Transitional distribution dynamics of housing affordability in Australia, Canada and USA
Bibliographic record
Abstract
Purpose The main purpose of this paper is to explore the transitional dynamics of housing affordability indicators of major cities in three developed countries: the USA, Canada and Australia, in the period after the global financial crisis. As the global housing markets are more interconnected today, it is essential to investigate the demographic movement pattern and their impacts on housing market dynamics. Design/methodology/approach Based on the Markov transition matrix approach and the stochastic kernel technique, a newly established framework named the mobility probability plot (MPP) is adopted to investigate the city-level trends of housing affordability in the three countries during the period 2008-2015. Findings The results suggest that the transitional dynamics of the USA’s housing affordability trend saliently differs from those of Canada and Australia: in the USA, MPP results reveal that when the price-to-income (P/I) ratio is higher than 3.5 times, it has a high tendency of moving downward in the next period. In Australia, housing affordability tends to continue deteriorating when the P/I ratios are in the range from 8.0 to 8.6. In Canada, the MPP analysis indicates that the P/I ratios tend to increase further when the ratios are between 5.7 and 7.0, and within the range of 8.3-9.5. Originality/value This paper adopts an innovative approach to explore the city-level trends of housing affordability in the three developed countries during the period 2008-2015. The distribution dynamics approach has several virtues: first, this approach does not merely focus on the issue of housing affordability but also includes an analysis of the underlying housing affordability distribution. Second, it can clearly show the mobility of the city-level units in terms of the P/I change. Third, it can predict the proportion of the entities in different P/I ratio bands in a number of years ahead and even in the long run.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".