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Enregistrement W2994081262

Concentration Alert: Why You Should Adopt Better Commercial Real Estate Risk Management Practices Even before New Guidelines Take Effect

2006· article· en· W2994081262 sur OpenAlexaboutno aff
John Barrickman, Gary Stein

Notice bibliographique

RevueABA banking journal · 2006
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueHousing Market and Economics
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésReal estatePortfolioQuarter (Canadian coin)BusinessFinanceActuarial scienceAsset (computer security)
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Over the past decade, banks and thrifts of all sizes have significantly increased their exposure to commercial real estate (CRE) lending. The largest percentage increases have occurred at institutions with $10 billion or less in total assets. At the end of the third quarter of 2005, construction and land development loans accounted for three times their 1995 percentage of gross loans and leases at institutions within this asset tier, and non-farm, non-residential CRE loans were nearly double their 1995 percentage. In response to this trend, the Federal Reserve, the FDIC, the OCC, and the OTS published proposed interagency guidelines and best on Jan. 9, 2006. It is unclear at the time of this writing if final guidance will be issued later this year and what it will say. [The comment period was extended in March to April 13.] Regardless of the final outcome, the guidelines as initially proposed are far-reaching and make for prudent business practices. More plainly, real estate lenders that do not adhere to the principles outlined in the proposed guidelines may underestimate the risk in their portfolios and subject themselves to substandard portfolio credit performance. The implications of enhancing risk monitoring--i.e., adopting the guidelines if and when they are finalized--are significant. First, many banks may be forced to change their current business model. Roughly one quarter to one third of all supervised institutions have portfolio CRE concentrations that exceed proposed capital thresholds. As a result, these lenders will need to meet heightened risk management practices and/or carry more capital to avoid enhanced scrutiny. Either way, the profitability of real estate lending may be diminished, and severely impacted banks will likely need to find alternative lending opportunities. Second, these same consequences will force many lenders to reconsider CRE pricing. Specifically, they will need to analyze and offset increased capital allocations and monitoring costs to maintain profitability. Banks with inadequate risk monitoring must also determine how to implement an improved risk management infrastructure. While the proposed guidelines specify board and management responsibilities as well as what banks must measure, getting there--i.e., changing day-to-day practices, and, even, more so, the lending culture--can be very daunting. The remainder of this article presents a framework for meeting two of the more esoteric yet intrinsic requirements implied in the proposed guidelines: achieving consensus on the bank's tolerance for risk and defining the model portfolio. These are also the most fundamental elements of sound risk management and critical first steps to setting loan policy and establishing effective portfolio monitoring. Tolerance for risk At the industry level, it is easy to distinguish the risk tolerance between sub- and superprime lenders. Ask a number of bankers at a single institution, however, and you often find inconsistency in how they articulate their own bank's tolerance for risk. This is alarming, as risk tolerance drives lending strategy and model portfolio definition, which in turn influences risk policies, procedures, systems, and controls. Lack of understanding and disagreement over risk tolerance also lead to disconnects between growth and credit quality goals; may cause frontline lenders to focus on the wrong opportunities; lead to wasted time in the credit committee; and ultimately, create unhappy management, lenders, borrowers, and shareholders. How do you achieve consensus regarding tolerance for risk? First and foremost by involving people at all levels of the organization. Consensus is not easy. Often, the board of directors and line have polar opposite goals. It is therefore critical for centers of influence-formal and informal thought leaders across the bank--to participate in the definition process to help drive cultural acceptance and companywide adoption. …

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,027
score de la tête « metaresearch » (Gemma)0,143
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: Commentaire
Score de désaccord entre enseignants0,030
Score d'incertitude au seuil0,142

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0270,143
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0040,007
Communication savante0,0100,014
Science ouverte0,0030,004
Intégrité de la recherche0,0300,021
Charge utile insuffisante (le modèle a refusé de juger)0,0150,007

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,047
Tête enseignante GPT0,277
Écart entre enseignants0,230 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2006
Routes d'admission1
Résumé présentoui

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