Exploring Irrational Expectations: Macroeconomic Factors in the Housing Boom
Bibliographic record
Abstract
Using a panel database of quarterly data from 1976-4 through 2008-2 for each of the 50 states and the District of Columbia, I show that home prices in the US build up significant inertia over time.Price changes in one quarter tend to be serially correlated with successive quarters.This effect has become even more pronounced in recent decades as home buyers and owner-occupiers have acted as speculators through the use of recent financial innovations, thus fueling momentum in residential real estate markets through the enhanced use of leverage.Moreover, the evidence indicates that inertia in home prices has become a national phenomenon.Contagion, propagated via the national news media, has prolonged the run-up in housing prices.As a result, regional home price correlations have increased markedly over the past three decades, such that the advantages to the diversification of residential real estate, as measured via portfolio analysis, have steadily decreased.Consequently, investors and banks must re-evaluate the risks of home loan portfolios in light of this inertia and increasing correlation among assets inherent in the current housing market.Over the next few years, this same inertia is likely to drive national home prices down by another 15-30% before any meaningful uptick in home prices occurs.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".