Pricing of Commercial Mortgage-Backed Securities: Evidence from Modern Conduit Issues John P. Harding University of Connecticut School of Business 2100 Hillside Road, Unit 1041 Storrs, CT 06269-2041 Email:
[email protected] Phone: (860) 486-3229 Fax: (860) 486-0349
C. F. Sirmans University of Connecticut School of Business 2100 Hillside Road, Unit 1041 Storrs, CT 06269-2041 Email:
[email protected] Phone: (860) 486-3227 Fax: (860) 486-0349
Sansanee Thebpanya Bangkok University School of Business Administration 40/4 Rama 4 Road Klongtoey Bangkok 10110 Thailand Email:
[email protected] Phone: (662) 350-3500 x1640 Fax: (662) 350-3500 x1633
March 22, 2004
Abstract The market for commercial mortgage-backed securities (CMBS) has matured. After undergoing explosive growth from 1995 through 1998, the market has survived the liquidity crisis of 1998, the recession of 2001 and the fallout of the 9/11 terrorism. In recent years, the CMBS structure has been used by issuers worldwide to facilitate the funding of commercial real estate. Global volume averaged close to $100 billion per year in 2001 and 2002. The CMBS structure has continued to evolve with increased diversification and better prepayment protection for investors. Information about the deals and especially the underlying collateral has increased dramatically since 1997. This paper analyzes the pricing of new CMBS securities from 1997 through 2002. We find that, despite the maturing of the market, CMBS investors still discriminate across deals and price their investments based on an analysis of deal specific factors such as average loan quality, pool diversification, and property type. By utilizing the increased data available for modern CMBS issues and explicitly modeling the joint endogeneity of subordination and pricing, we are able to confirm theoretical predictions that AAA investors should adjust the price they pay based on the level of prepayment protection incorporated into the underlying loans and the diversification of the pool of mortgages, even after controlling for the rating level. In addition to prepayment protection and diversification, we find that loan quality and current economic and market conditions influence pricing.
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The life cycle of most new products includes four distinct stages: Introduction, Growth, Maturity and Decline. For modern commercial mortgage-backed securities, the introduction phase was 1992-1995 when the Resolution Trust Corporation (RTC)1, working with the investment banking community, developed a new security structure to help liquidate its large portfolio of commercial mortgages.
During the subsequent growth period (1996-1998),
conduits2 adopted and modified the basic structure to facilitate the funding of new commercial mortgage originations. After a brief dip in 1995 when RTC issues stopped, CMBS volume grew very rapidly in 1997 and 1998. The average annual growth rate from 1995 to 1998 was 62%. During this period, new investors were attracted to CMBS by the very strong relative returns for this asset class.
The expanding investor base and growing infrastructure for providing
information contributed to a trend toward narrower spreads over Treasuries for all classes of CMBS, enhancing the returns. The growth phase for CMBS ended in the liquidity crisis of fall, 1998. Many conduits experienced significant pipeline losses and CMBS investors, particularly leveraged investors in junior tranches, experienced significant losses as spreads suddenly widened. Conduits and investors alike were accustomed to hedging the interest rate risk associated with CMBS using short positions in Treasury securities. In the fall of 1998, Treasury yields fell while yields on CMBS increased. As a result, most hedges were ineffective and several conduits and many investors withdrew from the market.
1
The Resolution Trust Corporation was created by Congress in 1989 to assist in solving the thrift industry crisis by managing the disposition of assets acquired as part of the closing of failed institutions. The RTC was disbanded at the end of 1995 and the remaining activities were transferred to the FDIC.
2
A conduit lender is an entity that creates pools of mortgage loans by originating or purchasing loans and then sells securities backed by the pools. They are referred to as “conduits” because they do not hold the loans for very long and are the means by which loans are transmitted from one holder to another.
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The CMBS market began its recovery in 1999 and the period since then appears to be the beginning of the maturity phase for CMBS. Although the dollar volume of new domestic issues remains below the 1998 peak, globally, new CMBS issues in 2001 and 2002 are well above (22%) the 1998 global issuance. In many ways, the overall market is stronger now than it was in 1998. The ratings agencies are more sophisticated in their analysis of commercial real estate and CMBS structures. Information and technology providers have developed on-line systems to facilitate direct analysis by investors and reduce asymmetric information. The swaps market has proven to be more effective for hedging than the Treasury market and consequently, the swap curve has become the benchmark for pricing. The CMBS structure has also evolved. New conduit deals are better diversified geographically and by property type. Prepayment protection has shifted from dependence on absolute lockouts followed by prepayment penalties to a combination of shorter lockout periods followed by a period where borrowers can defease their obligation using a portfolio of Treasury securities. These defeasance provisions provide better prepayment protection for investors and also provide borrowers additional flexibility to refinance, albeit at a price. In 1997, Harding and Sirmans (1997) analyzed the initial pricing of CMBS tranches issued between 1994 and 1996. This study found the quality of the underlying mortgages and property type mix significantly affected the pricing of new, senior, CMBS tranches. Somewhat surprisingly, the study did not show a significant effect for either pool diversification or the degree of prepayment protection.
