New-Build Speculation and the Financialization of Urban Development in the Global South: A Perspective from Phnom Penh, Cambodia
Bibliographic record
Abstract
This article analyzes how urban development and property management strategies shape and accelerate new-build speculation (i.e., speculation mechanisms attached to the development of newly built real estate projects) in Phnom Penh (the capital of Cambodia), a city experiencing a growing financialization of its real estate markets. By discussing dominant conceptions of property speculation within critical research in geography and urban studies, the article shows that speculation has primarily been seen as a fictitious and unproductive form of capital accumulation rather than as an effective mechanism of urban production. Analyzing the development of mixed-use, large-scale, and condominium projects, the article argues that not only does property speculation participate in shaping real estate development but also that the production and management of real estate projects “engineer” property speculation and the spatialization logics of real estate financialization. Four main mechanisms of new-build speculation are identified: the design of speculative urbanity, the refining of the liquidity of real estate assets, the spatialization of speculation, and the production of territorial investment platforms. Ultimately, the article discusses how the intensification of the construction of large-scale, mixed-use, and condominium projects in Global South cities is supporting a speculative turn of urban development in so-called frontier markets.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".