The Impact of Oil Price on the Housing Market: A Case Study of Stavanger
Bibliographic record
Abstract
Our master thesis investigates the intricate relationship between the price of oil and the\nhousing market in Stavanger. Our thesis is motivated by the impact of oil on the region's\neconomy, and its potential impact on the housing market. Stavanger, known as the oil capital\nof Norway, provides a unique context for this study, making it an ideal location to explore the\nintricate interplay between the oil market and the housing sector.\nThis leads us to our research question;\nHow does the oil price affect the housing market in Stavanger?\nTo try to answer our research question, we will use quantitative research. Our data consists of\n64 observations from the 1st quarter of 2006 to the 4th quarter of 2022. Variables we have\nincluded are the House Price Index, Brent Crude Oil Price, House Stock, Policy Rate, Median\nIncome, and Unemployment Rate for Norway and Stavanger. Using a first-difference\nmultiple linear regression analysis, we test for differences in a model including and excluding\noil price as a variable and look for differences in a national average for Norway compared to\nStavanger.\nOur results show a statistically significant impact of oil prices on housing prices in Stavanger\nand Norway. Still, the oil price has a higher correlation with the house prices in Stavanger,\nsupporting our hypothesis. Our regressions show signs of multicollinearity, and some model\nvariables showed no significant impact on housing prices. This indicates that our model has\nomitted variables.\nTo conclude our research, we found evidence of oil impacting the housing prices in\nStavanger, but further research needs to be done on the topic as our models show signs of\ninaccuracies.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".