Defining the utility and market value of a real estate: a multiple criteria approach.

ABSTRACT. This paper considers the application of methodology for the defining the utility and market value of a real estate. The theoretical basis of the methodology is developed. The proposed methods, the method of multiple criteria complex proportional assessment (COPRAS) and the method of defining the utility and market value of a real estate assume the dependence of priority, utility degree and value of investigated versions on a system of criteria adequately describing the alternatives and their direct proportionality to the values and weights of these criteria. The procedure of the defining the utility and market value of a real estate is discussed using an example.

KEYWORDS: Multiple criteria analysis; Utility and market value; Real estate

1. INTRODUCTION

Many decision making models and methods have been developed in the world for solving different problems in real estate sector. Kuo (1996) proposed a method of polynomial approximation to value the housing price dynamics and the valuation of mortgage default options. Gonzalez and Laureano-Ortiz (1992) concentrated on the issues involved in the application of the case-based reasoning techniques to a specific domain, property appraisal. Case-based reasoning has been recently favored because it seems to resemble more closely the psychological process humans follow when trying to apply their knowledge to the solution of problems: adapting solutions of similar problems handled in past experiences to address present situations. By modelling the market data approach of appraisal, using adaptations of case-based reasoning techniques, such as the similarity links and the critics, and integrating other techniques, (i.e., the use of comfort factors), a case-based reasoner for property appraisal is implemented addressing the issues just mentioned above (Gonzalez and Laureano-Ortiz, 1992).

Diappi and Bolchi (2007) investigated local housing market dynamics by applying an urban spatial model of gentrification based on Smith's rent gap theory. Smith's supply side approach explains the emergence of gentrifying neighbourhoods on the basis of investments spent in "large scale renewal projects" which only investors or developers looking for profits are able to carry out. They invest in degraded areas on the base of the gap between the actual rent and the potential rent after rehabilitation (rent gap). A set of factors are selected and a statistical early-warning method, which can monitor the Shenzhen real estate property market, is developed by Huang and Wang (2005). In addition, a system dynamics model has been developed, which can provide a simulation tool to predict the effect of regulatory policies on the real estate market. Evaluation results indicate that the pre-warning system can provide useful information to regulate the property market in Shenzhen.

Markland (1979) described a Monte Carlo simulation approach to the analysis of real estate investments under uncertainty. Wang (2005) described a knowledge-based decision support system for measuring the performance of government real estate investment using DEA models. Trippi (1989) examined industry factors, design goals, and functions of a system used to improve major real property asset acquisition, improvement, and divestment decisions. Fletcher et al. (2000) were concerned as to whether it is more appropriate to use aggregate or disaggregate models in forecasting house prices when using hedonic modeling. Baffoe-Bonnie (1998) analyzed the dynamic effects of four key macroeconomic variables on housing prices and the stock of houses sold at national and regional levels by using a nonstructural estimation technique. Hui and Yu (2006) analyzed the dynamics of Hong Kong's office rental market. This study provides a generator approach, on the basis of both system dynamics and econometric modeling. Dua et al. (1999) used Bayesian vector autoregressive models to examine the usefulness of leading indicators in predicting U.S. home sales. Wheaton et al. (1997) applied structural econometric methodology for estimating and forecasting the greater London office market. Magdisyuk (2001) considered some aspects of using a cascade-correlation network in the investment task, in which it is required to determine the most suitable project to invest money. Leung and Hui (2000) attempted to introduce the application of the option pricing theory to the valuation of property development projects by integrating both the capital budgeting and the strategic planning that were based on the London Docklands saga.

Lins et al. (2005) proposes a new methodology for the assessment of the value range for real estate units. The proposed approach-christened Double Perspective-Data Envelopment Analysis (DP-DEA)--is applied to a database comprising the prices and features of the units under assessment. It is shown that the DP-DEA presents some specific advantages when compared to the usual regression analysis method employed in real estate value assessment. Englund et al. (1998) presented an improved methodology for estimating asset prices for real estate and other durables. The method is used to analyze house price dynamics by exploiting an unusually rich and detailed body of data-extensive descriptive and financial information on every house sale in Sweden during a 12-year period.

