Key Indicators of High Credit Union Performance

Transcription

1 Key Indicators of High Credit Union Performance Credit Unions have been exposed to a challenging economic environment over the past several years. The turmoil has been marked by numerous failures and consolidations within the Credit Union industry, coupled with increased regulatory scrutiny. However, some Credit Unions have fared better than others. Variation in the performance of Credit Unions, each exposed to a relatively similar economic environment, prompts an obvious question. What are the macroeconomic and Credit Union-specific factors which are associated with high performance? In this research note, we answer this question by evaluating the attributes of Credit Unions which are associated with high performance, as measured by a Credit Union s return on assets (R.O.A.). In particular, we analyze a sample of 1,069 unique Credit Unions over the years using publicly available call report data. One innovation of this study is that the publicly available call report data are supplemented with data on relevant macroeconomic trends, including rates of house price appreciation, unemployment data, and interest rates. The conclusions drawn in our analysis are supported by evidence generated from a multivariate regression analysis of Credit Unions realized R.O.A. The results of our analysis can be summarized into two broad themes. First, macroeconomic factors have some power in explaining observed variation in Credit Union s performance. Rates of house price appreciation, patterns in unemployment, and interest rates demonstrate meaningful statistical relationships with realized Credit Union performance, even when controlling for a host of Credit Union-specific factors. The second main theme is the relative importance of a Credit Union s operations, as opposed to the performance of a Credit Union s assets. Put differently, our analysis indicates that how a Credit Union operates in terms of originating loans and controlling operating costs more meaningfully explains variation in return on assets than a Credit Union s asset quality. Methodology Our analysis relies on the estimation of a linear regression model, where a Credit Union s R.O.A. serves as the dependent variable in the model. The independent variables of interest include variables from the balance sheet and income statement as well as measures of asset quality and asset type. The virtues of a linear regression model include the ease of estimation and interpretation of results. The limitations of a linear model include the inherent assumption of linearity in key relationships as well as the assumption of a normally distributed error term. We are careful to reduce

2 the influence of outliers in the estimation by eliminating observations which are obvious outliers relative to average values for each variable. An analysis of Credit Union performance necessarily requires an assumption regarding the measurement of Credit Union performance. We choose to focus on R.O.A. because it measures the effectiveness of a given Credit Union s assets in generating income. While R.O.A. is commonly used as a performance metric in the industry, we hasten to add that it suffers from at least three limitations. First, R.O.A. ignores risk. Credit Union s can generate a high return on assets simply by assuming a large degree of risk in its operations or lending standards. Second, R.O.A. ignores timing. Lumpy investments can distort R.O.A. in a given year if a worthwhile investment does not immediately generate yields. Last, R.O.A. cannot properly account for intangible assets. Despite these limitations, R.O.A. still represents an effective means by which to evaluate Credit Union s performance over a long time series. Constructing the Sample The first step in the analysis is to download the full sample of Call Report data made available by the National Credit Union Association (NCUA). The sample begins in the year 2000 and ends in the fourth quarter of We begin the sample in the year 2000 because many of the variables required to construct key ratios are not available prior to the year We also limit the sample to annual observations rather than the full set of quarterly observations made available in the call reports. Quarterly data introduces unnecessary serial correlation in our estimates and artificially inflates the sample size, which can have the impact of overstating the statistical significance of some of the variables. Finally, we require a Credit Union to have at least $50 million in assets to remain in our sample. Implementing a data screen on size ensures that a large clustering of very small Credit Unions does not unduly influence our results. We supplement the call report data with data on local housing markets using house price indexes made available by the Federal Housing Finance Agency (FHSA). The FHFA constructs MSA-specific quarterly house price indexes using a repeat sales methodology. We match MSA-level house price indexes to a given Credit Union based on the MSA in which the Credit Union is based. This matching relies on the assumption that the local housing market represents the housing market conditions to which the Credit Union is most exposed. This assumption will be limiting for Credit Unions with a broad geographic reach. State-level unemployment data made available by the Bureau of Labor Statistics (BLS) are matched to each Credit Union based on the Credit Union state of

