Real estate market stability: evaluation of the metropolitan areas by using factor analysis and z-scores
Bibliographic record
Abstract
This article is a modern approach to analysing the real estate market stability in today’s era. Many statistical methods were used to measure price deviations, nonetheless, they were insufficient to identify the economic collapse in 2008. As sustainable growth in the real estate sector has become a major priority, some alternative measures to analyse market deviations should be developed. Most recent studies showed that home prices in San Francisco, New York, Vancouver and other cities are soaring up to new unprecedented historic heights. The issue on whether this price growth is another bubble risk factor remains debatable since more scientific evidence needs to be presented. Therefore, this paper develops a new “bubble” index which provides additional insights in the current market situation from a broader perspective. The empirical research, which was conducted on four different metropolitan areas worldwide, which demonstrated an outstanding home price growth over the period 2008 to 2016. By applying factor and z-score analysis to seven different sub-indexes and aggregating them all into one, this paper developed the methodological framework that allowed to assess whether there is an under/over value situation in the real estate market. The research results have confirmed that 4 metropolitan areas (San Francisco, Vancouver, London and Sydney) are indeed in the bubble risk zones, which can lead to a market correction or even a new recession. The research suggests that although it is difficult to compare model accuracies, employment of factor analysis and z-score methods provides strong predictability capacity since it perfectly mimics the prior economic crisis and leads to the results somewhat similar to those obtained by employing the UBS bubble index.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".