Seller Financing: Contracting Out of the Lemons and Moral Hazard Problems When They May Co-Exist
Bibliographic record
Abstract
Quality problems that are known to the seller of an asset, but will become known to the buyer only after the purchase have the potential to frustrate voluntary exchanges. When the problem is subtle or confounded by the extent of buyer inputs, requiring risk-sharing by the contracting parties, both parties would benefit from a mechanism, such as seller financing, which not only credibly signals to the buyer the veracity of the seller’s representations about the asset (s)he is trying to sell, but also offers the seller sufficient protections against the potential that the buyer may engage in post-sale opportunistic behavior about the maintenance of the asset. We analyze one-time-only mortgage contracts in the National Association of Realtors' Home Financing Transaction database for 1984-1996, (data not collected outside this period), and find empirical support for seller financing as an asset quality signal and, separably, as a mechanism for providing credit when conventional credit sources are tight. We also point out the broad, but not well-acknowledged, reach of seller financing, including the sub-prime loan debacle, the earnout mergers or reverse annuity mortgages, which are inherently embedded with both asymmetric information about the quality of the relevant assets and moral hazard about the asset acquirer’s post-purchase maintenance.
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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 | 0.015 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".