3 domicile. Finally, we rely on the Federal Reserve s published rates on the ten-year treasury as a proxy for market interest rates. Some Credit Unions in the full call report sample do not report the data required to construct each of the variables employed in our analysis. As such, our sample is limited to the Credit Unions which report the data elements required to construct our key variables at some point during our sample period. The sample size is also influenced by start-ups, failures, and consolidations, which result in some Credit Unions entering and leaving the sample. The final sample consists of 1,069 unique Credit Unions, not all of which are represented in each year of the sample. On average, each year of the analysis contains about 600 unique Credit Unions, on average. Summary Statistics In this section we report the key summary statistics for the Credit Unions included in our sample. Table 1 tabulates statistics on the time series trends in Credit Unions R.O.A. over the course of our sample. The table indicates strong performance during the years 2000 through 2007, as would be expected. Despite the onset of the financial crisis, average R.O.A s were still at respectable levels. The effects of the financial crisis, particularly the crash in the housing market sent R.O.A. s spiraling downward during the years 2009 and Though performance recovered in 2011 and 2012, average R.O.A. s remained well below the robust years of the early 2000 s. Table 1. Summary Statistics: Return on Assets Year Sample Size Return on Assets Mean Median Std. Dev % 0.95% 0.42% % 0.84% 0.48% % 0.93% 0.47% % 0.91% 0.48% % 0.80% 0.50% % 0.75% 0.53% % 0.70% 0.54% % 0.70% 0.52% % 0.54% 0.57% % 0.32% 0.91% % 0.27% 0.59% % 0.53% 0.55% % 0.58% 0.56% Table 2 reports the summary statistics of the macroeconomic variables and some of the key independent variables of interest. The time series trend in rates of house price appreciation and the unemployment rate are as expected. Declines in house prices began in 2008, coincident with the

4 onset of the Financial Crisis. State-level average unemployment rates also climbed during the crisis, though they did not peak until Interest rates have been on a steady decline over the past decade, reaching lifetime lows in Average Credit Union asset size increased over the sample period. We adjust for the influence of size on return on assets in our regression framework by controlling explicitly for the log of asset size. Loan activity, as measured by total loans over total assets has been on a steady decline since Delinquency rates and charge offs peaked during the Financial Crisis but have declined over the past two years. Yields on investments closely track the time series patterns in interest rates, as has the cost of funds. Finally, operating expenses appear to be on the decline following a period of small increases during the years of the Financial Crisis. Table 2. Summary Statistics on Key Variables Annual House Total Loans Net Price Unemp. Ten-Year Total Assets over Total Delinquency Charge Yield on Yield on Cost of Operating Year Appreciation* Rate** Treasury ($ Million) Assets Rate Offs Loans Investments Funds Expenses % 4.02% 6.03% $ % 0.42% 8.32% 6.63% 3.27% 3.63% % 4.14% 5.02% $ % 0.42% 8.41% 5.36% 3.42% 3.65% % 5.71% 4.61% $ % 0.47% 7.78% 3.45% 2.32% 3.57% % 5.89% 4.01% $ % 0.51% 7.16% 2.61% 1.68% 3.54% % 5.73% 4.27% $ % 0.50% 6.46% 2.41% 1.28% 3.58% % 5.32% 4.29% $ % 0.48% 6.25% 3.00% 1.42% 3.65% % 4.78% 4.80% $ % 0.45% 6.47% 3.82% 1.92% 3.76% % 4.56% 4.63% $ % 0.43% 6.84% 4.65% 2.42% 3.87% % 4.85% 3.66% $ % 0.56% 6.84% 3.88% 2.32% 3.86% % 7.82% 3.26% $ % 0.80% 6.51% 2.59% 1.71% 4.29% % 9.43% 3.22% $ % 0.82% 6.32% 1.91% 1.16% 3.76% % 8.90% 2.78% $ % 0.72% 6.13% 1.58% 0.84% 3.66% % 8.02% 1.80% $ % 0.63% 5.91% 1.26% 0.66% 3.60% * House Price Appreciation is measured at the MSA-level based on the MSA of domicile for a given Credit Union. ** Unemployment rates are measured at the state-level based on the state of domicile for a given Credit Union. Highlighted Results In this section we report the key results of the regression analysis. We note that before estimating the model we standardize the dependent and independent variables into units of standard deviations in order to facilitate the economic interpretation of the model. Standardizing the variables allows for a comparison of the relative magnitudes of each variable s association with R.O.A., i.e. we can better understand which variables matter the most. We are also careful to control for other unobserved macroeconomic factors (aside from house prices, unemployment, and interest rates) by including year indicator variables which adjust for changes in average R.O.A. across years. Through the inclusion of year indicator variables, the estimates on our variables of interest can be interpreted as the marginal impact of the variable on R.O.A., after controlling for unobserved factors which clearly influence time-series variation in R.O.A. We control for variables which are not