Jacob and Gichon (1999) also studied the pricing of new
CMBS issues during the same time period. Jacob and Gichon also found that the quality of the underlying loans significantly affected senior tranche pricing. That study did find that AAA bonds with longer lockout protection were priced more favorably than those with shorter lockout
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protection. However, like Harding and Sirmans (1997), Jacobs and Gichon (1999) did not find that diversification or the extent of prepayment penalties significantly influenced pricing. The empirical findings of these two studies are puzzling because, in theory, a well diversified pool with strong prepayment protection should provide more stable and predictable cash flows to the senior investors. One would expect that stability to be priced. This paper extends previous research on the initial pricing of CMBS in several ways. First, we use a comprehensive data set covering all conduit issues from 1997 through 2001. The data provide a complete description of the deal and the underlying mortgage pool—including better information on diversification and prepayment protection than was available earlier. Second, consistent with current pricing practices, we model the pricing of CMBS relative to the swap curve not the Treasury curve. We believe that studying the swap spread excludes the noise added by the variation in swaps to Treasury which can be driven by unique factors affecting the Treasury market (e.g., the decline in new issues associated with budget surpluses in the late 1990s and the periodic “flights to quality” associated with geo-political events). Finally, we estimate a two equation system of equations to more accurately capture the fact that pricing and structure are jointly determined as the deal is brought to market. Specifically, we believe the level of subordination required by the rating agencies3 and initial tranche pricing are jointly determined. If subordination is endogenous, it will be correlated with the error term in an equation describing pricing and the estimated coefficient will be inconsistent. Although our focus here is primarily on explaining the cross sectional variation in spreads, we also include several macro economic variables to control for predictable changes in
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spreads. The resulting model of the initial pricing spreads is more consistent with theoretical pricing models than were earlier empirical models because both diversification and the degree of prepayment protection are shown to influence the initial pricing in addition to the traditional underwriting variables and bond descriptors. Even after controlling for credit rating, our results show that senior tranche investors price expected cash flow uncertainty while junior tranche investors focus more on loan quality. Furthermore our models show that over the period from 1997 to 2001 there has not been a significant trend in pricing spreads- although there has been a clear trend toward lower levels of credit enhancement as measured by required levels of subordination. The remainder of the paper is organized as follows. The next section reviews the background and history of CMBS. The following section reviews the theoretical and empirical literature on CMBS pricing. We then discuss our model, the data and the empirical results. Background and History of CMBS In order to describe the evolution of the CMBS market, we gathered data from a number of sources including data collected by Commercial Mortgage Alert, the Federal Reserve System, Bloomberg, and the prospectuses from conduit transactions.
Commercial Mortgage Alert
collects data on all new CMBS issues. We used the Federal Reserve data and Bloomberg for interest rates on the day of issuance and other market conditions.4 Exhibit 1 displays the volume of new CMBS issues since 1985. The figure shows a very low level of activity prior to the RTC’s first issue in the summer of 1991. Exhibit 2 provides the data on new issues and also describes the evolution of the CMBS structure over the period. Many of the early issues (prior to 3
The level of subordination was used by both Harding and Sirmans (1997) and Jacob and Gichon (1999) as a proxy for loan quality on the premise that the rating agencies would require higher levels of subordination for pools with poor loan quality.
4
1992) bore little resemblance to the modern CMBS structure. Most issues comprised one or two tranches sold to the public and generally followed a simple “senior/junior” structure. Many deals in this era were backed by a single loan. Even in 1989, the average number of loans per deal was only 11.2. The period from 1992 through 1995 (the period we refer to as the introduction period) shows a sharp increase in the growth rate. The average annual growth rate of new issues, measured in dollars, was approximately 25% from 1991 through 1995. The growth rate from 1995 through 1998 was almost 2.5 times as large (62%/year). This period marked the rise of the conduit lenders. Deal structures also changed sharply during this time. The average deal size increased from $196 million in 1995 to $845 million in 1998. The crisis of 1998 and subsequent shakeout of players in the market led to a drop in domestic dollar volume in 1999 and 2000, but the number of new domestic issues in 2001 and 2002 has exceeded the number of deals in 1998. With dollar volume down and the number of deals increasing, the average deal size has declined to $660 million in 2002. Although the volume of U.S. CMBS issues has not exceeded the 1998 peak, global volume set a new record in 2001 and the volume in 2002 was essentially unchanged from 2001. Exhibit 2 also shows that the structure of the CMBS has increased in complexity over time as measured by the average number of tranches in a deal. Prior to 1993, the typical deal involved the sale of a single tranche to the public. Credit support was often provided by letters of credit or lease arrangements with highly rated tenants, not security structure. During the growth phase, the complexity of CMBS increased as issuers sold more tranches to the public. The trend toward increased structural complexity has continued even after the 1998 liquidity
4
For more details on the data sources, see the section entitled Data and Empirical Models.
