21 resultados para Depreciation allowances

em CentAUR: Central Archive University of Reading - UK


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Purpose – The purpose of this paper is to consider prospects for UK REITs, which were introduced on 1 January 2007. It specifically focuses on the potential influence of depreciation and expenditure on income and distributions. Design/methodology/approach – First, the ways in which depreciation can affect vehicle earnings and value are discussed. This is then set in the context of the specific rules and features of REITs. An analysis using property income and expenditure data from the Investment Property Databank (IPD) then assesses what gross and net income for a UK REIT might have been like for the period 1984-2003. Findings – A UK REIT must distribute at least 90 per cent of net income from its property rental business. Expenditure therefore plays a significant part in determining what funds remain for distribution. Over 1984-2003, expenditure has absorbed 20 per cent of gross income and been a source of earnings volatility, which would have been exacerbated by gearing. Practical implications – Expenditure must take place to help UK REITs maintain and renew their real estate portfolios. In view of this, investors should moderate expectations of a high and stable income return, although it may well still be so relative to alternative investments. Originality/value – Previous literature on depreciation has not quantified amounts spent on portfolios to keep depreciation at those rates. Nor, to our knowledge, has its ideas been placed in the indirect investor context.

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Depreciation is a key element of understanding the returns from and price of commercial real estate. Understanding its impact is important for asset allocation models and asset management decisions. It is a key input into well-constructed pricing models and its impact on indices of commercial real estate prices needs to be recognised. There have been a number of previous studies of the impact of depreciation on real estate, particularly in the UK. Law (2004) analysed all of these studies and found that the seemingly consistent results were an illusion as they all used a variety of measurement methods and data. In addition, none of these studies examined impact on total returns; they examined either rental value depreciation alone or rental and capital value depreciation. This study seeks to rectify this omission, adopting the best practice measurement framework set out by Law (2004). Using individual property data from the UK Investment Property Databank for the 10-year period between 1994 and 2003, rental and capital depreciation, capital expenditure rates, and total return series for the data sample and for a benchmark are calculated for 10 market segments. The results are complicated by the period of analysis which started in the aftermath of the major UK real estate recession of the early 1990s, but they give important insights into the impact of depreciation in different segments of the UK real estate investment market.

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The retention rate of a company has an impact on its earnings and dividend growth. Lease structures and performance measurement practice force real estate investment managers to adopt full distribution policies. Does this lead to lower income growth in real estate? This paper examines several European office markets across which the effective retention rates vary. It then compares depreciation rates across these markets. It is concluded that there is evidence of a relationship between retention and depreciation. Those markets with particularly inflexible lease structures exhibit low retention rates and higher levels of rental value depreciation. This poses interesting questions concerning the appropriate way to measure property performance across markets exhibiting significantly different retention rates and also raises important issues for global investors.

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Purpose – The paper addresses the practical problems which emerge when attempting to apply longitudinal approaches to the assessment of property depreciation using valuation-based data. These problems relate to inconsistent valuation regimes and the difficulties in finding appropriate benchmarks. Design/methodology/approach – The paper adopts a case study of seven major office locations around Europe and attempts to determine ten-year rental value depreciation rates based on a longitudinal approach using IPD, CBRE and BNP Paribas datasets. Findings – The depreciation rates range from a 5 per cent PA depreciation rate in Frankfurt to a 2 per cent appreciation rate in Stockholm. The results are discussed in the context of the difficulties in applying this method with inconsistent data. Research limitations/implications – The paper has methodological implications for measuring property investment depreciation and provides an example of the problems in adopting theoretically sound approaches with inconsistent information. Practical implications – Valuations play an important role in performance measurement and cross border investment decision making and, therefore, knowledge of inconsistency of valuation practice aids decision making and informs any application of valuation-based data in the attainment of depreciation rates. Originality/value – The paper provides new insights into the use of property market valuation data in a cross-border context, insights that previously had been anecdotal and unproven in nature.

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This paper publishes the results of the 1996 study, which repeats a cross-section analysis of around 125 City of London office buildings, and examines the longitudinal data contributed by a sample of 56 unrefurbished properties common to the 1986 and 1996 City of London datasets. An estimate of the average rate of rental and capital value depreciation is made; the effect of age is shown not to be straight-line; and the causes if depreciation are measured. The results are compared with the 1986 City of London findings.

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This paper identifies the long-term rental depreciation rates for UK commercial properties and rates of capital expenditure incurred to offset depreciation over the same period. It starts by reviewing the economic depreciation literature and the rationale for adopting a longitudinal method of measurement, before discussing the data used and results. Data from 1993 to 2009 were sourced from Investment Property Databank and CB Richard Ellis real estate consultants. This is used to compare the change in values of new buildings in different locations with the change in values of individual properties in those locations. The analysis is conducted using observations on 742 assets drawn from all major segments of the commercial real estate market. Overall rental depreciation and capital expenditure rates are similar to those in other recent UK studies. Depreciation rates are 0.8% pa for offices, 0.5% pa for industrial properties and 0.3% pa for standard retail properties. These results hide interesting variations at a segment level, notably in retail where location often dominates value rather than the building. The majority of properties had little (if any) money spent on them over the last 16 years, but those subject to higher rates of expenditure were found to have lower depreciation rates.

