Determinants of Commercial Real Estate Market Performance: The Case of Addis Ababa, Ethiopia
Bibliographic record
Abstract
Numerous social and economic problems are brought on by urbanisation, including worse-than-ever housing shortages in emerging nations and an increase in the number of people living unlawfully in slums without permits or property rights. This is also applicable to Ethiopia. The investigation of the factors influencing Addis Ababa’s commercial real estate market performance was the study’s principal goal. Explanatory sequential mixed method design was used in the study, which took a mixed research strategy. Senior specialists, top business leaders, and other professionals from 35 commercial real estate developers active in Addis Abeba made up the study sample for the quantitative phase. It was decided to employ 163 of the 231 structured and self-administered sets of questions that were provided. This resulted in an actual response rate of 71%, which was deemed both necessary and sufficient for running the relevant statistical analyses. 15 key informants from important government ministries or agencies, as well as relevant industrial sectors, were chosen to participate in in-depth interviews for the qualitative phase of the study. Only ten significant informants, however, volunteered to be interviewed. The quantitative study’s findings revealed that every factor—firm efficiency, supplier dependability, and customer purchase intentions, as well as credit availability, marketing strategy, legal considerations, land availability, infrastructure development, technological adoption, and leadership quality—had a significant and positive impact on Addis Ababa’s commercial real estate market performance. The qualitative analysis found that additional variables such as political and economic instability, the degree of coordination and stakeholder participation, political interference, and house purchasers’ purchasing power influenced commercial real estate performance. To improve performance, the researcher suggests that real estate enterprises adopt an effective strategy that focuses on performance-improving variables and collaborate closely with the government and other stakeholders. Finally, the proposed framework for measuring performance in the commercial real estate business should guide commercial real estate developers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".