Determinants of Apartment Prices within Housing Estates of Nairobi Metropolitan Area
Bibliographic record
Abstract
The objective of this study is to establish the determinants that significantly influence apartment prices that are located within housing estates of Nairobi metropolitan area. The determinants comprise of apartments features including: proximity to shopping malls, proximity to Nairobi’s central business district, proximity to schools, proximity to slums, presence of swimming pool, presence of balcony, size of the apartment, periodic rental income and land value. Both secondary and primary data sources were employed in the research and 30 housing estates where apartment are located were selected for data collection purposes. Multiple regression analysis was employed for the secondary data and the findings indicated that: land value and size of the apartments had a significant influence on apartment pricing. Descriptive statistical analysis findings indicated that proximity to shopping malls, proximity to Nairobi’s central business district, proximity to schools, presence of swimming pool, size of the apartments and land value had significant influence on apartment prices. Triangulation of secondary and primary data analysis results indicated a consistency rate of 50%. The recommendation of the study is that real estate stakeholders especially buyers should focus on size and land value of apartments as these significantly influence apartment pricing in Nairobi metropolitan area.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".