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Remarks by Governor Ben S. Bernanke
At the 2003 Community Development Policy Summit, Federal Reserve Bank of Cleveland, Cleveland, Ohio
June 11, 2003

Soft Hearts, Hard Data: The Use of Quantitative Analysis in Community Development

I am pleased to have this opportunity to participate in today's conference on community development policy and to learn about some of the diverse strategies that community development organizations have used in their efforts to increase economic opportunity and revitalize their neighborhoods. Thanks are due to the Cleveland Fed for providing this forum for the exchange of ideas about community development, as well as to all the participants for their willingness to come here to share their experiences and learn from those of others.

The subtitle of today's conference is "The Evolution of Market-Based Solutions." Markets function best in an environment of timely, accurate information, a fact that has been brought home to us in the past year by a series of high-profile scandals in areas such as accounting and stock analysis. Both the adverse effects of these events on investor confidence and the strong legislative and regulatory responses they have engendered indicate the importance that market participants and policymakers attach to the clarity and transparency of evaluative data. Similarly, as community development organizations try to build relationships with financial institutions and the private sector more generally, the availability of useful information about these organizations and the communities they serve takes on increasing importance.

Good data--and good data analysis--are critical in the work of community organizations. First, to the extent that laws and regulations have mandated certain activities or practices by private-sector organizations (for example, nondiscriminatory lending practices), hard data can be used by community organizations to identify deficiencies and the need for change. Second, good and well-analyzed data about under-served communities can help to reveal investment opportunities that could prove profitable for businesses while advancing the objectives of community development. Third, hard data can be used by community organizations to conduct quantitative analyses of their own activities. Good data on the effectiveness of projects and programs are needed for accountability and hence for credibility, which is essential for community organizations in their dealings not only with the private sector but also with the government and nonprofit sectors. In my remarks today I will focus on a few examples of how quantitative analysis, appropriately used, can advance the objectives of community development. I should note that the opinions expressed today are my own and are not to be ascribed to my colleagues on the Board of Governors of the Federal Reserve System.1

Data, Public Policy, and Regulation
One of the primary reasons for community groups to develop skills in data collection and analysis is the increasingly data-based nature of the regulatory framework that governs areas such as lending and investment in low-income neighborhoods. In the 1960s and 1970s, when powerful political and societal forces such as the civil rights movement were driving social change, grassroots advocacy and political organizing efforts were perhaps the most important factors influencing public policy toward minorities and the poor. In that period, qualitative and anecdotal evidence was sufficient to change minds and votes. However, in the decades since that formative period, more formal regulatory structures have evolved. For example, within the banking sector, three major pieces of banking legislation--the Equal Credit Opportunity Act (ECOA), the Home Mortgage Disclosure Act (HMDA), and the Community Reinvestment Act (CRA)--were enacted to address issues of access to credit. The objective of these laws was (and continues to be) to enlist the support of the banking system in fostering local economic development, for example, by requiring banks to provide credit and other financial services to minorities and in lower-income neighborhoods. Although these laws have often been controversial, overall I believe they have made an important contribution to the revitalization of many American communities.

Both policymakers and communities have an interest in ensuring, first, that laws prohibiting discrimination and encouraging lending to underserved areas are enforced and, second, that existing laws and regulations are modified over time so as to better serve their legislative objectives. For both of these goals and for both regulators and community groups, good data are an essential input to good analysis. As an example, HMDA specifies fairly extensive data reporting requirements for lending institutions, and the collection of these data has led to the creation of an invaluable knowledge base for studying the relationships between mortgage credit and the geographic and demographic characteristics of borrowers. Over the course of the more than twenty-five years since this reporting framework was established, these data have been a valuable tool for academics, advocates, lenders, lawmakers, and regulators for gaining a better perspective on mortgage market activities and for identifying illegal or unwarranted disparities in lending patterns. For example, several community groups conduct regular studies of HMDA lending patterns in their cities to evaluate the records of financial institutions in lending to lower-income and minority communities. Bank examiners also make extensive use of HMDA data in assessing banks' effectiveness in meeting the mortgage credit needs in their service areas. Researchers, including several at the Federal Reserve, regularly use HMDA data to explore various features of the mortgage market, such as the link between race and mortgage lending and also the impact of the CRA on the profitability of mortgage lending in low-income communities. As noted, in some cases, analyses of HMDA data have highlighted disparities that warranted further examination of the underlying policies and practices of lenders. But the data have also helped to reveal market opportunities for lending to creditworthy but underserved populations, and in that way they have served as a catalyst for the formation of public-private partnerships designed to increase mortgage lending in lower-income communities.

