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Is big always better?

Taking on conventional wisdom, Coopers & Lybrand took apart some data from GNMA servicers and found big is not always better. Furthermore, geographic diversity is not always the plus it's cracked up to be.

In business, is big really better? And what about the popular diversification advice to avoid putting all your eggs in one basket? After all, even Mark Twain cautioned us "to put all our eggs in one basket, but watch them."

Even though conventional wisdom today is clearly stacked on the side of growth through mergers and acquisitions, and diversification is a commonly sought strategic goal for many, we think it's critical to question whether these strategies are everything they are cracked up to be.

On one side of the growth and diversification issue are the financial and economic professionals who argue quantitatively that the soundest and least risky strategy for business management is through growth and diversification. On the other side of the same issue are the business policy analysts who argue qualitatively that undisciplined growth and diversification strategies may result in a reduced focus on business advantages and objectives, negating any of the presumed quantitative gains that the "number crunchers" claim to result from growth and diversification.

How relevant is this issue? It is hard to think of any corporate body holding a strategic planning session today that would not discuss the issue of growth and/or diversification. Much federal decision-making is molded by the attitudes policymakers have on growth and diversification, including such things as banking policy, antitrust law, and even how the savings and loan debacle will be resolved.

Consolidation and geographic diversification have received increased attention in many sectors of our economy for reasons that stretch from the simple desire to improve efficiency and lower risk to the need to diversify as the only means for survival. Consider the recent spate of large bank mergers. In the end, it is the American consumer who either gains or loses from the success or failure of these strategic decisions or policies.

To test the practical results of these strategic policy tenets in the context of the mortgage banking industry, we assessed the relative performance of servicers of mortgages backing Ginnie Mae mortgage-backed securities. Some of these servicers were large and diversified and some were small and market-concentrated. Using standardized information from 362 mortgage banking firms that submit the status of their loan portfolios to the Government National Mortgage Association (GNMA), the Risk Analysis Group of Coopers & Lybrand (C&L) set out to evaluate the effects of grows and diversification in the industry. This article discusses the results of that analysis.

Purpose and framework of the analysis

Since January 1990, the C&L Risk Analysis Group has supported GNMA in its efforts to monitor and address risk proactively. The basis of much of the group's risk analysis comes from the aggregation, segregation and synthesis of more than 7 million home mortgage computer records submitted quarterly to GNMA by servicers of GNMA single-family loan portfolios.

Conventional wisdom asserts that:

* Increased geographical diversification lowers the risk of business failure by reducing regional dependency; and

* Growth leads to increased efficiencies through economies of scale, which, in turn, improve competitive advantage.

The Risk Analysis Group decided to test this conventional wisdom by analyzing the loan portfolios of both large and small mortgage institutions with different levels of regional diversification. For the particular analysis discussed here, the Risk Analysis Group looked at the 362 active, non-defaulted institutions, each having GNMA loan portfolios containing more than 1,000 federally guaranteed home loans. The total loan sample studied represents more than 7 million loans and more than $396 billion in outstanding principal. The records analyzed represented the condition of the portfolios as of December 1991.

The measurement used to evaluate portfolio performance was what the Risk Analysis Group calls the DQ3RATIO, the percentage of portfolio loans that are three months or more delinquent. The DQ3RATIO is central to strategic management decisions through its effect on expected future cash flows and thus portfolio valuation. A portfolio with a high DQ3RATIO has a high percentage of non-performing loans and thus does not carry the same value as a portfolio with a low DQ3RATIO. The likelihood of a portfolio defaulting to GNMA increases as the DQ3RATIO increases.

The measurement used to evaluate portfolio risk for the different size and diversification categories investigated was the standard deviation of the DQ3RATIO. A large DQ3RATIO standard deviation for a category indicates high volatility and greater risk than a category with a lower DQ3RATIO standard deviation.

The Risk Analysis Group developed its own geographical diversification index to measure the degree of diversification of a loan portfolio. The procedure for calculating the diversification index was based upon the percentage of an institution's loans that were in each of five regions--East, Midwest, South, Southwest and West. For each portfolio, the percentage of loans in each of the five regions was calculated.

Regional loan percentages of 20 percent or more were given a score of 20, while percentages under 20 percent received a score equal to the percentage itself. The minimum diversification score using this method is 20, given to those institutions with 100 percent of their loans in one region. The theoretical optimum diversification score of 100 requires the portfolio to have exactly 20 percent of the loans in each TABULAR DATA OMITTED of the five geographical regions. Figure A shows how a typical diversification index was calculated.

Size and performance

Figure 1 shows the results of the analysis segregated by size of institutional portfolio. The portfolios were divided into three categories:

* portfolios with more than 10,000 loans;

* portfolios with 4,000 to 10,000 loans; and

* portfolios with 1,000 to 4,000 loans.

