The Financialization of Housing as a Growth Model: New Property Relations and Massive Suburbanization in Toronto/Brampton and Istanbul/Gktrk
Bibliographic record
Abstract
The financialization of housing has become a crucial discussion point since the financial crisis of 2008. This dissertation aims at focusing on the financialization of housing in the Greater Toronto Area and Istanbul Metropolitan Area. While the existing literature tends to describe the financialization of housing as the increasing impact of finance capital on the production of space, in this dissertation I argue that the financialization of housing appears as an economic growth model that transforms the socio-economic conditions of households at least in certain countries. \n \nThe dissertation examines the cases of Brampton in GTA and Gktrk in IMA (Istanbul Metropolitan Area) in order to underline the ongoing property relations and the rise of suburban-financial nexus as an economic growth model. The financialization of housing occurs in many different forms in different countries. In certain countries it appears as a simple dynamic of the housing market, i.e. it is just a matter of mortgage credits and the banking system linked to the global financial investments. In certain countries, it appears as the financialization of rental housing systems (e.g. Germany), and in certain countries it appears as the dominance of finance capital in order to speculate the investments in securities. In fact, the financialization of housing began to become the dominant economic growth model in certain countries. Canada and Turkey can be examined as the economies that pursue the strategy of using the financialization of housing as a boosting tool for economic growth. In these two countries, the real estate market and its connection to the financial flows have become the key growth engine of the economy. In this dissertation, I examine how in these two countries, the financialization of housing has become the leading economic growth strategy and how this process transforms the cities. The cases in this dissertation aims at contributing to this argument by going into details of how finance capital transforms the socio-spatial reality.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".