Evaluating real estate development project with Monte Carlo based binomial options pricing model
Why this work is in the frame
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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.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it