Bobler i strandkanten?: En analyse af sommerhusmarkedets prisdannelse og afvigelser fra det klassiske ejerboligmarked
Bibliographic record
Abstract
When Denmark went into lockdown during the Covid-19 pandemic, prices of summer houses surged dramatically due to travel restrictions and increased demand for domestic vacation alternatives. This sparked media speculation about a potential housing bubble. However, despite the end of the pandemic, prices have remained at a high level and continue to show an upward trend. This thesis examines the price development in the Danish housing market with a particular focus on summer houses in the period from 1997 through the second quarter of 2024. The aim is to analyze and compare price developments across submarkets and to highlight differences in buyer behavior and motivations, as well as how these may influence price developments over time – particularly in periods of economic shocks. The analysis includes a comparative study of the summer house market and the general owner- occupied housing market, with special attention to regional differences between North Zealand and Western Jutland. Using regression analysis and the fundamental housing economic model, the study identifies key fundamental factors such as interest rates, disposable income, and demographic trends that affect price formation. The thesis also discusses whether the housing market in general is prone to bubble formation. The summer house market is assessed as particularly vulnerable, partly because summer houses are not subject to permanent residence requirements and can therefore more easily be used as investment objects, increasing the risk of speculative behavior. Furthermore, summer houses are considered a luxury good, which makes the market more sensitive to economic fluctuations and thus more exposed to significant price volatility during periods of economic upturns and downturns. The results reveal considerable regional differences and indicate that a significant part of the price development can be explained by fundamental economic factors – but to a large extent also by expectation formation based on past price trends. The analysis further suggests that the summer house market cannot be regarded as a single, unified market; both price developments and purchase motivations vary substantially across regions and buyer types.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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.
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".