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Record W2553302343 · doi:10.5539/ibr.v9n12p103

A Study on Housing Price in Klang Valley, Malaysia

2016· article· en· W2553302343 on OpenAlexvenueno aff
Paul Anthony Mariadas, Mahiswaran Selvanathan, Tan Kok Hong

Bibliographic record

VenueInternational Business Research · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsSpeculationInflation (cosmology)Price indexOrder (exchange)Index (typography)Government (linguistics)PopulationInflation rateEconomicsHouse priceSample (material)BusinessAgricultural economicsFinanceMonetary economicsInterest rateEconometricsDemography

Abstract

fetched live from OpenAlex

<p>The main aim of this study is to measure the factors influencing housing price in Klang Valley, Malaysia. This paper examines empirically whether the increasing trend in the Malaysian housing price is associated to changes in the population, construction cost, housing speculation, and inflation rate. The paper is exploratory in nature. The data is collected via questionnaire survey form distributed to youngest respondents in the sample area which is Klang Valley region. Each single elements are calculated its average index respect to few level of influence under respondents opinion. The index will then treated as influencing level of the factors. The paper delivers empirical outcomes that the population, construction cost and housing speculation are the main factors of housing prices. However, fluctuations in housing prices could not necessarily be influenced by the inflation rate. The overall result of this paper strongly recommends that housing price in the Malaysian residential property market is increasing continuously. Therefore, efforts to control the hike in housing price is needed by government and policy controllers in order to maintain the affordable to own a house in Malaysia.</p>

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.175
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.124
GPT teacher head0.341
Teacher spread0.217 · 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.

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
Published2016
Admission routes1
Has abstractyes

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