This study examines the effects of contamination on the redevelopment and valuation of industrial land. The period covered by the study was one in which environmental legislation in the United Kingdom was undergoing significant changes. The Government's proposal to introduce registers of 'potentially contaminated sites' was fiercely opposed by different interest groups and was abandoned. New legislative proposals followed but will not take effect before 1997. During the same period, the guidance given to the valuers of industrial properties, and of other properties which may be affected by contamination, has been limited in scope and difficult to implement. It is argued that contaminated land is an important resource and that a 'risk assessment' approach should be adopted for valuation purposes and the appraisal of redevelopment proposals. The processes involved in the investigation of contaminated sites, the selection of treatment methods and the role of the valuer in these actions are considered. Alternative approaches to the valuation and appraisal of contaminated sites are described, both in situations where the existing industrial use is to continue and where redevelopment is proposed. Value was found to be affected both by the cost to treat the contamination and perceived 'risk factors', which are collectively termed 'stigma'.Besides valuers, many different actors are involved in the property development process. These actors are likely to hold differing views in respect of treatment methods, the value and desirability of redeveloping contaminated sites, according to the nature of their involvement. Questionnaire surveys were undertaken of valuers and other professionals involved in redevelopment, in order to test their perceptions of the risks involved. The views of a 'general population' sample were also obtained in respect of a number of environmental issues, in order to compare the views of two 'expert' groups with those of a wider population. Interviews were conducted with a number of leading valuers, so as to assess current practice in reflecting the possibility of contamination in valuations. The surveys enabled professional perceptions of the stigma effect to be determined. Case studies involving the redevelopment of contaminated sites were researched and the impact of stigma upon transaction prices was assessed. The findings of the research enabled a predictive model to be developed for use in the valuation of contaminated land and this was tested by reference to ten case studies. The perception of risk, associated with contaminated land, held by valuers was clearly identified to be higher than that perceived by the other group of'experts' involved in the development process. All of the professionals were generally supportive of current Government proposals but with some reservations. Property investors were identified as being the most cautious of all actors involved in property development. Further research proposals are described.

Abstract The investment is often coupled with the valuation. Before taking action, the decision that how to value an investment underlying is a scientific process. Usually, this work is heavily done and associated with an intangible value. Hence, somewhat in reality, the valuation and investment are also an artistic work. This study, using Taiwaneese quarterly data from 1992 to 2001, examines whether the RIV Model and Ohlson(1995) Model are applicable and valid in different industries. Both the RIV Model that equates the market value of a firm¡¦s equity to book value plus the present value of expected abnormal earnings and Ohlson(1995) Model which links the RIV with a linear information dynamic equation of abnormal earnings are accounting-based valuation Model. An emperical results show that the RIV Model is the best suitable for traditional industry, the Ohlson(1995) valuation Model is the best suitable for financial company, and the modified Model developed in this study is suitable for information technology industry. The implications implyed by this study are summarily as the followings¡G 1. Traditional Industry has stepped into mature or falling phase, and will be difficult to have abnormal earnings. The stock price can be explained largely by its book value. The forecasting value and terminal value are comparatively not so important. So the influence of predictive bias becomes less, and the accuracy of estimating intrinsic value will be higher than other industries. Therefore, RIV Model is comparatively suitable for the traditional industry. 2. Financial industry has been a mature one and its turnover is stable. The consecutive mutual relationship of the abnormal earnings is the highest among three industry from experimentation. The difference between RIV and Ohlson Valuation Model is that the latter derived from RIV connected with abnormal earning linear dynamic function, so financial industry is more suitable for Ohlson valuation model. 3. Information Technology Industry is in its growing phase. The turnover is too volatile, so ex-period market information (P-BV)t-1 must be added to acquire a better explanation power of the model. Therefore, The valuation model developed in this research is suitable for IT Industry.

