"A Business-Geographics-Based Real Estate EIS [Executive Information System]"
The following text is Gil Castle's final draft of the real estate column appearing in Business Geographics, March/April 1994
Copyright © 1994 GIS World, Inc.
Wednesday morning, 8:25 a.m. The Vice President of Acquisitions for a full service real estate investment company logs into her company's GIS-based Executive Information System (EIS) from the personal computer on her desk. She scans several E-mail messages, and then turns to the once-per-week task of reviewing unsolicited property offers from commercial brokers. She knows from past experience that only a fraction of the properties will fulfill her firm's investment criteria. The challenge is to separate promising candidate properties from the rest of the submittals as efficiently and reliably as possible.
During the previous week her administrative assistant created an electronic file for each broker's submittal in the firm's EIS. Into the file were placed: property specifications, including property size, age, address; financial performance data disaggregated into various revenue and expense categories; photographic images of the exterior, interior, floor plan, and so on. Some of the information was submitted by each broker on disk in an EIS-compatible format (a pre-condition for doing business with the VP's firm); other, more specialized information was electronically scanned from hard copy materials into the appropriate file by the administrative assistance.
The VP clicks an icon her computer screen to retrieve and display a map of the United States, and then has GIS tools within the EIS plot the locations of the candidate properties on that map. The properties are distributed among seven Metropolitan Statistical Areas (MSAs). The VP next clicks another icon for a map showing the investment "attractiveness" scores for the top 100 MSAs in the nation (see the real estate column in the July/August 1993 issue of BG). The map is updated periodically by her firm's Research Department, using a proprietary scoring model based on: the current and projected balance between demand and supply in each real estate market; portfolio diversification guidelines; return on investment objectives, and so on. She notes that the Research Department's latest map advocates investment in only one of the seven MSAs. Brokers have submitted prospectuses on three candidate properties in that particular MSA, all of which are community shopping centers.
Zooming into the high scoring MSA, the VP displays all the census tracts and the locations of the three properties. For each shopping center, the VP quickly defines primary and secondary trade areas using several automated methods: concentric rings, travel-time isoquants, and gravity models. The GIS automatically calculates and displays summary statistics on each trade area under each analysis method. Critical variables include median household income, number of households, retail sales per capita, and (again) proprietary indices developed by the Research Department based on these and other socioeconomic factors. On the GIS map on her screen, the VP then toggles different census tracts on or off to test how sensitive the trade area statistics are to alternative trade area boundaries. The results ultimately show that two of the three shopping centers are located in promising trade areas.
The VP retrieves a zip code zone map for the MSA, together with tabular data on the labor force in each zone. In a manner similar to defining trade areas, she determines that the labor pool within a reasonable commute time of each shopping center is sufficient for cost-effective operations now and in the foreseeable future.
Having conducted socioeconomic (demand-side) analyses, the VP is ready for real estate (supply-side) analysis. She retrieves a GIS display from a data base maintained by the firm's Vice President of Asset Management. He keeps track of the total inventory of commercial space, vacant space, pending construction and demolitions, etc., for key sub markets within each of the 100 MSAs. (A given MSA usually encompasses three to seven sub markets, which are areas with homogeneous real estate market characteristics.) He periodically updates his information with inputs from the firm's internal property management files, from local brokers and appraisers, and from data vendors such as REIS Reports and F.W. Dodge. Though the motivation of the VP for Asset Management is to ensure that the portfolio of properties already owned by the firm is performing at least as well as those of other companies, the information is also useful to the VP of Acquisitions for determining the competitive environment around a potential investment property. From her examination of the supply-side data base, she judges that only one of the candidate properties is in a sound competitive position.
Her next step is to determine a reasonable purchase price for the remaining candidate property. By clicking on a menu icon and the sub market surrounding the property, she instructs the EIS to establish a modem link with a comparable sales ("comps") service in the MSA, and then to automatically purchase and download relevant data. (Comps services keep track of real estate transactions via local government records, contacts with brokers and appraisers, etc. They repackage and sell the data to firms which want to know, for example, the selling price and property specifications for "all Class A office buildings greater than 100,000 square feet sold in the Central Business District sub market within the last 18 months.") A minute later the EIS displays a message that data on 12 comps has been received. The VP maps the locations of the 12 comps and the candidate property on the screen, together with key reference features (e.g., major roads). She clicks on various comps near the candidate property, to review the attributes of each. Based on those attributes and geographic proximity, she selects six comps on the GIS display and instructs the EIS to perform a comparables-based appraisal of the candidate property (see the real estate column in the November/December 1993 issue of BG). She tests the sensitive of the resulting value estimate by clicking on different comps and by providing alternative inputs to the appraisal model's adjustment matrix. She saves in a file the comps and model inputs she judges to be the most credible, along with the GIS display showing the locations of the candidate property, comps, and key reference features.
As a final step, she calculates the likely return on investment (ROI) of the candidate property at the estimated purchase price, using several standard ROI formulas — internal rate of return, free and clear return, broker's equity return, etc. Her ROI analysis encompasses varying assumptions on financing terms, the eventual disposition value of the property, holding period, and so on.
The VP of Acquisitions writes a brief E-Mail message to other members of the Executive Committee identifying the candidate property, together with the names of the GIS and financial files she saved. They will review her analyses on their computers in the next day or so, and provide her any additional insights they might have on the sub market, candidate property, or structuring of the transaction. The VP sends another E-mail message to her administrative assistant asking for all the hard copy materials on the property submitted by the broker, and instructing her assistant to arrange a meeting with the broker at the VP's office.
Wednesday, 9:30 a.m. As the VP turns to other duties, she thinks briefly about the innumerable hours — usually days — she and her staff used to spend tediously screening broker submittals before the firm's EIS was implemented. She knows her counterparts in other departments are enjoying similar increases in productivity and credibility via the firm's EIS.
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Though the preceding account is necessarily fictional, all requisite software, hardware and data are available "off the shelf" today. The principal reasons that few, if any, real estate firms have implemented a GIS-based EIS to date are: the lack of awareness of the technology's potential; the reluctance by senior executives to revamp traditional practices; and the front-end investment in time, money, and intellectual capital needed to specify appropriate analytic procedures, reliable data sources, fun-to-use menus and icons, etc. Within one or two years, though, several firms will gain a significant competitive advantage by constructing such an EIS — and the rest of the industry will soon follow.