Search

This paper investigates how property risk in Australian Commercial Mortgage-Backed Securities (CMBS) issued between 2000 and 2005 can be assessed and reported in a more systematic and consistent approach to be easily understood by institutional investors. Our framework shows that assessing and reporting property risk in Australian CMBSs, which are primarily backed by direct property assets, under the headings of investment quality risk, covenant strength risk, and depreciation and obsolescence risk can easily be done. Rating agencies can adopt a more systematic and consistent approach towards reporting of assessed property risk in CMBS. Issuers and institutional investors can examine the perceived consistency and appropriateness of the rating assigned to a CMBS issue by providing inferences concerning property risk assessment

Using artificial neural networks (ANN) and ordinal regression (OR) as alternative methods to predict Commercial Mortgage-backed Securities (CMBS) credit ratings, we examine the role that various financial and industry-based variables have on CMBS credit ratings issued by Standard and Poor’s from 1999-2005. Our OR results show that rating agencies use only a subset of variables they describe or indicate as important to CMBS credit rating as some of the variables they use were statistically insignificant. Overall, ANN show superior results to OR in predicting CMBS credit ratings.

According to the Reserve Bank of Australia (2006) the increased supply of Commercial Mortgage-Backed Securities (CMBS), with a range of subordination, has broadened the investor base in real estate debt markets and reduced the commercial property sector’s dependence on bank financing The CMBS market has been one of the most dynamic and fastest-growing sectors in the capital markets, for a market which was virtually nonexistent prior to 1990. The global CMBS market issuance which stood at AU$5.1 billion (US$4 billion) in 1990 had grown to AU$380 billion (US$299 billion) by the end of 2006. In Australia, a total of over 60 CMBSs with nearly 180 tranches totalling over AU$17.4 billion had been issued to December 2006 from when they were first introduced in 1999. To date few studies have been done on Australian CMBSs outside the credit rating agency circles. These studies are predominantly practitioner focused (Jones Lang LaSalle 2001; Richardson 2003; Roche 2000, 2002). O’Sullivan (1998) and Simonovski (2003) are the only academic studies on CMBSs. As such, this thesis examines issues relating to the development of Australian CMBSs and quantitatively and qualitatively analyses the structuring of Australian CMBSs. In assessing the growth of the Australian CMBS market, an interpretive historical approach (Baumgarter & Hensley 2005) is adopted to provide a cogent review and explanation of features of international and Australian CMBSs. This helps to understand the changing nature of the market and provides better understanding of the present and suggests possible future directions. The Australian CMBS market is matured in comparison with the larger US and EU CMBS markets as seen by the diversity of asset classes backing the issues and transaction types, tightening spreads, and record issuance volumes. High property market transparency (Jones Lang LaSalle 2006b) and predominance of Listed Property Trusts (LPT) as CMBS issuers (Standard & Poor’s 2005b), who legally have to

According to the Reserve Bank of Australia (2006) the increased supply of Commercial Mortgage-Backed Securities (CMBS), with a range of subordination, has broadened the investor base in real estate debt markets and reduced the commercial property sector’s dependence on bank financing The CMBS market has been one of the most dynamic and fastest-growing sectors in the capital markets, for a market which was virtually nonexistent prior to 1990. The global CMBS market issuance which stood at AU$5.1 billion (US$4 billion) in 1990 had grown to AU$380 billion (US$299 billion) by the end of 2006. In Australia, a total of over 60 CMBSs with nearly 180 tranches totalling over AU$17.4 billion had been issued to December 2006 from when they were first introduced in 1999. To date few studies have been done on Australian CMBSs outside the credit rating agency circles. These studies are predominantly practitioner focused (Jones Lang LaSalle 2001; Richardson 2003; Roche 2000, 2002). O’Sullivan (1998) and Simonovski (2003) are the only academic studies on CMBSs. As such, this thesis examines issues relating to the development of Australian CMBSs and quantitatively and qualitatively analyses the structuring of Australian CMBSs. In assessing the growth of the Australian CMBS market, an interpretive historical approach (Baumgarter & Hensley 2005) is adopted to provide a cogent review and explanation of features of international and Australian CMBSs. This helps to understand the changing nature of the market and provides better understanding of the present and suggests possible future directions. The Australian CMBS market is matured in comparison with the larger US and EU CMBS markets as seen by the diversity of asset classes backing the issues and transaction types, tightening spreads, and record issuance volumes.High property market transparency (Jones Lang LaSalle 2006b) and predominance of Listed Property Trusts (LPT) as CMBS issuers (Standard & Poor’s 2005b), who legally have to r

