Rising Inequality of Housing? Evidence from Segmented Housing Price Indices
Bibliographic record
Abstract
Abstract:\nThis article uses the Case-Shiller technique for constructing housing price indices on a Norwegian\ndata set of transactions for the period 1991-2002 consisting of 10 376 pairs of repeated sales. Using\na weighted least squares scheme in order to control for heteroskedasticity, we construct a general\nhousing price index by regressing differences in log prices for the subset of repeated sales of same,\nand thus identical, homes onto a set of binary time variables, one for each quarter in the period. The\nconstructed index shows that nominal prices for identical homes in general have increased by a\nfactor of 3.58 over the 11-year period, while the CPI increased by 1.28, creating substantial capital\nreturns for early purchasers. We then segment the data set into five different housing types in order\nto control for finite mixtures of hedonic features, and find that price indices for the smallest and\nlargest type show nominal increases by factors 4.40 and 2.77, respectively.\nKeywords: distribution, hedonic model, housing price bubble, housing price index, inequality,\nrepeated sales model, segmented housing types
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".