Use of repeat house sales to measure changes since the early‐ or mid‐1980s in two inner‐city neighbourhoods in Windsor, Ontario
Bibliographic record
Abstract
Changes in the prices of homes and the reasons for those changes may be more accurately predicted from repeat sales of the same homes after controlling for their changed attributes and differences in time between their sales and resales. This paper analyzes 346 of 583 sold houses in the Glengarry neighbourhood in Windsor, Ontario, that were sold more than once between 1981 and mid‐2017, and a corresponding 414 of 737 sold houses in the city's Wellington‐Crawford neighbourhood, sold more than once between 1986 and mid‐2017. After comparing types of resold homes with once‐sold ones, a repeat sales model predicts a first period of increasing annual percentage changes in resale prices compared to sale prices during the 1980s, followed by a second period of stagnation and possible decreases until 2011, and then increases during a third period after that. In addition, changes in resold homes’ attributes of the dwelling unit and neighbourhood are a second type of neighbourhood change in two inner‐city neighbourhoods during the past 30 or more years.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".