Dynamic Model: House Price Return, Mortgage rate and
Bibliographic record
Abstract
We apply vector auto regression models (VAR) and simultaneous equations models (SEM) to estimate the dynamic relations among house price returns, mortgage rates and mortgage default rates, using historical data during the time period of 1979 till 2007. We estimate that, holding all the other factors constant, two consecutive one-percent increases of default rates can drive OFHEO’s house price returns down by about 5 percent and Case-Shiller’s current house price return down by about 12 percent. Conversely, two consecutive 1-percent decreases of OFHEO’s or Case-Shiller’s house price returns can drag the current default rate up by 0.08 percent or 0.05 percent, respectively. We apply our models in making predictions using data up to the second quarter of 2008. Not surprisingly, the OFHEO’s and Case-Shiller’s indices exhibit different patterns and thus they yield different predictions as well. On an expected value basis, the future level of OFHEO’s house price returns will remain negative and reach the lowest value in 2010; it may take quite a few years for the house price returns to become positive. However, we get more optimistic forecasts using the Case-Shiller’s index, whereas the future house price returns would become positive since 2010, and mortgage default rates will peak by 2010 and decrease thereafter.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".