MétaCan
Menu
Back to cohort
Record W3205165825

MULTIPLIER GROSS INCOME ANALYSIS AS A PROPERTY ASSET VALUE DETERMINATION IN THE CITY OF YOGYAKARTA

2020· article· en· W3205165825 on OpenAlexaboutno aff
Uswatun Khasanah, Rifki Khoirudin

Bibliographic record

VenueSelamat Datang di Repository UAD (Universitas Ahmad Dahlan) · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Fiscal Policies
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)RentingValuation (finance)Property valueProperty marketGross incomeIncome approachEconomicsAgricultural economicsAdjusted gross incomeProperty taxResidential propertyPrice indexMarket valueBusinessFinanceReal estateRevenueGeographyEconometricsPublic economicsEngineering
DOInot available

Abstract

fetched live from OpenAlex

The development of land prices from year to year in the city of Yogyakarta continues to increase. This is because in 
\nthe previous few years the city of Yogyakarta had a lot of construction of hotels and apartments which triggered an 
\nincrease in land prices. For the development of rising land prices, in the second quarter of 2017, the property index 
\nrecorded a 0.12 percent increase compared to the previous quarter. With the fluctuation of property prices from year 
\nto year in the city of Yogyakarta, the valuation of property especially in this area will tend to be unpredictable. For 
\nthis reason, an alternative asset valuation method is needed with the Gross Income Multiplier (GIM) method. GIM 
\nitself is a method of valuing property by multiplying between gross rental income and the price of multipler gross 
\nincome so that the market value of a property is obtained. From the results of this study, the GIM figures from the 
\nresearch samples in the form of shop houses and business sites in the Yogyakarta City area were 36.

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)0.000
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 score0.034
Threshold uncertainty score0.688

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.028
GPT teacher head0.211
Teacher spread0.183 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueSelamat Datang di Repository UAD (Universitas Ahmad Dahlan)Same topicEconomic Growth and Fiscal PoliciesFrench-language works237,207