 -0.53358 0.06822 -7.82 <.0001 

St_Cloud_MN 1 -0.73529 0.11337 -6.49 <.0001 

St_Joseph_MO_KS 1 -0.36591 0.10775 -3.4 0.0007

St_Louis_MO_IL 1 -0.34546 0.05822 -5.93 <.0001 

Stockton_CA 1 0.32382 0.06076 5.33 <.0001 

Syracuse_NY 1 -0.51576 0.06465 -7.98 <.0001 

Tallahassee_FL 1 -0.4147 0.07368 -5.63 <.0001 

Tampa_St_Petersburg_Clwater_FL 1 -0.34974 0.05269 -6.64 <.0001 

Toledo_OH 1 -0.16477 0.0577 -2.86 0.0043

Topeka_KS 1 -0.54898 0.07529 -7.29 <.0001 

Trenton_Ewing_NJ 1 -0.25884 0.08595 -3.01 0.0026

Tucson_AZ 1 -0.08815 0.05507 -1.6 0.1095

Tulsa_OK 1 -0.36288 0.05287 -6.86 <.0001 

Tuscaloosa_AL 1 -0.16586 0.08714 -1.9 0.057

Tyler_TX 1 -0.2555 0.12539 -2.04 0.0416

Vallejo_Fairfield_CA 1 0.37098 0.06282 5.91 <.0001 

Page B-35 
Page B-36 

Virginia_Beach_Norfolk_VA_NC 1 -0.1418 0.04906 -2.89 0.0039

Visalia_Porterville_CA 1 0.15583 0.10062 1.55 0.1215

Waco_TX 1 -0.5323 0.09392 -5.67 <.0001 

Washington_Arl_Alex_DC_VA_MD_WV 1 0.17325 0.05393 3.21 0.0013

Waterloo_Cedar_Falls_IA 1 -0.59191 0.08949 -6.61 <.0001 

Wichita_Falls_TX 1 -0.56496 0.08751 -6.46 <.0001 

Wichita_KS 1 -0.51891 0.0573 -9.06 <.0001 

Wilmington_NC 1 -0.39179 0.10804 -3.63 0.0003

Winston_Salem_NC 1 -0.17344 0.07072 -2.45 0.0142

Worcester_MA 1 -0.30127 0.07368 -4.09 <.0001 

Yakima_WA 1 0.20396 0.11286 1.81 0.0707

Youngstown_Warren_Boardman_OH_PA 1 -0.41408 0.09971 -4.15 <.0001 

Yuma_AZ 1 -0.13035 0.09781 -1.33 0.1826
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 1⁄2 standard deviation of 

mean 168 165 

Number of cities within 1 standard deviation from 

mean 260 276 

Number of cities within 1 1⁄2 standard deviations from 

mean 327 339 

Number of cities within 2 standard deviations from 

mean 358 361 

Number of cities within 2 1⁄2 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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