Bibliographic record
Abstract
There is strong evidence of a seasonal pattern in Dutch house prices; prices tend to increase stronger in the second quarter of the year. This is reported both in popular reports from banks (e.g. Rabobank, the largest mortgage supplier of the Netherlands) and in academia (e.g. De Wit & Van der Klaauw, 2010). One of the potential explanations for this pattern is that households are timing their purchase of a new house before the summer holiday, such that they can move in (and do some DIY) during the summer holiday. By planning the move in a holiday the transition from one house to the next should be as smooth as possible: plenty of time to pack and move, do refurbishing et cetera. More specifically, by moving in the summer holiday kids are able to enter gently into their new schools. This argument leans on a large body of scientific research that proves that certain predictable changes in lifeís course trigger residential mobility (e.g. Clark & Huang, 2003). Alternative explanations have been suggested as well, such as Ngai et. al. (2009) market matching explanation. In this study we seek if we can explain the commonly reported Q2-peak in transaction prices of residential housing according to the life course hypothesis. We specifically focus on the potential influence of the summer holiday on the housing market. Especially to households with children it is beneficial to purchase a new house before the start of the summer holiday: households without children are less stringently tied to the summer holiday. Moreover, households without children are less stringently tied to house types (or at least to size). These two results of the life course hypothesis therefore leads us to expect that housing that is not suited for households with children (i.e. small multi-family dwellings) displays less seasonality. We use the database of the Dutch Association of Real Estate Agents and Real Estate Experts (henceforth: NVM) for our study. This database contains roughly 70% of all residential housing transactions in the Netherlands from 1985 onwards. The information we have on transactions includes dates (on and off the market), price and numerous characteristics. In the Netherlands schools are free to set most holidays. The summer holiday is the only holiday prescribed by the government. In setting the dates of the summer holiday the government distinguishes three regional areas for spreading holiday peaks (thus seeking to avoid congestion on roads and lengthening the holiday season for e.g. the catering industry). The summer holiday starts and ends at different dates for each area with a time gap of 3 weeks between the early and the late area. Our initial analysis focuses on the general holiday season and national patterns of seasonality using the common hedonic and seasonality dummy approach. We will then try to further exploit our extensive database and venture into a natural experiment where we try to find small shifts in seasonal patterns given the above described holiday spreading policy.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".