Identifying the preference of buyers of single-family homes in Dammam, Saudi Arabia
Bibliographic record
Abstract
Purpose The purpose of this study is to identify the preferred characteristics of buyers of single-family homes in Saudi Arabia with an emphasis on the city of Dammam. Design/methodology/approach Data were collected using face-to-face structured interviews conducted from November 2016 to May 2017 with 177 owners of single-family homes that were purchased between 2010 and the first quarter of 2017. Findings The findings indicate that homes can be divided into three types: villas, detached duplexes and semi-detached duplexes. Also, more than three-quarters of the respondents purchased their homes through mortgages from either lenders or the government. It seems we find that the advantages of the detached duplex, particularly its privacy level, over other types of single-family homes induce homebuyers to choose this home type. Originality/value In this study, the authors analyze housing preferences among various segments of the Saudi society, in the city of Dammam, to understand the housing supply in Saudi Arabia. Only a few studies have investigated the preferences of homebuyers in Saudi Arabia. Below the authors provide a literature review, discuss data and methods and results, as well as provide concluding remarks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".