Factors affecting the Distribution of Community Reinvestment Act (CRA) Loans across Household Income Groups

The purpose of the paper is to understand the soci-economic factors affecting the distribution of community reinvestment act loans across four income groups using county level information from 1996-2004 for the delta region. The specific objectives of the paper 1) Estimate an seemingly unrelated regression (SUR) to examine the factors affecting the distribution of loan across income groups 2) Prior to the estimation of SUR, test for autocorrelation, heteroskedasticity and time series properties of the variables. Background To promote depository financial institutions to serve the credit needs of moderate and lower income neighborhoods, the U.S. Congress passed the Community Reinvestment Act (CRA) in 1977. The CRA was introduced to prevent "redlining" or the practice of financial institutions excluding moderate and low-income neighborhoods from receiving adequate or fair financial services. Further, CRA was implemented to ensure that banks provided services to farm and non-farm communities. Economists, have examined of the Community Reinvestment Act with regard to issues related to banking and treasury, policies, politics and economic related analysis. However, the question still remains "Is redlining still present or it is simply a case of supply and demand?" Are banks providing services where they are needed in order to improve profitability or are they avoiding low-income areas because they are perceived to pose too much risk? Are the results of earlier studies an indication that more banks are located where there is more economic growth and that is the reason that more loans are coming from these more economically advanced areas? In this paper, we specifically examine the importance of higher levels of education, population growth, economic growth, income levels and sales growth on the amount and number of CRA loans approved. We use county level data for the delta region spread across three states: Mississippi, Arkansas, and Louisiana for the period, 1996-2004. We estimate seemingly unrelated regression with the amount and/or number of CRA loans as the endogenous variables. Econometric Methods and Data Seemingly unrelated regression also called Zellner estimation, is a generalization of ordinary least squares for multi-equation systems. Like ordinary least squares, the seemingly unrelated regression method assumes that all, regressors are independent variables, but seemingly unrelated regression uses the correlations among the errors in different equations to improve the regression estimates. The seemingly unrelated regression method requires an initial ordinary least squares regression to compute residuals. The ordinary least squares residuals are used to estimate the cross-equation covariance matrix. The seemingly unrelated regression for the four income levels: low income (<$100,000), moderate income ($100,000 - $250,000), medium income (> $250,000) and high income (< million dollars) can be represented by the econometric model as: (1.1) where is the vector of endogenous variable, i.e., the amount and/or number of CRA loans approved for the four income groups, a vector of exogenous variables that could potentially include higher levels of education, population growth, economic growth, income levels and sales growth, and are the number of delta region counties. Equation (1) is examined for the properties of autocorrelation, Heteroskedasticity and time series properties. Data to accomplish the objectives of this study will come from the CRA record for period 1996-2004 and the remaining variables are obtained from Department of Commerce, Bureau of Economic Analysis. Results/Expected Results and Discussion Our earlier analysis seems to indicate a direct correlation between the counties with higher levels of education, population, sales growth and the amount and number of CRA loans. According to the results of this study, the areas of the Mississippi Delta that have enjoyed favorable economic growth in the past 8 years are those that have also received the most CRA loan activity. DeSoto County by far is the Delta county that has seen the most growth and it is consistently the county that has received the most loan funds and the largest number of loans. The same results occur in Mississippi outside the Delta Region. Madison, Rankin, Harrison, and Jackson counties are urban areas that are growing faster than the rest of the state. These counties have consistently recorded more CRA loans than any other areas. According to the data the percentage of CRA loans that went to low income groups consistently was the smallest. Loans to low-income groups actually decreased overall from 1996 to 2004. The percent of CRA loans to moderate-income groups also decreased from 96 to 04. The percent of funds to medium-income groups rose, while the percent of loans in the high-income group stayed relatively the same over time. The total amount of CRA loans increased for all income groups during the study period with the exception of the low-income group which decreases slightly. Empirical application of the seemingly unrelated regression econometric model would allow us to examine the factors affecting the distribution of the CRA loans across the income groups. Further, the results from the paper would provide input to the policy makers, banking community, and investors to make a decision on the future of CRA loan distribution.