DETERMINING RETAIL RENTS THROUGH CASE-BASED REASONING

R E S E A R C HDETERMINING RETAIL RENTS THROUGHCASE-BASED REASONINGBrenna O'roarty1, Alastair Adair2, Stanley Mcgreal2 And David Patterson3.1Centre For Property Research, University Of Aberdeen2Centre For Research Of Property, Planning And Logistics, University Of Ulster 3Northern Ireland Knowledge Engineering Laboratory, University Of Ulster 1.IntroductionIn the United Kingdom, retail rents are traditionally assessed by the comparative method at rent review which is reliant on the quality of data and the experience of the valuer. The ambit of most retail rent determinations is to assess the open market value of property from the evidence available. Retail property is heterogeneous in nature and in making comparisons between properties subjective rental adjustments are made where differences in the locational, physical and lease term characteristics occur between the subject and comparable properties. This ability to adjust less than ideal evidence to reflect comparability has been termed the "valuer's art" however there has been a positive movement towards appraisals which may be mathematically supported (Mark and Goldberg, 1988). Such applications are commonly computer aided techniques.In assessing the rental value of retail property, an application which is objective, practical, flexible and which can work with a minimum of data is required. Rule-based expert systems, multiple regression analysis (MRA), artificial neural networks (ANNs) and case-based reasoning applications enable assessments to be undertaken in an objective manner. Each of the methods have their own strengths and weaknesses which need to be evaluated in regard to the specific domain prior to the selection and utilisation of the most appropriate approach. This paper initially reviews each of these approaches and then specifically applies case-based reasoning to the determination of retail rents.2. A review of computer assisted approaches to the valuation domain2.1Rule-based expert systemsRule-based expert systems attempt to replicate and have been applied to a wide variety of disciplines (Boyle, 1984). In the domain of property valuation their development is focused on the residential market for purposes of sale valuations (Czernowski, 1989), mortgage valuations (Scott and Gronow, 1989) and for mass appraisal property taxation purposes (Jenkins, 1994).The general objectives of any rule-based expert system are to ascertain a body of knowledge in a particular domain, to be able to apply this knowledge to problem situations (often in conditions of incomplete or uncertain information), to deliver effective and efficient solutions and to provide explanations and justifications for these solutions (Curtis, 1989). The specific objectives of a rule-based expert system are determined by identifying the role and benefits of the completed system within an organization together with consideration of the end user. This enables consideration of the type and depth of knowledge required with the reliability and effectiveness of a rule-based expert system dependent on the acquisition of knowledge (Brown and Stockley, 1992). In a valuation domain the aim of the rule-based expert system is to represent the knowledge and methodology employed by a valuer when undertaking specific tasks.The knowledge elicitation process is central to the success or failure of a rule-based expert system enabling a conveyance and transformation of problem solving expertise from various knowledge sources to a computer program. The valuers' knowledge may be classified into four broad categories namely; common sense, textbook, theoretical and operational knowledge (Scott and Gronow, 1989). Valuers' theoretical and textbook knowledge may be classified as public knowledge as it stems from published factsand theories and can be easily interpreted while knowledge held in valuers' minds may encapsulate unpublished knowledge such as common sense and operational information may be termed private knowledge (Brown and Stockley, 1992). Scott and Gronow (1989) contend that in addition to experience and ability an expert must further encompass knowledge which can anticipate the problem as opposed to solely applying 'surface knowledge' to solve it. In this respect the rule-based expert system is not a representation of data but a simulation of the expertise and knowledge of an expert valuer.The process of knowledge elicitation is extremely time consuming and can be problematic (Barletta, 1991). Furthermore it is argued that such problems stem from the complexity and disorganisation of human knowledge as well as the difficulties in communicating such implicit information to another party (Kidd and Wellbank, 1984). Indeed the more adept the subject expert is, the more 'unconscious' his/her knowledge. Moreover this knowledge which is implied within verbal explanations, comments and actions must be interpreted into knowledge data from a limited set of existing elicitation techniques. This is critical to the ultimate success of a rule-based expert system as it is dependent on the acquisition of sufficient and accurate information to carry out the stated task.The salient feature of rule-based expert systems is their ability to encapsulate rules of thumb and generalities, although recognizing that most experts have difficulty in dissecting their skills and decision making processes into the appropriate component parts (Gronow and Scott, 1986). However it is acknowledged that any factors influencing an expert's decision cannot be explicated within the bounds of a rule-based expert system (Brown and Stockley, 1992).The system may be tested or verified by informal validation, structured experiments and/or comparative validation. The process of informal validation involves system testing by individual experts while structured