HADTS: A decision technology system to support Army housing management

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH

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European Journal of Operational Research 97 (1997) 363-379

HADTS: A decision technology system to support Army housing management G u i s s e p p i A. F o r g i o n n e 1 Infi}rmation Systems Department, University of Maryland Baltimore County, Catonsville, MD 21228, USA

Abstract

The Department of Army must provide its personnel with acceptable housing at minimum cost within the vicinity of military installations. To achieve these housing objectives, the Army often must enter into agreements for the longterm construction of onpost housing or the leasing of existing offpost housing. A decision support system, called HANS, has been developed to project the necessary construction or leasing. HANS had some gaps in supporting the construction and leasing decisions. This paper describes the gaps and shows how a decision technology system, called the Housing Analysis Decision Technology System (HADTS), can help Army managers to overcome the support gaps. It also overviews HADTS's benefits, challenges, and limitations. Keywords: Computerized mapping; Geographic information systems; Decision support systems; Military housing management; Decision technology systems; Data visualization; Database management systems; Executive information systems

1. Introduction

The Department of Army wants to provide its personnel with acceptable housing in the areas within a 30 mile radius of its military installations. To achieve this housing goal, the Army often must enter into agreements for the construction of onpost housing or the leasing of offpost rentals. The Army must economically justify any leasing request with a laborintensive, errorprone, and conceptuallyflawed Segmented Housing Market Analysis (SHMA). A decision support system, called the Housing Analysis System (HANS), was developed to address the conceptual flaws and to facilitate (simplify and

i Email: [email protected].

semi-automate) the SHMA process. While HANS accurately projected the number of housing deficits, the system lacked other SHMA process support desired by senior Army officials. A decision technology system, called the Housing Analysis Decision Technology System (HADTS), was developed to deliver the additional functionality. HADTS integrates the functions of an executive information system (EIS), a geographic information system (GIS), and an upgraded version of HANS. Its deployment is expected to provide significant economic and management benefits to Army housing managers. This paper first describes the Army housing construction and leasing problem, examines how the original version of HANS addresses the problem, and identifies the gaps in HANS's support. Next, the paper presents the Housing Analysis Decision Tech-

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nology System and discusses how the system helps Army management to close the support gaps. The paper concludes with an overview of the HADTS's benefits, limitations, and challenges.

2. Housing deficit problem At any military installation, the projected supply of available government housing may be insufficient to meet the personnel demand expected at the site. Policy requires unaccommodated personnel to seek acceptable private rentals in the installation's predefined Housing Market Area (HMA). If the expected stock of private rentals in the HMA will be insufficient to eliminate the onpost housing deficit, the Department of Army will enter into agreements for the construction of onpost housing or the leasing of existing offpost housing. Government policy and regulations require the Army to economically justify any leasing request with a Segmented Housing Market Analysis (SHMA). 2.1. SHMA process

During a SHMA review, housing managers first compute the onpost deficit and forecast the private rental stock available to meet military housing needs. Next, they estimate the military's market share of the private stock and compute the number of adequate rental dwelling units available in the local market to offset any onpost deficit. The result is the gross military deficit, or the number of personnel that do not have adequate housing onpost or in the private market. The gross military deficit is reported by bedroom count (BC) for personnel in each of the twenty-one Army grades (ranks). There is a separate (grade by bedroom count or 21 x 6 = 126) matrix for unaccompanied (UPH) and family (AFH) personnel. Some cells in the housing deficit matrixes may show surpluses. In the interest of minimizing construction or leasing, Army policy is to offset deficits in other parts of the matrixes with these surpluses. Offsetting results in a final housing deficit, and this deficit becomes the basis for making construction or leasing requests.

To conform with government policy, the final deficit is reported by bedroom count (BC) for personnel in specified Army grade groups. Since there are six relevant grade groups, the final deficit computation generates a 6 X 6 matrix. There are separate matrixes for unaccompanied (UPH) and family (AFH) personnel. 2.2. Econometric enhancement

Government auditors were not satisfied with the economic analysis used by Army housing managers to project the private rental stock and to forecast the Army's market share of the stock. To address the concerns of government auditors, the original SHMA process was enhanced by incorporating an econometric model into the analysis. The econometric model used established economic concepts to determine the prices (rents) that equate demand and supply (clear the marke0 for private rental housing in market segments relevant to Army personnel. These marketclearing quantities represent the number of rental units that will be available to consumers (including military personnel) at marketclearing prices in the long run on average (Forgionne, 1992). Multiplying the SHMA-estimated shares by the econometrically forecasted market clearing quantities provided the offpost rentals expected to be available for Army personnel (Forgionne, 1991b). 2.3. Decision support system

Housing managers in the field frequently complained about the complexity of the process and the high training costs involved with a SHMA review. In addition, senior Army officials wanted to simplify and automate key segments of the SHMA process. These officials sought support for the econometric analyses mandated by the government auditors, the complex computations involved in the SHMA, and the report writing necessitated by Department of Defense policies. A decision support system, called the Housing Analysis System (HANS), was developed to provide the desired support. HANS' architecture, which applies established DSS principles has a database and a model base. The database contains offpost and onpost data relevant to

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the econometrically enhanced SHMA process. The model base includes the econometric model and the algorithms needed to perform the deficit offsetting computations. Military housing managers use HANS to organize the relevant data, structure the econometric model, and simulate housing deficits (Forgionne, 1991b, 1992). The simulations are performed automatically, and the results are displayed on the preprogrammed forms desired by military officials. These results consist of housing reports that project market conditions, housing deficit forecasts, and deficit forecasts adjusted by offsetting computations.

