Abel, D. J., S. K. Yap, R. Ackland, M. A. Cameron, D. F. Smith, and G. Walker. 1992. ... Andrews, Richard B. 1980. .... Wincott, D. Richard, and Glenn R. Mueller.
Journal of Housing Research • Volume 9, Issue 1 q Fannie Mae Foundation 1998. All Rights Reserved.
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Development of Spatial Decision Support Systems for Residential Real Estate Kim Peterson*
Abstract Geographic Information Systems (GIS) can enhance the efficiency and effectiveness of decision making in the residential real estate industry. They can organize, manage, and analyze information in ways that were not possible with traditional information management systems. Although GIS are now used to perform specific business functions, their use can be magnified and extended through the creation of enterprise-wide spatial decision support systems (SDSS). This article provides a conceptual framework for the development of enterprise-wide SDSS. The first part of the article discusses the nature of real estate decision making and investment analysis, paying special attention to residential real estate. It also reviews different approaches to SDSS development. The second part of the article discusses enterprise-wide information architecture planning and specifies a conceptual framework for SDSS development. It then discusses issues related to technology transfer and SDSS implementation. Keywords: Geographic information systems; Spatial decision support systems; Residential real estate
Introduction This article presents a conceptual framework for analyzing how Geographic Information Systems (GIS) and spatial decision support systems (SDSS) can be effectively implemented in large, complex organizations. It argues that effective and efficient decision support requires consideration of enterprise-wide information technology (IT) issues and needs and that decision support is best approached as part of an IT architecture planning process. As a basis for developing a conceptual framework, this article discusses the nature of real estate decision making and emphasizes residential analysis. It then defines GIS, expert systems (XS), and decision support systems (DSS) and discusses how they relate to SDSS. It also emphasizes the ways in which SDSS incorporate GIS functions. This article then specifies a conceptual framework based on IT architecture planning principles. These principles are well founded, and the framework derived from them provides a flexible approach to specifying how SDSS can be developed to serve enterprise-wide decisionmaking needs.
* Kim Peterson is a research associate at the Institute for Urban Land Economics Research, Inc. The author wishes to thank Ays¸e Can of the Fannie Mae Foundation for her guidance, and two anonymous referees for their helpful comments on an earlier draft of this paper.
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The Nature of Residential Real Estate Decision Making Residential real estate decisions are made by a variety of actors pursuing a broad range of objectives. These actors include home buyers and renters, builders, brokers, bankers, and the public agencies that provide physical networks and services such as streets, utilities, and schools. Many decisions have spatial dimensions and may be grouped according to functional area, recognizing that some functions are actor specific and some may be classified in more than one function. The following functional areas are among the most important: (1) planning, strategy formulation, and research; (2) construction and development; (3) finance, including construction lending, equity investment, mortgage finance, public and private venturing, and property taxation and assessment; (4) property management; (5) risk management; (6) marketing; and (7) regulatory compliance. Geographic information and expert systems have been developed to support residential decision making in each of these areas; selected examples illustrate their contributions (see Belsky, Can, and Megbolugbe 1996; Thrall and Amos 1996). In the areas of planning and construction, expert systems have been designed to analyze land use laws and other legal issues related to location, including determining whether a proposed land use meets zoning and other land use regulations (Waterman 1985). GIS have been developed to assist in site selection and location analysis for residential subdivisions (Barnett and Okoruwa 1993), and expert systems have been designed for managing construction activities (Levitt and Kunz 1985). Several vendors offer computer applications for estimating construction costs based on geographic location, which has done much to streamline the real estate appraisal business. In the area of risk management, GIS and SDSS have been developed to help mortgage lenders and insurers improve their underwriting procedures and price their policies. These applications help to determine whether a property is located in an area prone to natural disaster (e.g., floods or earthquakes) and to calculate rates based on automated assessment of neighborhood crime rates and distances to fire hydrants, fire stations, and police stations (Francica 1993; Kochera 1994). Applications have also been developed to automate the appraisal process (Can and Megbolugbe 1996; Robbins 1996; Rodriguez, Sirmans, and Marks 1995) and to utilize detailed site measurements and sales data provided via computer networks and CD-ROMs. When used to mark mortgage portfolios to market value, these systems help lenders improve credit loss forecasts. Mortgage lenders, originators, and consultants have adopted GIS for a variety of marketing functions (for examples, see Beaumont 1991a, 1991b; Clark 1993; Graham 1992; Hall 1993; Pittman and Thrall 1992), including identification of market areas for specific branch office locations, estimation of market potential for particular mortgage products within those areas, computation of expected capture rates, measurement of current market penetration, segmentation of markets based on geodemographics, selection of targets for direct mail advertising, and location-allocation modeling to help determine the optimum number and locations of branch facilities. Finally, GIS have been developed to assess the compliance of lenders with the Community Reinvestment Act (CRA), which stipulates that financial intermediaries meet the credit needs of their entire community, including residents of low- and moderate-income neigh-
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borhoods. These systems provide visual displays of the geographic locations of mortgage loan applications, approvals, and denials and combine these data with thematic map overlays showing census tracts by income level; the result is a visual display of patterns of service and underservice (Belsky, Can, and Megbolugbe 1996; Thrall, Fandrich, and Thrall 1995).
