Bibliographic record
Abstract
Explosive Bubbles in House Prices? Evidence from the OECD Countries SUMMARYThis dissertation comprises three self-contained chapters that all relate to the implications of expectations in the housing market.In general, house price changes are caused by changes to 'fundamentals' and/or speculative behavior.The fundamental price is notoriously difficult to asses as it is influenced by a large set of variables including, but not limited to, income, mortgage rates, property taxes, local factors such as neighborhood attributes, and expectations to future values of these factors.Additionally, households move infrequently as housing is an illiquid asset and transaction costs in the housing market are substantial.Furthermore, households might face borrowing constraints such that they are restricted from some parts of the housing market.Therefore, expectations to the future state of the housing market are an important driver of household choices and, by extension, housing prices.In this context, the three chapters of this dissertation investigate the role of expectations and their implications in inherently dynamic housing markets.The first chapter "Explosive Bubbles in House Prices?Evidence from the OECD Countries" is co-authored with Tom Engsted (Aarhus University, CREATES) and Thomas Q. Pedersen (Aarhus University, CREATES).In this chapter, we conduct an econometric analysis of speculative bubbles in housing markets.With econometric methods that explicitly allow for explosiveness, i.e. a rational bubble, we investigate the explosive nature of bubbles.First, we apply a univariate right-tailed unit root test procedure on the price-rent ratio in order to identify periods of exuberance.With this sample we then apply a co-explosive VAR framework to test for explosive bubbles.Using quarterly OECD data for 18 countries from 1970 to 2013, we find evidence of explosiveness in many housing markets, thus supporting the bubble hypothesis.A slightly shorter version of the first chapter has been accepted for publication in Journal of International Financial Markets, Institutions, and Money.The second chapter "Dynamic Residential Sorting -Investigating the Distribution of Capital Gains" estimates a dynamic residential sorting model of housing owners.The model explicitly takes account of transactions costs, borrowing constraints of vii viii SUMMARY households, and allows for forward looking behavior.The focus in this chapter is on how capital gains are distributed geographically and across the wealth distribution.The model is estimated using unique Danish register data from 1992 to 2011 of housing owners.The chapter finds substantial differences in capital gains as the highest wealth decile, i.e. the 10 percent wealthiest households, over the sample receives almost a 2 percentage points larger annual capital gain than the wealth type with the lowest housing investment.Furthermore, I find substantial differences in capital gains geographically.Lastly, it is found that the freeze of property taxes in 2002 enhanced capital gains dispersion and counterfactual simulations show that the progressive Danish taxation scheme from before 2002 could have mitigated parts of the dispersion.The third chapter "Valuation of Non-Traded Amenities in a Dynamic Demand Model" is co-authored with Christopher Timmins (Duke University) and Rune M. Vejlin (Aarhus University) and was partly written during my stay at Duke University.Using the population-wide Danish register data with precise measures of households' wealth, income, and socio-economic status, we specify and estimate a dynamic structural model of residential neighborhood demand.Our model includes moving costs, forward looking behavior of households, and uncertainty about the evolution of neighborhood attributes, wealth, income, house prices, and family composition.We estimate marginal willingness to pay for non-traded neighborhood amenities with a focus on air pollution.We allow household willingness to pay to vary in household characteristics and argue that low wealth and low income households face borrowing constraints.The willingness to pay of households who are likely borrowing constrained is found to be much more sensitive to changes in wealth than for other households.Our application finds that the dynamic approach adjusts for various biases relative to a comparable static approach.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 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".