Delays in Banks’ Loan Loss Provisioning and Economic Downturns: Evidence from the U.S. Housing Market

Abstract

I study whether banks’ loan loss provisioning contributes to economic downturns, by examining the U.S. housing market. Specifically, I examine the aggregate effects of banks’ delayed loan loss recognition (DLR) on house prices during the Great Recession and the channels through which these potential effects arose. I construct ZIP‐code‐level exposure to banks’ DLR before the crisis and compare high‐ and low‐exposure ZIP codes during the crisis to examine the aggregate effects of banks’ DLR on the housing market. I find that high‐exposure ZIP codes experienced larger decreases in mortgage supply, larger increases in distressed sales, and larger decreases in house prices during the crisis. In addition, I conduct individual bank‐level analyses and find that high‐DLR banks reduced their mortgage supply more than low‐DLR banks, and mortgages issued by high‐DLR banks were more likely to become distressed during the crisis. Taken together, these findings suggest that banks’ DLR was associated with nontrivial effects on the housing market during the Great Recession, and the effects of DLR on house prices were likely driven by both the credit‐crunch and distressed‐sales channels.

Current Expected Credit Losses (CECL) Standard and Banks' Information Production

“…Prior studies proxy the timeliness of LLPs as a positive relationship between current LLPs and changes in future non-performing loans [Nichols et al, 2009, Beatty and Liao, 2011, Bushman and Williams, 2015, Kim, 2022. Thus, if banks produce better information and that information is reflected in their LLPs, we expect the positive relationship between current LLPs and changes in future non-performing loans for the adopting banks to become stronger after CECL adoption.…”

“…The financial crisis of 2007-2009 sparked a debate about banks' financial reporting and their loan loss recognition in particular [Laux and Leuz, 2009, Barth and Landsman, 2010, Vyas, 2011, Beatty and Liao, 2011, 2014, Bushman and Williams, 2012, Huizinga and Laeven, 2012, Kothari and Lester, 2012, Acharya and Ryan, 2016, Wheeler, 2019, Bischof et al, 2021b, Kim, 2022. Regulators and others have blamed delays in loan loss provisioning under the existing accounting standard (FAS 5, ILM) for exacerbating the severity of economic downturns.…”

Using and Interpreting Fixed Effects Models

“…For example, deHaan et al [2021] plot the relation between index funds' fees and disclosure readability, but first residualize both variables to year FE to remove the effects of time trends. Or, Kim [2022] plots the relation between banks' loan loss provisions and local housing prices, but first residualizes both variables to controls and FE to better visually isolate the relation of interest.…”

Limitations of Implementing an Expected Credit Loss Model

“…Second, we contribute to the literature on the relation between banks' loan loss impairments and bank lending. Prior evidence suggests that delayed loss recognition is associated with greater risk in banks' loan portfolios and, consequently, higher reductions in bank lending during economic downturns (e.g., Beatty and Liao 2011;Bushman and Williams 2015;Wheeler 2019;Sehwa Kim 2022;Huber 2022), while timely fair value accounting does not produce such a procyclicality in bank lending (e.g., Xie 2016; Laux and Rauter 2017).…”