GIS and SDSS The applications mentioned in the preceding section demonstrate that GIS can be efficient and effective tools for managing both business and geographical information. They provide an important technology for the development of DSS that require spatial data as input or that have spatial implications. But the analytical requirements placed on these systems vary, depending on the specific knowledge domains they are designed to support. Some business areas can be served with existing commercial GIS functionality (e.g., mapping and simple spatial queries), whereas some require the incorporation of XS modules and sophisticated algorithms. The following definitions highlight important features of GIS, XS, DSS, and SDSS and provide a conceptual basis for matching applications to functions.
Geographic Information Systems A GIS is defined as a system of hardware, software, data, people, organizations, and institutional arrangements for collecting, storing, analyzing, and disseminating information about areas of the earth (Dueker 1989). Requisite functions include the ability to create, edit, and delete geographically structured data; link locational and attribute data; perform spatial analysis functions such as map overlay of multiple data themes; and display geographic information.
Expert Systems Expert systems are computer programs that apply artificial intelligence to narrow and clearly defined problems. They typically combine rules with facts to draw conclusions, rely heavily on theories of logical deduction, and are developed using heuristic methods or conventional computer programs (Ortolano and Perman 1990). The subject area of an XS is called its domain. The collection of facts, definitions, rules of thumb, and computational procedures that apply to the domain is called the knowledge base. The set of procedures for manipulating the information in the knowledge base to reach conclusions is called the control mechanism (or inference engine).
Decision Support Systems Numerous definitions and characterizations of DSS have been proposed. For purposes of this discussion, a DSS may be viewed as an interactive computer-based system that helps decision makers utilize data and models to solve unstructured problems (Sprague and Carlson 1982). An unstructured problem is not susceptible to algorithmic solution because it has not arisen before, because its precise nature and structure are elusive or complex, or because it
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is so important that it deserves custom-tailored attention (Simon 1960). In such situations, decision makers may need to research possible solutions and evaluate and modify the possibilities until an acceptable solution is obtained. Accordingly, a central theme for the development of DSS is the notion that the role of the DSS is not to undertake any kind of search for a problem solution but to assist the decision maker who undertakes that search (Davis and McDonald 1993).
Spatial Decision Support Systems When a DSS is developed for use with a domain that includes spatial data (one or more data attributes with a distribution over space), or when the solutions generated by the DSS have spatial dimensions, the analytical system may be referred to as an SDSS (Wright and Buehler 1993). SDSS enhance decision making by supporting processes that can be characterized as iterative, integrative, and participative (Densham and Goodchild 1989). This support is given through provision of models in a model base, a user-friendly interface, and easy access to appropriate data (irrespective of location or format). Characteristics specified by Densham and Goodchild (1989) can be used to formally define SDSS: These systems are normally provided for a limited problem domain, integrate both spatial and nonspatial data, typically facilitate the use of analytical and statistical modeling, and convey information to decision makers by way of a graphical interface. These systems also adapt to the decision maker’s style of problem solving and are easily modified to include new capabilities (Keen 1980).
SDSS Evolution Recent advances in GIS and SDSS have been driven in part by enhancements in the power and affordability of desktop computing. This microchip-based technology includes freestanding desktop machines but may also include networks of machines linked together and to servers, which may be other microcomputers, workstations, minicomputers, or mainframes. Two evolutionary paths can be identified (figures 1 and 2). Spatial decision support systems of the first path are evolving mainly from GIS (figure 1). These systems emphasize spatial data and higher-order spatial relationships (Goodchild 1992), and are typically applied to problems in which spatial analysis is the primary focus. For example, an SDSS from this path is seen as ‘‘some device (software and hardware) that provides information to a decision maker, ideally in an interactive framework, that can assist with a locational decision’’ (Fotheringham 1990, 1137). These systems are sometimes classified according to whether they rearrange existing information or generate new information, and the latter are further differentiated according to the presence or absence of consumer choice (Fotheringham 1991). Spatial data handling is highly sophisticated in these systems, and ‘‘toolboxes’’ of spatial analysis functions are typically included. Because they emphasize spatial analysis, these systems generally provide only limited DSS and XS components for nonspatial analysis and decision support. This narrowness of focus is reflected in the broken line linkage between the GIS and decision support boxes shown in figure 1.
