Bibliographic record
Abstract
Over the past decade, house prices have kept soaring in Chinese cities, making housing affordability one of the biggest social and political issues in urban China. Existing research on housing affordability in China has largely looked at megacities while medium- and small-sized cities and towns have been ignored. In order to fill this research gap, this study analyses the issue of housing affordability in a cross-tier selection of cities, ranging from 1st tier to 5th tier cities, based on both quantitative and qualitative data. Quantitative data makes use of the data collected for the Land and Housing Survey in a Global Sample of Cities in 32 Chinese cities in 2015 and 2016, and the qualitative data consists of 17 semi-structured expert interviews in five cities in 2019. The house-price-to-income ratio (HPIR) and rent-to-income ratio (RIR) have been adopted to measure affordability. The main finding of this research is that housing is largely unaffordable in Chinese cities, yet the severity varies among cities. Considering a HPIR of 3.0 and RIR of 25 percent to be affordable, we find ‘formal private housing’ for example, with a HPIR of 7.2 and RIR of 34 percent, to be (severely) unaffordable, yet while all sample cities’ HPIRs were above 3.0, the severity of unaffordability varies, with 1st and 2nd tier cities located in eastern China with an average HPIR higher than 10 whereas 3rd, 4th and 5th tier cities had HPIRs of around 5. Therefore, special attention should be paid to address the city variations when examining the issue of housing affordability in China. Furthermore, I assert that when facing a housing affordability crisis, it is essential to consider all housing sectors, including public housing and informal housing, the latter playing a positive role in providing relevant affordable housing to rural-urban migrant and the urban poor in Chinese cities.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".