Analyzing the Impact of Electricity Prices on Airbnb - A Linear Regression Approach
Bibliographic record
Abstract
The digital age has transformed the economy, giving rise to the sharing economy and platforms like Airbnb, which have revolutionized the hospitality industry by allowing individuals to rent out their homes. As the cost of living continues to rise, it becomes essential to understand how fluctuations in utility prices, such as electricity, influence rental markets. This study specifically examines whether electricity prices affect Airbnb listing prices. The basis of this study is Copenhagen, Oslo, and Stockholm, and the data spans from the fourth quarter of 2021 to the fourth quarter of 2023. This period includes significant global events, such as the Russian-Ukrainian war, which have influenced energy markets significantly. Using panel data consisting of data from Inside Airbnb and reputable public sources for electricity prices, we apply simple linear regression through Python to analyze the relationship between electricity prices and Airbnb prices. The results show that there is a slightly negative, nearly non-existent, relationship between them. These findings suggest that as electricity prices increase, Airbnb prices are either slightly reduced or stay the same. This could indicate that Airbnb hosts might not be able to offset their increased electricity costs to guests and consequently must suffer the results of rising energy costs, for example when global events affect the economy. This research provides valuable insights for policymakers, economists, and stakeholders in the short-term rental market, highlighting the need for strategies to handle the effects of energy price volatility. Future research should focus on including a broader range of countries, separating the effects of Covid-19 and the Russian-Ukrainian war, extending the study period, considering lagged electricity prices, conducting qualitative interviews with Airbnb hosts, and ensuring consistent data collection methods.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".