Bibliographic record
Abstract
This paper argues that the Home Bias phenomenon prevails in the real estate market, which is inferred from psychology, economic, and financial literature. Utilizing the trait of the Home bias behavior, which can reduce the risk of information asymmetry, I modify the classical pure trading model and employ the parameter of relative risk aversion as the proxy variable of Home Bias to translate the relationship among Home Bias phenomenon, the property prices, and the expected returns. The comparative static analyses indicate that Home Bias behavior is negatively related to the property prices and positively related to the property returns. The marginal effects on property prices are heightened in situations of high time preference and relative low Home Bias. Conversely, the marginal effects on property returns are larger if the time preference parameter is smaller. As a household buyer with high time preference is located far away from a property, his bargaining power is easily affected by home bias behavior. Further, this paper focuses on the home bias elasticity of property prices and returns for the sake of unit-free property. Inelastic coefficients of elasticity of prices and returns indicate that the capability of households to lower property overvalued prices (i.e. increase investment returns) from reducing information asymmetry by using Home Bias behavior is still limited.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".