experiments comprise measuring the similarity of hypothetical cases presented to both experts and the system. Comparative validation entails considering the system's results against market evidence. However while rule-based expert systems explicating the valuation process are beneficial they fail to question or test the validity of the underlying rationale of the methods adopted in assessing retail rental values. In this context the system is fundamentally reliant upon adjustments which are prone to subjectivity which may be founded on little more than an inappropriate rule of thumb (Adair et al, 1996). Indeed Barletta (1991) argues that such rule-based expert systems do not inherently learn and merely mirror the actions of an expert. Moreover the rule-based expert system is rendered redundant if a problem arises which is outside the scope of the original system, or if market considerations and/or perceptions are altered so that the expert's knowledge reasoning subsequently differs from that initially extracted.2.2Multiple regression analysis (MRA)MRA techniques enable the estimation of the parameters of a hypothesized, usually linear relationship between a single dependent variable and several independent variables (Adair and McGreal, 1988a; Mark and Goldberg, 1988). In the context of retail rent assessments the rental value of a property is the dependent variable and the locational, physical and lease term characteristics describing the property might form the independent variables.In the property valuation domain, by viewing each property as a bundle of theoretical rights (Jackson, 1991; Rosen, 1974; Olsen, 1969) MRA enables valuers to examine and critically evaluate the determinants of property value. Such techniques parallel the decision making process of valuers (McCluskey and Adair, 1994; Gronow and Scott, 1986). Jackson (1991) details the process of traditional valuation and the corresponding stages of regression analysis, highlighting the similarity of approach and logical sequencing of assessment procedures. However the traditional approach involves a comparative value assessment and by nature is subjective, whereas the use of MRA enables the objective ordering and adjusting of the components.The development and practical application of MRA for property valuation purposes was enhanced by the early development of computer systems in the 1950s. Such techniques could not only predict value but provided a means whereby valuers could objectively analyze comparables. This was further enhanced by the development of sophisticated weighting techniques enabling the objective adjustment of variables (Adair and McGreal, 1988a;1988b; Wendt, 1974). While Renshaw (1958) recognizes that it may not be possible to isolate all the factors relevant within property transactions it is possible to establish a relationship between property value and a select sub-set of variables.The earliest use of such computerised valuation systems in practice is generally credited to Orange County, California followed by Madison, Wisconsin (Pendleton, 1965). Indeed according to Fraser (1984) a whole host of cities and municipalities in the US subsequently employed various levels of computer assisted valuation (CAV). In the UK the method has been adopted amongst other examples by the Lothian Region (Scotland) for rating indices but the adoption of such techniques has been minimal and slow despite the many advantages associated with such techniques namely objectivity and uniformity of approach, speed and cost saving benefits (Gallimore and Ward, 1992; Adair and McGreal, 1988c).Such benefits are creating an increasing awareness amongst some UK valuers that MRA is a developing area of valuation practice. In terms of mass appraisal for property taxation purposes MRA has been one of the most important advances and is commonly demonstrated concerning the appraisal of residential property (Anand et al, 1996; McCluskey and Adair, 1994; ten Have, 1994), however it has experienced more limited application in the context of retail rent assessments (Bakewell, 1991; Jackson, 1991; MacFarlane and Fibbens, 1990). Whilst such research boasts a high predictive capacity concerning retail rents (R2=99.72% Jackson, 1991; R2=95.88% Bakewell, 1991; R2=85.85%, 92.81% and 98.44% MacFarlane and Fibbens, 1990) many of the mathematical assumptions underlying the technique may be violated thereby creating difficulties.An additive MRA model imposes a linear relationship (Czernowski, 1989) and thus does not enable expression of the varying relationships within some factors, for example unit value and size of a property. Similarly elements which have an influence across a number of variables cannot be incorporated. Multiplicative models may overcome such deficiencies enabling data to be transformed using log, log-log, and square root transformations. Indeed such transformation of data may further satisfy the requirement that data has multi-variate normality. Although it is commonly suggested that it is the predictive capability of a model which is of importance in the valuation domain, Whipple (1974) argues that interpretative problems arise if multiple transformations are employed. Moreover it may be further argued that the purposes of explanation or prediction are inseparable as the predictive equation should be supported by its explanatory power and theoretical interpretation (Mark and Goldberg, 1988; Whipple, 1974).The effect of multi-collinearity is particularly relevant to the prediction of retail rental values as property characteristics display a propensity to be inter-related (Jackson, 1991). However it has been argued that where the model is employed for predictive purposes the effects of multi-collinearity may be overlooked (MacFarlane and Fibbens, 1990) although problems in attaining reliable or stable coefficients arise under such