Making the hard sale: Migrant sales agents and the precarious labours of Philippine real estate brokerage
Bibliographic record
Abstract
In recent years, scholars have taken interest in the significant remittance contributions of Filipinos living and working abroad to what some have called the Philippine ‘real estate boom’. In the case of real estate property as investment, however, much remains unknown with regards to how major Philippine real estate developers capture, facilitate and ensure the continuous flow of migrant investments into condominium units in the Philippines. This article examines how Philippine real estate developers make use of a particular kind of sales labour to reach various Overseas Filipino Worker (OFW) populations. It argues that most major developers rely on a hierarchical, networked and transnationalized labour arrangement, composed mainly of licensed and unlicensed sales agents. Drawing from interviews with sales agents working in and between the cities of Metro Manila and Dubai, it then complicates dominant depictions of real estate sales agents by demonstrating the precarious conditions they work under as well as how this flexible labour arrangement often works in two ways. First, this article demonstrates that through their sales labour, specific Filipino migrant community dynamics such as community leadership, charity and other migrant ‘life projects’ such as investments, side hustles and debt get absorbed into the very circuitry of real estate accumulation. Second, through sales agents’ recruitment of OFW ‘marketing partners’, Philippine real estate developers are able to enter and expand into spaces of migrant advocacy and of migrant in/formal work to enrol OFWs either as potential investors, as labour or both.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".