Who Is “Misleading” Whom in Real Estate Transactions?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The observation that real estate agents sell their clients' homes cheaper and faster than their own homes has been well identified in the literature and interpreted as evidence of an agency problem originated from information asymmetry. This article studies whether this well‐known result holds true for all types of agents and clients, and whether information asymmetry is the full story. By using the Multiple Listing Service (MLS) data from Indiana, we find that, after controlling for observables, mainly homes owned by institutional clients are sold cheaper and faster than agent‐owned homes, and the differences are mainly driven by less and moderately experienced agents. Besides information asymmetry, we also find evidence of motivation heterogeneity—institutions themselves are very motivated to sell, and therefore are willing to sell cheaper in order to sell faster.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it