U.S. House Price Dynamics and Behavioral Finance
Bibliographic record
Abstract
There has been considerable debate in recent years regarding the role of behavioral factors in determining housing prices. The question of whether psychology matters in the housing market has been settled long ago: the answer is yes. Rather, economists are now debating in what ways psychology impacts market behavior and how large an effect this impact has on housing prices. One oft-cited example of a clear behavioral bubble in housing is the sharp boom-bust in the Vancouver housing market during the early 1980s (see figure 1). In the 18 months between January 1980 and July 1981, real house prices grew 87 percent. In the subsequent 18 months, real prices fell by nearly 44 percent, plateauing at a level only 6 percent above where prices were three years earlier before the boom began. While news and rumors about Britain’s returning Hong Kong to China may have swayed sentiment in the Vancouver market, where many wealthy Hong Kong residents own second homes, it is very difficult to use fundamental factors in explaining the sudden boom-bust pattern witnessed in the early 1980s. In this paper we examine the relative roles played by economic fundamentals and market psychology in explaining U.S. house price dynamics using two different boom periods, one in the 1980s and the other one in the early-to-mid-2000s. We begin by considering what proportion of the variation in the house price-rent ratio within metropolitan areas can be explained by fundamentals using a single-period version of the user cost model with static expectations of price growth, as in Himmelberg, Mayer, and Sinai (2005). We then consider how much additional variation can be explained by a handful of behavioral finance theories262
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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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".