Modeling the Housing Market’s Decline: How Much, How Fast, and What People Value
Bibliographic record
Abstract
One of the most severe threats to the U.S. economy is the current credit crisis that was sparked by the collapse of the sub-prime housing market. This collapse has spread to affect the housing market as a whole and has left many families unable to sell their homes, some falling into foreclosure, others getting stuck with two or more mortgages, and most watching the value of their most important asset- their home- fall sharply. It is important to examine this decline to obtain insight into when the decline began, how sharp the decline was, and whether or not there are signs of the decline slowing or reversing. This study attempts to examine the decline in the housing market in Modesto, CA- one of the hardest-hit communities in terms of foreclosures and unsold inventory in the country. Typical housing market research is done using multivariate hedonic regression models. As such, this study employs a hedonic log-lin model using data obtained from the MLS on 6500 homes sold between April2005 and March 2008. This model controls for many standard variables included in typical housing research, but includes some unique variables for items such as Home Owner's Association (HOA), HOA dues, and Real Estate Owned (foreclosed) homes. Initial findings reproduce the decline in housing prices and show that the most recent quarter shows an increased loss in value over the previous quarter, with prices peaking in the first half of2006.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".