Real Estate Market Stability: Evaluation of the Metropolitan Areas Using Factor Analysis
Bibliographic record
Abstract
This article is a modern approach to analysing real estate market stability in today’s era. Since economic collapse in 2008, sustainable growth in real estate sector has become a major discussion and avoidance of another housing market bubble is a priority. Although certain measures have been taken by the governments to control economic direction, most recent analysis showed that home prices in San Francisco, New York, Vancouver and other cities are soaring up, leading to new unprecedented historic highs. Whether this price growth is another bubble risk factor is still negotiable, since more scientific evidence needs to be presented. Therefore, this paper develops a “bubble” measure which gives additional insights in trying to assess the current market situation in a more broader perspective. The empirical research was conducted on four different metropolitan areas around the world which demonstrated an outstanding home price growth in the time period of 2008 – 2017. By applying factor analysis to seven different sub-indexes, aggregating them all into one and using benchmark tools this methodological framework allowed researchers to see whether there is an under/over value situation in the real estate market and whether this growth is sustainable. The research results have confirmed that indeed 4 metropolitan areas (San Francisco, Vancouver, London and Sydney) are in the bubble risk zones that could lead to a market correction or even a new recession. Research suggest that growth is no longer sustainable from within the cities natural demand since average income/mortgage ratio has surpassed its normal levels. As the markets become more unstable a price drop should be expected in the near future.DOI: http://dx.doi.org/10.5755/j01.ee.29.2.19380
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 | 0.002 | 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 | 0.000 |
| 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 | 0.000 |
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".