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Data made available annually pursuant to the Home Mortgage Disclosure Act of 1975 (HMDA) provide an opportunity to explore changes in mortgage market activity along a host of dimensions.1 HMDA requiresmost mortgage lending institutions with offices in metropolitan areas to publicly disclose information about their home-lending activity each year. The data include the disposition of each application for mortgage credit; the type, purpose, lien status, and characteristics of the home mortgages.

Reports on the role of information that credit-reporting agencies maintain on consumers' credit-related experiences in credit markets in the U.S. Effects of data limitations on consumers' access to credit; Concern among financial analysts about the accuracy, completeness, timeliness and consistency of consumer credit records; Elements of credit models used to generate credit history scores.

Focuses on the special leading programs of the Community Reinvestment Act (CRA) in the United States. Implications of the CRA programs for banking institutions; Impact of the CRA on low-income populations; Characteristics of the special lending programs; Profitability of the programs in comparison with the overall mortgage lending.

Explores the relationship between consolidation and lending to purchase homes. Trends in banking consolidation; Consolidation and market-level changes in mortgage lending; Consolidation and mortgage lending at the banking organization level.

If the research goal in applying classical discriminant analysis techniques is to test for differences in the general characteristics of groups, then standard tests from multivariate analysis of variance are appropriate. Statistically significant differences among group means and dispersions indicate that the groups arose from separate populations. If, however, the goal is to explore group structure in terms of group overlap and the locations of individual observations relative to each other and to the group means, then it can be shown that the significance tests provide incomplete and often misleading representations of the data. Similarly, in problems involving just more that just two groups, the null hypothesis can be rejected, when in fact only one of the several groups arose from a different population.

Because wealth estimates from survey data have usually fallen substantially short of independent aggregate estimates, survey data have not been seen as adequate for assessing questions dependent on a good representation of the entire distribution of wealth, such as estimates of wealth concentration. The 1983 Survey of Consumer Finances (SCF), which contains a supplementary sample of very high income households drawn from a tax-file sample frame, is the first U.S. survey since the 1963 Survey of Financial Characteristics of Consumers that offers hope of accurately measuring the entire wealth distribution. In this paper, we discuss the design of the survey, the critical issue of proper weighting to merge the supplementary sample with an area probability sample, and the role of imputation. We show that the use of ordinary area probability samples alone leads to probable bias in the measurement of highly concentrated assets such as stocks and bonds. We compare the SCF data with aggregates derived from the flow-of-funds accounts of the Federal Reserve Board. While methodological issues cloud exact comparisons, it appears overall that the SCF estimates are at least as credible as other aggregate measurements. Finally, we use the data to assess the change in concentration of wealth from 1963 to 1983. We estimate that the concentration of wealth in terms of households did not change significantly over this period. [ABSTRACT FROM AUTHOR]