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crisis. In 2001 and 2002, the average deal contained roughly twelve different tranches – a 33% increase over the average in 1998. Exhibit 3 provides more detailed information on the recent changes in CMBS structure— especially the increased use of defeasance to provide better prepayment protection to investors. Much of the detailed data reported in the top panel of Exhibit 3 is only available from the prospectus for the issue. Because we only gathered detailed information from the prospectuses for conduit deals issued since 1997,5 the analysis in that panel is limited to those transactions. The information in the lower panel is based on Commercial Mortgage Alert data and covers all CMBS issues during the period. Several trends are apparent in the exhibit. First, the significant change in prepayment protection is highlighted by the increase in the percentage of loans with defeasance provisions and the length of the lockout/defeasance protection. Prior to 1997, the use of defeasance in commercial mortgage loans was minimal.
Between 1997 and 2001, the
percentage of loans with defeasance provisions increased from 25% to 93%. The benefit for investors is that the period of protection from lockout and defeasance increased from just over five years to more than eight years based on data for all CMBS issues. As a result, most AAA tranches now have a high degree of prepayment protection covering almost all of their expected lives. At the same time, borrowers have more flexibility to sell or refinance their properties when market conditions dictate.6 For the conduit deals, we are able to look at the lockout and defeasance individually. In 1997-1998, for loans with no defeasance, the average lockout period was about five years. For these loans, the lockout period was followed by a period of prepayment penalties which did not
5 6
Many of the prospectuses from before 1997 are only available in hard copy format. See Quan and Torous (2001) for a more detailed study of the costs and benefits of defeasance.
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give investors absolute certainty about their cash flows.7 For loans with defeasance, the lockout period is somewhat shorter (approximately three years), but the combined lockout plus defeasance period is longer than nine years. In the last two years, loans without defeasance have had longer lockout periods, but these loans represent a very small fraction of the total loans (less than 10% in 2002). Another significant trend is the reduction in the level of subordination below the AAA level. This trend has been discussed in several trade publications (see, for example, periodic reports by Ernst & Young or Prudential Securities during the period from 2000 through 2002). Most observers attribute the decline to a combination of factors including the overall good performance of loans backing conduit issues, the increased experience of the rating agencies with the CMBS structure and the increased awareness of conduits and other originators of the rating agency criteria. A similar trend toward lower subordination has occurred with the required subordination levels for junior tranches. For example, the average level of subordination below BBB has declined from 12.36% to 9.07% from 1997 to 2001. Because lower subordination levels mean that an issuer can sell a larger volume of securities at the lower rates required for senior tranches, lower subordination can be as beneficial to the issuer as lower spreads. The precise tradeoff depends on market conditions at the time of issuance. Pricing There is substantial variation in the pricing of CMBS tranches, even after controlling for credit rating. For example, the top panel of exhibit 4 shows the mean, minimum, maximum and standard deviation of AAA CMBS spreads for conduit issues from 1997 through 2001. The
7
Although the penalties for some loans required a payment sufficient to make the investor whole (e.g. full yield maintenance), the penalties on other loans were a fixed number of points and frequently/ declined with seasoning.
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market turmoil of 1998 is reflected largely in the unusually large standard deviation of 16 basis points for that year. Nevertheless, there is substantial variation in spreads to the swap curve after holding year and rating constant. The exhibit suggests that investors discriminate among the different new issues and favor some deals/structures over others. There is also significant variation in spreads over time as shown by the shift in mean from 32 basis points in 1997 to 51 basis points in 1999. The larger spreads in the last few years do not mean, however, that the relative pricing of CMBS has changed because, as noted above, the level of subordination has declined over the same time period. The lower panel of exhibit 4 shows the same information for BBB securities.8 Here the cross sectional variation is even larger as reflected by both the larger standard deviation and the wider range of observations. The exhibit also shows that the increased level of spreads after 1997 is much greater for BBB securities than for AAA securities. Nevertheless, neither the AAA nor the BBB tranches exhibit a monotonic trend over the period from 1999-2001. Literature on Pricing of CMBS Most theoretical pricing models for mortgages have their roots in the option pricing methodology that began with the seminal work of Black and Scholes (1973) and Merton (1973). In these theoretical models, the mortgage is viewed as comprising a riskless, fixed-term, debt obligation (which can be readily valued using the current term structure of Treasury rates) and an option that provides the borrower certain rights to terminate the debt prior to maturity through either prepayment or default. The right to prepay is modeled as a call option on the riskless debt and the default option is viewed as a put option on the underlying collateral. If one can value the imbedded option to terminate the mortgage before maturity, the market value of the mortgage
