International Real Estate Review
Bibliographic record
Abstract
The paper studies the residential micromarket of the Gurgaon region of the Delhi National Capital Region in India, to identify the key determinants of real estate investment selection and perform empirical analysis of property prices. A primary survey suggests that the goodwill of the developer is the most important factor for investors in the case of residential properties that are under construction (forward projects). Other factors include location, amenities, project density and construction quality. These factors enjoy almost equal importance in selecting completed projects (spot projects). The factor information can be used to construct property quality rating classes. High risk adjusted returns are provided by high quality spot projects and low quality forward projects. A long run equilibrium relationship is observed between spot projects and forward prices with the former playing the lead role. Gross domestic product and non-food bank credit are the macroeconomic variables that can predict property prices. The highest pre-tax internal rate of return is observed for forward projects in the first quarter holding itself while for spot projects, it is around the eighth quarter. The research has implications for property developers, real estate investors and market regulators. The study contributes to the real estate investment literature on emerging markets.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.045 | 0.014 |
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".