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Record W2994081262

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

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

Bibliographic record

VenueABA banking journal · 2006
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsReal estatePortfolioQuarter (Canadian coin)BusinessFinanceActuarial scienceAsset (computer security)
DOInot available

Abstract

fetched live from 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. …

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.027
metaresearch head score (Gemma)0.143
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.030
Threshold uncertainty score0.142

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.143
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0040.007
Scholarly communication0.0100.014
Open science0.0030.004
Research integrity0.0300.021
Insufficient payload (model declined to judge)0.0150.007

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.047
GPT teacher head0.277
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2006
Admission routes1
Has abstractyes

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Same venueABA banking journalSame topicHousing Market and EconomicsFrench-language works237,207