circumstances. Stepwise regression procedures are considered to alleviate the problem of multi-collinearity as they distinguish between variables which make a significant contribution to prediction and those which do not (Gustafson, 1985), with backward stepwise regression being the preferred method of practitioners (Eckert, 1985). Adair and McGreal (1988a) argue that a further advantage of stepwise regression is that it enables highly correlated independent variables to be identified and that a significant problem is not encountered as long as the R2 value between two variables is lower than the regression equation R2 value. However Mark and Goldberg (1988) argue that while stepwise regression techniques may reduce a total set of potential variables to a more manageable number there are several difficulties associated with their employment and relevance in the valuation context.Firstly the elimination of variables which belong in the true equation due to the size of their F statistic (denoting collinearity) may result in biased coefficients. Secondly such techniques rely on the data to select variables as opposed to the theory. Thirdly as the criterion for inclusion or deletion of variables is generally based on the significance and size of R2 values, the size of estimated coefficients remain unexamined in terms of their reasonableness or expected direction of relationship (signs). For example when predicting rents using non stepwise regression procedures Jackson (1991) was able to detect a negative relationship between the date of transaction and rental level even though rents were actually increasing over time. Upon further investigation it became clear that this was attributable to the fact that the larger properties had earlier rent review dates resulting in the total rental amount seeming to decrease over time. Changes in the sign of Beta coefficients may further denote the presence of multi-collinearity (Adair and McGreal, 1988a; Boyle, 1984) and although stepwise procedures have been suggested as a solution to multi-collinearity Whipple (1974) suggests that such techniques will not eliminate its effects from the predictive model.Gronow and Scott (1986) argue that the Achilles heal of MRA is that large amounts of data are required. Indeed the accuracy and hence usefulness of the derived model and its results is dependent upon the sample size, the quality of the data together with the skill in selecting the variables. The assumptions underlying the technique create additional problems in its practical application to retail rents as key variables such as frontage, depth, area and frontage to depth ratio have expected high correlation and interaction.2.3Artificial neural networks (ANNs)ANNs are generated using a computer programme which has the ability to learn from experience and to make internal models of the real world (Worzala, 1994; Tay and Ho, 1994; Do and Grudnitski, 1992).. Although there are several network architectures available, layered feed-forward networks with back propagation are the most commonly employed systems (Borst, 1995; Tay and Ho, 1994; Worzala, 1994). These comprise three types of layers of nodes: the input layer, the hidden layer and the output layer. The input layer contains data from the measures of explanatory or independent variables. These data are passed forward through the nodes of the hidden layer(s) to the output layer which is the outcome value (dependent variable(s)).The large number of interconnections create a high degree of parallel processing as all of the input variables are connected to the hidden layer which are in turn connected to the outcome layer. Each interconnection is attributed a weight. The processing unit performs a weighted sum of the inputs and then uses a non linear threshold function (sigmoid function) (f) to compute its outcome. This imitates the transformation of information as it passes through the brain's synapses. The number of input nodes varies with the number of characteristics representing the specific domain. Similarly the number of outputs is not fixed but in the context of retail rent review assessments the only outcome desired is the rental value. The number of hidden layers and the number of nodes characterizing each will depend on the complexity of the problem. It has been argued that if there are too many hidden layers the ANN will fail to learn the underlying pattern, while with too few the ANN will not recognize the full intricacy of the pattern inherent in the data (Anand et al, 1996). The optimum topology may be self-determined by the user or the ANN itself.A traditional criticism of ANNs has been the "black box" nature of the back propagation training process as its lack of explainability negates any advantage associated with the method's accuracy, ease of use or economic viability (Borst, 1995; Tay and Ho, 1994). However Borst (1995) suggests that this accusation is no longer tenable as significant progress has been made in this area with graphical representation of the effect of changes in neurodes on individual properties now easily achievable. Furthermore a method for interpreting the connection-weights of back propagation exists which enables the effect of various input nodes on the outcome node to be examined and ranked (Tay and Ho, 1994).In relation to the property industry ANNs have been applied as a tool for predicting residential property values in a number of studies which report that in comparison to MRA, ANNs achieve greater accuracy (Borst, 1995; McCluskey and Adair, 1994; Tay and Ho,1994 ). However Worzala et al (1994) undertook a comparative study of MRA and ANNs using two different neural network software packages and conclude that ANNs must be employed with caution in the property valuation field for two main reasons. Firstly significantly different experiences between each of the ANN software