An In-depth Analysis of Factors Impacting Housing Satisfaction: A Systematic Review and Post-Occupancy Evaluation
Bibliographic record
Abstract
Housing is a critical component of urban development, shaping various aspects of community life, including social, economic, environmental, and cultural dimensions.Understanding the factors that influence housing satisfaction is essential for creating living environments that enhance residents' quality of life.This study aims to identify and analyze the key determinants of housing satisfaction, with a particular focus on improving the end-user experience.The research employs a systematic literature review, selecting studies based on specific criteria, including their relevance to housing satisfaction and the diversity of factors they examine.The analysis highlights several key factors that significantly impact housing satisfaction, such as the quality of architectural design, environmental sustainability, and socio-economic conditions.Additionally, the study explores how these factors interact to influence overall satisfaction, providing a more nuanced understanding of their effects.The findings contribute to the theoretical framework of housing satisfaction by integrating these diverse factors and offer practical implications for architects, urban planners, and policymakers.By addressing these determinants, the study provides strategies for creating more sustainable and livable residential environments, ultimately enhancing the well-being of residents and contributing to the broader goals of sustainable urban development.
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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 | 0.015 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".