This study provides two empirical studies in market-based accounting research. One study focuses on using out-of-sample valuation errors to evaluate various estimation approaches for firm-valuation models. The second empirical study uses portfolio analysis to evaluate an empirical accounting-based firm valuation model developed in the UK context.The first study uses out-of-sample valuation errors as an alternative metric capturing the effectiveness of various estimation approaches in generating reliable estimates of coefficients in accounting-based valuation models and, accordingly, less valuation bias and higher valuation accuracy. Valuation bias is expressed as the mean proportional valuation error, where estimated market value less the actually observed market value divided by the actual market value is the proportional valuation error, and valuation accuracy is measured by both the mean absolute and the mean squared proportional valuation error. We find that deflating the full equation including the constant term of the undeflated model and, hence, estimating without a constant term in the deflated model provides less bias and more accurate value estimates relative to including a constant term in the regression equation. Also estimating the valuation model on high- and low-intangible asset firms separately, instead of pooling the full sample for estimation, provides better performance in all cases. As expected, the results suggest that an extended model including the main accounting variables found to be associated with market value in the UK is better specified than a benchmark model, widely adopted in prior research, where market value is regressed on book value and earnings alone. Inclusion of 'other information' also seems to improve the performance of the models. However, there is no clear evidence that one particular deflator out of the five we investigate outperforms the others, although book value and opening and closing market value appear to generally perform better than sales and number of shares.The second empirical study tests for the existence of a 'mispricing' effect associated with accounting-based valuation models in the UK. It investigates a specific firm valuation model where market value is expressed as a linear combination of book value, earnings, research and development expenditures, dividends, capital contributions, capital expenditures and other information. All these accounting variables have been found value-relevant in prior studies in the UK. Firms are ranked by in-sample proportional valuation errors. Results show that although firms in the higher rank deciles tend to have higher abnormal returns than firms in the lower rank deciles, the difference between the two extreme portfolios (or the hedge returns) is statistically insignificant. As a consequence, accounting-based valuation models do not seem to provide superior estimates of intrinsic value to market values. We can conclude that the UK stock market is semi-strong form efficient, in the sense that it does not appear to be possible to generate positive abnormal returns based upon publicly available accounting information embedded in the valuation models studied.

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Master of Science / Department of Agricultural Economics / Mykel R. Taylor / Land value research has been conducted over many decades with efforts being focused on a broad spectrum of topics encompassing many different issues. The research in this thesis will focus on understanding the relationship between net farm income, cash rent, and land value. This research could provide insight and direction in determining future land value behavior. Understanding land prices is important to many different segments of the agricultural industry. Those involved in the industry want to know where land values are going and what the future looks like. Although certain segments may not be directly affected by land value movements, if value decreases the environment of the agriculture industry is changed. Farmers and ranchers are interested in future land values as they make purchase and sale decisions or as they consider future growth of their operation. Agribusinesses understand the affect a decrease in land value would do to farmer’s decisions regarding capital purchases. Additionally, agriculture finance institutions are interested in the future movement of land value as they are concerned about the affects adverse movements in land value would have on their customer’s balance sheet and ultimately their collateral position. In this paper the relationship between land value and cash rent; where land value is a function of historical cash rent and cash rent is a function of net returns to the land will be tested for its’ existence in Kansas. Data were collected for the nine crop reporting districts in Kansas from 1973 through 2012.

Researchers recommended investing according to the long only momentum (MOM) strategy to generate excess returns for private investors. The general problem of this study was that it was unclear when to enter and when to exit declining financial markets to avoid larger losses and to improve the overall performance with the MOM strategy. Therefore, it was important to understand the influence of a timing indicator on the MOM strategy. The purpose of this study was to examine the relationship between different moving average (MA) settings, the MOM strategy, and the performance of the returns from the construction of small U.S. stock portfolios. The research question was what MA setting as a strategy adjustment could improve the MOM strategy performance for small portfolios of U.S. stocks. A quasi-experimental research design was chosen to answer this research question. For the methods and analysis, simple- and exponential- MA, 2 econometric models, and abnormal Sharpe ratios were computed on the sample basis of 30 Dow Jones Industrial Average (DJIA) stocks. The computations allowed me to determine the optimal trading frequencies for the MA MOM strategy. The key result was that the MA MOM strategy could improve the MOM strategy on average by 0.16% per month. The optimal trading frequency for the MA MOM strategy with $5,000 was tri yearly through which (0.90 - 1.85 %) net monthly return could be achieved. The MOM strategy can be adjusted by a simple moving average (SMA) indicator on a 6 versus 36-month basis as a recommendation. This study might contribute to positive social change by adjusting the MOM strategy, which specifically impacts private investors in declining stock markets to improve the overall performance when trading the MA MOM strategy.