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Real estate depreciation continues to be a critical issue for investors and the appraisal profession in the UK in the 1990s. Depreciation-sensitive cash flow models have been developed, but there is a real need to develop further empirical methodologies to determine rental depreciation rates for input into these models. Although building quality has been found to be an important explanatory variable in depreciation it is very difficult to incorporate it into such models or to analyse it retrospectively. It is essential to examine previous depreciation research from real estate and economics in the USA and UK to understand the issues in constructing a valid and pragmatic way of calculating rental depreciation. Distinguishing between 'depreciation' and 'obsolescence' is important, and the pattern of depreciation in any study can be influenced by such factors as the type (longitudinal or crosssectional) and timing of the study, and the market state. Longitudinal studies can analyse change more directly than cross-sectional studies. Any methodology for calculating rental depreciation rate should be formulated in the context of such issues as 'censored sample bias', 'lemons' and 'filtering', which have been highlighted in key US literature from the field of economic depreciation. Property depreciation studies in the UK have tended to overlook this literature, however. Although data limitations and constraints reduce the ability of empirical property depreciation work in the UK to consider these issues fully, 'averaging' techniques and ordinary least squares (OLS) regression can both provide a consistent way of calculating rental depreciation rates within a 'cohort' framework.

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Beginning with an extensive literature review, this new research summary report analyses the process of rental depreciation, its effect on the performance of rents, and the impacts of capital expenditure on depreciation. It is based on an analysis of more than 700 commercial and industrial properties and newer property types and it is the most comprehensive national study of rental depreciation since the 1986 CALUS report. The report will be of interest to valuers, fund managers, institutional investors and their advisors and researchers.

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As new buildings are constructed in response to changes in technology or user requirements, the value of the existing stock will decline in relative terms. This is termed economic depreciation and it may be influenced by the age and quality of buildings, amount and timing of expenditure, and wider market and economic conditions. This study tests why individual assets experience different depreciation rates, applying panel regression techniques to 375 UK office and industrial assets. Results suggest that rental value depreciation rates reduce as buildings get older, while a composite measure of age and quality provides more explanation of depreciation than age alone. Furthermore, economic and local real estate market conditions are significant in explaining how depreciation rates change over time.

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Formal and analytical risk models prescribe how risk should be incorporated in construction bids. However, the actual process of how contractors and their clients negotiate and agree on price is complex, and not clearly articulated in the literature. Using participant observation, the entire tender process was shadowed in two leading UK construction firms. This was compared to propositions in analytical models and significant differences were found. 670 hours of work observed in both firms revealed three stages of the bidding process. Bidding activities were categorized and their extent estimated as deskwork (32%), calculations (19%), meetings (14%), documents (13%), off-days (11%), conversations (7%), correspondence (3%) and travel (1%). Risk allowances of 1-2% were priced in some bids and three tiers of risk apportionment in bids were identified. However, priced risks may sometimes be excluded from the final bidding price to enhance competitiveness. Thus, although risk apportionment affects a contractor’s pricing strategy, other complex, microeconomic factors also affect price. Instead of pricing in contingencies, risk was priced mostly through contractual rather than price mechanisms, to reflect commercial imperatives. The findings explain why some assumptions underpinning analytical models may not be sustainable in practice and why what actually happens in practice is important for those who seek to model the pricing of construction bids.

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Many aspects of the conditions required to maximize the ewe's response to ram introduction in the late anoestrous season remain unclear. The aim of this research was to determine whether grazing space allowances could influence the efficacy of the ram effect. In August 1995, at Reading (latitude 51degrees27'N), following a 3-month isolation period from rams, two groups of nulliparous Mule ewes, aged 15 months, were introduced to four rains in a low (12 ewes/ha; treatment L, n = 124) or in a high stocking rate (84 ewes/ha; treatment H, n = 126). From the beginning of August until the end of August oestrous behaviour was recorded by daily checks of mating marks on ewes. Rams were removed and in October all ewes were scanned (day 50) for pregnancy. No significant differences were found in the parameters investigated. Eighty-two percent of the L and 75.4% of the H ewes exhibited oestrus, with a pronounced peak on day 23 following ram introduction and a compact concentration in the 21-25-day period. The oestrous synchronisation rate in this 5-day period was 69.4 and 68.3%, respectively for L and H. The mean interval from ram introduction to oestrus was 23.17+/-2.4 days in L and 23.0+/-2.2 days in the H group. Conception rates were 84.3 and 87.4% for L and H groups, respectively. These results suggest that the response of anoestrous ewes to the introduction of rams was not affected by grazing space allowances and that yearling Mule ewes respond well to the ram effect in the late anoestrus season. (C) 2003 Elsevier Science B.V. All rights reserved.