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can be calculated as the difference between the market value of the riskless debt and the market value of the joint option to prepay or default. The owner of a mortgage holds a long position in the riskless debt but is short the imbedded joint option. For a review of this literature, see Kau et al. (1995). The options imbedded in mortgages are complex American options and, in general, can only be valued numerically. Further complicating the valuation of the options is the fact that the option to terminate the mortgage before maturity is a single option providing the borrower the right to either default or prepay—but not both. As a result, one cannot simply value the call, value the put, and add the two values. Consequently, many researchers have simplified the problem by assuming away one or the other of the two components of the joint option. In residential mortgages, where default is rare, many articles have been written that focus exclusively on the prepayment option. Because commercial mortgages generally have strong prepayment protection, many early studies of commercial mortgages have assumed no prepayment. See for example, Titman and Torous (1989). Valuing commercial mortgage-backed securities adds another layer of complexity because the cash flows to the various tranches depend on both the cash flows from the underlying mortgages and the structure of the CMBS. For example, if a mortgage in a pool defaults, the servicer will liquidate the collateral typically generating cash proceeds less than the mortgage balance. The cash proceeds are allocated to the senior tranches as a prepayment and the loss is allocated to the most junior tranche. Kau, et al. (1987, 1990) were the first to explicitly model commercial mortgage-backed securities contingent on their underlying mortgages. They apply contingent-claim techniques to price commercial mortgage instruments subject to default and prepayment risk. Their model of 8
Securities rated BBB+ or BBB- were excluded in calculating the exhibit.
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commercial mortgages depends on the term structure of interest rate and the value of the underlying property.
Both variables enter the model due to their likelihood to trigger
prepayment or default of the mortgage loan. They provide numerical analyses of the pricing of CMBS and the effect from other loan features such as prepayment penalties. However, they do not discuss how the structure of the CMBS can impact the value of each tranche. Childs et al. (1996a) employ a contingent-claim methodology to price multi-class commercial mortgage-backed securities. Their model involves a two-step procedure. In the first step, the optimal default boundaries for individual borrowers are determined using the traditional numerical option pricing methodology. For example, if default is triggered by a decline in property values, the first step would define the level of property value that would trigger default each month over the life of the loan. In the second step, the authors use Monte Carlo simulation to generate cash flows from the pool of mortgages and then allocate those cash flows to each tranche in a CMBS structure based on the specified priority rules. Unlike Kau et al. (1987, 1990), Childs et al. (1996a) assume that the commercial mortgages are “locked out,” (i.e. cannot prepay before maturity) therefore the uncertainty of cash flows is entirely attributable to default. A major difference between Childs et al. (1996a) and other studies of CMBS is that Childs et al. (1996a) consider the structure of the deal and are able to value each tranche separately. They show that, in theory, pool diversification should affect the value of CMBS by adding value to the senior tranche investors while decreasing the value of the first-loss tranche.9 The empirical research on pricing CMBS has been limited. Maxam and Fisher (2001) studied the pricing of 40 AAA- and AA-rated CMBS during the 1994 to 1996 period. They used kernel density techniques to explain the pricing of senior tranches as a function of property price
9
In most CMBS transactions, the first loss tranche is privately placed. Because our data is limited to publicly distributed securities, our data does not include first loss tranches.
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volatility, loan-to-value ratio, mortgage rate, yield curve steepness, interest rate volatility, and the amortization characteristics of underlying loans.
The results show that default-related
variables such as property volatility, amortization, and loan-to-value ratio significantly influence pricing. The authors conclude that even though the senior tranche CMBS are immune from default loss, they are not immune from early return of principal due to default and therefore the default risk of the underlying loans negatively affects the pricing of these tranches. Harding and Sirmans (1997) and Jacob and Gichon (1999) model the spreads of newly issued CMBS as a function of property-, loan-, and bond-level variables. Both studies discuss the factors that theory suggests should be related to initial pricing spreads and both studies analyze the period from 1994 to 1996. The results for AAA and AA tranches show that the quality of the mortgages10 and property type are significantly related to the initial pricing of these securities. Lower loan quality or “higher-risk” collateral11 was found to be associated with wider spreads in these two studies. Higher concentrations of multifamily collateral are associated with lower spreads; while a higher concentration in either hotel or office collateral results in wider spreads for these senior tranches. For BBB tranches, Harding and Sirmans (1997) find that tranche order within the same rating category is significant while Jacob and Gichon (1999) find that quality of collateral, size of the tranche, and average life of the tranche are significant variables. Maris and Segal (2002) study initial pricing spreads from 1992 through 1999, but their focus is more on explaining the longer term trends in average spreads than in explaining the 10
Both studies use the required level of subordination as a proxy for loan quality in addition to the standard measures of average loan-to-value ratio and average debt service coverage ratio. The rationale for using the level of subordination is that if the rating agency analysis shows greater risk from any source (poor location, high level of competition in the area, poor operator track record), a higher level of subordination will be required. 11 In general, commercial property investors view multifamily properties as lower risk because they have shown less sensitivity to the overall business cycle. Office buildings and hotels are viewed as being more pro-cyclical and consequently having greater systematic risk.