2.4. Support gaps HANS accurately projected the number of housing deficits, and it demonstrated the power of information systems to support Army housing management. The system also exposed further deficiencies in the support for the SHMA process. These deficiencies involved marketcondition forecasting, deficit calculations, data management, report writing, and mapping.

2.4.1. Marketcondition forecasting At the time of HANS's development, the Army had unreliable data on the economic variables relevant to rental quantity and price (rent) forecasting. As detailed elsewhere, these data restricted HANS's original econometric model to a single quantity component with only two simultaneous equations and a few exogenous variables (Forgionne, 1992). The limited model with the unreliable data occasionally resuited in erratic marketclearing quantity and rent predictions. Moreover, the original SHMA process assigned a single subjectively projected Army share to all market segments and estimated the marketclearing quantities independently of this share. In practice, the Army's share will be determined in conjunction with (rather than separately from) the marketclearing quantity and rent. Military personnel in distinct market segments (bedroom counts) likely will obtain different shares of the marketclearing quantities. The market share forecasting flaws contributed to the occasional erratic marketclearing quantity and rent predictions.

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2.4.2. Deficit calculations The Army makes construction and leasing requests for groups, rather than individual grades, of personnel. The original HANS reflected this focus and used grade group, rather than individual grade based, formulas to perform deficit offsetting computations. In the process, potential deficit reductions were missed or underestimated. 2.4.3. Data management Much of the relevant onpost data needed for the SHMA process are captured, stored, and can be retrieved through Army information reporting systems. However, the onpost data were not organized into the variables needed to perform the SHMA process. Required offpost data originally were collected, captured, and recorded manually, and in an often sketchy manner, during the SHMA process. Typical offpost data sources included banks, local realty boards, public utility commission reports and statistical abstracts, state statistical abstracts, and vendors of local housing market statistics. There was little (if any) sharing of information between the Army information reporting systems and the original HANS-supported SHMA process. 2.4.4. Report writing The original HANS generated a series of reports on market conditions and on projected Army housing deficits for the specified installation. Deficits were reported by grade and by the groups of grades needed to conform with the housing construction and leasing categories specified by Department of Defense policies (Forgionne, 1991b, 1992). The original reports, however, did not give a detailed breakdown of the offsetting operations and computations. Such a categorization would be useful for Army officials seeking to evaluate alternative housing deficit reduction policies. The division would also provide the policy implication information communicated by these officials to installation managers. 2.4.5. Mapping While the original HANS accurately projected the number of private rentals available to reduce onpost housing deficits, the system did not locate the rentals. The original HANS also did not display characteristics about the available rental properties. Such spa-

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The other would substantially amplify HANS's heuristic programming model (going from approximatley 50 to about 500 heuristics) to perform individual grade based, rather than grade group based, offsetting. At the same time, these officials sought additional information systems that would close the mapping, report writing, and data management gaps in the SHMA support. An integrated information system, called the Housing Analysis Decision Technology System (HADTS), was developed to provide the desired enhancements. This system was developed iteratively, using the Adaptive Design Strategy (ADS), by two researchers working in conjunction

tial and attribute data would greatly enhance the Army housing managers' ability to implement leasing directives.

3. Housing Analysis Decision Technology System (HADTS) When U.S. Census data became available, senior Army officials sought two important upgrades to HANS. One would significantly expand HANS's econometric model to better reflect market realities and improve the model's forecasting capabilities.

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with affected Army executives (senior housing managers). 3.1. HADTS components

HADTS integrates the functions of a Geographic Information System (GIS), an Executive Information System (EIS), and an upgraded version of HANS. The GIS is delivered through ATLAS/GIS software, while the EIS and upgraded HANS are delivered through the SAS System for Information Delivery. Fig. 1 shows the relationships between these HADTS components. As Fig. 1 illustrates, the GIS extracts Census data, creates Housing Market Area (HMA) maps, and

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displays userspecified market conditions on the maps. Among other things, the EIS extracts HMA market conditions from the GIS and installation housing characteristics from Army information systems, captures the extracted data, and forms the database needed to perform the HANS analyses and evaluations. HANS utilizes the upgraded econometric and heuristic programming models to compute the results displayed on the housing deficit reports. 3.2. HADTS architecture

To conserve resources and to meet the needs of the Department of Army's personnel, HADTS is made available through an easy-to-use computer sys-

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t e m that c a n b e r e a d i l y used at h e a d q u a r t e r s , the m a j o r A r m y c o m m a n d s ( M A C O M s ) , or the m i l i t a r y i n s t a l l a t i o n s b y n o n t e c h n i c a l p e r s o n s . Fig. 2 g i v e s a conceptual architecture of HADTS.

