Relationship between Indoor Living Environment and Housing Prices: A Case Study of the Taojinjiayuan Residential Quarter in Guangzhou, China
Bibliographic record
Abstract
The indoor living environment of residential buildings is an important part of the habitat environment, affecting the living experience and well-being of the residents, which in turn influences the price of housing. However, few existing studies systematically concentrate on the integrated influence of the various elements of the indoor living environment on prices, and even fewer analyze the relationship between the indoor living environment and housing prices of different houses within the same residential quarter from a microperspective. Therefore, we use the Taojinjiayuan Residential Quarter, located in central Guangzhou City, China, as a case study area and analyze the extent and direction of the effect of the indoor living environment on housing prices. The study found that the quantitative evaluation results of the indoor living environment were reasonable. The integrated indoor living environment factors are closely related to housing prices. Orientation, view, and acoustic environment are significantly and positively related to housing prices, which have a different intensity of influence. These findings are beneficial to real estate developers, building designers, and residential users in quantitatively understanding the value of the indoor living environment.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".