Evaluating real estate development project with Monte Carlo based binomial options pricing model
Bibliographic record
Abstract
This paper proposes three evaluation models for evaluating the value of strategic waiting of real estate development project. In Model 1, the ratio of land cost to total real estate sales in period (t) and period (t + 1) is uncorrelated (random). In Model 2, the ratio is unchanged (constant). Model 3 integrates Models 1 and 2 with the ‘land value persistence factor’. The larger the factor, the more the land cost tends to consider only the previous land price. This study uses the Binomial Option Pricing Model and Monte Carlo Simulation hybrid method to solve these three models. In addition, this research also proposes a method for estimating the net present value of project expansion on the time axis. The results show that five main factors influencing the expected value of the option value are the real estate price rate of change, present value of total real estate sales, duration, land value persistence factor, and present value of land. Regardless of the land value persistence factor, the longer the time, the expected value of the option value tends to increase. However, when the land value persistence factor is larger, the expected value of the option value increases more.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".