5 included in tabulated results presented in Table 3. Unreported control variables include loan-type controls such as the fraction of credit cards, leases, business loans, real estate, and first mortgage loans. We also control for the net worth ratio and yearly fixed-effects which adjust for unobserved variation in R.O.A. across years. Table 3 reports results of the analysis. The reported coefficients can be interpreted as follows. A one-standard deviation increase in a given independent variable is associated with a given percent of one-standard deviation change in R.O.A. For example, the 21.3% coefficient reported for the Total Assets variable should be interpreted in the following manner. A one-standard deviation increase in asset size is associated with a 21.3%-of-one-standard-deviation increase in R.O.A. The p- value of each estimate is less than 1%, indicating that the estimates are all statistically significant. Table 3. The Assocation of Key Independent Variables with Return on Assets Annual House Price Appreciation Unemp. Rate Ten-Year Treasury Total Assets ($ Million) Total Loans over Total Assets Delinquency Rate Net Charge Offs Yield on Loans Yield on Investments Cost of Funds Operating Expenses 11.1% -0.02% -18.2% 21.3% 40.0% -12.6% -24.2% 58.2% 28.1% -57.5% -45.6% Note: Percentages indicate the percent of one-standard deviation. The results indicate that macroeconomic variables matter, at least statistically speaking. Our estimates indicate that, after controlling for all other factors, a one-standard deviation increase in the one-year rate of house price appreciation is associated with 11.1% of one-standard deviation higher R.O.A. Interest rates have an association of a similar magnitude, but in the opposite direction. Unemployment rates are correlated with R.O.A. in the direction one would predict; higher unemployment is associated with lower R.O.A., but the magnitude of the relationship is substantially weaker than that of house prices and interest rates. Estimates on Credit Union-specific attributes yield interesting results. As expected, the yield on loans has the strongest economic association with R.O.A. The yield on investments also demonstrates a strong association with R.O.A., though it is not as economically important as the yield on loans. However, an interesting result is the magnitude of the relationship between total loans and R.O.A. Even after controlling for the yield on loans and investments, the amount of loans originated by Credit Unions has a significant economic relationship with R.O.A. On the cost side, the cost of funds, operating expenses, delinquency rates, and charge offs are negatively correlated with R.O.A., as expected. The cost of funds demonstrates the largest negative

6 association with R.O.A. But of particular interest is the result which indicates that operating expenses have a substantial impact on R.O.A., even after controlling for loan quality and the cost of funds. This result is potentially surprising because conventional wisdom holds that loan quality (charge-off rates, delinquencies) is the most important factor in explaining credit union performance. 1 Interpretation of Results In this section we briefly discuss the implications of the results. Two key themes emerge from the analysis. First, macroeconomic variables demonstrate a statistical relationship with R.O.A., even when controlling for observable Credit Union-specific factors. This result is important because regulators have put an increased emphasis on Credit Unions better understanding their exposure to macroeconomic factors. Our results indicate this emphasis is well motivated, particularly with regard to understanding house price exposure. House prices matter for R.O.A. because they influence returns in two important ways. First, they influence the likelihood of loan default and second, they influence loss given default. Higher collateral values result in lower rates of default because delinquent borrowers have the option to sell their home rather than default. Second, higher home values are associated with lower losses, even conditional on default occurring. A second key theme emerging from the analysis is that operational decisions are economically important to the performance of a Credit Union. Credit Unions with a higher fraction of loans to total assets are associated with higher R.O.A. s. This suggests that loan activity matters for overall Credit Union performance, even after controlling for loan performance. Second, a Credit Union s cost of funds and operating costs have a stronger economic association with poor R.O.A. than measures of loan delinquencies and asset write-offs. A result indicating that operating costs are negatively associated with R.O.A. may seem obvious, as is a result which suggests that making loans results in making more money. However, 1 One potential caveat to this result is the fact that our sample, by construction, includes Credit Union s which have survived. Credit Union s with poor asset quality may have failed, and thus dropped out of our sample, because of the poor loan quality in their portfolio. A survival bias could have the effect of dampening the impact of loan credit quality in our estimates. In coming months, we will publish the results of an analysis which more explicitly analyzes the role of poor-credit quality loans in Credit Union failures.

7 we note with considerable emphasis that the contribution of this study is to document the relative importance of each of these factors. The data indicate that of all the measurable factors which are known to influence R.O.A., over the past decade, heightened lending activity and careful control over funding and operating costs are important covariates with realized R.O.A. Taken together, these results suggest that operational decisions may be more important for Credit Union performance than asset quality, within reason.