Bibliographic record
Abstract
This paper investigates the interaction between residential housing prices and mortgage credit in Luxembourg over the period 1980Q1-2017Q1. We use a vector error correction framework to model this interaction and allow for feedback effects between the two variables. In the long-run, higher housing prices lead to a mortgage credit expansion, which in turn puts upward pressure on prices. The growing demand for mortgage credit is also sustained by positive net migration to Luxembourg. Construction activity is another important determinant of housing prices, in line with existing supply-side limitations on dwelling availability. These dynamics lead to a structural imbalance between housing supply and demand, with the latter being fueled by demographic factors, tax incentives and fiscal subsidies, as well as the low interest rate environment. While price dynamics are partially explained by these structural factors, our results suggest that over the last few years residential housing prices have been characterized by a moderate, but persistent, overvaluation with respect to market fundamentals. Between 2012Q1 and 2017Q1, the average overvaluation is estimated at 6.85% but its trend is decreasing in the last quarters. Results also show that housing prices have a slow rate of adjustment to deviations from fundamentals (only 2.2% of the misalignment is corrected each quarter) and they do not directly adjust to disequilibria in the mortgage market. These _ndings are supported by impulse response analysis, which suggests that shocks to the endogenous variables lead to permanent increases in housing prices.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".