‘You Can Touch the Bricks’: The role of Asset Tangibility in Landlord Investment Decision Making
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".