Search results

This paper aims to test and compare two innovative methodologies (utility additive and evolutionary polynomial regression) for mass appraisal of residential properties. The aim is to deepen their characteristics, by exploring the potentialities and the operating limits.

With reference to the same case studies, concerning samples of residential properties recently sold in three Italian cities, the two procedures are tested and the results are compared. The first method is the utility additive, which interprets the process of the property price formation as a multi-criteria selection of multi-objective typology, where the selection criteria are the property characteristics that are decisive in the real estate market; the second method is a hybrid data-driven technique, called evolutionary polynomial regression, that uses multi-objective genetic algorithms to search those models expressions that simultaneously maximize accuracy of data and parsimony of mathematical functions.

The outputs obtained from the experimentation highlight the potentialities and the limits of the two methodologies, as well as the possibility of jointly applying them to interpret and predict the real estate phenomena in a more realistic representation.

In all countries, mass appraisal techniques have become strategic for the definition of management and enhancement policies of public and private property assets, in the case of investments of technical and economic refunctionalization (energy, environment, etc.), and for the alienation of buildings no longer suitable for public needs (military barracks, hospitals, areas in disuse, etc.). In this context, the use of mass appraisal techniques for residential properties assumes a leading role for sector operators (buyers, sellers, institutions, insurance companies, banks, real estate funds, etc.). Therefore, the results of the applications outline the potentialities of the two methodologies implemented and the opportunity of further insights of the topics that have been dealt with in this research.

I. Introduction The general manager of a state owned manufacturing plant in a developing country has to decide how to allocate production between two products. The first product is more profitable, but the second is a priority product which the government wants to produce inside the country. Demand for the second product is uncertain, so that producing too much may decrease profits without increasing market satisfaction, while producing too little could result in unfavourable government and public reaction. This problem is typical of many decision‐making situations in the modern world—the dilemma posed by multiple, conflicting objectives in an uncertain environment.

The purpose of this paper is to present a methodology for estimating the “price,” or the not-to-loss value, of individual highway assets, which reflects not only the assets’ capital value but also economic productivity, by adopting a productivity-based asset valuation framework. The price tags can be used in prioritizing highway assets in support of transportation asset management processes.

The methodology adopts the utility theory to consider multiple performance measures reflecting the economic productivity generated by the assets, as well as their capital value. Key performance measures are first selected, and their values are retrieved from highway asset management databases. Next, the utility functions representing decision makers’ preferences convert the performance measures into utility values, which adjust the replacement cost (RC) of each highway asset to estimate price tags. To demonstrate its applicability, case studies were conducted for the highway networks of Texas and Washington State in the USA.

The methodology yielded price tags that better reflect the importance of highways’ roles in the economy in comparison to methods where only RCs are used. Furthermore, it was proven to be flexible enough to accommodate local conditions such as varying data availability.

The research provides a practical and reasonable way to prioritize critical highway assets in purport of maintenance and rehabilitation resource allocations, based on their economic productivity as well as physical condition and historical cost information, enhancing the overall efficiency and effectiveness of highway asset management.

This study was carried out to analyze the importance of consumer preference data in forecasting demand in apparel retailing.

To collect preference data, 729 hypothetical stock keeping units (SKU) were derived using a full factorial design, from a combination of six attributes and three levels each. From the hypothetical SKU's, 63 practical SKU's were selected for further analysis. Two hundred two responses were collected from a store intercept survey. Respondents' utility scores for all 63 SKUs were calculated using conjoint analysis. In estimating aggregate demand, to allow for consumer substitution and to make the SKU available when a consumer wishes to buy more than one item in the same SKU, top three highly preferred SKU's utility scores of each individual were selected and classified using a decision tree and was aggregated. A choice rule was modeled to include substitution; by applying this choice rule, aggregate demand was estimated.

The respondents' utility scores were calculated. The value of Kendall's tau is 0.88, the value of Pearson's R is 0.98 and internal predictive validity using Kendall's tau is 1.00, and this shows the high quality of data obtained. The proposed model was used to estimate the demand for 63 SKUs. The demand was estimated at 6.04 per cent for the SKU cotton, regular style, half sleeve, medium priced, private label. The proposed model for estimating demand using consumer preference data gave better estimates close to actual sales than expert opinion data. The Spearman's rank correlation between actual sales and consumer preference data is 0.338 and is significant at 5 per cent level. The Spearman's rank correlation between actual sales and expert opinion is −0.059, and there is no significant relation between expert opinion data and actual sales. Thus, consumer preference model proves to be better in estimating demand than expert opinion data.

There has been a considerable amount of work done in choice-based models. There is a lot of scope in working in deterministic models.

The proposed consumer preference-based demand estimation model can be beneficial to the apparel retailers in increasing their profit by reducing stock-out and overstocking situations. Though conjoint analysis is used in demand estimation in other industries, it is not used in apparel for demand estimations and can be greater use in its simplest form.

This research is the first one to model consumer preferences-based data to estimate demand in apparel. This research was practically tested in an apparel retail store. It is original.

The purpose of this paper is to present a review of the literature on alcohol consumption, the externality cost of alcohol consumption, and the effectiveness of policy options.

