The recovery rate for retail and commercial customers in Germany: a look at collateral and its adjusted market values

Abstract

Based on a unique data set of 909 defaulted retail and commercial (selfemployed and SMEs) credit customers in Germany, whose original loans were made by 123 different banks, our article confirms a significant positive influence of collateral, and of amicable agreements between the debtor and the bank (redemption), on the recovery rate [1 -loss given default (LGD)]. In a further analysis of collateral, systematic biases between the realized market price and the expected market values of real estate are revealed, even though the appraisal reports should have already considered all factors influencing the value. Using valuations that were adjusted for these recognized biases, we can increase the explanatory power of the underlying models. Moreover, we compare these models to models that apply, as is common practice in the banking industry, flat haircuts to collateral values and show the superior performance of our proposed approach.

The Impact of Borrower-Based Instruments on Household Vulnerability in Germany

“…In a booming market with prices increases of 10% or more, the property sold will fetch the going market price. The calibration matches the empirical observation of 78% for the average recovery value of defaulted German properties, as reported by Ingermann et al (2016).…”

Stress Testing the German Mortgage Market

“…with ∆p i,t = p i,t p i,t−1 − 1 and a natural lower bound at zero and a maximum discount rate of 50% in the most adverse possible market environment. The implied value of 25% for the years 2006-2011, when average house price were flat in Germany, corresponds well with the average recovery values of 78% reported by Ingermann et al (2016) for a portfolio of 1,236 defaulted German properties for the same time period. Finally, the time-invariant annual depreciation rate δ is set to be 1.5%.…”

Forecasting recoveries in debt collection: Debt collectors and information production

“…Bellotti & Crook () report adjusted values between 10.5% and 11.1% in a linear regression on a comprehensive set of debtor, contract, spatial, and macroeconomic characteristics; Gürtler & Hibbeln () report values between 4.4% and 18.9%; Loterman, Brown, Martens, Mues, and Baesens () examine six different loan data sets and find values between 1.2% and 44.12%. The value is considerably higher in studies that include information on collateral—up to 76.9% for Ingermann et al () or up to 61% for Qi & Yang (). Given that the values not including collateral are often low, predicting the recovery rate is considered a difficult problem.…”

“…Our empirical modeling analysis uses the fractional regression model of Papke & Wooldridge (), a common approach in modeling the recovery rate of bank loans (Dermine & de Carvalho, ; Ingermann, Hesse, Bélorgey, & Pfingsten, ). Furthermore, Beck et al () use this approach to model collection rates.…”