3.2.1. Inputs H A D T S h a s a d a t a b a s e that c a p t u r e s a n d stores spatial a n d a t t r i b u t e d a t a for the H M A s a n d r e l e v a n t o n p o s t data. Spatial d a t a i n c l u d e s l o n g i t u d e a n d latitude c o o r d i n a t e s t h a t are u s e d to d r a w the H M A m a p s a n d f e a t u r e s o n the m a p s , i n c l u d i n g city a n d c e n s u s tract b o u n d a r i e s , b o d i e s o f water, h i g h w a y s , streets, the i n s t a l l a t i o n ' s location, a n d the l o c a t i o n o f

a v a i l a b l e rental p r o p e r t i e s . A t t r i b u t e data c o n s i s t s of." (i) the e c o n o m i c v a r i a b l e s n e e d e d to p e r f o r m H A N S ' s deficit a n a l y s e s a n d (ii) h o u s i n g c h a r a c t e r i s t i c s o f i n t e r e s t to A r m y h o u s i n g m a n a g e r s . T h e e c o n o m i c v a r i a b l e s include the H M A ' s total p o p u l a t i o n , land area, a v e r a g e p o p u l a t i o n age, average years o f s c h o o l i n g , m e d i a n h o u s e value, a v e r a g e travel t i m e to w o r k , m e d i a n h o u s e h o l d i n c o m e , total p r e c o l l e g e s c h o o l e n r o l l m e n t , a v e r a g e f a m i l y size, the n u m b e r o f m a l e s , total h o u s i n g stock, a n d vac a n c y rate. H o u s i n g c h a r a c t e r i s t i c s i n c l u d e m e d i a n r e n t s a n d rental h o u s i n g q u a n t i t i e s c a t e g o r i z e d b y b e d r o o m count. R e l e v a n t o n p o s t data c o n s i s t s o f the

PS = GRS × GAP PVS = GRV × GAP PMS = MSR × GAP PTM = MR x GAP GAP = TGS - TDG - TRG PM = P T M - (0.5 x PMS) US = GUS/GPR RB = (PM -- PVS + US + OM) x PLF RF = (RB × B R ) - LOF RU = (PS - PM - LOU) x PLU HA = (1.5 × BAQ) + VltA GA = GCP/GPR where PS = projected force strength, GRS = ratio of the current strength in the grade, GAP = Army stationing plan (ASIP) by personnel classification, TGS = classification total, TDG = temporary duty personnel, TP~G = training personnel, PVS = projected dependents voluntarily separated from their spouses, GRV = ratio of the current voluntarily-separated strength in the grade, PMS = projected number of soldiers married to soldiers, MSR = ratio of the current married-to-soldiers strength in the grade, PTM = projected total number of married soldiers, MR = ratio of the current married strength in the grade, PM = projected number of married soldiers, US = married soldiers unaccompanied by their spouses, GUS = unaccompanied married soldiers by personnel classification, GPR -- proportion of the grade in the personnel classification, RB = requirements base, OM = personnel onpost from services other than the Army, PLF = AFH program limit (typically 90% or 100%), RF = AFH housing requirements, BR = ratio of current strength having a specified bedroom count requirement, LOF = AFH soldiers owning an offpost residence in the lIMA, RU = UPH housing requirements, LOU = UPH soldiers owning an offpost residence in the HMA, PLU --- UPH program limit (usually 95% or 100 %), HA = housing allowance, BAQ = basic housing allowance, VHA = housing allowance adjustment for the HMA, GA = available onpost housing, and GCP = housing capacity by pcrsonncl classification. NOTES: The formulas and ratios a r e s e t by Army policy; UPtt refers to unaccompanied personnel; and AFH denotes soldiers with families. Fig. 3. General housing formulas.

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elements needed to estimate housing requirements, government owned and controlled assets, personnel renting offpost, installation populations, and effective military demand dollars (housing allowances). There is also a model base that contains statistical procedures, location formulas, data conversion rules, the upgraded econometric model, and the upgraded deficit reduction heuristics. The statistical procedures are used to categorize attribute data within the HMAs and to calculate summary statistics for the economic variables and housing characteristics within the HMAs. Location formulas, proprietary within ATLAS/GIS, are used to convert U.S. Census Bureau TIGER (Topologically Integrated Geographic Encoding and Referencing) degrees into radians. The radians are then used to develop polygons that define the block groups, counties, and the HMAs. Data from existing Army information systems are not in the format needed to perform HADTS's analyses and evaluations. Predefined rules are used to convert the raw Army data into the needed HADTS formats. Heuristics, called crossleveling and redesignation, are used to implement DOD policies and Army guidelines for reducing deficits in bedroom counts and grades among unaccompanied and family personnel. 3.2.2. Data conversion formulas

One set of formulas converts the extracted economic and military characteristic data into the variables needed for HANS's analyses, evaluations, and reports. Some of these variables become inputs into HANS's econometric analyses, and such analyses output (project) available private (offpost) housing by grade and bedroom count. A second set of formulas transforms the remaining variables into projected housing requirements and government owned and controlled (onpost) housing by grade and bedroom count. Fig. 3 delineates the general form of the formulas. 3.2.3. Econometric model

HANS's upgraded econometric model includes an expanded quantity component and a market share extension. The expanded quantity component has six sections (one for each bedroom count) each with 6 equations and 12 variables defined by senior Army housing managers' judgement and by economic housing theory (Blackley and Ondrich, 1988; Good-

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q D i = QSi QDI = qd(R./, V)

Qs~ = qs(R,, S,) Si = s(ST, H) V = v(DT, T, Y) Y = y(A, ED, M, P) where i = bedroom count (studio/efficiency, one, two, three, four, and five-plus bedrooms), QD = quantity of rentals demanded, QS = quantity of rentMs supplied, R = average gross rent, V = median house value, S = housing stock, ST = median age of houses, H = number of households, DT = population density, T = average travel time to work, Y = median household income, A = average population age, ED = average years of schooling, M = number of males in the population, and P = average persons per household. Fig. 4. Econometric model's updated quantity block.

