Bibliographic record
Abstract
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-
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".