Notice bibliographique
Résumé
The FHA is a business and a government agency. It is supposed to help people buy homes. It is also expected to operate at a profit. It has competitors in the private sector: the private mortgage insurers (PMIs) on the low-risk side, since the late 1950s; and the subprime lenders on the high-risk side, since the early 1990s. It also competes with the GSEs. The FHA has no protection from this competition. The FHA mortgage ceiling keeps the FHA out of the market for highbalance mortgages; it doesn’t keep anybody out of the FHA market. If the subprime lenders or the GSEs can take away the FHA’s business, it’s theirs. The FHA has an obvious advantage over PMIs and subprime lenders. FHA insurance carries the full faith and credit of the government of the United States. Conversely, it has the disadvantage of being a government agency—being less flexible and having to obtain congressional approval for major changes in its activities. The net result is that the FHA does serve a market segment that its competitors apparently can’t, and it serves that market without losing money. Quigley states that the FHA’s market share has declined systematically since the late 1950s. It is infuriatingly difficult to construct a consistent time series on FHA activity, or the home-mortgage L et me start by explaining my perspective on federal housing credit programs. During 2001-05, I served as Federal Housing Administration (FHA) Commissioner at the Department of Housing and Urban Development (HUD) and also Assistant Secretary for Housing. I managed the FHA programs and was, therefore, responsible for half a trillion dollars of mortgage insurance exposure backed by the full faith and credit of the government of the United States. I was also the “mission regulator” for Fannie Mae and Freddie Mac— not the safety and soundness regulator (I need to make that very clear)—responsible for the housing goals, new program approval, and a few other matters. I was also at HUD during the administration of the first President Bush, running the Office of Policy Development and Research. In that capacity, I was responsible for developing an FHA reform proposal that was enacted in 1990, and also was the regulator for the governmentsponsored enterprises (GSEs), regulating both their safety and soundness and public purpose. Earlier, I was chief economist at HUD in the mid-1970s. So, I have a fairly long historical perspective. Quigley’s (2006) paper is well worth reading as an introduction to federal housing credit activities. He has a good sense of what is important. I follow his order in my comments: the FHA’s business, the FHA’s public purposes, and then similarly for the GSEs. I begin with a general point: Quigley is absolutely right about the path-dependency of housing policy. I’ve felt that way for years: “pres-
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,009 | 0,004 |
| Communication savante | 0,004 | 0,005 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,074 | 0,042 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,019 | 0,011 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».