The Swedish property crisis in retrospect: a new look at appraisal bias
Bibliographic record
Abstract
Purpose In the early 1990s, Sweden suffered from a severe property crisis. This study aims to analyze the market for income properties in Sweden over a 20‐year period, 1980‐2000, taking a fresh look at describing the depth of the property crisis. The study specifically attempts to examine if appraisal bias was present when the state‐owned Nordbanken bank foreclosed on a large number of properties. Design/methodology/approach Using transaction data, the article estimates a set of hedonic price indices. The result is used to calculate predicted market values. To assess if the appraisals are biased they are compared with both the predicted market value and the actual transaction price. Findings The study does not find any indications of the appraisals being systematically biased. For the comparison with transaction price, however, a caveat in drawing these conclusions is that the appraisals could have had a direct impact on the reservation prices. The results further suggest that there is added information in appraisal beyond those characteristics that are available in public registers. Originality/value The study presents a new set of price indices based on a limited set of property characteristic. Most indices in actual use are based on appraised values. This study has shed light on the depth of the Swedish property crisis and enabled us to assess the quality of appraisals in general.
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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 | 0.028 | 0.120 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".