Probably the most interesting thing to note from this segregation is the abnormally high DQ3RATIO of the group of institutions with the largest portfolios when compared to the two smaller groups. Figure 1 shows that smaller institutions substantially out-perform larger institutions in maintaining lower levels of delinquent loans.

Is it surprising that the DQ3RATIO of the largest group is significantly higher than the two smaller groups? Not necessarily. The economic breakeven point of a loan portfolio depends upon unit-processing cost factors associated with both performing as well as non-performing loans. If increased size provides economies of scale that reduce overall unit-processing costs, then these economies can be used to cover the increased level of costs associated with a higher rate of non-performing loans. In effect, breakeven analysis allows new efficiencies (e.g., reduced unit processing costs) to cover new inefficiencies (e.g., higher delinquency rates and subsequent foreclosure costs).

From an overall government policy viewpoint, there may be more to this table than is readily apparent. The federal government covers a substantial portion of the loss of a foreclosure through its FHA and VA home loan insurance and guaranty programs. Therefore, a business entity does not have to factor these covered losses into their own breakeven analysis. Because of this, a significant portion of the increased cost of reduced performance is passed onto the government and the insurance funds that support these programs.
FIGURE A
 Loan Diversification
 Region Percent Score
 East 43.4% 20.0
 Midwest 14.3% 14.3
 South 21.4% 20.0
Southwest 6.4% 6.4
 West 14.6% 14.6
 Total 100.0% 75.3


Another observation about Figure 1 is that the DQ3RATIO standard deviation increases as loan size groups get smaller. This is an expected result. Due to size alone, greater volatility and risk is expected with the smaller portfolios, as measured by the statistically calculated standard deviation for the DQ3RATIO. Because of this volatility, the likelihood of an institution defaulting its portfolio to GNMA is greater for the smaller portfolio groups.

In fact, history shows that a higher percentage of small institution portfolios have defaulted to GNMA than large portfolios. However, the number of institutions defaulting is not necessarily the best measure for government risk. Even though only 4 of the 31 previously defaulted portfolios that GNMA is currently managing had more than 10,000 loans in them at the time of default, these four portfolios represent 85 percent of the total amount of defaulted loans. Large institutions may not default as often as smaller institutions; however, when the large institutions do default the impact is much more significant.

Diversification: A perspective based on size

Figure 2 shows the results of the analysis as the data was segregated into the same three size categories of Figure 1 and then further segmented into three diversification categories:

* diversification scores greater than 60;

* diversification scores between 40-60; and

* diversification scores between 20-40.

According to the group's methodology, the higher the diversification score, the greater the geographic diversification of the portfolio.

An analysis of Figure 2 leads towards an argument against diversification. At first glance, diversification does not seem to be much of a factor for the institutions with 10,000 loan portfolios or larger. Neither the DQ3RATIO nor the DQ3RATIO standard deviations vary much between geographic diversification categories. However, DQ3RATIO's vary substantially between the five regions analyzed, as will be discussed later. For this reason greater volatility would be expected in the DQ3RATIO standard deviation for the less diverse portfolios. Since the volatility isn't apparent in the findings, it implies that some other factor must be at work. It is likely that the TABULAR DATA OMITTED more targeted focus of servicers of regional portfolios versus regionally diversified portfolios may in fact be working here to keep risk down.

In fact, of the four large institutional portfolios that have previously defaulted to GNMA, two have a diversification score higher than 60, one falls in the 40-60 range, and one has a score of less than 40. The largest of all previous defaulted portfolios had a geographic diversification index of 83.5. This leads to the conclusion that, in truth, risk may be more a factor of management, itself, than of management's decisions regarding geographic diversification.

For the smaller two groups of portfolios, the case against diversification seems even stronger. Figure 2 shows that the DQ3RATIO's for these groups are substantially lower for the less geographically diversified portfolios, yet the standard deviations are not substantially different. Focus seems to be very important for smaller entities. Note that the average number of loans in the portfolio for the smaller two groups are basically the same for each of the diversification categories.

It is probably no coincidence that the largest portfolios tend to be more diversified than the smaller portfolios. To attain growth, diversification may be necessary. It is somewhat surprising, however, that the benefits of geographic diversification do not show up in the analysis. At best, the analysis is neutral on the merits of geographic diversification for large portfolios. Furthermore, it is the small, diversified portfolio that seems to present the greatest risk of higher delinquencies, according to our findings (See Figure 2). These portfolios may, on average, lack the sophistication and resources needed to adequately handle the negative features of a diversification policy.