The utility of the HMDA data has been enhanced by periodic adjustments to reporting requirements to reflect innovation, growth, and regulatory changes in the mortgage market. Reporting requirements and the types of lenders subject to those requirements have been expanded to better reflect the range of market participants and types of mortgage lending. As you may know, for example, the most recent changes to the HMDA reporting requirements have increased the information that must be reported on loan pricing relevant to the analysis of the subprime lending market.

As the existence of HMDA data has improved our understanding of the general mortgage market, conversely the absence of relevant data for other markets has sometimes been a barrier to understanding. For example, although a great deal of anecdotal information indicates the presence of unethical, and even fraudulent, mortgage credit practices in some segments of the market, the lack of relevant data has made it exceedingly difficult to quantify the extent of such predatory lending activities. Lack of data about borrower characteristics and credit terms has similarly complicated the process of designing laws and regulations that strike an appropriate balance between curtailing unscrupulous lending, which is desirable, and impeding the appropriate extension of credit to subprime borrowers, which is not. This problem does not have an obvious solution, as obtaining good data on illegal, or at the least unsavory, practices is difficult, given the incentives of those who engage in such practices to conceal their activities. More sophisticated methods such as sampling must therefore be used by regulators to address these issues, much in the same way that the Internal Revenue Service performs random audits to obtain information about noncompliance with the tax laws.

Data and Economic Opportunity
Another important use of data for community development is to demonstrate to potential private investors that lower-income communities often present substantial investment opportunities. An example of how data development has enhanced the ability of communities to demonstrate the economic potential of their neighborhoods is the Neighborhood Market Drill Down approach to measuring market value designed by the nonprofit organization, Social Compact. Social Compact, a coalition of business leaders interested in promoting investment in undervalued communities, undertook this initiative based on their view that traditional market analyses may serve to reinforce negative stereotypes associated with inner-city communities and hence underestimate the economic potential of these communities. According to this view, the application of inappropriate data and methods of analysis to the inner city unnecessarily discourages private investment, resulting in a reduction of local services and economic opportunity and possibly the perpetuation of the cycle of disinvestment, as some residents and businesses choose to abandon the underserved area. Social Compact contended that significant inner-city business opportunities might be overlooked because the methods of data analysis used in evaluating market potential in suburban communities do not account for the unique characteristics of inner-city neighborhoods, such as higher population density, the presence of cash economies, and micro-market development patterns. To capture these dynamics and translate them into measurements of purchasing power, Social Compact designed new methodologies for gathering and analyzing data on the commercial potential of urban neighborhoods.

Social Compact's "drill down" methodology was applied in sixteen Houston neighborhoods in 2001 at the request of a local developer and some major area corporations. Data on tax assessments, building permits, and auto registrations were used to obtain an accurate count of households, while credit report information and other data on bill payments from surveys were used to estimate consumer purchasing power. In addition, growth and development activity was verified through immigration data, school enrollment statistics, and building construction information. The results of these analyses demonstrated that the residential population of the neighborhoods studied was nearly 27 percent larger than that indicated by the Census 2000. Moreover, housing values had been underestimated by almost 40 percent, and the estimated aggregate household income was $170 million higher than what conventional measures captured. These data confirmed the existence of untapped market opportunities in the sixteen neighborhoods examined. Over the course of the following year, public-private partnerships and financing resulted in the redevelopment of 700,000 square feet of retail space and a 246-unit condominium complex in these areas.

Data and the Performance of Community Organizations
A final example of the application of data analysis in community development is the evaluation of the performance of community development organizations themselves, a function that has become increasingly important as these organizations have matured.

As you know, community development organizations have a rich history as agents for change in low-income, minority, and isolated communities, but the nature of their role has evolved significantly over time. In particular, these groups have gone well beyond simply being advocates for change to becoming providers of solutions themselves, in areas ranging from social services delivery to training to economic development. Community groups have also played an important role in helping residents become greater stakeholders in their own communities, for example, by creating mechanisms to increase the involvement of local residents in the development planning process and by promoting increased rates of home ownership and small business ownership.

Recent statistics reported by the National Community Capital Association (NCCA) give some indication of the scope of development activities undertaken by community development financial institutions (CDFIs), a subset of community development organizations, in the lower-income communities they serve. A survey by NCCA and its partners collected data for fiscal year 2001 on 512 of the 800 to 1,000 CDFIs operating in the United States. The survey results indicated that these institutions held some $5.7 billion in outstanding loans. During 2001, the surveyed CDFIs financed more than 43,000 housing units, funded nearly 7,500 businesses, capitalized 500 community service organizations, and created or retained approximately 53,000 jobs, according to the NCCA.