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cross sectional variation. Maris and Segal argue that spreads should widen as the risk of default increases and that corporate bond spreads and predictors of economic growth are good proxies for default risk.
Using the traditional option valuation results, they include the level and
volatility of interest rates in their model to capture the value of the prepayment option. Although Maris and Segal use individual tranche yield spreads as their dependent variable, they are able to include very few deal or tranche descriptors on the right hand side of their model because their analysis period includes the early years when very little data was available. The authors find that yield spreads to Treasuries were positively related to the spread between AAA corporate bonds and Treasuries and the Stock-Watson recession predictor index. However, even after controlling for economic factors and the limited number of deal characteristics available to them, Maris and Segal find a significant trend in spreads that narrows from 1992 to 1997 and then reverses sharply. The authors speculate that the initial trend toward narrower spreads was attributable to a learning process whereby market participants lowered their required risk premia as they became more familiar with the product. The authors further speculate that the reversal might be due, in part, to an increase in caution resulting from the adverse events of 1998. Riddiough (2002) studies the evolution of the CMBS market and discusses the role of rating agencies in the process of issuing CMBS. He analyzes cross-sectional and time-series variation of CMBS attributes during the period of 1994 through 1996 using 119 AAA-rated security tranches. He finds that pool characteristics and market effects (e.g., REIT returns) explain 83 percent of the variation in subordination level.
For the security yield spread
estimation, he finds that the coefficients on loan-to-value ratio, debt service coverage ratio, pool diversification and property type mix are not significant. He concludes that rating agencies adjust subordination levels in a manner that is satisfactory to investors.
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Data and Empirical Model Data. We built the data base used to examine the initial pricing of CMBS tranches from various sources. We purchased the Commercial Mortgage Alert (CA) data covering all CMBS issues since 1985. The CA data provides basic information describing the issue including deal size, number and size of tranches sold to the public, coupon rates and life for each tranche and the credit ratings for each tranche. The CA data includes summary statistics for the underlying collateral pool such as the number of loans in the pool, average loan-to-value (LTV) ratio, average debt service coverage ratio (DSCR) for the pool and the property type distribution. The data also includes a description of the property type distribution. Commercial Mortgage Alert also provides initial pricing information for most publicly-traded tranches.
The pricing
information generally consists of specifying a benchmark for pricing (e.g. the 10-year Treasury or the 10-year swap rate) and a spread over the benchmark. For example, a particular tranche might be reported as having been sold to provide a 50 basis point spread over the 5-year swap rate. We augmented this data by obtaining the prospectuses for all conduit12 deals brought to market between 1997 and 200113. We chose to focus on conduit transactions because they are the largest sector of the CMBS market.14 Furthermore, non conduit transactions are frequently “unique deals” that are highly dependent on a small number of properties or a single lender. The pricing of these special types of issues is not expected to be homogenous across different
12
We used the Commercial Mortgage Alert designation of a conduit transaction. The conduit designation includes “fusion” deals that include a small number of loans that are larger than average within a large pool. 13 As noted earlier, the time period was, in part, determined by the availability of prospectuses in the SEC’s Edgar system. 14 According to Commercial Mortgage Alert, conduit deals accounted for an average of 58% of total CMBS issues between 1997 and 2001.
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transactions. From the prospectus, we obtained detailed information about the loan pool and a description of the type of prepayment protection incorporated into the underlying loans. For example, we were able to obtain the length of the lockout and defeasance periods, the average loan age, the use of cross collateralization and information about the minimum and maximum LTV and DSCR. We selected all deals characterized by Commercial Mortgage Alert as conduit deals issued between 1997 and 2001. The 1997 start date was chosen based on the availability of prospectuses.
