Vassilis Assimakopoulos

Real estate appraisal: a review of valuation methods

The valuation of real estate is a central tenet for all businesses. Land and property are factors of production and, as with any other asset, the value of the land flows from the use to which it is put, and that in turn is dependent upon the demand (and supply) for the product that is produced. Valuation, in its simplest form, is the determination of the amount for which the property will transact on a particular date. However, there is a wide range of purposes for which valuations are required. These range from valuations for purchase and sale, transfer, tax assessment, expropriation, inheritance or estate settlement, investment and financing. The objective of the paper is to provide a brief overview of the methods used in real estate valuation. Valuation methods can be grouped as traditional and advanced. The traditional methods are regression models, comparable, cost, income, profit and contractor’s method. The advanced methods are ANNs, hedonic pricing method, spatial analysis methods, fuzzy logic and ARIMA models.

The theta model: a decomposition approach to forecasting

Abstract This paper presents a new univariate forecasting method. The method is based on the concept of modifying the local curvature of the time-series through a coefficient ‘Theta’ (the Greek letter θ), that is applied directly to the second differences of the data. The resulting series that are created maintain the mean and the slope of the original data but not their curvatures. These new time series are named Theta-lines. Their primary qualitative characteristic is the improvement of the approximation of the long-term behavior of the data or the augmentation of the short-term features, depending on the value of the Theta coefficient. The proposed method decomposes the original time series into two or more different Theta-lines. These are extrapolated separately and the subsequent forecasts are combined. The simple combination of two Theta-lines, the Theta=0 (straight line) and Theta=2 (double local curves) was adopted in order to produce forecasts for the 3003 series of the M3 competition. The method performed well, particularly for monthly series and for microeconomic data.

Integrating industrial maintenance strategy into ERP

During the last decade, many companies have made large investments in the development and implementation of enterprise resource planning (ERP) systems. However, only few of these systems developed or installed have actually considered maintenance strategies. Maintenance is a complex process that is triggered by planned periodic repair (scheduled or planned maintenance), equipment breakdown or deterioration indicated by a monitored parameter (unplanned or emergency maintenance). This process requires planning, scheduling, monitoring, quality assurance and deployment of necessary resources (workshop, manpower, machines, equipment, tools, spare parts, materials). Proper design and integration of maintenance management into ERP systems enable enterprises to effectively manage their production planning and scheduling, as well as to analyze their maintenance history so as to carry out cost analysis and produce future projections of failure trends. The present work presents the design of an object‐oriented maintenance management model and its integration into an ERP system. The proposed model was designed towards the development of innovative industrial software regarding the optimum management of maintenance in a wide range of business areas.

Theta intelligent forecasting information system

The need effectively to integrate decision making tasks together with knowledge representation and inference procedures has caused recent research efforts towards the integration of decision support systems with knowledge‐based techniques. Explores the potential benefits of such integration in the area of business forecasting. Describes the forecasting process and identifies its main functional elements. Some of these elements provide the requirements for an intelligent forecasting support system. Describes the architecture and the implementation of such a system, the theta intelligent forecasting information system (TIFIS) that that first‐named author had developed during his dissertation. In TIFIS, besides the traditional components of a decision‐support onformation system, four constituents are included that try to model the expertise required. The information system adopts an object‐oriented approach to forecasting and exploits the forecasting engine of the theta model integrated with automated rule based adjustments and judgmental adjustments. Tests the forecasting accuracy of the information system on the M3‐competition monthly data.

Real estate valuation with artificial intelligence approaches

Real Estate valuation in urban areas is a very difficult task that has absorbed the interest of many academics in the past years. Many qualitative and quantitative variables affect the value of an estate in urban areas. As a result, multivariate models are more suitable in the appraisal process. One of the most common approaches is multiple linear regression technique (MLR) that is always used as a benchmark in various studies. A very promising way of dealing with uncertainty in real estate analysis and producing sufficient evaluations is the use of Artificial Neural Networks (ANNs). Also, an expert method combining the strengths of MLR and ANN is tried out successfully. The purpose of this study is to compare these approaches on the basis of the data from the Attica urban area in Greece.

A first approach to e‐forecasting: a survey of forecasting Web services

A great challenge for today’s companies is not only how to adapt to the changing business environment but also how to gain a competitive advantage from the way in which they choose to do so. As a basis for achieving such advantages, companies have started to seek to improve the performance of various operations. Forecasting is one of them; it is important to firms because it can help ensure that effective use of resources is made. In the market there are a number of off‐the‐shelf system products, which provide forecasts. The new trend, of moving traditional software packages to Web services, has pushed forecasting to a new dimension, named by the authors as “e‐forecasting”. In this paper, a first approach to e‐forecasting is made by throwing light on several aspects and a survey is presented which aims at identifying existing Web forecasting services.

A study of financial volatility forecasting techniques in the FTSE/ASE 20 index

Forecasting financial market volatility is an important task that has absorbed the interest of many academics in the late twentieth and early twenty-first centuries. This strong interest of the academic world reflects the importance of volatility in several financial and business activities. Volatility forecast, crucially affects investment choice and is the most important parameter affecting prices of market listed options, of which trading volume has proliferated in the last years. The purpose of this article is to compare various volatility forecasting approaches using data on the Greek FTSE/ASE 20 stock index.

Options trading driven by volatility directional accuracy.

Analysts have claimed over the last years that the volatility of an asset is caused solely by the random arrival of new information about the future returns from the underlying asset. It is a common belief that volatility is of great importance in finance and it is one of the critical factors determining option prices and consequently driving option-trading strategies. This article discusses an empirical option trading methodology based on efficient volatility direction forecasts. Although in most cases accurate volatility forecasts are hard to obtain, forecasting the direction is significantly easier. Increase in the directional accuracy leads to profitable investment strategies. The net gain is depended on the size of the changes as well; however successful volatility forecasts in terms of directional accuracy was found to be sufficient for positive results. In order to evaluate the proposed methodology weekly data from CAX40, DAX and the Greek FTSE/ASE 20 stock indices were used.

An object oriented approach to forecasting

Abstract This paper introduces the use of object oriented design techniques in the area of forecasting. It focuses on representing the knowledge underlying the forecasting process using classes, objects and properties organized in a tree structure. Three major classes have been identified: time-series, quantitative forecasting tools, and results. Objects constitute specific instances of these classes, while properties represent their characteristics perceived as decisive in the forecasting process. Reasoning is performed by rules that act upon the three basic structures of representation mentioned above and determine the inference relations that exist among them. In the working applications developed, knowledge bases corresponding to specific forecasting methodologies were integrated with the proposed object oriented representation.

The advanced forecasting information system PYTHIA: An application in real estate time series

Purpose – The main scope of the paper is to demonstrate the capabilities of PYTHIA forecasting platform, to compare time series forecasting techniques, which were used to forecast mortgage loans in UK, and to show how PYTHIA can be useful for a bank.Design/methodology/approach – The paper outlines the methods used to forecast the time series data, which are included in PYTHIA. Theta, the time‐series used to forecast average mortgage loan prices, were grouped in: all buyers – average loan prices in UK; first‐time buyers – average loan prices in UK; and home‐movers – average loan prices in UK. The case of all buyers – average loan prices in UK, was presented in detail.Findings – After the comparison of the methods, the best forecasts are produced by WINTERS and this is maybe due to the fact that there is seasonality in the data. The Theta method comes next in the row and generally produces good forecasts with small mean absolute percentage errors. In order to tell with grater certainty which method produces...