Cannaday et al. (2005) developed a multivariate repeat-sales model that is able to separately control for the effects of age and time, as well as other assets with changing attributes in the construction of price indices. Martinaitis et al. (2007) proposed a two-factor method for appraising building renovation and energy efficiency improvement projects. Ioannides (2003) examined effects of social interactions in the form of reaction functions for homeowners' valuation of their properties at the level of the immediate residential neighborhood, with neighborhoods consisting of a randomly chosen dwelling unit and about ten nearest neighbors. The paper provides empirical support for the notion, common in the real estate world, of the importance of neighboring properties in property valuations. Gibbons and Machin (2003) provided the first empirical evidence for the UK on the effect of primary school performance on property prices. Bourassa et al. (2006) presented the sale price appraisal ratio (SPAR) method for constructing house price indexes. Authors compared the official New Zealand indexes for three urban areas with repeat sales and hedonic indexes created from the same transactions data, and observed that the SPAR method produced an index very much like those produced by repeat sales methods.

Mendez (2006) estimated the value of legal property titles on the Costa Rican urban housing market using hedonic regressions on the value of the house and then studied specific segments of the population that vary in their economic activities and incentives as related to legal housing titles. Arguea and Hsiao (2000) presented a latent variable framework to provide consistent and efficient estimates of market values of amenities. They used samples obtained from the American Housing Survey (AHS) to estimate the effect of neighborhood quality on housing prices.

However, fewer attempts have been made to employ methods of multiple criteria decision making (MCDM) to solve a number of problems in real estate sector (see Zavadskas and Kaklauskas, 1996; Zavadskas et al., 1997; Maliene et al., 1999; Zavadskas et al., 2001; Zavadskas et al., 2004a; Zavadskas et al., 2004b; Kaklauskas and Gikys, 2005; Kaklauskas et al., 2005). In this paper, the authors present a methodology for the defining the utility and market value of a real estate. The proposed methods, the method of multiple criteria complex proportional assessment (COPRAS) and the method of defining the utility and market value of a real estate assume the dependence of priority, utility degree and value of investigated versions on a system of criteria adequately describing the alternatives and their direct proportionality to the values and weights of these criteria. The potential of the approach has been explored in Framework 5 (2000), Framework 5 (2001), Framework 6 (2003) and ACE PHARE programme (Kaklauskas, 1998), for instance. Real-world applications demonstrate the effectiveness of this approach in solving wide-range problems (see Ministry of Construction and Urban Development of the Republic of Lithuania, 1998; Ministry of Economy of the Republic of Lithuania, 2001). In view of our theoretical and practical results, we believe that the proposed approach is especially suitable for decision contexts where multiple dimensions of problems must be evaluated and due attention to interests of participants involved must be given. The proposed methodology allows the decision maker to negotiate his/her preferences and needs.

The remainder of this paper is structured as follows. Section 2 presents the methodology for the determination of the utility degree and market value of a real estate. An example is given in Section 3 to illustrate the use of the methodology. Finally, some concluding remarks are provided in Section 4.

2. A MULTIPLE CRITERIA APPROACH

2.1. Collection of initial data and determination of the criteria weights

The determination of the utility degree and market value of the real estate under investigation and the establishment of the order of priority for its implementation has less difficulty if the criteria numerical values and weights are obtained and when multiple criteria decision making methods are used.

The data for the analysis of real estate projects are presented as a grouped decision matrix that involves a set of n alternatives, to be compared with respect to a set of m criteria (Table 1). For evaluating competing alternatives (the real estate to be valued and comparable real estates), a complex analysis of its economical, technical, qualitative, infrastructure and other aspects is needed. Quantitative and conceptual descriptions provide this information. Quantitative information is based on criteria system, units of measurement, values and weights of the criteria. The determination of quantitative criteria numerical values is based on the use of various statistical methods, analysed projects, recommendations, price-lists, reference books, building codes, specifications and other documents.