31 resultados para investment analysis

em CentAUR: Central Archive University of Reading - UK


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Investments in direct real estate are inherently difficult to segment compared to other asset classes due to the complex and heterogeneous nature of the asset. The most common segmentation in real estate investment analysis relies on property sector and geographical region. In this paper, we compare the predictive power of existing industry classifications with a new type of segmentation using cluster analysis on a number of relevant property attributes including the equivalent yield and size of the property as well as information on lease terms, number of tenants and tenant concentration. The new segments are shown to be distinct and relatively stable over time. In a second stage of the analysis, we test whether the newly generated segments are able to better predict the resulting financial performance of the assets than the old dichotomous segments. Applying both discriminant and neural network analysis we find mixed evidence for this hypothesis. Overall, we conclude from our analysis that each of the two approaches to segmenting the market has its strengths and weaknesses so that both might be applied gainfully in real estate investment analysis and fund management.

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Office returns in the City of London are more volatile than in other UK markets. This volatility may reflect fluctuations in capital flows associated with changing patterns of ownership and the growing linkage between real estate and financial markets in the City. Using current and historical data, patterns of ownership in the City are investigated. They reveal that overseas ownership has grown markedly since 1985, that owners are predominantly FIRE sector firms and that there are strong links between ownership and occupation. This raises concerns about future volatility and systemic risk.

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Biological emergencies such as the appearance of an exotic transboundary or emerging disease can become disasters. The question that faces Veterinary Services in developing countries is how to balance resources dedicated to active insurance measures, such as border control, surveillance, working with the governments of developing countries, and investing in improving veterinary knowledge and tools, with passive measures, such as contingency funds and vaccine banks. There is strong evidence that the animal health situation in developed countries has improved and is relatively stable. In addition, through trade with other countries, developing countries are becoming part of the international animal health system, the status of which is improving, though with occasional setbacks. However, despite these improvements, the risk of a possible biological disaster still remains, and has increased in recent times because of the threat of bioterrorism. This paper suggests that a model that combines decision tree analysis with epidemiology is required to identify critical points in food chains that should be strengthened to reduce the risk of emergencies and prevent emergencies from becoming disasters.

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The principle aim of this research is to elucidate the factors driving the total rate of return of non-listed funds using a panel data analytical framework. In line with previous results, we find that core funds exhibit lower yet more stable returns than value-added and, in particular, opportunistic funds, both cross-sectionally and over time. After taking into account overall market exposure, as measured by weighted market returns, the excess returns of value-added and opportunity funds are likely to stem from: high leverage, high exposure to development, active asset management and investment in specialized property sectors. A random effects estimation of the panel data model largely confirms the findings obtained from the fixed effects model. Again, the country and sector property effect shows the strongest significance in explaining total returns. The stock market variable is negative which hints at switching effects between competing asset classes. For opportunity funds, on average, the returns attributable to gearing are three times higher than those for value added funds and over five times higher than for core funds. Overall, there is relatively strong evidence indicating that country and sector allocation, style, gearing and fund size combinations impact on the performance of unlisted real estate funds.

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In this paper we investigate the commonly used autoregressive filter method of adjusting appraisal-based real estate returns to correct for the perceived biases induced in the appraisal process. Since the early work by Geltner (1989), many papers have been written on this topic but remarkably few have considered the relationship between smoothing at the individual property level and the amount of persistence in the aggregate appraised-based index. To investigate this issue in more detail we analyse a sample of individual property level appraisal data from the Investment Property Database (IPD). We find that commonly used unsmoothing estimates overstate the extent of smoothing that takes place at the individual property level. There is also strong support for an ARFIMA representation of appraisal returns.

