Optimal private housing supply under various schemes of public housing provision
Bibliographic record
Abstract
Allocating land for public housing is an essential step in providing affordable housing for low-income citizens, as practiced in Hong Kong and Singapore. Locating public housing projects in suburban areas causes a spatial mismatch between public housing tenants and job opportunities, while placing public units in urban areas exacerbates the shortage of urban land , which may result in a more compact urban development that worsens the living conditions of urban private flats. We extend the stochastic bid-rent framework to derive the optimal supply decisions of profit-seeking developers under various schemes of public housing provision . The residence and travel choices of heterogeneous households are simulated under the optimal supply decisions. We find that placing a certain number of public flats in urban areas will lead to a higher profit for developers. With more public housing in urban areas, developers condense the urban development and provide more urban micro flats to capture the privilege of accessibility, which lowers the quality of private urban flats. On the other hand, compact development accommodates more residents in urban areas, thereby alleviating congestion in suburban areas. This improvement in accessibility, however, results in rent increases for suburban private flat residents, who will suffer a loss of consumer surplus . Similarly, policy measures that aim to improve living standards or shorten the commuting time for residents (e.g., imposing restrictions on minimum flat size or upgrading suburban transport facilities) may affect the welfare of other stakeholders adversely. Thus, it is unlikely that a land allocation that improves the welfare of all stakeholders can be found, rendering the importance of considering this trade-off judiciously.
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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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".