The ultimate goal of structuring Commercial Mortgage-Backed Securities (CMBS) transactions is to obtain a high credit rating as this has an impact on the yield obtainable and the success of the issue. Though bond rating agencies claim that their ratings reflect each agency’s opinion about an issue’s potential default risk and rely heavily on a committee’s analysis of the issuer’s ability and willingness to repay its debt and therefore researchers would not be able to replicate their ratings quantitatively (Kim 2005), we follow previous researchers who gone ahead and replicated bond ratings on the premise that the financial variables extracted from public financial statements, such as financial ratios, contain a large amount of information about a company’s credit risk (Huang et al. 2004). We use artificial neural networks (ANN) and ordinal regression (OR) as alternative methods to predict CMBS ratings. OR results show that rating agencies use only a subset of variables they describe or indicate as important to CMBS rating as some of the variables they use were statistically insignificant. Overall, ANN show superior results to OR in predicting CMBS ratings.

The paper explores the development of the Australian Commercial Mortgage-Backed Securities (CMBS) market from 1999 to 2006 and outlines similarities and dissimilarities with the United States (US) and European (EU) CMBS markets. Whilst the US has been the market leader in terms of issuance volumes and diversity of asset classes backing the issues, the other two regions have not lagged far behind and have replicated the US CMBS model to suit their local needs. In comparison to the much bigger US and EU CMBS markets, the Australian CMBS market is well matured as seen by the diversity of asset classes backing the issues and transaction types, tightening spreads and record issuance volumes. The strong commercial real estate market outlook supports further CMBS issuance, with Listed Property Trusts (LPTs) continuing their dominance as issuers

The ultimate goal of structuring Commercial Mortgage-Backed Securities (CMBS) transactions is to obtain a high credit rating as this has an impact on the yield obtainable and the success of the issue. However, issues of proprietorship have resulted in the methodology of credit rating mostly being shrouded in mystery. The methods and input variables used in rating are not publicly disclosed. We use artificial neural networks (ANN) and multinominal logistic regression (MLR) as alternative methods to predict CMBS ratings. ANN shows superior results to MLR in predicting CMBS ratings

Balancing demand and supply is at the heart of the corporate real estate planning challenge. Real estate commitments, which are often long-term by nature, are at odds with the quickening competitive environment in which many companies operate today. The “structuring dilemma” is grounded in the uncertainty of a company’s external environment, as well as in the ways the company’s corporate real estate decision-making processes respond to uncertainty. The need for flexibility has seen most companies opt to divest from owning to leasing real estate. However, leasing also does not ensure ultimate flexibility if the durations of the leases are long and if the lease agreements do not contain options to renew and/or terminate the lease; and expand and/or contract space. Case studies were undertaken at ABB Sverige AB, TietoEnator AB, Siemens AB and Pharmacia AB, to investigate how these companies were matching their corporate real estate (CRE) to their corporate business strategies. The companies had identified real estate as contributing to business profitability. Although only one company had a formulated real estate strategy, they were all pursuing real estate goals to minimise long-term real estate costs and to optimise the use of space. Lease durations were being matched with business projections and options were embedded in the leases for flexibility. However, further investigation in all the companies is needed to evaluate the contribution of real estate to business profitability through measures such as the percentage of real estate costs to total business costs.