The Effect of the Principal Component Index for Housing Quality Satisfaction on Housing Price: Urban vs. Rural Analysis
Bibliographic record
Abstract
The 2015 Questionnaire Survey on Housing Conditions by the Construction and Planning Agency surveyed four categories of satisfactions on housing quality: living convenience, surrounding environmental quality, satisfaction on interior environment, and satisfaction on exterior environment. This study pioneeringly investigates the effect of housing satisfaction on Taiwanese housing price for six municipalities and other rural area. Since the above four survey categories of housing quality are highly overlapping, to avoid variable interaction, we construct an index for housing quality satisfaction using principle component analysis to reduce dimensionality. After controlling the moderation effect of market condition, the results show that residential area, house age, floor location, and number of floors all significantly affect housing price. More importantly, the index for housing quality satisfaction is positively related to housing price, indicating that better housing quality helps in raising housing price. However, the positive relation does not exist in Taipei City or Kaohsiung City. Consistent with previous studies, stress on high housing price may weaken the need for quality consideration, especially the case of Taipei City. Finally, market variation does not lead to difference in the relation between housing quality satisfaction and housing price.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".