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Record W4379387924 · doi:10.33423/jabe.v25i2.6101

An Overview of the Capital Raising Activities Among Proptech Firms

2023· article· en· W4379387924 on OpenAlexvenueno aff
Liang Fu, Ran Lu‐Andrews, Yin Yu-Thompson

Bibliographic record

VenueJournal of Applied Business and Economics · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsRaising (metalworking)Real estatePerspective (graphical)Capital (architecture)Strengths and weaknessesBusinessIndustrial organizationMarketingEconomicsFinanceGeographyComputer scienceEngineering

Abstract

fetched live from OpenAlex

This article presents an overview of the capital raising activities among property/real estate technology (i.e. Proptech) firms. This overview highlights the strengths and weaknesses of the Proptech sector in recent years. This article provides a detailed summary and description of how the capital raising activities distribute across different Proptech categories and geographic locations in different market conditions. The authors find that the capital-raising activities in Proptech have cooled down despite the rising real estate prices last year. The authors hope that this review can present a more comprehensive picture of the Proptech development and attract more researchers to investigate the costs and benefits of Proptech to the real estate markets. This research contributes to the understanding of Proptech sector more comprehensively by utilizing a unique hand-collected dataset. The results present a different perspective on the recent trends of Proptech firms as they feature both the promising trends and concerning issues within the field.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.329
Threshold uncertainty score0.573

Codex and Gemma teacher scores by category

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

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 head0.045
GPT teacher head0.227
Teacher spread0.182 · 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.

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

Citations1
Published2023
Admission routes1
Has abstractyes

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