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Record W3200382064 · doi:10.46503/oeri9337

Analysis of the Spanish real estate market as investment opportunity

2021· article· en· W3200382064 on OpenAlexaboutno aff
Diego Gonzalo Astudillo Cobos

Bibliographic record

VenueFinance Markets and Valuation · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsReal estateBusinessFinanceInvestment (military)PortfolioQuarter (Canadian coin)Real estate investment trustLandlordFinancial systemMonetary economicsEconomics

Abstract

fetched live from OpenAlex

After the bubble burst, the real estate sector has become a very attractive market for funds or large investors. Financial entities were going through a very difficult situation and these opportunistic funds found the best time to invest in real estate. One of the most important funds in this market is Blanckstone, it has a large portfolio of assets and is considered the largest landlord in Spain. At the end of 2013, several indicators were favorable for the real state sector. However, since the last quarter of 2019, this recovery already showed signs of exhaustion and the contraction was greater this year since productive activity in most sectors was paralyzed. However, several authors agree that a market collapse will not occur, since they are since current conditions are better compared to those that occurred during the bursting of the bubble, for example the private sector presents a better financial position and there is no excessive building.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.313
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.053
GPT teacher head0.244
Teacher spread0.192 · 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 teacher head, not a consensus.

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
Published2021
Admission routes1
Has abstractyes

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