There were a total of 167 conduit deals during that period identified by
Commercial Mortgage Alert. After excluding deals with incomplete information or for which we could not obtain a prospectus, we had a sample comprising 135 deals and 930 public tranches. We obtained information on market conditions at the time of pricing from Bloomberg and the web site of the Federal Reserve Bank of St. Louis.15 We used Bloomberg data for the swap curve and the St. Louis Federal Reserve Bank data for Treasury rates. In addition, we obtained a leading indicator of economic conditions from the Stock-Watson web site.16 Empirical Model. Before bringing a deal to market, an issuer must obtain credit ratings from two of the three major rating agencies. To obtain these ratings, the issuer must provide detailed information to the rating agency about the proposed deal and the pool of loans that will be used as collateral. The objective of obtaining a rating is to provide investors with a consistent indicator of the likelihood that the cash flow from the underlying mortgages will be sufficient to
15 16
www.stls.frb.org. ksghome.harvard.edu
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meet the promised payments of principal and interest on the certificate.17 While the rating agencies do not quantify the probability of default associated with each rating class, history shows that less than 1% of all bonds with original ratings of AAA default during the first ten years.18 For mortgage-backed securities, the cash flows from the underlying mortgages are subjected to “stress tests” and the cash flows for higher rated tranches are expected to withstand increasingly severe economic conditions as reflected by both default rates on individual loans and the severity of loss. In order to accomplish this, progressively greater credit support is need at higher rating levels. The analysis of the rating agencies is extensive. See for example, “CMBS: Moody’s Approach to Rating U.S. Conduit Transactions” (September 15, 2000). The agencies typically begin with the reported cash flow from the underlying property. They verify reported numbers and make adjustments to leases, vacancy and operating expenses based on their own analysis. Using these adjusted cash flows, they make their own estimates of both LTV and DSCR. The agencies also look at other characteristics of the pool including measures of diversification. Less subordination is required for pools that are well diversified in all dimensions: geographically, by property type and by loan size. Additional subordination is required for pools with large dollar concentrations in a small number of loans.
In addition, the rating agencies take into
consideration the quality and track record of the originator, the servicer and the master servicer.19
17
For example, Moody’s describes its AAA rating as follows: AAA securities “carry the smallest degree of investment risk and are generally referred to as ‘gilt edged.’ Interest payments are protected by a large or by an exceptionally stable margin and principal is secure. While the various protective elements are likely to change, such changes as can be visualized are most unlikely to impair the fundamentally strong position of such issues.” 18 See Moody’s (2003) and Standard and Poor’s (2003). 19 The master servicer administers the pool of mortgages on behalf of and for the benefit of the security holders. Although duties can vary, normal responsibilities include collection of payments, preparing reports on mortgage performance, advancing payments to the trustee for any loans that are delinquent and transferring non performing loans to the special servicer. The special servicer assumes administrative responsibilities when a loan goes into
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After an initial review, the rating agency provides the issuer a schedule of the subordination levels required to achieve the various rating levels. For example, a 25% AAA subordination level means that for a $100 million pool, an issuer could sell $75 million AAA securities and a total of $25 million of securities with claims subordinate to those of the AAA security holders.20 Normally, the $25 million of subordinated securities will be allocated to several different rating categories ranging from AA to unrated. Different levels of subordination are set for each rating category to limit the risk of default on the rated tranche to a level comparable to that of similarly rated securities of other types such as corporate or municipal bonds. However, the determination of a subordination level is not an exogenous event independent of the current prices in the market. After the initial subordination levels are reported by the rating agency, the issuer can respond by making modifications to the pool of loans and/or the structure to achieve better overall execution.21 For example, if AAA securities are expected to sell at 90 basis points lower yields than BBB securities, a 1% reduction in the AAA subordination level (on a billion dollar issue) means the issuer can sell $10 million more lowercost AAA securities and $10 million less of higher-cost junior securities. Using the BBB rate as a proxy for the average cost of junior securities, a 1% reduction in the AAA subordination level saves the issuer $90,000 per year of interest expense on a $1 billion deal.22 This savings is
default and conducts the “work out” or foreclosure process. The responsibilities of both sevicers are specified in a formal servicing agreement. 20 Most CMBS include more than one AAA tranche. Within the AAA rating, there will normally be some securities with first claim to principal repayments creating tranches with different weighted average lives. 21 Because rating agency standards are well known, the issuer may, in fact, anticipate the rating agency’s response to a given pool of mortgages and adjust the pool before the initial submission to the rating agency. This scenario does not change the conclusion that pricing and subordination are jointly determined. 22 In general, the issuer will sell claims to all future net interest cash flow (i.e., any excess interest income over the interest expense associated with all of the tranches) over time in the form of an interest-only (IO) strip. The sale of IO securities converts a claim to future cash flows into current cash proceeds.
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roughly equal to the savings from a 1.2 basis point reduction in the spread for the AAA tranche23.
The issuer’s objective is to obtain the best overall execution from the sale of
securities. The issuer does not care whether that “best execution” entails selling a smaller amount of AAA securities at a narrow spread or a larger amount at a somewhat wider spread. Thus, depending on market conditions, the issuer might eliminate certain loans deemed risky by the rating agency in order to achieve a lower level of subordination. These changes would likely lower the average LTV and increase the average DSCR of the pool, but the lower risk associated with a higher quality pool would be offset by higher risk from a lower level of subordination. Thus, it is clear that subordination and pricing are jointly determined. Because subordination and pricing are jointly determined, we estimate a two equation system of equations: Subordination % = f (loan quality, diversification, spread, other factors)
(1)
Spread =f (loan quality, diversification, subordination, prepayment protection, other factors) (2) To estimate this system empirically we must “identify” each individual equation. To identify the two equations, we must find some variables that influence subordination and not pricing and vice versa. The criteria used by the ratings agencies are described in various publications. See, for example, Moody’s (2000), Fitch (2001) and Wheeler (2001). One of the variables used by both Moody’s and Fitch is an index of diversification known as a Herfindahl index. Herfindahl indices are used to measure market share concentration. Consider an industry with a fixed number of participants. The Herfindahl index is calculated by squaring each firm’s market share
23
Using a subordination level of 25%, a 1.2 basis point savings in AAA yield would save the issuer $90,000 per year on a total AAA issue of $750 million.