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Figure 1. GIS-Driven Evolution of SDSS
Figure 2. DSS-Driven Evolution of SDSS
Applications of this type of SDSS are readily found in retail location analysis. For example, spatial interaction models of different types can be selected from menus in various GIS software packages (e.g., SPANS [TYDAC Technologies, Inc.] and TransCAD [Caliper Corporation]). Other examples include SDSS developed by public utilities to support asset optimization decisions related to processing service requests, engineering design, load management, and work management (Epstein and Odenwalder 1993). Applications have also been developed for the provision of social services by state and local governments (Vachon
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1993) and for land use planning in both public and private contexts (Budic 1994; Kinsey and Avin 1992), but the focus of all of these systems remains predominantly locational analysis. SDSS of the second path (figure 2) are evolving mainly from or in concert with DSS. These systems emphasize decisions in which spatial analysis is of only coequal or secondary importance. They are typically tailored to a specific knowledge domain and emphasize the manipulation and analysis of data relevant to that domain. High-level spatial data handling procedures and fine-grain geographic precision are not available in many of these systems, but they often provide access to, or are compatible with, sophisticated DSS and XS components that have been developed for the knowledge domain or problem area of interest. Examples of SDSS of this second path may be found in a wide variety of application areas including land development suitability analysis (Han and Kim 1990; Lein 1990), construction management (Levitt and Kunz 1985), comprehensive land use planning and regulation (Davis and McDonald 1993; Maidment and Evans 1993), identification of hazardous materials and their handling in the case of accidents (Cooke 1992), allocation of territories to sales personnel (Bryan 1993), and the specification of product mix for alternative retail sites based on geography, traffic flows, and customer profiles (Kroh 1995). Although SDSS of both paths address specific user needs, the first path holds limited promise for significant near-term improvements in decision support. This limitation follows mainly from the kind of analysis that is typically performed and the relatively smaller market in which this analysis takes place. In contrast, the problems addressed by SDSS of the second path are based mainly in business processes, not geography, and in many cases do not call for the same kind of high-level geographical precision for which digitized maps and GIS toolbox functions are required. The market for this type of analysis is much larger, because there may be hundreds of businesses that can use this type of system for every public agency or business dealing with locational decisions. The evolution of SDSS along the second path will also be fueled by three features of desktop computing: the increasing power and affordability of desktop systems, the widespread adoption of these tools by businesses and individuals, and the increasing use of DSS for business analysis. Microcomputer-based spreadsheet and database management systems frequently function as DSS and have already been widely incorporated into organizational procedures for analyzing problems and making decisions. Some business schools even use enhanced spreadsheet software to teach economics and statistics courses, and millions of these DSS have been installed in businesses, schools, and homes. It follows that SDSS and desktop GIS products that can function in concert with these DSS are likely to benefit from their widespread popularity. The importance of this compatibility is reflected in recent assertions that GIS provide a new paradigm for the organization of information and the design of information systems (Dangermond 1995; Vonderohe et al. 1993). The central tenet of this paradigm is the use of location as a basis for restructuring information systems and developing new ones. The latest desktop GIS provide for easy, seamless linking with spreadsheets and database management systems (DBMS), and a wide array of value-added vendors provide census data and other
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information in spatially referenced formats. In addition, at least one vendor has released a spatial database engine (SDE) for use with Oracle database management systems (ESRI 1995). This SDE makes highly accurate spatial data available as a common data type within corporate and government database environments, meaning that GIS data can now be fully incorporated with nonspatial data in a single relational database architecture. The evolution of SDSS from DSS is also being promoted in the applications development environment. Some microcomputer-based GIS provide for the development of custom applications to fit special user needs, and at least one vendor has designed its GIS desktop products so they can be customized with the same programming software used to build spreadsheet and DBMS applications.1 This ability to customize will encourage tighter integration of GIS functions with business-oriented DSS and facilitate creation of a broad array of SDSS.
SDSS Development and IT Implementation of GIS and SDSS tools throughout a large, complex enterprise requires careful planning, and specification of an IT architecture is implied. In developing this architecture it is important to account for the current enterprise-wide IT infrastructure, especially the DBMS currently in use and important legacy systems. The benefits typically cited for creating and implementing an IT architecture are substantial and include the following (Rosser 1995): 1.
The ability to achieve interoperability—that is, different systems working together, especially in sharing data
2.
The ability to speed the implementation of new systems by having many choices already settled and the skills and learning in place
3.
Lower costs due to reduction of support effort as the number of products and processes is reduced
4.
The general upgrading of system quality and increased ability to make modifications more easily in the future
5.
Communication of a common direction throughout IT and end-user departments, a byproduct of going through the architecture planning process
6.
In the effort to reduce the total number of components and processes employed, an architecture may forestall the crisis of complexity that is projected for networked computing as more and more demands are made and expectations rise.
1
ESRI provides its own application development language for ArcView but also supports Microsoft’s Visual Basic.
