The failure of models that predict failure: Distance, incentives, and defaults

Rajan, Uday; Seru, Amit; Vig, Vikrant

doi:10.1016/j.jfineco.2014.09.012

Cited by 331 publications

(172 citation statements)

References 41 publications

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“…It is acknowledged in prior studies that business loans are riskier than car loans [7]; the effect of borrower"s annual income on the PD is well-known [18], as is the relationship between credit history and PD [19].…”

Section: Accepted Manuscriptmentioning

confidence: 99%

The use of profit scoring as an alternative to credit scoring systems in peer-to-peer (P2P) lending

Serrano‐Cinca

Nieto

2016

Decision Support Systems

212

124

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Section: Accepted Manuscriptmentioning

confidence: 99%

The use of profit scoring as an alternative to credit scoring systems in peer-to-peer (P2P) lending

Serrano‐Cinca

Nieto

2016

Decision Support Systems

212

124

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“…Securitisation could also enhance the dependence of mortgage credit supply on variations in local housing markets due to variations in the market value of the collateral (Loutskina and Strahan, 2011). Rajan et al (2010) explain this by the increased dependence of lenders on public signals, such as LTV ratios, as opposed to private information.…”

Section: Literature Reviewmentioning

confidence: 99%

Bank integration and co-movements across housing markets

Milcheva

Zhu²

2016

Journal of Banking & Finance

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This paper investigates whether bank integration measured by cross-border bank flows can capture the co-movements across housing markets in developed countries by using a spatial dynamic panel model. The transmission can occur through a global banking channel in which global banks intermediate wholesale funding to local banks. Changes in financial conditions are passed across borders through the banks' balance-sheet exposure to credit, currency, maturity, and funding risks resulting in house price spillovers. While controlling for country-level and global factors, we find significant co-movement across housing markets of countries with proportionally high bank integration. Bank integration can better capture house price comovements than other measures of economic integration. Once we account for bank exposure, other spatial linkages traditionally used to account for return co-movements across region -such as trade, foreign direct investment, portfolio investment, geographic proximity, etc. -become insignificant. Moreover, we find that the co-movement across housing markets decreases for countries with less developed mortgage markets characterized by fixed mortgage rate contracts, low limits of loan-to-value ratios and no mortgage equity withdrawal. JEL Classifications: C23, G15, F36, R3 Abstract This paper investigates whether bank integration measured by cross-border bank flows can capture the co-movements across housing markets in developed countries by using a spatial dynamic panel model. The transmission can occur through a global banking channel in which global banks intermediate wholesale funding to local banks. Changes in financial conditions are passed across borders through the banks' balance-sheet exposure to credit, currency, maturity, and funding risks resulting in house price spillovers. While controlling for country-level and global factors, we find significant co-movement across housing markets of countries with proportionally high bank integration. Bank integration can better capture house price comovements than other measures of economic integration. Once we account for bank exposure, other spatial linkages traditionally used to account for return co-movements across region -such as trade, foreign direct investment, portfolio investment, geographic proximity, etc. -become insignificant. Moreover, we find that the co-movement across housing markets decreases for countries with less developed mortgage markets characterized by fixed mortgage rate contracts, low limits of loan-to-value ratios and no mortgage equity withdrawal. JEL Classifications: C23, G15, F36, R3

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“…Soft adjustments are by construction less verifiable and thus more likely to be biased than quantitative adjustments, because ex post detection for a single firm case is difficult due to the unverifiability. For example, Rajan et al (2010) find that as incentives for decision makers to collect value-relevant information diminish, market participants rely increasingly on hard factors rather than value-relevant soft factors in the pricing of securitized subprime mortgages, which ultimately leads to an under-prediction of default risk in this scenario. Despite the fact that hard adjustments are less subjective than soft adjustments, even they provide discretion to rating analysts, who have to choose how big a multiplier to use to capitalize operating lease rent expense or whether to classify a securitization as non-recourse.…”

Section: Empirical Approachmentioning

confidence: 99%