Diversification: A perspective based on region

The Risk Analysis Group analyzed diversification from a regional perspective separately from the prior perspective based on portfolio size. Figure 3 shows the DQ3RATIO for each region plus the DQ3RATIO for all the loans that fall outside of the particular region. For example, the DQ3RATIO for all the loans located in the Eastern region is 3.04 percent, while the DQ3RATIO for all the loans outside of the Eastern region is 2.48 percent. In total, the DQ3RATIO for the entire population of loans is 2.56 percent. Clearly, the economic problems that the East and the South are currently facing show up in this table, where those two regions post the two highest DQ3RATIOs.

Further, each of the 362 institutional portfolios were categorized as having one primary regional focus, which was the region where the portfolio had the highest percentage of loans. Figure 4 shows a composite summary of the number of institutions, the average number of loans in the portfolio and average diversification for each regional category.

Figure 4 also shows a matrix reflecting how each regional group performed within its own region, as well as how it performed in the other four regions. For example, predominantly Eastern servicers showed a DQ3RATIO of 3.01 for the East region and a DQ3RATIO of 2.77 in the Southwest. When compared against norms for the regions from Figure 3 this means that the Eastern group had a DQ3RATIO of 0.03 percent (3.01 - 3.04) below the norm for the East and a DQ3RATIO of 0.36 percent (2.77 - 2.41) above the norm for the Southwest. In other words, the Eastern group performed better than the norm in their own region and worse than the norm in the Southwest region.

In every region, except the Southwest, the regional groups performed better than the overall norm for their region. Even for the Southwest group, where performance was worse than the norms in every region, the performance was closer to the norm in its own region than in any other region. As can be seen from the bottom line of Figure 4, when compared against norms for the population analyzed, every region performed better within its own region than for the combined areas outside its home region. In total, the DQ3RATIO for home regions was 2.44 percent, while the DQ3RATIO for loans outside the home region was 2.70 percent.

Food for thought

A business that initiates activity outside of its home region, such as through loan originations in new markets or purchases of servicing or other acquisitions in mortgage banking, gains a hedge against the economic swings at home. However, at the same time it risks sacrificing potential competitive advantages in its own home market. Competitive pressures may tempt business leaders to diversify for growth purposes without performing time-consuming, yet essential, activities, such as the analysis of these potential markets, investors and competition.

As the proximity to and the familiarity with a given region or specific business activity decreases, it becomes more difficult to sustain existing competitive advantages. The cost of maintaining the same level of quality control, reporting and communication are likely to increase with geographic diversification.

This analysis of the FHA/VA residential mortgage market illustrates the difficulties involved with maintaining portfolio/business performance away from a more traditional home region focus. Proper analysis and appraisal are essential to minimizing loan quality risk, yet they become increasingly difficult outside one's home region. Comprehensive appraisals require ongoing data on local economic trends, supply and demand conditions, competitive dynamics, zoning laws, builders' reputations and so forth. Proper field evaluations, property inspections, due diligence, and the establishment of strong relationships with local housing and labor authorities are more difficult in unfamiliar territory.

The benefits of geographic diversification and growth through consolidation are highly touted today. These include lower unit costs through the elimination of wasteful and duplicate overhead expenses, increased opportunities through synergistic market expansion and a stronger competitive position in the marketplace. However, to any discussion of the benefits of growth and diversification must be added a recognition of related costs--all costs.

For example, in the case of federally subsidized or insured business activities, it is important to factor in the cost of the insurance. This is a hidden cost to consolidation, as evidenced by the costs of the savings and loan bailout. As legislators consider reforms to help the banking industry navigate through the current era of consolidation, they must balance the needs and objectives of both the institutions and their insurers in making sound public policy.

From the analysis of the mortgage banking industry discussed here, the benefits of growth and geographic diversification are not readily apparent and probably are overstated. As banks, thrifts and mortgage bankers consolidate their risk into larger and larger entities, it is more important than ever that the federal insurance programs monitor their risk liability. Even though the management intent behind a strategy of consolidation may be good, the evidence presented here shows that institutions should be very careful with their assumptions about the benefits to be derived from such strategies.

In summary, the analysis of a large group of servicers of GNMA portfolios shows that the generalizations imbedded in the financial theories that "bigger is better" and "diversification lowers risk" are not sufficient, by themselves, to serve as the basis for sound corporate and federal policies. In fact, growth and diversification may not be as desirable as popular belief suggests. From this analysis, it appears that a shrewd focus on business competencies is more important than the mere concept of growth and diversification for its own sake. Finally, risk may be more a factor of management itself, than of management's decisions relating to growth and diversification.
COPYRIGHT 1992 Mortgage Bankers Association of America
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 1992 Gale, Cengage Learning. All rights reserved.

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Title Annotation:servicers of Government National Mortgage Association
Publication:Mortgage Banking
Article Type:Cover Story
Date:Aug 1, 1992
Words:2804
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