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New Foreclosure Phenomenon: Amid a Mortgage Boom, There Have Been Three Years of Record Foreclosures. Subprime Is a Key Reason, but Is the Cycle Ending

2003· article· en· W2993963934 sur OpenAlexaboutno aff
Órla O’Sullivan

Notice bibliographique

RevueABA banking journal · 2003
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueBanking stability, regulation, efficiency
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésForeclosureQuarter (Canadian coin)BoomRecessionEconomicsBusinessFinancial systemMonetary economicsActuarial scienceFinanceKeynesian economicsHistoryEngineering
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

Five hundredths of one percent may not seem like much to the average person. To an economist, specifically the Mortgage Bankers Association's chief economist, it's enough to assume that years of worsening mortgage foreclosures have come to an end. The number of now foreclosure proceedings has risen consecutively since the second quarter of 2000. That was when the internet bubble burst, marking the start of the current economic downturn in the U.S. and the first test of subprime mortgage lending, which really only took root after the last recession of the early 'Nineties. By mid-2002 foreclosures were at their worst level ever since MBA began tracking them in 1972. By mid-2003, however, the latest available data point, the percentage of loans entering the process of foreclosure had dropped by five basis points, to 0.32%. That's a pretty' big drop for that indicator, MBA's Doug Duncan said, upon releasing the second-quarter data, in mid-September. Foreclosures are slow to register, he explained. The lengthy legal lead-up to repossession proceedings means that the percentage of loans entering foreclosure usually move just two or three basis points in either direction. The total stock of homes in foreclosure declined marginally to 560,000 out of 50 million mortgages. MBA doesn't track how loans exit foreclosure. Even though marginally mo,-e borrowers did begin to fall behind on their mortgages in the second quarter, Duncan foresees no more than a possible upward blip in next quarter's new foreclosures, followed by a long-term decline. He adds that future foreclosures hinge oil unemployment levels. High flyers without wings MBA's expectation is not shared by companies privy to the jumbo-loan market, rather than the conventional mortgage market, which MBA's data reflects. Eastern Savings Bank, a $700-million asset thrift outside Baltimore, lends nationally to high flyers operating on one wing--perhaps a $3-million without a nickel in the bank, explains Jonathan Feldman, senior vice-president of workouts. Eastern Savings makes low (60-70%) loan-to-value mortgages but still has unusually high delinquencies. Of its loans, 16% were delinquent at least 90 days in 2000, before the national foreclosure problem began, 23% at the start of this year, and a marginally improved 21% by mid-year. Feldman recognizes his niche is atypical, adding, however, A lot of people hale credit that's beyond, their means. Foreclosures.com, a Sacramento-based company, has for 11 years been gathering data from local courts and elsewhere for would-be buyers of pre-foreclosed property. Interviewed before the release of MBA's latest data, Alexis McGee, president, said the late stages of the foreclosure cycle, and have lot been reached anywhere nationally. In California, we're seeing the hey inning of a three- or four year cycle. Feldman adds, A lot of foreclosures have been avoided because properties are appreciating, and that might not last. Counterintuitive trend The past few years have been a notable period ill mortgage history because foreclosures have been at record levels, paradoxically alongside record origination levels and escalating home prices. An increase in foreclosures historically has indicated a troubled housing market, since a hot market would enable a homeowner facing default to sell quickly and avoid foreclosing. The difference in today's foreclosure pattern is substantially explained by subprime lending, which is facing it's first test in an economic slowdown. Over-leveraged buyers some perhaps on 125% loan-to-value mortgages, may have no equity left. Unemployment hovered around a nine-year record in September while personal bankruptcy, filing last year were at a 22-year high. Pockets of reportedly high foreclosures include wealthy Silicon Valley in California, heart of the ailing tech sector, and in poorer parts of New York. …

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)0,002
score de la tête « metaresearch » (Gemma)0,006
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,010
Score d'incertitude au seuil0,032

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,006
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0020,003
Études des sciences et des technologies0,0020,002
Communication savante0,0040,006
Science ouverte0,0010,003
Intégrité de la recherche0,0020,006
Charge utile insuffisante (le modèle a refusé de juger)0,0100,002

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 Opus0,025
Tête enseignante GPT0,229
Écart entre enseignants0,204 · 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'étudeObservationnel
Domainenon disponible
GenreEmpirique

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

En bref

Citations1
Publié2003
Routes d'admission1
Résumé présentoui

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