Bibliographic record
Abstract
Introduction The Conservative governments of Margaret Thatcher and John Major introduced a range of initiatives aimed at breathing new life into the private rental housing market. These included one measure – housing investment trusts – that was specifically aimed at attracting investment in the market by financial institutions (DoE, 1995). Since coming to power in 1997, ‘New’ Labour has made clear that it too sees an important role for the private rental sector (PRS) and that it would welcome investment by financial institutions (DETR, 2000a). While there has been a modest revival in the size of the PRS over the past decade (see Chapter One), there has been very little investment in the sector by financial institutions. Indeed, financial institutions currently own a negligible amount of private rental housing. There are a number of reasons why they have made no significant investment in the sector, but one of them is the lack of market information about the private rental market. In particular, very little regular and reliable information has existed about residential rents and rates of return (Crook et al, 1995; Coopers and Lybrand, 1996). This situation contrasts with the extensive market information that is available about equities, gilt investment markets and a range of commercial property market indices (Morrell, 1991, 1995). Likewise, there are a number of respected indices for house prices, including those developed by the Halifax Bank and the Nationwide Building Society. If the financial institutions are to enter the PRS on a significant scale, market information about rent levels and rental yields is necessary to inform their investment decisions (Crook and Kemp, 1999). In considering whether to invest in the sector, fund managers for the financial institutions need to know how the returns from residential lettings compare with alternative investments, such as commercial property and equities. They will also need to know how the returns on the properties they have purchased compare with the average returns being made in the PRS as a whole. Market information of this kind makes it possible for fund managers to carry out performance benchmarking exercises to help inform their investment decisions. This chapter is based on work undertaken to fill this information gap about rents and returns in the PRS.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 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 teacher head, 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".