Neighborhood profiles and house values : dealing with spatial autocorrelation using kriging techniques
Bibliographic record
Abstract
The current piece of research focuses on assessing the effect of micro-level, neighborhood profiles on house values and market differentiation in the presence of spatial autocorrelation. While principal component analysis (PCA) is used as a sorting device to identify space-structuring factors, kriging is resorted to in order to deal with spatial dependence among model residuals. The hedonic approach, on which is based this investigation, is applied to the residential market of the Quebec City region. The data bank includes some 2 405 cottages sold on the Quebec Urban Community (QUC) territory from January 1993 to January 1997. Sale prices of sampled cottages range from $50 000 to $250 000, with mean price standing at $123 183. The modeling process is applied to the global sample as well as to two randomly chosen, equal-size sub-samples. Isotropic exponential and spherical variogram functions are used for interpolation of model residuals. Once reinserted in the hedonic equations as independent vectors, kriged variables are shown to substantially improve both explanatory and predictive performances. Thus, the global model adjusted R-Square is raised to 95.8% from 88.8% previously while the standard prediction error drops from 11.3% to 6.9% of mean sale price. In so doing, most spatially structured variations are captured by the model, leaving the remaining spatial autocorrelation statistically non significant while regression coefficients are unbiased. Such a procedure is a pre-requisite for reliable hedonic modeling.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".