Evaluation of the Affordability Level of State-Sector Housing Built in Iran: Case Study of the Maskan-e-Mehr Project in Zanjan City
Bibliographic record
Abstract
Supplying affordable housing for urban lower- and medium-income classes has always been considered an important planning objective. The policies of Iran’s fourth and fifth development plan (referred to as Maskan-e-Mehr or Kindness Housing Project) is intended to supply more affordable housing for the urban low-income classes. For this purpose, the state sector has strove to reduce the average house price as much as possible through cession of land in cities and new towns surrounding metropolises. In turn, families just have to pay the housing construction. Unfortunately, the lack of suitable services and facilities in these areas has resulted in an increase in household transportation costs. Therefore, the present research work deals with evaluating the affordability level of these houses through a new index of housing affordability, which simultaneously considers housing and transportation costs. The study revealed that high transportation costs that result from inadequate accessibility to public transportation and neighborhood services, and high payback of the monthly installments of the housing loan from the other side has resulted in spending more than 45.2% of the family income on housing construction and more than 25.8% on commuting. This means, in general, about 71% of the families’ income is spent on housing and transportation combined; these figures show lack of affordability of these houses for target groups (low-income classes).
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".