man, 1988; Turnbull, 1989) and estimated statistically with regression analysis. This component forecasts marketclearing supplies and rents for rental housing by bedroom count (BC) in the installation's HMA. Fig. 4 gives the general form of the updated quantity block. The market share extension has 252 sections each with a single equation (one for each comination of Army grade, bedroom count, and personnel category) and 3 variables defined by senior Army housing managers' judgement and by economic housing theory (Carruthers, 1989; Kaplan and Berman, 1988; Tumbull, 1988) and estimated statistically with regression analysis. Each market share equation takes the following general form: MSjk i = ms (SPjk, ERjk, VRi) where j = Army grade (1 through 21 for El-E9, W I - W 5 , and O1-O7 + , respectively), k = personnel category (UPH or AFH), MS = Army's market share of aggregate rental housing, SP = installation strength to population ratio (as a measure of the Army's relative market presence), E R = effective military demand dollars to rent ratio (as a measure of Army personnels' relative purchasing power), and VR = vacancy rate. These market share equations forecast the military's share of the marketclearing rental quantity for each of the six market segments (bedroom counts) relevant to Army personnel in the installation's HMA. Multiplying the estimated marketclearing supplies by the predicted market shares gives the offpost

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rentals that will be available to Army personnel by category (unaccompanied or family), grade, and bedroom count. Army requirements less government owned and controlled assets less available offpost rentals give the unreconciled deficits/surpluses that can be anticipated by grade for each BC.

3.2.4. Deficit reduction heuristics. An upgraded and expanded assignment model automatically reassigns (offsets) deficits and surpluses among BC and grades (rather than grade groups) in accordance with the latest DOD policies, rules, and regulations, first using available offpost rentals and then using government owned and controlled assets. When private assets are used in the offsetting, the heuristics take the following general form: APA = R # P A TOD = (OD - APA) < > Z ROD=TOD> Z where R = a matrix containing values of 1 if the entry involves no grade exceptions and, where appropriate, meets the pertinent 80% rule, and 0 otherwise; PA = a matrix containing the pertinent private assets; OD = a matrix containing the pertinent onpost deficits; Z = a matrix containing values of 0; ROD = a matrix containing the revised (reset) onpost deficits; RPA = a matrix containing the revised (reset) private assets; and the other matrixes are defined by the matrix equations. The symbol # denotes elementwise matrix multiplication; () indicates matrix element maximums; and > < symbolizes matrix element minimums. When onpost assets are used in the offsetting, the heuristics take the following general form: IOD = F D # O D IOS = FS#OS TOD = OD + IOS COD = TOD < > Z ROD=COD> < Z ROS = COS < > OS

where FD = a matrix containing values of 0.5 if the entry represents a UPH grade group 2 (E5-E6) onpost surplus being used to redesignate or reallocate a UPH grade group 1 ( E l - E 4 ) onpost deficit, 2 if the entry represents a UPH grade group 1 onpost surplus being used to redesignate or reallocate a UPH grade group 2 onpost deficit, and 1 otherwise; FS = a matrix containing values of 2 if the entry represents a UPH grade group 2 (E5-E6) onpost surplus being used to redesignate or reallocate a UPH grade group 1 ( E l E4) onpost deficit, 0.5 if the entry represents a UPH grade group 1 onpost surplus being used to redesignate or reallocate a UPH grade group 2 onpost deficit, and 1 otherwise; OS = a matrix containing the pertinent onpost surpluses; ROS = a matrix containing the revised (reset) onpost surpluses; the other matrixes are defined as before or by the matrix equations; and the symbols are specified as before.

3.2.5. Processing The decision maker (a military housing executive or staff management assistant) uses computer technology to perform housing analyses and evaluations with HADTS's EIS, GIS, and HANS components. Currently, the system executes on an IBM-compatible 486 microcomputer with 8MB of RAM, a color graphics display, and a printer compatible with the microcomputer. It runs the SAS information delivery system and the A T L A S / G I S geographic information system through the O S / 2 operating system. This configuration was selected because it offered a more consistent, less timeconsuming, less costly, and more flexible development and implementation environment than the available alternatives. From a flow perspective (Fig. 1), data moves from the GIS (and other Army information systems) through the EIS for further HADTS processing. Hence, the arrow's direction is from the GIS to the EIS in Fig. 1. From the user-interface perspective (Fig. 2), the EIS acts as a frontend to HADTS's database management system (DBMS), GIS, and

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HANS. Consequently, the arrow's direction is from the EIS to the GIS in Fig. 2. Like many geographic information systems, HADTS's GIS organizes the collected spatial and attribute data (in vector format), captures the data, and stores the key offpost variables as a dBASE IV-based, DBF-formatted, relational database (Bruno, 1992; Fischer and Nijkamp, 1993; Franklin, 1992; Grupe, 1992a,b; Huxhold, 1991). HADTS's GIS then structures the HMA maps, locates available rental housing on the HMA maps, and simulates economic variables and housing characteristics within the HMAs. The system also enables the housing manager to interactively modify the HMA, display tabular statistical reports that summarize housing characteristics in the HMA, and print hard copies of the HMA maps and summary statistics. By using the DBMS, the user can extract HMA market conditions from the GIS and installation housing characteristics from Army information systems, display the data, modify the displayed data, and store the SHMA-relevant information. The HANS component then utilizes the DBMS-generated onpost and offpost data to automatically forecast market conditions and Army housing supplies from the upgraded econometric model, perform upgraded deficit reduction heuristics, and generate detailed reports of the results automatically without human (manual) intervention. As indicated by the top feedback loop in Fig. 2, offpost and onpost data, reports, and maps created during HADTS's analyses and evaluatioris can be captured and stored as inputs for future processing. These captured inputs are stored as additional or revised fields and records, thereby updating the data base dynamically. The user executes the functions with mouse controlled point-and-click operations on attractive visual displays that make the computer processing virtually invisible (transparent) to the user. 3.2.6. Outputs Processing automatically generates visual displays of the outputs desired by housing managers. Outputs include HMA maps and associated deficit forecasts and reports. The maps define the boundaries of the HMA, give the road and street patterns, identify important landmarks, locate the military installation within the HMA, and highlight the locations of rental

