Bibliographic record
Abstract
Low interest rates continue to prime global housing markets, notwithstanding relatively sluggish economic growth and elevated financial market volatility. According to IMF estimates, roughly three-quarters of national markets are experiencing appreciating real house prices, based on the latest available data. Momentum in general favours advanced nations over emerging markets, though gains can be seen across regions. Notable exceptions are Brazil and Russia, where deep recessions, rising unemployment and high interest rates continue to put significant downward pressure on housing demand and prices. Canada, Australia, Sweden and the U.K. remain among the top performing residential markets internationally. The ongoing rapid pace of house price appreciation has prompted authorities to further tighten some macroprudential rules. This includes increased downpayment requirements (Canada), higher investor lending rates (Australia), stricter mortgage standards (Sweden) and new taxes on second homes and rental properties (U.K.). U.S. house prices continue to trend up amid strengthening sales and tight inventory. Solid fundamentals — pentup demand, a robust job market and rising household formation — should extend the recovery even in the face of moderately higher borrowing costs. Affordability remains supportive, with average prices still about 20% below the pre-crisis peak adjusted for inflation, and the U.S. Federal Reserve engineering only a gradual firming in policy. Housing markets also are gradually firming in the euro zone. Average inflation-adjusted house prices across the region edged up 2% over the past year, a modest but defining turning point after several years of decline. However, conditions remain uneven, with strengthening labour markets supporting solid price gains in some member countries, notably Ireland, Spain and Germany, while other markets, including France and Italy, continue to languish alongside a more tepid economic recovery. The majority of property markets in Latin America and Asia are showing moderate activity and price growth. China’s housing recovery is broadening, with roughly two-thirds of major centres reporting annual price growth through April. However, authorities face a tough policy balancing act in their attempt to cool skyrocketing prices in top-tier cities while at the same time support the nascent recovery in oversupplied smaller centres. Foreign capital inflows also are contributing to the recovery in global property markets, as investors search for geographical and asset diversification, and higher potential returns. This extends not just into residential real estate, but commercial properties and agricultural lands as well. A large share of these flows has been destined to the luxury property market in top-tier cities. Market sentiment remains vulnerable to shifts in the economic and financial climate. Sales of high-end luxury properties have cooled in a number of large markets over the past year, including New York, Hong Kong and London. The softening in demand mirrors the economic slowdowns in China and the Middle East, and deep recessions in Russia and Brazil, all key source markets of luxury foreign buyers. Affordability also is taking on added importance, with relatively lower prices and favourable exchange rate conversions benefiting some second-tier cities, including in Canada, Australia and the euro zone.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.044 | 0.004 |
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".