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A Conceptual Framework for SDSS Development Previous efforts to develop conceptual frameworks for GIS and SDSS have grown out of application areas as diverse as transportation research (Vonderohe et al. 1993), urban planning (Innes and Simpson 1993), market analysis (Beaumont 1991a, 1991b), and environmental analysis (Abel et al. 1992), but all have shared a common core of concerns. The design of these frameworks have all taken into account that rapid changes in information technology are radically affecting the ways in which GIS and SDSS are built and used, that we should rethink these systems completely rather than modify them in a piecemeal fashion, and that we need to focus on decision-making processes and organizational structures to understand how these systems may influence work practices, roles, responsibilities, and the nature of tasks. The conceptual framework developed in this article is based on IT planning principles that define and distinguish three types of information architecture: data, process, and network technology (Zachman 1987). The structure of this framework is shown with real estate– related examples in figure 3.2 This framework may be used to analyze the IT needs of an organization as a whole, in which case all enterprise areas and subareas would be evaluated. In the present case, it is used to organize thinking about only those areas of the enterprise that deal with real estate decision making and analysis. It provides guidelines for determining what data must be included in an enterprise-wide SDSS (column 1), what business analysis and decision-making processes must be supported (column 2), and where these services must be provided in terms of both organization structure and geography (column 3). The three cells in the top row of figure 3 list general classes of data, processes, and locations that the information architecture is intended to address. The cells in the second row list specific entities belonging to each class.
Data Architecture Cell 1 of figure 3 lists the classes of data entities that are important to the business enterprise as a whole or to specific departments. It is generally impractical or impossible to design information architecture for all data classes, so the decision regarding which subset of items to choose is based on the values and strategies of the business. Similar decisions to narrow the focus of the architecture are made for the other general-level description cells (i.e., cells 2 and 3 of figure 3). For example, there will probably be insufficient resources to automate all relevant business processes (cell 2), and out of the total list of locations in which the business operates (cell 3), there are probably insufficient data processing resources to place hardware and software at every location. At this most general level, an operative data question for a large residential lender might be ‘‘What things must we know about in order to make residential mortgage loans?’’ Classes of housing market participants come immediately to mind, including space producers, space consumers, and infrastructure providers (Graaskamp 1980). Space producers include those who assemble capital, provide materials or expertise, and construct dwelling units on site; 2
Only the top two rows of Zachman’s architecture planning framework are provided here. The remaining four rows have been omitted because they deal with IT design issues, which are beyond this article’s scope.
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Figure 3. Framework for SDSS Architecture
General Level
Data Architecture
Process Architecture
Network Architecture
Entities important to the business
Processes the business performs
Locations in which the business operates
1
2
3
Classes of Entities:
Classes of Processes:
Classes of Locations:
Space Producers Space Consumers Infrastructure Providers
Mortgage Loan Underwriting
Office Locations (home and branch)
Property Management
Organization Departments
Features of Urban Environments*
Construction Lending Market Research
Specific Level
4
5
6
Specific Entities:
Specific Processes:
Specific Locations:
Anderson-Kiefer Builders
Projection of Population Trends
Branch Office: Santa Rosa Plaza
NCREIF Guidelines for Analysis
Loan Underwriting Department
Proprietary Heuristics
Property Management Department
Empire Mortgage Company Mortgage Underwriting Department
Note: Based on Zachman’s Framework for Information Systems Architecture (1987, p. 285). *Andrews (1980) identifies land use, physical, social/cultural, psychological, economic, and institutional/political environments. Features are associated with each. For example, features of an area’s physical environment include topography, ground cover, soils, atmosphere, buildings, streets, and so on.
architects, mortgage bankers, lumbermen, lawyers, city planners, and apartment owners/ operators are all included in this group; space consumers include households and individuals seeking to purchase or rent places to live. Infrastructure providers include enterprises that create and maintain physical networks of streets, sewers, and utilities; they also provide services such as education, police, and fire protection and administer operational systems for deed registration, government regulation, adjudication, and various forms of economic activity.3 3 A similar perspective is provided in the analysis of institutional forces as important determinants of urban area development (Shlay 1988). The focus here is on developers, speculators, and financial organizations as classes of entities. Financial organizations can in turn be segmented into subclasses of mortgage bankers, banks, insurance companies, pension funds, and so on.
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A second general question might be ‘‘What things must we know about to establish and maintain our branch offices?’’ This question is important because of the complexity of selecting locations, maintaining owned buildings, and evaluating the relative attractiveness of leasing space in other kinds of facilities (e.g., placing branch offices in supermarkets).4 Decision support may also be needed in the property management division if the enterprise manages residential properties. Data classes can also be organized in terms of urban environments, including land use, physical, social/cultural, psychological, economic, and institutional/political environments (Andrews 1982). For example, analysis of economic environments may focus on real estate markets as an important subarea. Data needed to support this type of analysis would include levels and rates of growth for populations of individuals, households, and businesses; economic data related to interest rates, employment, and income; and demand parameters such as household size, quantities of housing space demanded, expenditures per capita, and levels of move-up demand (Myers and Mitchell 1993). Historical capture and absorption rates (by real estate type) could also be required. Whereas classes of entities are specified in cell 1, specific instances are implied in cell 4 of the framework. For example, the general class of space producers identified in cell 1 includes local residential builders and mortgage brokers. When managers in the firm’s mortgage underwriting department specify an entity such as a home builder, what they have in mind is a developer or contractor that builds single-family homes. Similarly, within-enterprise entities could include the mortgage underwriting department, the property management department, and construction lending. Relationships among data types—the business rules or strategies that relate one entity to another—are also specified in column 1 of figure 3. A business rule or strategy for a large mortgage lender might be ‘‘to make loans so as to satisfy CRA requirements.’’ Another relationship might be ‘‘to place new branch facilities in only those census tracts that are projected to experience annual population growth in excess of x percent.’’