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In a workshop setting, two pieces of recorded music were presented to a group of adult non-specialists; a key feature was to set up structured discussion within which the respondents considered each piece of music as a whole and not in its constituent parts. There were two areas of interest, namely to explore whether the respondents were likely to identify the musical features or to make extra-musical associations and, to establish the extent to which there would be commonality and difference in their approach to formulating the verbal responses. An inductive approach was used in the analysis of data to reveal some of the working theories underpinning the intuitive musicianship of the adult non-specialist listener. Findings have shown that, when unprompted by forced choice responses, the listeners generated responses that could be said to be information-poor in terms of musical features but rich in terms of the level of personal investment they made in formulating their responses. This is evidenced in a number of connections they made between the discursive and the non-discursive, including those which are relational and mediated by their experiences. Implications for music education are considered.

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Decision theory is the study of models of judgement involved in, and leading to, deliberate and (usually) rational choice. In real estate investment there are normative models for the allocation of assets. These asset allocation models suggest an optimum allocation between the respective asset classes based on the investors’ judgements of performance and risk. Real estate is selected, as other assets, on the basis of some criteria, e.g. commonly its marginal contribution to the production of a mean variance efficient multi asset portfolio, subject to the investor’s objectives and capital rationing constraints. However, decisions are made relative to current expectations and current business constraints. Whilst a decision maker may believe in the required optimum exposure levels as dictated by an asset allocation model, the final decision may/will be influenced by factors outside the parameters of the mathematical model. This paper discusses investors' perceptions and attitudes toward real estate and highlights the important difference between theoretical exposure levels and pragmatic business considerations. It develops a model to identify “soft” parameters in decision making which will influence the optimal allocation for that asset class. This “soft” information may relate to behavioural issues such as the tendency to mirror competitors; a desire to meet weight of money objectives; a desire to retain the status quo and many other non-financial considerations. The paper aims to establish the place of property in multi asset portfolios in the UK and examine the asset allocation process in practice, with a view to understanding the decision making process and to look at investors’ perceptions based on an historic analysis of market expectation; a comparison with historic data and an analysis of actual performance.

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Investing in real estate markets overseas means venturing into the unknown, where you meet unfamiliar political and economic environments, unstable currencies, strange cultures and languages, and so although the advantages of international diversification might appear attractive, the risks of international investment must not be overlooked. However, capital markets are becoming global markets, and commercial real estate markets are no exception, accordingly despite the difficulties posed by venturing overseas no investor can overlook the potential international investment holds out. Thus, what strategies are appropriate for capitalising on this potential? Three issues must be considered: (1) the potential of the countries real estate market in general; (2) the potential of the individual market sectors; and (3) the investment process itself. Although each step in foreign real estate investment is critical, the initial assessment of opportunities is especially important. Various methods can be used to achieve this but a formal and systematic analysis of aggregate market potential should prove particularly fruitful. The work reported here, therefore, develops and illustrates such a methodology for the over 50 international real estate markets.

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In this article, we investigate the commonly used autoregressive filter method of adjusting appraisal-based real estate returns to correct for the perceived biases induced in the appraisal process. Many articles have been written on appraisal smoothing but remarkably few have considered the relationship between smoothing at the individual property level and the amount of persistence in the aggregate appraisal-based index. To investigate this issue we analyze a large sample of appraisal data at the individual property level from the Investment Property Databank. We find that commonly used unsmoothing estimates at the index level overstate the extent of smoothing that takes place at the individual property level. There is also strong support for an ARFIMA representation of appraisal returns at the index level and an ARMA model at the individual property level.

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The rapid growth of non-listed real estate funds over the last several years has contributed towards establishing this sector as a major investment vehicle for gaining exposure to commercial real estate. Academic research has not kept up with this development, however, as there are still only a few published studies on non-listed real estate funds. This paper aims to identify the factors driving the total return over a seven-year period. Influential factors tested in our analysis include the weighted underlying direct property returns in each country and sector as well as fund size, investment style gearing and the distribution yield. Furthermore, we analyze the interaction of non-listed real estate funds with the performance of the overall economy and that of competing asset classes and found that lagged GDP growth and stock market returns as well as contemporaneous government bond rates are significant and positive predictors of annual fund performance.