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Record W4232199614 · doi:10.24124/2010/bpgub1431

Evaluating investment in real estate projects

2010· dissertation· en· W4232199614 on OpenAlexaffabout
Mohammed Ghane

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsSciencetech (Canada)University of Northern British Columbia
Fundersnot available
KeywordsReal estateCapitalization rateValuation (finance)Real estate investment trustCost approachFinanceIncome approachBoomBusinessReal estate developmentBustFinancial economicsCash flowCorporate Real EstateRate of returnEconomicsEngineering

Abstract

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This study demonstrates the application of a financial model to evaluate the expected return from investing in real estate projects.Based on the analytical framework which is widely adopted in the market, the study examines the dynamics of supply and demand in the space and asset markets and its impact on valuation of real assets in Canada.The study presents a financial and market analysis for a hypothetical project using actual data of real estate properties close to Vancouver in British Columbia.The valuation is based on the fact that real estate provides potential future cash flow for investors, similar to any other asset in the capital market.The study also links the empirical results of this valuation with the supply and demand theory in order to understand the boom and bust that happened in real estate during the last few years.The analysis shows that the high increase in property prices in 2003-2007 has led to a sharp reduction in cap rates which has a great impact on lowering investor returns from real estate properties.The study concludes that the current rent level is below the long-term equilibrium and, therefore, holding a property for rent does not meet the expected return criteria.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.068

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.087
GPT teacher head0.312
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2010
Admission routes2
Has abstractyes

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