Analyzing European Bank Non-Core Assets


This paper presents a case study of investment dynamics and opportunities in European non-core assets (“NCAs”) and illustrates early stages in our investment process as applied to these assets.[1]

Specifically, this study describes how we generally seek to:

  • Source and filter the deals in our pipeline to identify the best opportunities

  • Assess intrinsic value and parse portfolio characteristics

  • Determine optimal pricing

  • Model stress scenarios that may have significant impact on asset value

To demonstrate how certain aspects of the investment process have worked in practice, we take a closer look in this case study at a large portfolio of Spanish NCAs. This portfolio consisted primarily of loans to small and medium enterprises (“SMEs”).

The portfolio described in this study, which we will refer to as the “Spanish NCA portfolio,” consisted of 421 loans with a face value of approximately €175 million, held by a regional Spanish bank.

Note: This paper is authored by the D. E. Shaw group. Certain first-person statements ( i.e., those using “we” or “our”) relate to the D. E. Shaw group generally, while others relate to the firm’s Asset-Backed Strategies team (the “ABS team”) specifically. In particular, it should be noted that the investment process and approach described herein are deployed by the ABS team.

[1] “Non-core assets” is intended to cover non-core asset classes from which U.S. or European financial institutions have been withdrawing under competitive or regulatory pressures. The term “non-core assets” includes loans and other receivables, performing and non-performing, as well as assets that typically back bank loans and securitizations. While there is no settled definition of a “non-core asset,” in general, these assets can be thought of as no longer being part of the owner’s core business and/or as being offered for sale.

Investment Approach

Strong sourcing relationships and quantitative analytical capabilities are foundations of our investment process, which aims for maximum scope and efficiency in evaluating potential deals.

  • In the sourcing process, we seek deal flow from advisers, bank management teams, servicers, and lawyers in our network. We have strong, direct relationships in these fields, as demonstrated by the numerous sellers with whom we have completed multiple transactions.

  • The NCA portfolios we have favored typically consist of numerous, relatively small loans that may be secured by a variety of collateral. This heterogeneous quality of the underlying loans can make such portfolios more difficult to value and restructure. In our experience, such portfolios often are less-competitively bid than those composed of smaller numbers of relatively large loans backed by other assets, such as commercial real estate-backed transactions.

  • We have designed and built proprietary data collection and quantitative systems to assist practitioners in core activities, such as identifying/sourcing potentially attractive opportunities, due diligence and structuring, post-deal management, and exit. These efforts have helped to build a database that tracks approximately 65 million loans, representing more than $12 trillion in balances.

  • Our first pass on all deals is typically based on in-house analysis using a statistical approach, which we believe produces meaningful results while streamlining the process and avoids the costs that would be incurred by outsourcing these steps.

  • Once we have selected the deals we want to consider for a binding offer, we engage outside vendors and commence a more robust loan-by-loan analysis.

Each NCA portfolio is analyzed according to the set of characteristics we consider most relevant to the intrinsic value of each loan and its associated collateral.

  • To assess the value of the loans in each NCA portfolio, we consider typical market factors such as jurisdiction, loan status, collateral type, lien type, loan size, geography, default year, and legal state, on a loan-by-loan basis. In the Spanish NCA portfolio, the predominance of secured loans entailed significant analysis of underlying collateral.

  • We estimate collateral values based on various inputs, drawing heavily on our proprietary datasets. In the Spanish NCA portfolio, we assigned collateral value only to real estate, so we describe below the estimated market value of relevant collateral as real estate value (“REV”) as of the time of our due diligence of the portfolio.

  • We pay close attention to the legal state of each asset, which can materially impact the purchase price (see “Scenario Analysis” below). For each asset in the Spanish NCA portfolio, either the debtor was subject to a bankruptcy proceeding (i.e., a Spanish “concurso”) or the applicable loan was subject to litigation.

The Spanish NCA portfolio consisted primarily of secured SME loans

C o u n t r y I n v e s t me n t F u n d i n g D a t e Primary Asset Type Face Value (€ mn) Number of Positions Avg Face Value per Position (€ mn) Real Estate Value (€ mn, est) S p a in S e p te m be r 2 0 1 6 S M E L o a n s 1 7 4 . 7 4 2 1 0 .4 1 8 5 . 8 L ega l S t a t e De f a ul t Y e a r G eo g ra p h y L o a n S ize L i en T y p e % o f F a c e V al u e Se n i o r J un i o r N o n e < 5 0 0k 50 0 k - 1 m n >1 m n N o r t h S o u t h C e n t e r I s la n d s < = 20 1 0 20 11 - 20 1 2 > = 20 1 3 B a n kru pt c y Li t i g at i o n 0 10 20 30 40 50 60 70 80 90 100

Source: the D. E. Shaw group. Please see the notes at the end of this paper for key information regarding this chart.

Real estate collateral supporting SME loans in NCA portfolios can take a variety of forms, including residential, commercial, and industrial properties as well as land.

  • The collateral for the Spanish NCA portfolio was primarily developed property, which consisted mainly of residential real estate, and smaller amounts of commercial and industrial property as well as land. The following are three examples of non-land collateral that supported SME loans in this portfolio.

P u r c h a s e P r i c e o f L o a n 2 9,9 4 1 P u r ch a s e P r i c e o f L o a n 6 4 , 1 4 6 P u r c h a s e P r i c e o f L o a n 5 9,99 9 F a c e V a l u e o f L o a n 1 0 7,39 2 F a c e V a l u e o f L o a n 676, 2 5 7 F a c e V a l u e o f L o a n 1 6 2 ,3 5 2 C o l l a t e r a l T y p e R e s i d e n t i a l C o l l a t e r a l T y p e C o m me r c i a l C o l l a t e r a l T y p e I n d u s t r i a l L o c a t i o n S e v il l a , A n d a l u c ia L o c a t i o n G i r o n a , C a t a l a L o c a t i o n S e v il l a , An d a l u c i a