Process Architecture As with the data architecture, each of the cells in the process architecture column of figure 3 has a different meaning depending on level of generality, although each may be described in terms of an input-output diagram. In the discussion that follows, general kinds of processes in which a large residential lender engages are discussed first, followed by specific processes related to mortgage investments in residential properties. At the general level, process means class of business process in which to invest resources for automation or decision support. This definition implies procedures such as mortgage loan underwriting, property management for homes or apartment projects acquired through loan default, construction lending, and housing market research. A large residential lender might
4 The classes of entities of interest to facilities managers may include transportation networks; fleets of vehicles; parking facilities; buildings and rooms; multibuilding heating, cooling, and electrical systems; the location of office furniture and equipment (including computer terminals); and space leased to or from other firms.
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choose to emphasize mortgage loan underwriting and a comprehensive program of housing market research but to defer support for procedures used in a construction lending process. At the specific level, decisions would be made regarding the particular procedures to provide in the decision support apparatus. A functional flow diagram might be specified in which process would be a business process (not an information systems process), and inputs and outputs would be business resources such as people, cash, or material. For a large mortgage lender, examples include processes for projecting population and employment trends in specific housing markets under different scenarios and procedures for delineating market areas for branch office locations. Many specific algorithms or procedures are available for supporting these processes. The National Council of Real Estate Investment Fiduciaries specifies guidelines for conducting a variety of analyses, including market area delineation, demand analysis, supply analysis, absorption analysis, vacancy analysis, and market rental rate analysis (Wincott and Mueller 1995). Also important are the procedures, guidelines, and rules of thumb developed and currently employed within the enterprise. These heuristics are likely to be based on proprietary data such as loan-to-value ratios for mortgages grouped by geographic area. Specific rules employed by institutional investors would probably take a form similar to the following heuristics, which could be used for underwriting apartment building loans: (1) accept no projects with fewer than 8 units, and prefer those with 24 units or more; (2) avoid outdoor balcony entrances (motel type); (3) value should be 6.5 to 7.5 times gross income; and (4) expenses should range between 35 and 45 percent of gross revenue at 100 percent occupancy. These analysis procedures may be provided in a number of specific applications (e.g., an SDSS for underwriting apartment project loans), although SDSS may also be structured as toolboxes or generators.5 Given the way in which XS and DSS are used to support decision making for narrowly defined problem areas, specific SDSS make the most sense. Individual decision makers would probably use only a small number of these specific SDSS, but because many of the decision procedures supported may use the same general types of data and may need to access the same proprietary and/or historical databases, these SDSS would be most efficiently provided on a network through icons in a Windows-like graphical user interface.
Network Architecture After deciding on the data entities and processes to be supported, attention can turn to the locations at which system access will be provided. This consideration obviously interacts with data and process concerns, but it is likely to be decided more or less independently, as with the decisions for the entities in cells 1 and 2. At the general level, the list in cell 3 would indicate the classes of locations from which the lender operates and at which data and processing capabilities may be required. Examples of network nodes at this level could include the home office and branch office locations. Also
5 Sprague (1980) differentiates between three different types of DSS—DSS toolboxes, DSS generators, and specific DSS—all of which are applicable in the spatial domain. DSS toolboxes are used to construct DSS generators, which are used to develop prototype implementations quickly.
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implied are departments within the organization itself, regardless of whether these departments are geographically disparate. At the specific level, a node would be seen as business units or aggregations of business resources (e.g., people, facilities, responsibilities, etc.) at specific geographical locations. The nodes shown in cell 6 include business units defined in terms of specific geography (e.g., the lender’s branch office at Santa Rosa Plaza) and organization structure (e.g., the loan underwriting department and property management department, both located at the home office).
Technology Transfer Implementation Issues Given this conceptual framework for planning an enterprise-wide SDSS architecture, implementation issues may be briefly discussed. Considerable difficulty may be associated with implementing any significant new computing system, and most of the enterprises that undertake information architecture planning are not successful (Spewak 1992). Critics contend that failure occurs because information architecture planning proceeds from two deeply flawed assumptions (Davenport 1994): (1) the architect has perfect control over components and their interaction, and (2) those components are immutable and understood throughout the organization. These assumptions may be reasonable in the construction of a building, but they are inconsistent with the realities of most IT environments. Numerous obstacles have been cited as contributing to failures in enterprise architecture planning, many of which relate to the structure of the IT department and to practices employed in the planning process. For example, insufficient resources and failure to gain the full awareness and support of top management are often cited (Spewak 1992). But more fundamental problems exist with respect to adoption of new technologies by the people who will use them. These problems are addressed here.
