Housing financialization in Lisbon´s historical center
Bibliographic record
Abstract
This dissertation explores the phenomenon of the financialization of housing in the historic city center of Lisbon during the last years, between the years of 2012 and the present. By housing financialization what is meant is residential properties being primarily utilized as vehicles of investment, at the expense of their social use of being homes. This study looks at whether signs of housing financialization are present in the country’s capital, and focuses on what is believed to be one of its particular manifestations: the rise of short-term rentals, commonly referred to as vacation rentals. A growing incidence of homes are placed on the short-term rental market through online platforms such as Airbnb. Finally, this study aims at examining if short-term rentals are a new frontier of housing financialization in Lisbon, and to further understand the means through which investment is channeled into this sector. This dissertation first addresses the topic of housing financialization as a global phenomenon, and analyzes its distinct mechanisms being employed in diverse geographies. Upon this framework attention is directed towards the context of Portugal, and more specifically Lisbon, and how past politics and economic policies of the country shaped the development of the housing market The analysis grants specific emphasis to the period post crisis, in which a series of neoliberal policies, the growing touristification of the city and a push for urban rehabilitation greatly reconfigured the city’s residential real estate market. Paving the path for the rise of short-term rentals and the entrance of a deluge of capital investment. This study followed a qualitative interview model and began with a series of exploratory in-depth interviews with key experts in the real estate market. Upon analyzing the content of the initial interviews there was a narrowing of the scope of the study and a lens was placed upon the subject of short-term rentals. A number of interviews were carried out with key actors in the short-term rental market, which have been the core of the findings of this study lie. The mode in which this sector directs investment into residential real estate is addressed, and insight was gained into the facets of the sector that attract investors.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".