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properties on the HMA roads and streets. Deficit forecasts project the corresponding market conditions and their effects on Army housing deficits/surpluses. The results are displayed as a series of housing reports that list the HMA marketclearing rents and rental quantities by bedroom count and deficit computations by bedroom count and grade. Deficit computation reports include the offpost rentals that will go to Army personnel, Army housing requirements, available onpost housing, net deficits before reassignments, net deficits after reassignments, and the distribution of reassignments. As indicated by the bottom feedback loop in Fig. 2, the user can utilize the outputs to guide further HADTS processing before exiting the system. Typically, the feedback will involve sensitivity analyses in which the user modifies the HMA boundaries and observes the effects on market conditions or the user adjusts onpost variables and observes the effects on housing deficits. 3.2.7. Decision support principles HADTS applies a number of important decision support principles. The system utilizes appropriate data (onpost and offpost statistics) and robust models (data conversion, location, econometric, and deficit reduction formulas) to improve decision making for a complex managerial problem (Dadam and Linnemann, 1989; Targowski, 1990; Wang and Walker, 1989). User actions are guided toward, and focused on, the pertinent aspects (extracting the relevant data, accurately forecasting private housing supplies, and correctly applying Army offsetting policies and procedures) of the decision problem (Adelman, 1992; Benbasat and Nault, 1990; Silver, 1991). Processing occurs in a timely (short processing cycle) and intuitive (point and click operations) manner, allowing users to concentrate on problem content and analysis (Forgionne, 1991a; Turban, 1993). System feedback loops facilitate userinspired problem experimentation and promote learning and knowledge capture (Sengupta and Abdel-Hamid, 1993; Watson et al., 1992). These principles can be illustrated by examining excerpts from a HADTS session. 3.3. HADTS session There is an HADTS icon on the O S / 2 desktop. By double clicking this icon, the user accesses the

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Housing Analysis Decision Technology System. Once in HADTS, the user performs the housing analyses and evaluations by navigating with point and click operations through the displays overviewed in Fig. 5. As this figure illustrates, the operations involve installation identification, mapping, and deficit reporting.

3.3.1. Installation definition The Welcome display enables the user to access the EIS. Once in the EIS, the user with the correct password can update HADTS's database, obtain system help, and proceed to the installation definition screens. These screens require the user to interactively identify from screen icons in sequence the installation's Major Army Command (MACOM), specific installation (fort), and the current year. These operations automatically subset the HADTS database, provide the data needed for further processing, and access the HADTS processing display. From this display, the user can select the type of processing desired - - GIS (maps of the HMA) or HANS (inputs and reports).

3.3.2. Mapping Selecting the GIS button will put the user in ATLAS/GIS. A program, written in ATLAS's scripting language, automatically accesses the relevant map information, displays a map of the installation's HMA, and provides a custom pulldown menu for userspecified actions. By making appropriate selections from the custom pulldown menus, the user can: (i) display summary housing statistics for the HMA map, (ii) interactively modify the HMA boundaries, and (iii) perform various database functions. The summary housing statistics, which include median rents and rental housing quantities, are displayed adjoining the corresponding HMA map. Fig. 6 illustrates an annotated map display. Department of Defense (DOD) policy defines the acceptable commuting distance and time for military personnel stationed at an installation. A change in policy can necessitate a modification in the boundaries of the HMA. Using the computer's mouse, the military housing manager can modify these boundaries interactively, observe the effects on key economic variables and housing characteristics, and quickly report the results to DOD officials.

3.3.3. Deficit reporting Selecting the HANS button from the HADTS processing screen puts the user in the enhanced Housing Analysis System (HANS). Once in HANS, the system displays the REPORTS screen shown in Fig. 7. Selecting the What If buton will display forecasts of housing statistics for the installation's Housing Market Area (HMA). These statistics include selected HMA market conditions, military characteristics, housing requirements, available offpost housing, and available onpost housing for the installation. Fig. 8 illustrates a small portion of this multiscreen What If window. While the forecasts can be altered at any time, typically the user will first display housing reports and then examine the sensitivity of the reported deficits to changes in the projections. Selecting the Reports button on the REPORTS screen will put the user in the REPORT SELECTION screen shown in Fig. 9. Once in this screen, the user can select the summary report for a person-

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3.3.4. Application of decision support principles

nel category (UPH or AFH) or any detail report. As illustrated in Fig. 10, the summary report gives the final military deficit by grade group and bedroom count for the selected personnel category. Detail reports give summaries of the explanatory computations that justify the summary report. These intermediate descriptions include reports on market conditions, housing requirements, available offpost housing, available onpost housing, gross deficits, offset deficits using offpost housing only, offset deficits using both offpost and onpost housing reports, and reassigned onpost housing by grade and bedroom count. Fig. 11 shows an example detail report. In the figures, a negative deficit represents a housing surplus.