According to the World Health Organization (WHO), the population over the age of 65 in Taiwan has reached over 7% of the total population, making Taiwan an aging society and the senior consumer market a target market with significant potential. This study attempts to identify, quantify, and target the potential of senior living. First, the study identifies the senior living market demand, and product examples within the senior living market. Secondly, the report quantifies the senior living market demand. Lastly, the study proposes a senior living community resort as an investment. The study targets the senior population over 60 years old, indigenous peoples over 55 years old, are capable of independent living, and spouses no less than 50 years old. The study method is based upon secondary research and comparative analysis. This research analyzes the current residential market for seniors, identifies trends in the senior residential market, and based on existing research on the senior living market; discuss product design, target market, and pricing strategies for a senior residential product. From this research, we provide an investment valuation model that can be used to develop an investment proposal for a community-style senior living resort. This study aims for two main contributions from this research. First, current studies on seniors currently do not address the investment possibilities of senior living resorts or provide an investment valuation model for a workable investment proposal. Secondly, this research report provides actual management experience and industry information appropriate for Taiwanese businesses that require an investment valuation model for the development of an investment plan of a community-style senior living resort.

Dividend Valuation Model is a well-known stock pricing model. However, many empirical studies of foreign stock markets do not support the Dividend Valuation Model; most of these studies think stock price is too volatile to explain by expected dividend. Therefore, this article would like to use Stochastic Cointegration to reexamining Taiwan stock market, and observe whether Taiwan stock market supports Dividend Valuation Model. The empirical results showed that stock price and dividends exist a positive comovements relationship in the plastic, steel, electronic, and the banking & insurance industries, but empirical results does not completely support the theoretical value of cointegration vector. Therefore, this study has not been sufficient evidence to support Taiwan stock market is efficient.

A general player valuation model in ice hockey and football that would estimate player transfer price based on performace atributes as well as on personal and club atributes doesn't exist. There are no general rules on player transfer market that would set the player transfer price. The main aim of the dissertation thesis is to set up a model that would estimate player transfer price on the player market at a level when the selling club would earn at least the same amount that he invested to he player. Specific model for football and ice hockey is also introduced in the thesis. The specific model estimates transfer price based on the price of a same quality player abroad. General and specific models fit together. They form a guideline for sport managers, agents as well as for public while estimating player transfer price. The models are introduced, the transfer prices are estimated and compared with known transfer price on the player market. The major acquisition of the models is transparent price settng for player transfers, focusing on player transfers from Czech Republic abroad. The models are easily reversible for different collective sports.

Neural networks are seldom used as a modelling tool by statisticians. This is often due to the lack of knowledge in the eld of neural networks as neural networks are frequently perceived as mysterious methods that evolved from the eld of computer science. In this dissertation an attempt will be made to show that neural network methods are closely related to statistical methods. In particular we will show how a backpropagation neural network can be used for statistical applications like regression and classi cation which will include the setting up a of neural network for di erent objectives and also using a neural network for predictive inference. Through simulations we will show an e cient method to t a neural network in practical applications. A neural network will then be employed in a practical application to illustrate how to use a neural network in a regression or classi cation context. This application will also show the necessity of statistical knowledge when using a neural network as a modelling tool. / Dissertation (MSc)--University of Pretoria, 2010. / Statistics / unrestricted