ASSESSING THE EFFECT OF ACCESSIBILITY TO AMENITIES IN THE LOCATION RENT SEARCHING FOR MARKET SEGMENTATION OF PREFERENCES
Bibliographic record
Abstract
In previous research (ThÈriault et al., 2005 & 2007), we developed a novel approach for assessing centrality and accessibility to urban amenities distinguishing among city centre, labour market and other types of services, like schools, shopping centres, groceries and health facilities. Complementary indices for each type of amenity derived from suitable opportunity sets (based on willingness to travel thresholds) are integrated within a hedonic model of housing markets. Results are highly significant and permit in-depth comparison (and ordering) of the marginal value of accessibility (by car, bus and walking) to several types of amenity within an integrated hedonic framework, while controlling for multicollinearity related to urban form and transportation networks, using principal component analysis. However, there is remaining significant spatial autocorrelation among the model residuals. Thus, spatial drift is eventually harming the robustness of estimates of the coefficients and of their standard errors. The purpose of this paper is to explore ways to get rid of this spatial autocorrelation or, at least, to handle the spatial drift which could be present in the perception of accessibility (and its valuation) among buyers. Building on a comprehensive hedonic model using thousands of single-family house transactions made during the 1993-2004 period in the Quebec Metropolitan Area, this paper compares the efficiency of OLS (ordinary least square), SAR (spatial autoregression) and Quantile regression techniques for handling spatial autocorrelation in the hedonic model and, eventually, for identifying factors behind the spatial drift that could influence buyerís valuation of urban centrality and accessibility to amenities. Moreover, assessment of the marginal effects of market segmentation on the valuation of accessibility to specific amenities (e.g. schools versus labour market, schools versus shopping centres) provides bases for discussing spatial drift estimations in-line with urban economic theory.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".