Housing Affordability, Public Policy and Economic Dynamics: An Analysis of the City of Lisbon
Bibliographic record
Abstract
The increasing growth of population living in cities, associated with the commoditization of investment in real estate, has impacted real estate prices and created obstacles for average income families to meet their housing needs. This problem is generalized to virtually all cities, but it has assumed larger proportions in cities where economic activities (tourism, financial services, high-tech industry) have flourished after the financial crisis. Lisbon is one of those cases. The growth of short-term rentals led to an increase in the property prices well above the average income growth, eroding housing affordability. This paper will focus on analyzing Lisbon´s affordability and understanding its main determinants. The analysis is carried out from the compilation and processing of data from 2004 to 2019, in the context of the municipality of Lisbon, using statistical instruments of linear regression in an exploratory and predictive approach. The results suggest a great influence of factors such as tourism, the foreign population with resident status, the propagation of short-term rentals and public policies on the worsening of housing affordability. In view of these conclusions, the preponderance of the type of public policies implemented and their relationship with the most prominent factors on housing affordability is debated.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".