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‘You Can Touch the Bricks’: The role of Asset Tangibility in Landlord Investment Decision Making

2025· article· W7118179618 on OpenAlexaboutno aff
Andrew Watson

Bibliographic record

VenueCritical Housing Analysis · 2025
Typearticle
Language
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsnot available
FundersUniversity of Glasgow
KeywordsLandlordInvestment (military)Asset (computer security)Salience (neuroscience)WelfarePerception

Abstract

fetched live from OpenAlex

For three decades, Private Rented Sector (PRS) growth has been driven by part-time, small-scale, profit-seeking landlords in several Western nations. While the characteristics and motivations of these landlords have been examined in some geographies, far less is known about their investment decision-making, particularly their reasons for choosing the PRS over alternative investment options. This matters because these decisions shape the sector’s growth and tenant welfare. The study begins to address this gap by exploring the role of asset tangibility in landlord investment decisions, drawing on research from other investment domains. A mixed methods study was conducted, comprising an online survey of 1,033 Scottish landlords and follow-up interviews with 33 landlords and PRS professionals. Findings suggest that some landlords exhibit a bias towards the ‘bricks and mortar’ tangibility of PRS investment, which shapes risk perceptions and aspects of their investment decision-making. The findings have several implications. For landlords, there are concerns around investment efficacy; for policymakers, questions about landlord financial literacy; and for tenants, risks to their welfare from landlord decision-making. While the findings are not directly transferable, they are likely to have salience in other nations with established PRSs, including Australia, Canada, the United States, and parts of Europe.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.286
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.005
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.020
GPT teacher head0.282
Teacher spread0.263 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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