Análisis DAFO y PEST

To supplement the quantitative analysis, several interviews were conducted with experts from each context. The results of these interviews are presented at the end of Chapters 4 and 5. Before proceeding, a brief introduction to this approach is necessary.

The analytic hierarchy process (AHP, Saaty 1977) technique is based on the pair-wise preference comparison of elements (attributes or alternatives), and results in a comparison matrix in which the relative importance of each ele- ment is determined as a ratio between 0 and 1. This technique is suitable for many kinds of analyses, including appraisal problems. The basic principle of the method is given below.

In sharp contrast to the classical multi-attribute value-tree modelling approach, which is based on the assumption that utility functions can be explained, the AHP does not assume that the evaluator is able to express the overall elicitation of the problem as a single function. Instead, the AHP is based on the assumption that the relevant dominance of one attribute over another can be measured with a systematic, pair-wise comparison of preferen- ces at each level of a hierarchy of factors, presented as a value tree (e.g., Ball & Srinivasan, 1994). The overall objective of the decision stands at the top of the hierarchy, with lower-level objectives or attributes at the lower levels (e.g., Zahedi, 1986).

The comparison begins at the lowest level of the tree, where the elements (attributes or alternatives) are usually elicited with an ordinal scale from 1 to 9, with the values corresponding to verbal expressions. A value of 1 means that ‘both are of equal importance’, and a value of 9 means that ‘A has an extreme importance over B’. The comparisons are then converted into cardi- nal rankings (e.g., Erkut and Moran, 1991). Balancing the pair-wise ranks in this way involves the use of measurement theory, as pair-wise judgments cannot be assumed consistent across the entire set of comparisons (e.g., Ball and Srinivasan, 1994).

explained in terms of a matrix equation. Consider the elements: A₁, A₂, ... A_n within one level of the tree hierarchy. In practice, the maximum number of elements to compare within a single comparison matrix is nine (the ‘Expert Choice’ software actually has a maximum of seven elements), although there is theoretically no upper limit to the number of elements to compare. The comparisons among all of the elements (A₁:A₂, ... , A_n-1:A_n ) then generate the following matrix:

The total number of comparisons is (A_n-1 x A_n)/2. For example, a matrix of four elements generates six comparisons. Each comparison generates a pair-wise ratio, (e.g., w₁/w₂, w₂/w₁). All of the ratios along the diagonal are obviously equal to 1, as it is not necessary to compare elements with themselves. The overall weight is indicated by the priority vector.

The most common way to estimate the relative weights from the matrix of pair-wise comparisons is the ‘eigenvalue’ method (see e.g., Zahedi, 1986, for a full discussion).

The matrix formula A_w = n_w applies only for the theoretical ideal situation in which the comparison is fully consistent. This is usually not the case in observed pair-wise comparisons (unless the comparison is unambiguous and the matrix is very small, e.g., 3 elements that compare 2:1, 2:1 and 4:1), and the estimate ␭max is therefore used instead of the exact n. To enable approx- imation of a less than fully consistent comparison matrix, there must be more observations than weights. In fact, as Saaty (1990) demonstrated, ␭max is always greater than or equal to n and, as it approaches n, the values of A become more consistent. In the terminology of AHP, this property has led to construction of the consistency index (CI) as follows:

CI = (␭max – n)/ (n – 1), (5)

The consistency of the comparisons is measured with the consistency ratio (CR), which is calculated according to the expected results of consistent pair- wise comparisons across the matrix, as follows:

CR = (CI/ACI) x 100, (6) A₁ w₁ / w₁ w₁ / w₂ w₁ / w₃ ... w₁ / w_n w₁ w₁ A₂ w₂ / w₁ w₂ / w₂ w₂ / w₃ ... w₂ / w_n w₂ w₂ A = A₃ w₃ / w₁ w₃ / w₂ w₃ / w₃ ... w₃ / w_n w₃ = n w₃ (4) . . . . . . . . . . . . A_n w_n / w₁ w_n / w₂ w_n / w₃ ... w_n / w_n w_n w_n

The ACI is the average index of randomly generated weights (Cited in Zahe- di, 1986). Using analogous terminology from statistics, substituting ␭max for n generates a number of equations that exceeds the number of unknown parameters to be estimated. The CR should be very small. There are several opinions about the relevance of the CR; for example, it may be used as a filter. This measure is disregarded in the exercise that is reported below.

Finally, local weights are transformed into global weights. The most attrac- tive choice is determined by aggregating the local priorities into global priori- ties (i.e., ‘quality-ranks’ or Q-values). This process quantifies the relative con- tribution of each element in the value tree to the overall goal.

Using the analytic hierarchy process (AHP), quality ranks were generat- ed for various bundles of locational attributes, using interactive data. In this exercise, the respondents were required to meet two criteria: (1) a pursuit as stakeholder, based on professional responsibility in business or administra- tion and (2) a deep local knowledge of the spatial housing-market structure, gained through professional experience. The experts represented transaction- related services (e.g., estate agents and assessors), land and property own- ership (e.g., builders, municipalities as landowners and other investors) and user-oriented interest groups (e.g., planners, rental agents and other admin- istrators). There is no fundamental reason either for or against adapting the method by including the owners and renters of housing as experts. In con- trast to the better-informed professional expert groups, these informants are likely to have somewhat less variation in the attributes determining their location choices or property-appraisal decisions, as households tend to have much less information at their disposal than do professionals in the field.

The ‘behavioural paradigm’ in residential valuation, which is propagated by Daly and colleagues (2003), places more emphasis on demand or consumer- driven factors that relate to preferences and intangible components of quality. Further, it evaluates the performance of a given method with regard to these aspects. This approach offers both a contrasting alternative and a supplement to the main approach (as in the case of this study). Because of problems that are associated with listed-price data (e.g., scarcity, unreliability and low qual- ity), conventional methods do not apply. Arguably, the inclusion of consumer behaviour and quality in the method can improve its conceptual soundness. Multi-criteria decision-making analysis is therefore the most conceptually sound approach to valuation, as it explicitly deals with such elements. Partic- ular goods, including housing, may have fashionable symbolic meanings (sign value), and demographically similar groups may have fundamentally different ways of life (Bourdieu). Scarcity value is one obvious aspect of this. Adding the conceptualisation of the ways in which particular products (in this case, resi- dential areas and housing packages) become fashionable (Beck) and attractive targets for trendsetters the argument moves toward the discussion on imma- terial sign values in consumer sociology (Cited in Uuskallio, 2001). As recent-

ly noted, ‘new life-styles have developed that emphasise “hedonistic individu- alism” and that are characterised by patterns of consumption that emphasise symbolic values in conjunction with articulated life-styles’ (Mingione & Scott, cited in Kloosterman & Lambregts, 2001). The same consumer-oriented proc- esses of attributing symbolic and sentimental value to the home also occur in less-developed societies such as Turkey (Tekkaya, 2002).

3.5 The comparative perspective and the