As the HADTS user selects installation definition, processing, and reporting screens, he or she is sequentially guided toward, and focused on, the pertinent aspects of the complex housing management problem. Such selections automatically: (i) extract the pertinent onpost and offpost data, (ii) use the data to operationalize and implement robust data conversion, location, econometric, and deficit reduction formulas, and (iii) display the maps and deficit reports relevant to the user. Intuitive point-and-click and timely (typical five-minute system session) operations enable the HADTS user to concentrate on the results from, rather than the technical computer and mathematical processing and interpretation needed

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40 to

214/sqm

[]

21,1 to

528/sqm

[]

528 to

1295 /sqrn

[]

1295 to 11171 /sqm

Miles 0 2 4 6

Fig. 6.

8

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374

for, the housing analyses and evaluations. HADTS's housing statistic and map boundary sensitivity analyses facilitate userinspired housing policy experimentation and promote management learning, while the system's automatic storage of processed results expedites knowledge capture.

HADTS DATA INPUT SCREENS INSTALLATION: Oahu Consolidated Housing LATEST UPDATE: April 26, 1995

ECONOMIC FACTORS 4. Benefits

To ensure that the information system accurately replicated the inputs, HADTS's data conversion rules were tested against historical data for existing military installations. In the testing, housing statistics displayed from the system were compared with the corresponding actual values. According to the results, HADTS reproduced the actual data exactly. HADTS's econometric model was tested against Census data from the counties surrounding existing Army installations. In the testing, projected eco-

AVERAGE AGE: 34.11 HOUSEHOLDS: 197294 POPULATION: 836228 TRAVEL TIME: 27.65 LAND AREA: 1546501 INCOME: 42442

MEDIANSTRUCTURE AGE: 23 NUMBEROF MALES: 425996 AVERAGESCHOOLING: 12.69 AVERAGEHOUSEHOLD SIZE: 2.98 S C H O O LENROLLMENT: 447441 HOUSE VALUE: 294295

HOUSING INFORMATION Bedroom Count (BC) Studio One Bedroom Two Bedroom Three Bedroom Four Bedroom Five Bedroom

Rental Supply

Median Rent ($)

16071 34468 40557 26992 8046 1278

509.92 616.57 756.87 956.58 1100.07 1243,56

Vacant Houses

Housing Stock

2525 6089 4179 2622 723 241

21667 55510 75155 87850 30680 10821

Fig. 8. What if screen.

OAHU CONSOLIDATED HOUSING The HOUSING ANALYSIS SYSTEM (HANS) forecasts housing deficits by grade end bedroom count. To perform the analysis with the latest available data, place the cursor on the HANS REPORTS button and press the ENTER key, or click the HANS REPORTS button with the computer's mouse. The data was last updated on April26, 1995. It is also possible to determine the effects on the forecasts of changes In offpost and onpost variables. To perform these sensitivity analyses, place the cursor on the WHA T IF button and press the ENTER key, or click the WHAT IF button with the computer's mouse. A form will be displayed to enter the desired variable changes. Then you can return to this screen and select the HANS REPORTS butlon to obtain the results of the changes. Selecting the GO BACK button will return you to the previous display.

Fig. 7. Reports screen.

nomic variables from the quantity block were compared with the corresponding actual Census values. According to the results, the estimated equations predicted between 81.52% to 95.84% of the variance in the supply (quantity) data and between 42.77% to 80.92% of the variance in the rent data. Root mean squared (RMS) error percentages ranged between 0% to 14.5572%, with most values less than 5%, from the bedroomcount supply and rent equations. Also, projected variables from the market share block were compared with the corresponding actual Army records. According to the results, the estimated equations predicted between 0.08% to 99.96% of the variance in the market share data. Root mean squared (RMS) errors ranged from 0.00003 to 0.0334 for all market share equations. Such performance generally was far superior to the statistical results from the earlier econometric model (Forgionne, 1992). Hypothetical, but realistic, data on housing requirements, onpost (Army) assets, and private assets

G.A. Forgionne / European Journal of Operational Research 97 (1997) 363-379

375

Fort G r e e l y 1993 OAHU CONSOLIDATED

HOUSING

FAMILY

In this display, you Identify the desired deficit report, To make a selection, place the cursor on the desired raport and press the ENTER key or click the button with the system's mouse, You can return to the previous display by selecting the GO BACK buffon, Detailed descriptions of the reports can be obtained by selecting the HELP button. Selecting the ALL REPORTS button prints every report on the list but does NOT display the reports on the screen.

Grade

Market Conditions UPH UPH UPH UPH UPH UPH UPH UPH , UPH

Offpost Housing Requirements Onpost Housing Net Requirements Deficits Offpost Offset Deficits Onpost Offset Deficits Reassigned Housing Grade Group Deficits

AFH AFH AFH AFH AFH AFH AFH AFH

Offpost Housing Requirements Onpost Housing Net Requirements Deficits Offpost Offset Deficits Onpost Offset Deficits Reassigned Housing

Studio

NET DEFICITS

t 1 BR

0 0 0

0 0 73

0 0 71

E4 E5 E6 E7 E8 E9 Wl W2 W3 W4

0

164

0

0

585

0

0 0 0

513 57

331 781

w~---

33 I

83

0 0 0 0

11 I 51 87 61 31

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, I}

3 I 0 31 5 35 3 (951) 93 0 17 0 702 711 941 12 42 6 (81) (7) 0 (36) 42 0 10 1 0

I

0 0

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i

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F i g . 11. E x a m p l e detail report.

Fig.

