Comparing Securitized and Balance Sheet Loans: Size Matters

Abstract

We assemble a unique dataset of commercial mortgages with information on loan characteristics at origination and subsequent performance. The most significant difference between securitized and balance sheet loans is the size of the loan. The loans in the highest loan size decile have a 43% percent chance of securitization whereas the ones in the lowest decile have only a 1% chance. This result is consistent with diversification being a key motivation for securitization. We also find that loans that require substantial monitoring are less likely to be securitized. Finally, securitized loans get resolved less quickly after defaulting.JEL classification: G21, G23, G20.

The coevolution of banks and corporate securities markets: The financing of Belgium’s industrial take-off in the 1830s

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How Do Firms Finance Nonprimary Market Investments? Evidence from REITs

“…In a recent study, Ghent and Valkanov () use the mortgage information collected by RCA to examine the relationship between commercial mortgage characteristics and securitization.…”

“…In a recent study,Ghent and Valkanov (2015) use the mortgage information collected by RCA to examine the relationship between commercial mortgage characteristics and securitization. 15 SNL Financial is a provider of news, financial data and expert analysis on banking, insurance, financial services, real estate, energy, media & communications and metals & mining.…”

Intermediary Segmentation in the Commercial Real Estate Market

“…Black et al (2017Black et al ( , 2018 show that banks specialize in lending against risky properties where monitoring and renegotiation are important. Meanwhile, Ghent and Valkanov (2016) show that CMBS disproportionately hold loans against larger properties, consistent with a superior ability to diversify risk. 5 We advance this literature along a number of dimensions.…”

“…Black et al (2017Black et al ( , 2018 use the same data sources for CMBS and bank loan portfolios, but their analysis does not include life insurers. Downs and Xu (2015) and Ghent and Valkanov (2016) use data which includes insurers but does not come from regulatory filings, causing many fields pertaining to loan 4 There is a large theoretical and empirical literature on this topic. Important examples include: Townsend (1979); Sharpe (1990); Diamond (1991); Rajan (1992); Hart and Moore (1998); Denis and Mihov (2003); Gande and Saunders (2012); Hale and Santos (2009) ;Becker and Ivashina (2014).…”