Diffusion of Innovation Technology transfer offers important insights for the planning and implementation of enterprise-wide SDSS. It complements the architecture planning perspective that has been used to develop this conceptual framework and offers important insights into larger organization-wide IT issues. Many models of technology transfer are available, only one of which is addressed here: diffusion of innovation.6 The guidelines proposed here follow from the work of Rogers (1983), who reviewed research on innovations in many fields, and from the work of researchers who have focused on innovations applied to land use planning (see, for example, Dueker 1987; Dueker and DeLacy 1990; Innes and Simpson 1993). Five general guidelines are suggested to help assure effec6 Technology transfer has been widely studied by and is of special interest in the software engineering community, which continuously develops and attempts to apply new methodologies and management practices. In this area the norm is constant change in approaches to developing and maintaining software, yet success in implementing new technologies has been limited.
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tive diffusion of innovation (Rogers 1983): simplicity, observability of benefits, relative advantage, ability to make small trials, and compatibility. Simplicity. A technology must be understood by those involved in building and using it, and it must also be meaningful to them. The problem with many GIS and SDSS is that they are inherently complex and difficult to understand, especially those that are implemented as multipurpose, multiuser systems. Some managers have tried to compensate for this complexity by defining GIS at the outset as tools for just one or two tasks. The often-expressed hope is that, as users become accustomed to the system, they will find more uses for it (Innes and Simpson 1993). This approach can be readily applied to SDSS implementations, because the DSS or XS modules that are provided in SDSS are typically designed to support a narrowly defined problem or issue. Other GIS innovators attempt to introduce more complex multipurpose systems with the aid of an image or metaphor that captures the overall complexity of the system and conveys it as a simple idea. Researchers have found managers in urban planning agencies experimenting with images for GIS that included ‘‘an architecture,’’ ‘‘an information engine, driving applications from a database,’’ and ‘‘the ‘glue’ that binds departments together’’ (Innes and Simpson 1993). Both approaches may be used to implement SDSS in large, complex enterprises. In both cases, enhancing user understanding and building a simple, shared meaning of the system throughout the organization will help ensure effective diffusion of innovation. Observability of Benefits. Success in adopting any new technology is more likely if its value can be seen and verified. In other words, those who adopt GIS or SDSS must know what they are getting and be able to assess its worth. Unfortunately, accurate prediction and assessment are difficult if there are relatively few routine activities that can be readily automated. Difficulties are especially likely with respect to the ill-structured real estate investment problems that SDSS are used to address, since a goal of SDSS is to provide support for problems that are anything but routine (e.g., on the surface, apartment loan underwriting may seem to be a routine task, but the range of issues is wide and varies from one project to the next, requiring the provision of expert advice and accumulated knowledge to analysts). In this context, it is likely that benefits will not be immediately observable as savings of time but rather as long-term benefits that accrue as a consequence of higherquality real estate decisions. Well-structured procedures are available for analyzing the consumption benefits of land information systems (see Stewart, Weir & Co. 1985) and GIS (see Budic 1994). These procedures may be modified to assess the benefits of adopting SDSS. For example, one may analyze impacts on the separate factors of operational and decision-making effectiveness (Budic 1994). Operational effectiveness is typically defined in terms of accuracy of positional and attribute data, availability of current data, data collection time, and accessibility of maps and tabular data contained in the GIS or SDSS. Decision-making effectiveness is defined in terms of time needed to make decisions, explicitness of decisions, identification and clarification of conflicts, communication and interpretation of information, and confidence in analyses generated with the GIS or SDSS.