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and adding all the squared shares. Highly concentrated markets will have a high index number, while less concentrated markets have lower index values. The rating agencies calculate an analogous index using the share of the pool represented by each individual loan. While investors consider diversification, it is unlikely that they use this particular measure because, in general, the detailed information is difficult to obtain during the initial pricing period.
Similarly,
according to Quigg (1993), rating agencies consider the age of the loans included in the pool as an indicator of loan quality. Like the Herfindahl index this measure requires detailed analysis of the schedule of individual loans, and we find little in the CMBS pricing literature to suggest that investors give it significant weight.
We therefore use these two variables to identify the
subordination equation. Identifying the pricing equation is easier as we believe that investors are influenced by market conditions at the pricing date (such as the level of yields and yield curve shape) but this information is not mentioned in any of the rating methodology literature. The rating agencies focus on the longer term performance of the loans. In addition, rating agencies focus primarily on default risk. Consequently, lockout provisions and prepayment penalties are much more significant in pricing than in determining subordination. Therefore, we use variables describing current economic conditions on the pricing date and variables describing the prepayment protection to identify the spread equation. We discuss the specific variables included in each model by major category: loan quality, diversification, prepayment protection, bond descriptors, market indicators and other factors. Exhibit 5 provides a complete list of the variables in each model and the hypothesized sign for the estimated coefficients. Exhibit 6 provides summary statistics for all variables.
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Loan Quality The primary measures of pool loan quality are the traditional underwriting variables: loan-to-value ratio and debt service coverage ratio. We expect that lower quality pools will require higher levels of subordination. The further impact of these variables on pricing spreads is likely to vary with the tranche rating. AAA investors may believe that the possibility of a direct loss of principal is remote; however, they may still demand higher spreads for lower quality pools because of the cash flow effects of defaults. Investors in lower rated tranches may have more concern that a high level of default could result in a lower credit rating resulting in a decline in the market value of their tranche. We include the average LTV ratio and average DSCR in both the spread and subordination models. In addition, we include an indicator of the presence in the loan pool of any loans with LTV >1 and an indicator of the presence of any loan with a DSCR 8 years were used to maintain comparability with the BBB spreads. Tranches rated BBB+ or BBB- were excluded.
.
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Variable Measures of Loan Quality Loan to value ratio (LTV) Debt Service Coverage Ratio (DSCR) LTV Indicator >1. DSCR indicator 1 in pool + -* + Indicator of presence of loan with DSCR35% Ind. that multifamily loans represent > 35% of pool -* Indicator Office > 35% Ind. that office loans represent > 35% of pool + Indicator retail >35% Ind. that retail loans represent > 35% of pool ? Bond & Deal Variables Deal Size Dollar amount of all securities sold in the deal (,000) ? Tranche Size Dollar size of the particular tranche (,000) ? -** Tranche Life Average life of tranche at 0% prepay (months) + +*** Other Variables Cross Collat. Indicator Ind. that> 20% of loans have cross-collateralization ? +*** Average Loan Seasoning Average age of loans in the pool + Notes: Exhibit 5 defines the variables used in the models for both spread and subordination level. The columns to the right report the expected direction of the effect (predicted) and the estimated effect (actual) from the AAA model. The significance of the actual effect is denoted by the asterisks: *,**, and *** indicate significance at the 10% level, 5% level and the 1% level, respectively. A blank in the actual column indicates the variable was not significant in the model. .
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Exhibit 6 Summary Statistics
Mean Dependent Variables Number of Observations (tranches) Number of Deals Spread to Swap Curve % Subordination Measures of Loan Quality Loan to value ratio (LTV) Debt Service Coverage Ratio (DSCR) LTV Indicator >1. DSCR indicator 35% Indicator Office > 35% Indicator retail >35% Market Indicators 10-year Treasury (%) Rate Volatility (%) Yield Curve Slope (%) AAA-BBB Spread (%) Stock-Watson Recession Index Bond & Deal Variables Deal Size ($,mil) Tranche Size ($, mil.) Tranche Life (yrs.) Other Variables Cross Collat. Indicator Loan Seasoning (months)
AAA Stand. Dev.
