An Analysis of Future Delinquency for Hotel CMBS Loans

2019

IJCHM

Purpose This study aims to investigate the determinants of credit spreads in hotel loans securitized into commercial mortgage-backed securities (CMBS) between 2010 and 2015. Design/methodology/approach The sample represents 1,579 US hotel fixed interest rate whole loans with an aggregate mortgage value of $26.6bn at loan origination. The relationship between credit spreads and property, loan and market characteristic is examined via multiple regression analysis. Additionally, the method of 2-stage least squares is used to control for endogeneity bias and identify the effect of the loan-to-value (LTV) ratio on credit spreads. Findings The multiple regression models explain 80 per cent of the variation in credit spreads and show a significant association of credit spreads with hotel and loan characteristics and market conditions. The findings indicate the debt coverage ratio to be the most important predictor of credit spreads followed by the loan maturity term, implied capitalization rate, LTV and yield curve. The results show the debt yield premium to be a stronger predictor of credit spreads than the debt yield ratio. The spread between the debt yield ratio and mortgage interest rate could be used in future research as an instrumental variable to identify the effect of the LTV on credit spreads. Research limitations/implications This study is limited to the CMBS market and the period after the financial crisis. Additional limitations include sample selection bias, exclusion of multi-property loans and variable interest rate loans. Practical implications Interest rate increases in an expanding economy would likely increase the cost of borrowing for hotel owners leading to higher debt service payments and lower profitability. If an increase in interest rates is offset by a decline in credit spreads, hotel owners will still benefit from the ensuing stability in borrowing interest rates. The evidence also suggests that CMBS lenders favor select service and extended stay hotels. Owners and operators of these efficient and profitable hotels will likely obtain loans with lower credit spreads given their lower risk of default. Originality/value The current study provides evidence on the effects of loan and property characteristics in the pricing of loan risk and serves to inform CMBS market participants about the factors that drive credit spreads in hotel mortgage loans.

Section: Literature Reviewmentioning

confidence: 99%

Determinants of credit spreads in hotel CMBS loans

2019

IJCHM

“…Their findings reveal the level of distress is associated with a lower likelihood of resolving a loan. In another study, Corgel and Walls (2010) estimate the NOI, DSCR, and debt service payments using a sample of hotels to determine the future delinquency of lodging CMBS loans. While Corgel and Walls's (2010) study is limited by sample size, Quan and Lebret's (2006) research is conducted using data prior to 2004, well before the GFC.…”

Section: Hospitality Commercial Mortgage Researchmentioning

confidence: 99%

Predicting the Likelihood of Lodging CMBS Loan Default

Cornell Hospitality Quarterly

2018

This study investigates whether the traditional underwriting loan metrics, loan-to-value (LTV), debt coverage ratio (DSCR), and debt yield (DY) ratio, can predict lodging commercial mortgage-backed securities (CMBS) loan defaults. Using a data set of 5,266 fixed-rate lodging whole loans that were securitized into CMBS between 1996 and 2015, the results of the study provide evidence of significant relationships between all three metrics and the likelihood of default. The LTV varies positively with default, while the DSCR and DY are negatively related to default. These results hold for a subsample analysis of loans originated prior to the global financial crisis (GFC). Finally, the results show the DY spreads to be adequate proxies for the DY ratio.

“…Their findings revealed that the level of distress was associated with a lower likelihood of resolving a loan. In another study, Corgel and Walls (2010) analyzed a sample of 365 hotels to determine the future delinquency of hotel CMBS loans. The analysis involved estimating NOI, DCRs, and debt service payments as the primary indicators of loan delinquency.…”

Section: Hospitality-related Researchmentioning

confidence: 99%

“…The analysis involved estimating NOI, DCRs, and debt service payments as the primary indicators of loan delinquency. Corgel and Walls (2010) concluded that deterioration in hotel DCRs would reach a peak in 2010 before gradually improving in 2011. Although Corgel and Walls's (2010) study was limited by sample size, Quan and Lebret's (2006) research was conducted using data prior to 2004, well before the current financial crisis of 2008-2009. A few hospitality studies have examined the predictors of hotel values using non-distressed hotel transaction prices.…”

Section: Hospitality-related Researchmentioning

confidence: 99%

The Effects of Securitization, Foreclosure, and Hotel Characteristics on Distressed Hotel Prices, Resolution Time, and Recovery Rate

Cornell Hospitality Quarterly

2016

This study investigates the effects of securitization, foreclosure, and hotel characteristics on the sale prices of distressed hotels as well as their influence on resolution time and recovery rate. Using a sample of 4,763 financially distressed hotels between 2010 and 2014, this study provides evidence that hotel size, securitization, foreclosure, and disposal methods are important predictors of distressed property prices, resolution time, and recovery rate.