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Relative Advantage. Adopters of any innovation must believe that benefits will exceed costs in both monetary and human terms (Stinchcombe 1990). For implementation of GIS and SDSS, benefits and costs are likely to fall unevenly on different participants. Adoption is likely to be costly for individuals because the time required to learn the system must be diverted from directly productive tasks and because change generally induces stress. These costs may be minimized, however, to the extent that the GIS or SDSS can be integrated with existing desktop decision-making support frameworks such as spreadsheets and database management systems. If GIS and SDSS can be provided along with these nonspatial DSS in a user-friendly graphical interface (e.g., based on Windows, OS2, Macintosh, or Unix), then implementation may be easier. Other cost savings may be realized if the enterprise can tap into government databases and GIS to access information for research and investment analysis. This approach is already being tried, with some local and state governments providing access to their GIS for information related to the preparation of environmental impact assessments, local economic conditions, and the laws and regulations governing land use. In addition, the U.S. Department of Housing and Urban Development has adopted desktop mapping software to help expedite federal grants to cities and towns. Its Office of Community Planning and Development has distributed a Windows CD-ROM developed using desktop mapping software. The system is intended to eliminate much of the paperwork confronting grant applicants, urban planners, agency administrators, and individuals trying to understand how federal money is being spent at the local level (Bowen 1994). Ability to Make Small Trials. A fourth general guideline for technology transfer is that technology should be introduced incrementally and that the changes introduced with it should be reversible in the early phases. A complex technology that requires large-scale change at introduction is unlikely to be implemented, and large-scale failures early on may derail the funding of future implementation steps. This guideline suggests that linking GIS and SDSS with existing desktop spreadsheets and DBMS may be an effective approach to implementation. If access to enterprise proprietary or historical data files must be provided, or if paper document files must be converted to electronic format, these steps may require considerable resources and should probably be undertaken on a segmented basis (e.g., by geographical regions). Compatibility. To ensure its successful adoption, technology should be compatible with the culture, language, skills, practices, understandings, and organizational and social structures of the organization that will use it (Innes and Simpson 1993). Again, compatibility with inplace desktop software should provide the best opportunity for successful adoption. Fortunately, desktop computing is well established in many large business organizations either as freestanding systems or via terminals attached to servers, minicomputers, or mainframes by means of networks, making adoption of GIS functions or SDSS promising from a compatibility standpoint. It makes sense to begin implementation by automating the existing tasks that DSS and XS modules in the SDSS have been created to address. The next steps of applying the system to new tasks or new ways of doing existing ones should probably follow from a formal planning process, preferably enterprise architecture planning, as described earlier. For this purpose, steering committees have been used to build consensus for the implementation of GIS
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in planning contexts (Innes 1992; Warnecke et al. 1992), and similar approaches should be considered for both GIS and SDSS in real estate investment contexts.
Organization-Wide IT Issues In addition to these implementation issues, at least four organizational issues are likely to influence how decision support is provided throughout a large enterprise involved with residential real estate and how GIS and SDSS are provided within this general framework. These issues include providing access to critical data stored in different types of enterprise databases, maintaining and analyzing historical data, distributing data and the processing to deal with them across multiple locations, and the adoption of object-oriented technology.
Access Relative to column 1 in the conceptual framework (figure 3), information technology must not only store, maintain, retrieve, and distribute data that are acquired from external sources and through ongoing collection and research efforts but also provide this kind of access to historical enterprise information. Historical data include both proprietary and external data; proprietary data include, but are not limited to, operational and resource information of all kinds, plus the transaction histories of enterprise customers and suppliers. For land use–related enterprises, external data include business data and census information on population and housing that are acquired from government agencies and other external sources. Decision-making processes may rely on critical data stored in different types of databases, on different systems, and in different locations throughout the enterprise, so building an enterprise data warehouse may become necessary. The principal objective of a data warehouse is to provide decision makers with easy access to business information, typically by copying data obtained from operational systems and external information providers and then loading them into separate integrated warehouse databases. These data are then checked for errors and reformatted for easy access and comprehension by users. Warehouse databases are linked to desktop computers throughout the enterprise by means of a network or system of networks (sometimes referred to as a wide area network, or WAN); decision makers can thus access any warehouse database from any computer on a network that is connected to it. A variety of data access software is available for processing queries from the desktop and for copying results to other desktop software such as spreadsheets, databases, presentation graphics, and word processors for further analysis and reporting.
Historical Data A second organization-wide issue related to data architecture (figure 3) involves maintaining and analyzing historical data. These data may be used for enterprise strategy formulation, problem solving, and research. Frequently, an organization has records of important trends related to a decision at hand. These data may include proprietary records of project vacancy rates, rental rates, and property appreciation rates over time; historical census data and
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national housing survey data may also be used to advantage. Analyzing these historical data to find patterns that illuminate the present is loosely described as ‘‘data mining’’ and has been fueled in many enterprises by advances in quantitative analysis procedures, including neural nets and abductive reasoning.7 Historical proprietary data may reside in paper document files as well as in magnetic format, which means that computer imaging technologies may be required. To implement a document image management system, enterprise paper files and documents must be selected and scanned into digitized images, which are then compressed and stored on optical disk. As many as 20,000 pages of documents can now be stored on a single CD. Once stored, images can be retrieved in seconds and used for decision support or integrated into other information systems.
Distributed Processing Given the specification of data, processes, and physical locations for enterprise-wide SDSS, system planners must decide on the computing hardware and software that will deliver GIS and SDSS functions. This is a design issue and is technically beyond the scope of this article, but it influences the success of SDSS implementation and is therefore briefly discussed. Three distributed application models may be considered (Rofrano 1992). 1. Distributed processing distributes functions or resources across two or more interconnected processors; these processors can be any combination of mainframe computer, minicomputer, or microcomputer (programmable workstation). Distributed processing is a generic term that includes cooperative processing and client-server computing. 2. Cooperative processing describes an application whose functions are divided between a mainframe processor and a programmable workstation. It is a term coined by mainframe users and provides a host-centric view in which the workstation adds value to the mainframe by providing a better human interface and possibly additional processing. 3. Client-server computing is a term created by personal computer users to describe an application whose functions are divided between a programmable workstation and a local area network (LAN) server (computer). It is a workstation-centric view in which the LAN server adds value to the workstation by carrying out work on its behalf. Some users extend the definition to include enterprise servers, which are often mainframes or minicomputers that have taken on new roles. In this framework, the client is the machine making the request and the server is the machine that does the work to satisfy the request.