These statistics help to illustrate the importance and range of community development activities, as well as the capacity and level of financial sophistication of many of the organizations operating within this sphere. However, data of this sort, while illustrative of the scale of the development effort, do not capture many other important aspects of community development. For example, statistics on loan type and volume, in and of themselves, do not provide sufficient context for assessing the economic value of community development activities; nor can they tell the full story of the impact of affordable housing and small business development programs on lower-income communities.

The absence of fully descriptive data on the performance of community development organizations is especially problematic at a time when these organizations are experiencing significant cutbacks in funds from their traditional capital sources, including government and foundations. Given these cutbacks, successful community development requires, first, that available public and nonprofit funds be put to the best uses and, second, that the sources of funding be expanded to include a greater share of private-sector sources of capital. Achieving both goals requires a more precise definition in quantitative terms of what it means to be successful. The challenge confronting community development organizations is to effectively use data and analytical methods to communicate their accomplishments. Meeting this challenge is an important step toward attracting a broader array of funding and investment.

As I have noted, community groups often measure their effectiveness in terms of the number of housing units constructed, small businesses established, or jobs created as a result of the organization's efforts. These data are often collected to demonstrate programmatic success, and in the past have typically been a condition of funding. Such statistics communicate improvements in the economic prospects of residents and in the success of a particular project or line of business, which are important measures of one level of success. However, they do not entirely convey the extent to which these activities contribute to the economic viability of the neighborhood, for example. Further, program-related data do not represent the financial performance of the loan portfolios supporting lending and development activities or the effectiveness of the organization operating the programs. All of these types of information are critical to an overall evaluation of success.

As socially motivated investors seek information that conveys both financial and social returns--the double bottom line--community organizations that can more precisely present that information will have more success in acquiring funding. Assessing the financial returns on community investments is perhaps the easiest part of the task in principle, though often demanding in practice. Most difficult is defining measures to capture intangible social benefits, such as those that accrue to a neighborhood as residents become engaged in community planning activities, improve their financial literacy, and increase their access to employment opportunities through job training. And measures of the organizational capacity of community organizations themselves should be of great interest to potential investors in their activities. What scale of activities can a particular organization support? What are its areas of expertise-technical, financial, legal, or otherwise? What resources has it mobilized? What is its track record in other communities?

The process of collecting and analyzing data is not cheap and is not the comparative advantage of many organizations. To the extent that doing so is possible, however, the consistent collection and analysis of standardized data can offer a variety of opportunities for diversifying funding sources. Information on financial performance helps private investors assess the risk profile of an organization or a project and determine an acceptable level of return. Coupled with social impact data, financial information can provide assurance to CDFIs and other socially motivated investors that their portfolios of community development investments offer both the financial and nonfinancial returns that they require. Government and foundation support is easier to justify and, one hopes, more likely to be obtained when the public benefits that community development offers can be measured, even approximately.

Today a variety of initiatives are under way to explore strategies for collecting data about community development activities that will be relevant to investors and donors. One such program, Wall Street Without Walls, seeks to assist community development organizations in gaining access to private capital markets for funding. Another program, the CDFI Data Project, involves collaboration of a group of community development financial institutions and foundations to develop effective procedures for conducting peer analyses of CDFIs. By developing comprehensive performance measures, the CDFI Data Project hopes to increase private-sector investment in the most effective organizations. Although these initiatives differ somewhat in philosophy, both have the objective of promoting the consistent collection of standardized data as a means for increasing accountability, transparency, and performance among community development organizations.

The Challenge of Data Collection and Analysis
As I have emphasized, the collection and especially the analysis of data present difficult challenges to community organizations. Quantifying social objectives such as "empowerment" is just one problem. Another difficulty is isolating the effects of a community development organization's activities from the many other factors that may cause a community to improve or decay.

Compounding the conceptual challenge of measurement are the not insignificant matters of cost and technological capacity that data collection demands. Data collection, analysis, and warehousing are extremely resource-intensive undertakings that require a high degree of technical expertise at every phase. Obtaining appropriately skilled staff and maintaining the technological infrastructure to support the data can be costly. As a result, substantial data collection efforts may simply be impractical for smaller organizations. Strategic collaborations among interested parties and the outsourcing of data collection and analysis may be the best option in some circumstances.

The difficulties notwithstanding, as you continue your exploration of market-based strategies for community development, I encourage you to consider methodologies for capturing the benefits and costs of the resulting programs. By doing so, you position yourselves to demonstrate the impact of your activities to those in the public and private sectors who are deciding where to lend their support. And not incidentally, in the process you may learn how to do things better. In both ways you increase the likelihood that you will make a real difference to the communities you serve.

Thank you.


1. I thank Carolyn Welch for assistance in preparing this talk. Return to text

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2003 Speeches