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Record W2964846256 · doi:10.1108/ijhma-02-2019-0011

Identifying the preference of buyers of single-family homes in Dammam, Saudi Arabia

2019· article· en· W2964846256 on OpenAlexaboutno aff
Ameen Bin Mohanna, Ali Alqahtany

Bibliographic record

VenueInternational Journal of Housing Markets and Analysis · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)OriginalitySingle-family detached homePreferenceGovernment (linguistics)Value (mathematics)BusinessSingle familyMarketingAdvertisingGeographySociologyEconomicsFinanceQualitative researchSocial scienceComputer science

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.359

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.234
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations23
Published2019
Admission routes1
Has abstractyes

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