After a year when the once-prolific Redwood Trust (RWT) issued only four prime jumbo residential mortgage-backed securitizations, the real estate investment trust is getting out of the gate early and in fine form in 2015.
The REIT is prepping its first prime jumbo RMBS through its Sequoia platform, under which it issued four jumbo RMBS transactions in 2014, after issuing roughly one a month in 2013.
Sequoia Mortgage Trust 2015-1 is backed by 478 mortgages with an aggregate principal balance of $338,795,838. The loans carry an average balance of $708,778.
Kroll Bond Rating Agency and Fitch Ratings both issued presale reports for SEMT 2015-1 and awarded AAA ratings to the offering’s Class A tranche, which includes a credit enhancement of 15%.
Both KBRA and Fitch cited the strong collateral quality of the underlying borrowers as a positive of the deal.
“The collateral pool consists of 30-year, fixed-rate, fully documented loans to borrowers with strong credit profiles, low leverage and substantial liquid reserves,” Fitch said in its presale. “Third-party, loan-level due diligence was conducted on 83% of the pool, the results of which, in Fitch’s opinion, indicate strong underwriting controls.”
The underlying borrowers carry a weighted average FICO score of 769 and KBRA notes that the collateral pool features “significant” borrower equity.
“The collateral pool’s 69.9% weighted average loan-to-value ratio and 70.8% WA combined LTV ratio provide a substantial margin of safety against potential home price declines,” KBRA said in its presale. “While 56.8% of the mortgages have CLTVs of 75% or greater, there are no loans with CLTVs greater than 80%. Approximately 32.3% of the mortgage loans have a CLTV of exactly 80%.”
Fitch also called out the significant liquid assets and high income of the borrowers as a positive.
“A number of borrowers in this pool are high net-worth individuals with significant liquid assets,” Fitch said. Borrowers with significant liquid reserves are generally better positioned to withstand a temporary income disruption and have a lower risk of default. Approximately 61.6% of the borrowers have liquid reserves exceeding five years of monthly mortgage payments, and approximately 16.1% have reserve amounts greater than their mortgage loan balance.”
According to Fitch’s data, the weighted average reserve-to-loan ratio is roughly 63.3% and the borrowers’ weighted average annual income is approximately $353,174.
KBRA also noted the “prudent” debt-to-income ratios of the deal, which carries a weighted average DTI of 32.1%.
Both KBRA and Fitch recognized Redwood’s experience as an aggregator, investor and securitization issuer as a positive of the deal.
“Redwood has been an active contributor in the residential mortgage market for over a decade as a loan aggregator, issuer and investor in RMBS securitizations,” KBRA said in its report.
“Historically, Redwood has generally invested in and securitized high quality jumbo prime mortgages, which have performed well relative to the universe of non-agency securitizations,” KBRA added. “There are minimal, if any, delinquencies on the mortgages backing the previous SEMT transactions issued since 2010. Redwood is incentivized to maintain its focus on loan quality as it retains an interest in the securitization through its ownership of subordinate securities.”
As with most jumbo securitizations, California makes up the largest portion of the geographical concentration. According to KBRA’s data, 38.3% of the loans are located in California, which is the highest level of California concentration among Redwood’s securitizations since SEMT 2014-1, Redwood’s first jumbo offering in 2014.
KBRA also noted that the offering has six loans that exceed $2 million, representing 4.3% of the mortgage pool.
“High net worth borrowers often buy very expensive homes with so-called ‘super-jumbo’ or ‘mega-jumbo’ mortgages,” KBRA noted. “While the terms and underwriting of such loans may generally be conservative, they still present risk in the context of a pool where a single such loan may comprise close to 1% of the pool balance. Loan-level models are derived from analyzing a large universe of loans, and such models work best when applied against pools with a large number of loans of relatively uniform size. For smaller pools with outsized loans, the risk of ‘outlier’ loss events grows.”
