Assessing the risks from high house prices
Bibliographic record
Abstract
▀ Global house price growth is slowing but remains relatively solid and, therefore, supportive of the global upturn. There are rising risks, however: prices are stagnating or falling in some highly valued markets; some emerging markets are facing local financial stresses; and OECD housing valuations, while well below the 2006–07 peak, are now comparable to earlier peak levels. ▀ Our in‐house world house price index shows real (inflation‐adjusted) growth falling from 4% in mid‐2017 to 2.7% in Q2 2018. This is slightly above the long‐term average rate since 1997. But trends across economies are very varied – price growth is rapid in Hong Kong, the Netherlands and Mexico but negative in Canada, Italy, Brazil, Turkey and Sweden. ▀ There are some signs that high valuations are now weighing on price growth, with most highly‐valued markets seeing stagnant or negative price growth. There are a few notable exceptions that may be risk hot‐spots for the future: Hong Kong, Ireland, the Netherlands and New Zealand are combining rapid price growth with relatively high valuations. ▀ Median OECD house price valuations are below the 2006–07 peak but are higher for a several risky markets. Historical experience suggests that high valuations – of 125% or more of the long‐term average – point to a 60% chance of prices falling over the next five years. This matters because house prices can have a big impact on economic activity, even if the link may have loosened in the G7 in recent years. ▀ Looking across a range of housing risk indicators, property market dangers look concentrated in a number of smaller advanced economies and are less severe for the largest economies. The potential ‘trigger’ of rising interest rates is limited or missing for most advanced countries (although it is not strictly needed for prices to start falling) but is present for some emerging countries.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".