Mean
317 133 35.17 25.65%
BBB Stand. Dev 264 126
14.12 4.11%
126.83 11.56%
41.75 2.25%
68.96% 1.43 6.77% 15.04%
3.28% 0.11 ---
68.87% 1.44 7.14% 15.08%
3.27% 0.11 ---
45.11% 23.31% 198.6 1.12 0.2354 0.0166
---
47.62% 23.81% 199.3 1.12 0.2332 0.0167
---
1.63 8.27%
95.3 0.16 0.0423 0.0084 0.70 --
0.75 8.73%
96.5 0.14 0.0420 0.0085 0.22 --
26.04% 21.65% 27.95% 8.17% 9.20% 12.78% 6.77% 18.05%
8.97% 9.57% 7.23% 4.51% 5.64% ----
25.91% 21.70% 27.72% 8.32% 9.27% 12.70% 6.35% 16.67%
8.78% 9.29% 7.20% 4.54% 5.65% ----
5.54 0.64 0.36 0.77 0.0812
0.59 0.75 0.09 0.17 0.0476
5.55 0.60 0.36 0.77 0.0798
0.59 0.74 0.09 0.16 0.0468
1,101 333 7.42
496 258 2.31
1105 30 10.68
508 28 1.56
2.4
23.02% 4.8
25.56% 4.8
--
-2.4
Notes: Exhibit 6 provides summary statistics for all variables used in the subordination and spread models. Definitions of the variables are provided in Exhibit 5 With the exception of tranche size and tranche life, all variables are measured at the deal level. The reported values differ between the AAA and BBB samples because not every deal includes both AAA tranches and BBB tranches. With the exception of rate volatility, market indicators were measured on the day of issue. Rate volatility is measured monthly.
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Exhibit 7 Three Stage Least Squares Regression Results for AAA Tranches Subordination Model coef. t-stat Endogenous Variables Spread to Swap Curve % Subordination Measures of Loan Quality Loan to value ratio (LTV) Debt Service Coverage Ratio (DSCR) LTV Indicator >1. DSCR indicator 35% Indicator Office > 35% Indicator retail >35% Bond & Deal Variables Deal Size Tranche Size Tranche Life Other Variables Cross Collat. Indicator Average Loan Seasoning Number of Observations R2 RMSE Χ2 Statistic p-value
-0.0002 --
-2.02
0.5616 -0.0341 -0.0054 0.0056 0.0119 0.0167 -0.0032 -0.0173 --0.1746
Spread Model coef. t-stat --129.9994
-2.53
13.19 -3.04 -1.73 2.21
102.5211 -7.8552 -0.7661 3.3593
2.99 -1.39 -0.48 2.62
6.8 5.72 -2.67 -3.03
2.9258 2.3182 -0.1427 4.0426 14.3939 --
3.21 1.88 -0.19 1.39 1.09
---
-2.2861 -3.8616
-2.22 -2.53
------
-15.8071 11.1370 0.4425 7.8531 37.0573
-6.97 3.6 0.04 1.7 3.43
-1.22
0.0638 0.0406 0.0020 0.1381 -----
4.91 3.07 0.16 6.78
---0.0053 0.0004 317 0.9172 0.0121 3548.27 0.0000
2.75 1.19
-----2.2082 -2.1140 -1.3117
-1.68 -1.25 -1.12
-0.8901 -1.1616 1.9797
-0.48 -2.24 8.13
--317 0.8077 5.8271 1482.54 0.0000
Exhibit 7 reports the results of estimating equations (1) & (2) with three stage least squares. Definitions of variables are provided in Exhibit 5. Coefficients on quarterly time dummies are suppressed to conserve space.
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Exhibit 8 Three Stage Least Squares Regression Results for BBB Tranches Subordination Model coef. t-stat
Spread Model coef. t-stat
Endogenous Variables Spread to Swap Curve -0.0341 0.01 -% Subordination --325.0979 -0.94 Measures of Loan Quality Loan to value ratio (LTV) 0.3966 11.09 119.3546 0.83 Debt Service Coverage Ratio (DSCR) 0.0077 0.75 -78.7045 -3.43 LTV Indicator >1. -0.0003 -0.12 -3.1712 -0.54 DSCR indicator 35% --0.1794 -0.03 Indicator Office > 35% --6.2242 -0.86 Indicator retail >35% --2.4016 -0.51 Bond & Deal Variables Deal Size --0.3788 -0.04 Tranche Size --7.6774 -1.72 Tranche Life -2.3335 1.99 Other Variables -0.0011 -0.66 -Cross Collat. Indicator Average Loan Seasoning 0.0009 2.98 -Indicator: BBB+ 0.0143 6.97 -4.1161 -0.57 Indicator: BBB-0.0163 -4.11 42.9388 6.22 264 264 Number of Observations R2 0.8421 0.8714 RMSE 0.0087 20.59 Χ2 Statistic 1409.05 1814.51 p-value 0.0000 0.0000 Exhibit 8 reports the results of estimating equations (1) & (2) with three stage least squares. Definitions of variables are provided in Exhibit 5. Coefficients on quarterly time dummies are suppressed to conserve space. .
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1
1
