Is Smart Housing a Good Deal? An Answer Based on Monte Carlo Net Present Value Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The smart cities are considered to be an engine of economic and social growth. Most countries started to convert their existing cities into smart cities or construct new smart cities in order to improve the quality of life of their inhabitants. However, the problem that facing those countries while applying the concept of smart cities is the costs, especially for the residential sector. Despite the high initial and even operation costs for adopting different technologies in smart housing; the benefits could exceed those costs within the lifespan of the project. This article is shedding the light on the economics of smart housing. This study aims to evaluate the net present value (NPV) of a smart economic housing model to check the viability and feasibility of such projects. The calculation of the NPV based on Monte Carlo simulation provides an interesting methodological framework to evaluate the robustness of the results as well as providing a simple way to test for statistical significance of the results. This analysis helps to evaluate the potential profitability of smart housing solutions. The research ends up by proving the feasibility of this type of project.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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