MULTIPLIER GROSS INCOME ANALYSIS AS A PROPERTY ASSET VALUE DETERMINATION IN THE CITY OF YOGYAKARTA
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".