7 Neural network software facilitates construction of complex mathematical models of the way a collection of brain cells, called neurons, operates—that is, learn from experience, develop rules, and recognize patterns. The financial industry has already adopted neural nets; traders and asset managers have been using these models for trend analysis and pattern recognition, credit evaluation, and marketing. Abductive models are used in many of the same problem areas as neural nets; they employ mathematical functions to represent complex and uncertain relationships, and networks to break problems into manageable pieces. Unlike neural nets, the functions they use to compute outputs from inputs may vary throughout the network.
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Distributed processing issues are relevant to enterprise-wide SDSS because of the evolutionary path SDSS are expected to follow and because of the vast amounts of data a large real estate investment enterprise is likely to process. The evolutionary path was specified earlier and is based in desktop computing and the proliferation of LANs. Vast quantities of data can be connected in this way, including operating histories of the real estate projects the enterprise has invested in, loan applications or payment histories and records of default, various types of business and economic data provided by vendors, and census data for populations and housing—all provided over time (e.g., data for the entire United States from the last three decennial censuses). These proprietary and external data caches may be accessed on one or more warehouse database servers that are maintained at a central location (e.g., the home office) or at branch locations; all these machines may be connected through a variety of telecommunications networks.
Object-Oriented Technology Object-oriented database (OODB) software was introduced about 10 years ago as a method for storing and retrieving various types of data used in computer-aided design and manufacturing applications. The basic organizing principle in these systems is the packaging of both data and procedures into structures related by some form of inheritance mechanism. An OODB is therefore a database designed to store and retrieve these structures without reducing them to some arbitrary internal format such as columns and rows. Object-oriented methodologies have been viewed as especially useful for spatial analysis because of their ability to accommodate the complexity of spatial objects and the relationships among them (Armstrong, Densham, and Bennett 1989; Dueker and Kjerne 1987). Object-oriented methodologies are also useful for developing prototype systems because they support a modular approach to application software development and provide a programming environment in which code reusability is an important feature. This efficiency could be especially beneficial for SDSS development in a large enterprise because it provides an efficient method for developing the wide array of area-specific SDSS that the enterprise may need. Object-oriented systems could also be used with large-scale imaging systems that may be used to convert and manage proprietary paper document files. But OODBs also have a negative side. Because they store complicated data and relationships directly rather than map them to relational columns and rows, OODBs may be difficult to relate to enterprise data warehouses and other relational databases. This drawback has significant implications with respect to SDSS, which may need to access both kinds of information.8 The adoption of object orientation is currently a topic of controversy in the IT industry. Some authorities criticize the ways in which relational and object-oriented concepts are being integrated (Darwen and Date 1995). Others argue that this controversy, although theoretically necessary and important, is detrimental to strategy formulation relating to object orientation issues (Tasker and Von Halle 1995). Despite the controversy, object orientation is 8 Efforts are currently under way to address this compatibility issue, and object orientation is being investigated not only with respect to object-relational problems but as a way to address issues of interoperability among multiple, heterogeneous databases in general (see, for example, Pitoura, Bukhres, and Elmagarmid 1995).
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likely to have a major impact on systems development in many enterprises and to influence the development of enterprise-wide SDSS.
Conclusions The general conclusions to be drawn from this research are that real estate decision making in general, and residential decision making in particular, can benefit significantly from the development of spatial decision support tools, including GIS and SDSS. Although efforts are well under way to incorporate decision support and analytical functions into full-featured GIS, a more promising approach follows from the evolution of SDSS from desktop DSS. This evolutionary path is faster and taps the considerable momentum supporting the development of desktop hardware and software, including integrated program suites that combine spreadsheets, databases, presentation graphics, and sophisticated query tools. Numerous guidelines are available for planning this technology transfer. The principles of innovation diffusion suggest that provision of enterprise-wide GIS and SDSS for spatial decision support is feasible. However, development and implementation processes must be considered within a larger context that includes issues related to the organization as a whole. The first of these issues involves providing access to proprietary and historical data that may be stored in different locations and in different forms throughout the enterprise. The second issue involves the ability to maintain and analyze these data to identify hidden patterns. Third is the need to distribute data and processing to many different locations. And the fourth issue is the growing popularity of object-oriented technology. Taken together, these issues imply the need to consider data warehousing and imaging technologies, sophisticated mathematical and statistical analysis procedures, distributed processing, the benefits and drawbacks of object-oriented technology, and system development within the context of organization-wide IT budgets. To develop and implement this kind of spatial decision support across multiple levels of a large real estate enterprise, an information architecture planning process is required. Although this kind of planning is not without its pitfalls and problems, the benefits of effective and efficient development and implementation of enterprise-wide SDSS and GIS justify the effort.
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