Evidence on the cost to society of alcohol consumption, the amount of excise tax collected, the demand response of consumers, and the effectiveness of alcohol‐control policies is reviewed.

Alcohol excise taxes generally, but not everywhere, fail to recover the externality costs placed on society that arise from alcohol consumption. Where externality costs are greater than excise revenue higher excise taxes are one effective and appropriate policy response. Complementary policies to higher excise taxes are likely to include: the provision of more information about harmful effects to consumers, especially the young; greater enforcement of drunk‐driving laws and zero tolerance drunk‐driving laws for young drivers. Restrictions on the opening hours of late night venues may have a modest impact on reducing costs, while advertising restrictions are unlikely to be effective.

Typically. articles on alcohol consider a single issue. This review paper brings together information from both the health stream of alcohol studies and the economics stream of alcohol studies and provides a useful survey and synthesis of the literature.

A reliable estimate of markup is essential for successful bid proposals. This paper presents a decision support model for construction bidding. The developed model can assist contractors in estimating markup, and owners and/or their agents in evaluating bid proposals. The model is generic and can be used as a tool to evaluate different alternatives in engineering, procurement, and construction. It utilizes the multi‐attribute utility theory and the analytic hierarchy process and makes use of their advantages. Unlike models developed for similar purposes, the proposed model provides a decision support environment for the two functions; that is, estimating markup and evaluating bids. It also enables the user to construct the decision hierarchy that best suits his/her company’s business environment and bidding strategy in a flexible manner. It accounts for the decision maker’s attitude towards risk. Two numerical examples are presented to demonstrate the use and capabilities of the proposed model.

Leading organizations do not stop at the gathering and analysis of performance data; rather, these organizations use performance measurement to stimulate improvement efforts and successfully translate strategy into action. In other words, they use performance measurement for managing their organizations. The most commonly used performance evaluation tool, the balanced scorecard (BSC) has been used extensively in the manufacturing, government, banking, retail, insurance and financial services sectors. This paper looks at potential applications and benefits of using the BSC as a framework to evaluate the performance improvement resulting from IT/IS implementation by a construction organization. The paper firstly seeks to adapt the original BSC concept to construction and then attempts to develop a performance measurement framework in the form of a tiered “Construct IT” BSC. The developed framework allows for the measurement of IT/IS performance at three different decision‐making tiers (i.e., project, business unit and enterprise tiers). At each tier, a group of performance indicators are used to describe key aspects of activities that occur frequently enough to warrant meaningful measurement and comparison. The proposed framework applies the analytic hierarchy process (AHP) and multi‐attribute utility theory (MAUT) to facilitate aggregating the obtained diverse performance measurements, thus giving rise to an overall IT/IS performance improvement measure (score) at each decision‐making tier. To illustrate the application of the proposed framework, a project tier example is provided.

The Renewable Energy Directive 2009/28/EC of the European Union provides another element to cross-border cooperation by allowing Member States to fulfill their 2020 renewable energy sources (RES) targets by implementing joint projects in third countries through the cooperation mechanisms. The purpose of this paper is to assess the country risk, to support bilateral cooperation for RES electricity generation projects.

A multicriteria decision support methodology has been developed taking into account three evaluation parameters, namely, the investment framework, the social conditions and the energy and technological status. An additive value model has been constructed, and the UTilitès Additives (UTA) – UTA* (UTASTAR) disaggregation method has been implemented to infer the criteria weights. The obtained ranking of alternatives has been subjected to robustness analysis, and finally the proposed methodology has been applied to five North Africa countries, so as to draw key results.

The pilot application of the methodological approach proposed and the model developed was fully compatible with the decision maker’s ranking on a set of fictitious countries and facilitated the assessment of a country’s current situation with regards to its investment, social conditions and energy and technological status. The results regarding the five North African countries examined, indicated the country’s investment framework as the most important factor, from foreign investors’ perspective, affecting a country’s suitability for the implementation of RES projects through a cooperation mechanism and Morocco, as well as Tunisia as the countries with the most suitable conditions for a successful implementation of such projects.

To the best of authors’ knowledge, there are only very few studies trying to assess opportunities and risks emerging from the implementation of joint projects between European and third countries in the field of electricity generation from RES. There are even less studies using (UTASTAR) method on real-world decision-making problems, and almost none are dedicated to energy sector-related problems.

Economists have long sought methods whereby efficient collective decisions can be made. When a group is confronted with the problem of choosing one policy from several alternatives, some decision‐making mechanism must be employed. Whether the mechanism is choice by democratic vote, the whim of the reigning dictator, coin tossing, or some other, the choice is made by the application of some decision‐making mechanism.

A repair contract problem has been analysed. Each contract alternative implies in a specific cost of the contract and a commitment of repair time. The decision maker has to choose the best alternative, taking into account the consequences modelled through an additive utility function. These consequences consist of system performance and the cost of the contract. The system performance is represented by system interruption time. The solution corresponds to a contract which optimises the additive utility function. The paper presents formulation and derivations in order to find the solution. A numerical application of the decision model proposed is presented including a sensitivity analysis step.