9. R e p o r t selection screen.

nary results from the implementation indicate that the decision technology system will have significant economic and management benefits, offer important lessons, and present key challenges for Army housing management.

were used to test the final set of upgraded deficit reduction heuristics. In the testing, modelcomputed surpluses and deficits were compared to the values expected at each stage of the offsetting process. This testing revealed that the heuristic programming model always generated the correct surpluses and deficits at all offsetting stages. Based on the test results, The Department of Army has decided to implement HADTS. Prelim/Oahu

4.1. Economic benefits Before the original version of HANS, the SHMA process involved a very complex process that reConsolidated April

UPH

Grade

26,

GRADE

GROUP

EI-E4 E5-E6 E7-E9 W1-W3 and W4-W5 and O6-O10 Totals

O1-O2 03-05

Studio -17507 0 0 0 0 0 0

1995

Group

(Positive

Housing

Deficits

Totals

Only)

IBR

2BR

3BR

4BR

5+BR

0 -4647 14 43 336 17 410

0 0 0 0 0 0 0

0 0 0 0 0 0 0

0 0 0 0 0 0 0

0 0 0 0 0 0 0

F i g . 10. G r a d e g r o u p report

Totals

0 0 14 43 336 17 410

376

G.A. Forgionne / European Journal of Operational Research 97 (1997) 363-379

quired extensive training for housing managers. As reported in detail elsewhere, by decreasing the volume of documentation, by simplifying the educational process, and by simplifying and automating much of the SHMA process, the original version of HANS was expected to save the Army approximately $3 million per year in implementation costs (Forgionne, 1991b, 1992). HADTS further simplifies and automates the SHMA process, reduces training requirements to a minimum, and increases computer processing efficiency. These substantial improvements are expected to offer significant additional savings for the Department of Army. The substantial additional savings can be attributed to the new EIS and GIS components and the significantly upgraded HANS component.

4.1.2. GIS savings While HANS substantially decreases deficit projection costs, the system does not locate the deficit reducing rentals. After completing the SHMA process, representatives (either Army personnel or local real estate agents) were sent into the HMA seeking available facilities. With the GIS, an Army housing manager can conduct the lease search in a matter of minutes at a nominal expense. By simplifying the search, the GIS has cut lease location expenses very significantly. Each HMA with deficits averages about 1 000 leases per year at an average cost of $500 per lease, and a search typically costs the Army approximately 5% of the lease expense. Since about 50 installations will require offpost leasing, the GIS is expected to save the Army a gross

4.1.1. EIS savings Prior to the EIS, HANS's database was formed in a largely manual fashion. Some data were extracted from files provided by source (other Army information system) suppliers through several, incompatible computer programs written by headquarters systems specialists. Other data were collected and captured manually by housing managers or their staff. The various data then were entered manually into HANS's database, and the data were not captured and stored for future use. With the EIS, all pertinent data are extracted and captured directly from the supplier files and stored as a data warehouse automatically by the system. The EIS utilizes the warehoused data to form HANS's database automatically in a matter of minutes without the intervention of systems specialists and housing managers. Such automated operations save the expenses associated with headquarters computer programming and with management or staff manual processing. These savings are estimated to be approximately $53,000 at headquarters annually plus about $21,000 per installation per year. Since 73 installations will require data access, extraction, focusing, and reporting, the EIS is expected to save the Army a gross

$500000 x 50 X 5% = $1250000

$53000 + ($21000 x 73) = $1586000 per year. The EIS was developed and integrated within HADTS for approximately $37 000.

per year in lease location costs. The GIS was developed and integrated within HADTS for approximately $50 000.

4.1.3. Upgraded HANS savings By improving the quantity block and by adding a market share block, the updated econometric model will enable managers to more fully describe housing market dynamics. Such a description should improve the accuracy of HANS's housing forecasts. To provide an indication of the possible improvement, data from the latest available Army reports were used to compare results from the earlier and updated econometric models. This .comparison revealed that the upgraded econometric model would have reduced housing deficits by a total of 5 342 units. While actual construction costs will vary, the pertinent housing is budgeted at $120000 per unit. Thus, the use of the updated econometric model would have saved ($120000 X 5342) = $641040000 over the reported period (the current fiscal year) for the relevant installations. Further savings would be expected from the installations in future years. The upgraded econometric model was developed and integrated within HADTS for about $26 000. By incorporating individual grade based heuristics into the offsetting process, the updated heuristic

G.A. Forgionne / European Journal of Operational Research 97 (1997) 363-379

programming model will enable managers to reduce housing deficits substantially. To provide an indication of the possible reductions, data from available reports were used to compare housing deficits computed with the earlier and updated heuristic programming models. This comparison indicated that the updated heuristic programming model would have reduced housing deficits by a total of 3 193 units. At a budgeted cost of $120000 per unit, the use of the updated heuristic programming model then would have saved ( $120000 × 3193) = $383160000 over the reported period (the current fiscal year) for the relevant installations. Further savings would be expected from the installations in future years. The upgraded heuristic programming model was developed and integrated within HADTS for approximately $28 000.