applications employed were identified. Although the same data were employed there were inconsistencies between the results. Secondly even when the same model with identical data was retrained using identical software, exact results failed to be replicated. This is caused by the random selection of the initial weights for each of the nodes of the hidden layer. Moreover the ANNs did not always outperform the MRA applications and rarely did the two ANNs packages coincide in outperforming MRA together, rather the two ANNs alternated in out performing MRA. In addition Worzala et al (1994) argue that ANNs are not easy to use as the optimum setting for the model is not readily apparent and small changes can result in very different findings.2.4Case-based reasoning (CBR)CBR is an artificial intelligence methodology which uses past experience to solve current problems and in this respect mirrors the comparative method of assessment which uses evidence of past rental agreements to assess present rents. Barletta (1991) contends that the ability of CBR to reason from past cases is appealing because it corresponds to the process an expert adopts in addressing present questions quickly and accurately in a wide spectrum of domains.CBR was originally conceptualised as an artificial intelligence memory based approach to reasoning by Schank (1982) and has its roots in the case method approach developed by the Harvard Business School in the 1920s (Churbuck, 1992). In contrast to traditional expert systems, CBR is not rule based and is founded on the premise that cases should be used as the foundation of an expert system. This is of particular relevance to retail rental assessments as any value reflects a multitude of locational, physical and lease term characteristics, which may vary and indeed can alter in their impact on a property's rental value according to the tenant's retail function (O'Roarty et al, 1995a). This is further influenced by demand and supply in the market.CBR essentially employs a data set termed a case library. This is a set of cases which describes the features of past problems or events in a particular domain together with the solutions or outcomes. Regarding retail rental assessments each relevant property characteristic is represented by a field in the data set and the rent achieved represents an outcome field. A CBR system enables a hypothetical case to be presented and searches for similar cases to be considered in relation to the decision. Determining the appropriate case features is the chief knowledge engineering task demanded from CBR. This removes the requirement to codify expertise derived through extensive interviewing into logical rules as involved in rule-based expert systems. Rather CBR employs case indexing techniques from which it derives its power to remove relevant cases quickly and accurately. A number of indexing strategies are available for use with CBR software systems, the most appropriate of which varies under different circumstances. Barletta (1991) argues that indexing processes usually fall into one of three kinds: nearest neighbour, inductive and knowledge-guided or a combination of the three types may be preferred.Nearest neighbour matching enables weights to be placed against fields in respect of the hypothetical or input cases and retrieves cases from memory based on this weighted sum of features. This approach may be advantageous where there are few cases or the retrieval goal is not easily defined, however deciding upon a set of global weights that will accurately retrieve in all situations can be arduous, particularly where the impact of individual case features is not entirely independent of other case features. In this respect inductive approaches to indexing may be superior particularly where the retrieval goal is well defined. Induction is the process of inferring general principles from specific examples using a clustering process from which the system builds a decision tree based on features from past cases which discriminate between various outcome values. The clustering procedure objectively and rigorously analyses cases to determine the features which most clearly distinguish between outcome values and then employs these case features to build an index for inductive retrievals. Moreover it enables the context of a feature in relation to other features to be considered. Pure induction solely derives rules from the features of the case library and in this respect fails to take advantage of an expert's domain knowledge. However knowledge-guided induction methods combine the objectivity of pure induction with such expertise. Domain specific knowledge is codified in CBR systems for knowledge-guided induction using symbol generalizations and ordering, a qualitative model (Q Model) and prototypes. Such approaches may be employed in isolation or in combination.Symbol generalizations and ordering can be used to describe a case library whose fields can be structured into related categories. For example, the location of a property may be split into various predefined categories such as a shopping centre or high street. The cluster mechanism may then split on the individual properties or general symbols. This generalised symbol enables the clustering mechanism to make observations about the case data which will further improve the effectiveness of the cluster tree. Where it is possible to order symbols in a hierarchy the clustering process considers this value relationship. For example, high street property may be further broken into the categorical symbols of prime and secondary. By ordering these features so that a secondary position is considered as having a smaller impact on value than a prime location, the clustering procedure may compare cases and values which are less than or equal to a symbol, in this instance location, to cases with values