Pricing the Daylight Comfort Frontier in Urban Housing: A Nonlinear Hedonic Reanalysis Framework for Turin Apartments
Bibliographic record
Abstract
Daylight is widely valued in residential decision-making, yet the economic literature still tends to treat its contribution to housing value as a linear attribute or to proxy it with legacy compliance metrics. This article develops a benchmarkgrounded nonlinear hedonic framework to investigate whether the market rewards daylight quantity alone or, more plausibly, a daylight comfort balance that combines useful illuminance, sunlight access, and manageable exposure. The study is anchored in the Turin condominium sample reported by Loro et al., which includes 100 apartments modeled through ClimateStudio and characterized by contextual, architectural, energy, and daylight variables. The empirical motivation is specific and bounded: in the published ordinary least squares benchmark, estimated on 90 units and checked on a 10-unit control sample, useful daylight illuminance achieved (UDI.A) and annual sunlight exposure (ASE) were significant, whereas average daylight factor (DFm) and spatial daylight autonomy (sDA) were not, and the model explained about 59% of variation in listing price per square meter. Building on that published pattern, the manuscript strengthens the theoretical motivation, clarifies the interpretation of listing-price capitalization as a noncausal marketsignaling relation, and specifies a parsimonious semiparametric hedonic model with spline-based daylight terms and restricted interaction effects among UDI.A, ASE, blinds-closed time, and vertical sky component. The contribution is methodological and interpretive: a defensible comfort-frontier hypothesis, a transparent reanalysis protocol, and a benchmark-based validation path for future microdata implementation. By making explicit why comfort-proximate daylight metrics may matter more than legacy diffuse-sky indicators, the paper speaks directly to valuation practice, climate-based daylight assessment, and performance-based housing design.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".