4.1.4. Total savings HADTS, then, is expected to save the Army a total of $2 860 000 per year in SHMA implementation and lease location costs and $1 024200000 in budgeted construction expenses. The Army spent about $141000 to develop and implement the HADTS system that delivers these economic benefits. Such economic gains are quite timely in light of federal budget restrictions and the Army's current direction of large scale force reductions and base closures. 4.2. Management benefits In the original SHMA process and in the manual search for lease locations, there has been a nearly exclusive reliance on tedious manual procedures. These procedures often resulted in inaccurate, incomplete, and redundant data collection. Such data problems can leave Army personnel inadequately housed, compromise morale, and jeopardize military preparedness. HADTS identifies all data relevant to the SHMA process, including locations of rental properties, and provides a mechanism that facilitates data entry while reducing errors and eliminating redundant inputs. In addition, HADTS enables the housing manager to improve the decision making required in the

377

SHMA and lease location processes. The decision technology system provides: 1. quicker analysis of the rental housing market and its impact on Army housing, 2. operationally and computationally errorfree SHMA and leaselocation processes, 3. more timely policy analyses and evaluations, 4. rapid sensitivity analyses of HMA boundary, market condition, and policy changes, 5. efficient flagging of data and information deficiencies, and 6. more effective evaluation of fieldgenerated housing requests. These enhanced capabilities will enable Army officials to more efficiently and effectively manage the $55 billion in housing assets under their control. Partially because of government auditors concerns, there has been significant movement within the armed services to standardize the process of requesting military housing construction and leasing. Toplevel policy makers realize that all the services use a similar process, and HADTS's success has convinced them that the process can be substantially enhanced with a decision technology system. Consequently, the Army's HADTS-supported SHMA and lease location processes are being considered as the standard for all the armed services.

4.3. Limitations and challenges Lessons were leamed from HADTS implementation and development. Improved accuracy in housing deficit projections are important to the Army and society. Underestimating deficits can leave Army personnel inadequately housed, lower morale, and jeopardize military preparedness. Unnecessary housing construction can waste scarce natural resources and, in the process, alienate local residents, environmentalists, and other interest groups. Reducing housing deficits can help the Department of the Army avoid these undesirable consequences. Since the earlier econometric model had a limited quantity, and no market share, block, the housing manager was left with an incomplete understanding of the data requirements needed for the SHMA process. As a result, managers often collected and captured irrelevant and redundant data. The upgraded

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econometric model identifies all offpost and onpost data relevant to the housing forecast process, and the HANS system provides a mechanism that facilitates data entry while reducing errors and providing accurate, reliable, and consistent data. These lessons identified some limitations that present profound challenges to Army housing management. These limitations and challenges involve data sharing, intelligent model management, and mapping enhancements.

4.3.1. Data sharing The econometric models within HADTS are estimated statistically from available data. At the present time, there is limited data available to estimate the equations in the market share segment of the model. These data limitations precluded a meticulous application of theoretical market share housing models and resulted in deficit forecasting anomalies for some Army installations. The data needed to overcome the limitations exist in other military information systems. These data, however, must be located and extracted from these other systems and used to update HADTS's database. The updated database then must be used to statistically estimate upgraded versions of the market share segment of HADTS's econometric model. Programs have been written and embedded within HADTS to perform the database updating when the appropriate government agencies work out technical data sharing arrangements. 4.3.2. Intelligent model management As new data becomes available, variables may be added or deleted from, and parameters may change in, the original formulations (Billman and Courtney, 1993; Cook, 1993; West and Courtney, 1993). To provide up-to-date econometric models: 1. pertinent data must be extracted from the database, 2. the data must be used to revise, through regression analyses, the original econometric models, and 3. the revised econometric models must replace the original formulations in HADTS's modelbase. The typical HADTS user will not have the technical expertise to perform these tasks without considerable assistance. There is interest among senior Army officials in developing a HADTS enhancement that will perform

the updating tasks automatically without human (manual) intervention. In the approach under consideration, new entries by the housing manager will be append existing data and create a revised database. It also will trigger a program that calls the revised database, executes regression analyses with the revised data, and stores the results as a revised model base. Another embedded program, triggered by the revision, will run HADTS with the revised model base.

4.3.3. Mapping enhancements Maps are displayed and the corresponding offpost housing data are generated through ATLAS/GIS, while the offpost data are utilized within SAS to create deficit reports. Using two different software tools (SAS and ATLAS/GIS) reduces computer processing efficiency and increases system maintenance requirements. These difficulties can be alleviated by replacing the ATLAS/GIS tool with SAS/GIS when it becomes available. Currently, the Army GIS displays the locations of rental housing within the HMA, but the system does not identify the characteristics of each rental. Housing managers would like a photograph, the specific street address, the bedroom and bathroom count, and any special features about each property. The Army GIS could be enhanced to incorporate these additional characteristics.

5. Conclusions

The HADTS architecture is based on a combination of database, econometric, heuristic programming, mapping, and decision support techniques. Its deployment has enabled the Department of Army to realize significant economic, management, and political benefits. Future enhancements, motivated by the challenges from the current system, promise to increase the power of HADTS and to further improve the Anny's ability to manage its housing assets. Partially because of government auditors' concerns, there has been significant movement within the armed services to standardize the processes of requesting and locating rental housing and locating leasing. Toplevel policy makers realize that all the services use similar processes, and HADTS's success

G.A. Forgionne / European Journal of Operational Research 97 (1997) 363-379

may convince them that the processes can be substantially enhanced with decision technology support. Consequently, the Army's HADTS-supported SHMA and lease location processes can be offered as the standards for all the armed services.

Acknowledgements The research for this paper was supported by a grant from Consortium Research Management Incorporated (CRMI). Personnel at CRMI, Hughes Service Corporation, and the U.S. Army Housing Support Center, including Paul Bachman, Carl Hughes, Jack Crabb, and Jim Tarelton, provided considerable assistance toward the successful completion of the project. Their valuable assistance is gratefully acknowledged.

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