greater than the symbol.The Q Model may affect the accuracy of the cluster tree in two ways; firstly it can influence the sequence of fields to be split under clustering and secondly by creating what are known as virtual q-nodes, additional case features are developed which combine and summarize information from several other features. Where a Q Model is employed the clustering process will initially take account of the features described to determine which features to differentiate on. In essence the Q Model describes and visually represents the relationship between case fields and outcome fields. Fields may be defined as having directly or inversely proportional relationships with the outcome field or as having no specific direction in the relationship to the outcome field.However such relationships are not represented as rules using formulae as with traditional expert systems. Thus the Q Model guides rather than dictates. Virtual q-nodes can be employed where the relationship between a particular feature and an outcome feature is dependent on the characteristic of another feature. For example a commercial property with a return frontage is perceived as having a positive impact on value by shoe retailers and a negative influence on value by fashion retailers (O'Roarty et al, 1995a). The relationship between return frontage and the outcome field rental value is dependent on the retail trade. Thus a virtual q-node enables a contextual relationship to be considered in the clustering procedure. Similarly prototypes enable domain specific knowledge to be expressed within the clustering process. Essentially prototypes may be used to group cases according to certain classifications. When combined with clustering the initial positioning of cases in the cluster tree is dictated by the prototype which may be derived from an expert's domain knowledge of a feature critical to the outcome field. Indeed prototypes can be placed under other prototypes enabling them to be organized into a general to specific hierarchy when indexing the case library. Prototypes may be employed together with the domain knowledge held in the Q Model.The result of pure inductive or knowledge guided inductive reasoning is a cluster tree which represents a hierarchial structure of the case library. The memory organization is related to the knowledge available to perform the indexing procedure and the retrieval needs of the system. Cases are represented as nodes at the end of each decision tree branch which detail the number of cases and the relevant outcome fields. By selecting a node the actual case may be retrieved for more detailed consideration by the user. The goal of case retrieval is to present the most similar past case(s) that is relevant to the current input situation. A retrieval case may be the best representation of the input case but may require adaptation in respect of features which differ from each other.3.Evaluation and selection of a tool to assist retail rent assessment modellingIn evaluating the relative merits of different modelling approaches it is apparent that CBR provides an objective, flexible and reliable technique which encompasses domain knowledge and offers several distinct advantages to rule-based expert systems, MRA and ANNs. This technique expresses knowledge as cases which is a more intuitive process than representing knowledge as rules. The benefits of this are two-fold. Firstly a great time saving is achieved as there is no manual extraction of a complete and consistent set of rules from an expert. This enables more time to be devoted to the process of collecting reliable and representative property data. Secondly less computer programming is required with CBR as the system builds the optimal decision structure. Furthermore rule-based expert systems can prove inflexible in the long term as they cannot adapt to changes in the market without the system being re-programmed. Conversely CBR systems easily adapt to new situations (new data or cases) as this is integral to their learning capabilities (Weiss and Kapouleas, 1989). Moreover CBR systems can justify their assessments by providing the general reasons which indicate why one case (or property) is distinguished from another and by illustrating the individual cases of comparable properties supporting the index used. Where no conclusive classification of properties has been made this aids the user in making a final decision. In contrast rule-based expert systems merely repeat the chain of rules employed as provided for the development of the initial system.MRA and other associated modelling techniques have beeen the focus of efforts to automate property valuation. However while useful, such techniques have a tendency to become impractical as they do not encompass the heuristic knowledge of property professionals or tenants. Whilst MRA procedures may parallel the decision process of valuers, CBR has the ability to model it by employing heeuristic knowledge to assess present rents by reasoning from past rental evidence (Gonzalez and Laureano-Ortiz, 1992).. The inductive-indexing capabilities of CBR further provides four distinct advantages over ANNs (Barletta, 1991). Firstly it can represent and learn from a wider range of feature types, secondly the time incurred to train an inductive CBR system is much less than for ANNs, thirdly the output of the inductive system is easily interpreted and finally CBR systems may be used to perform other tasks. In contrast there is presently no methodology which exists to aid in the design of an ANN structure to meet the demands of a specific domain area and Barletta (1991) argues that this renders it still very much an art form rather than a science. Moreover computationally ANNs require two or three orders of magnitude, more data and CPU time to arrive at an accurate network configuration than CBR systems.。