COMPARISON OF SPATIAL HEDONIC HOUSE PRICE MODELS: APPLICATION TO REAL ESTATE TRANSACTIONS IN VANCOUVER WEST
Bibliographic record
Abstract
This study compares hedonic house price models for single family properties in Vancouver West, Canada. The real estate literature has shown that traditional hedonic models based on OLS are unable to handle spatial effects inherent in housing markets, prompting the application of spatial econometric methods. This study compares four hedonic house price models: (i) classical OLS model, (ii) OLS model with neighborhood code dummies, (iii) Spatial Durbin Model, and (iv)Geographically Weighted Regression. The latter two models are common spatial econometric techniques that researchers have used. Models are compared based on model 2 R , out-of-sample prediction error, and ability to remove spatial effects from the data. Results indicate that Geographically Weighted Regression is the best performing model. In addition, classical OLS overestimates effects and is unable to address spatial effects. All four models predict a similarimpact of property attributes on sale price.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".