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339

Patterns of Office

Employment Cycles

Leon Shilton*

Abstract. Based upon an the analysis of quarterly office employment and total employment changes from 1975 through 1994, this research concludes that for a majority of the fifty metropolitan areas, office employment cycles are converging towards seven year cycles. However, many of the patterns are emerging and for one-third of the cities, the office employment changes are a random walk. While changes in office employment and total employment are correlated, neither series lags nor leads the other. Office employment grew faster than total employment, but office employment changes were more volatile.

Introduction

The poor investment performance of the office property sector during the last recession revives the analysis of office development timing. Looking at office employment changes, developers muse as to when in the real estate cycle to start development. Meanwhile, property managers devise leasing strategies to buffer their properties against office employment volatility.

This research seeks to answer:

n

Cycles: Is there a generic office employment cycle in the United States? Can the optimal time for office development be discerned?

n

Latitudinal: Is the cycle length standard or does it vary across metropolitan areas?

n

Composition: Are structural changes in the economic base affecting this cycle?

n

Lag: Does office employment in a metropolitan area lag or lead changes in its total employment?

For fifty metropolitan areas, this research classifies at the three-digit Standard Industrial Classification (SIC) level the office employment growth patterns during the two business cycles since 1974.

Background

Previous analysis of local office space cycles usually focused on the impact of new supply on vacancy levels (Born and Pyhrr, 1994) and rents over time (Shilling, *Fordham Graduate School of Business Administration, New York, NY 10023 or shilton@mary. fordham.edu.

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Exhibit 1 The Real Estate Cycle

Sirmans and Corgel, 1987; and Wheaton and Torto, 1988). Rents and vacancy changes were found, at least nationally, to follow the usual depiction of the sinal wave curve of the business cycle (see Exhibit 1). Because of faulty information processing prevalent in the real estate industry (Shilton and Tandy, 1993), however, there is no common agreement about these statistics in any given metropolitan area. The use of these statistics to measure a cycle is therefore questionable.

As an alternative, this research tests how cyclical is office employment, the underlying driver of office demand. In contrast to the extensive studies of manufacturing employment cycles, the analysis of office employment and how it drives demand for office space has been limited (Born and Pyhrr, 1994). The view that office space demand is either a residual of manufacturing / service activity and / or some function of total aggregate employment (Carn, Rabianski, Racster and Seldin, 1988) resulted in a focus on the cyclic nature of total employment. Thus, the subject of the patterns of office employment and how they might affect office space demand has not been fully explored.

Noyelle and Stanback (1984) concluded that the economic base of a city could no longer be described chiefly as a function of its manufacturing base. In a service-oriented economy, the office employment profile may depend on the

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nonmanufacturing function of the city (trade, banking, services, etc.) and not the type and magnitude of manufacturing employment (Shilton and Webb, 1991).

The employment data of the SIC group 60–69—Finance, Insurance and Real Estate— and of other selected SIC groupings from business services employment sector group SIC 70–79 (Wheaton, 1987) are commonly used to estimate office space demand. This hybrid proxy, however, still underestimates employment for the number of industries that use office space (Kelly, 1983; and Shilton, 1985, 1995).

Using fine grained office employment data bases at the three-digit SIC level for a twenty-year period for fifty cities, this study attempts to discern the changes in office employment and what, if any, distinct patterns emerge.

Theory

The need for office space over a business cycle stems from the demand by the local population for services rendered by office employees and the demand for business services by local industries engaged in the manufacturing, wholesale and trade, and import-export activities. The local demand for office services depends on the socioeconomic status of the local population which in part, but not exclusively, depends on the employment profile of the urban area (Shilton and Webb, 1991).

OL5 ƒ(PSES), (1)

where OL is office employment in an urban area due to local service functions and

PSESis the local population in the urban area. In contrast, primary office employment

is:

N N

OEp5 ƒ

S D

O

Ij 1

O

A ,k (2)

j51 k51

where:

OEp5 Primary office employment in a given urban area;

ƒ5 A function denoting that proportion of the primary (export-producing) manufacturing / service employment that is office related;

Ij5 The j

th industry; and

Ak5 The k

thancillary office support service.

A positive correlation exists between the ratio of office employment in the jthindustry

to total office employment and the ratio of office support services to total office employment:

srjrA

rOrlrA 5s s , (3)

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wherer is the correlation for the ratio of office employment in industry j and office support service employment in service k. Office employment is a function of the wholesale / retail trade and service sectors that depend on the robustness of the economy at the local, regional and national levels. Secondary office employment, primarily related to trade, is:

N N

OEt5 ƒ

S D

O

Tj 1

O

A ,k (4)

j51 k51

where OEt is office employment in the wholesale / retail industries and the ancillary

support services attributed to those industries, Tjis a wholesale or retail industry and

the other terms are as specified previously.

Changes in local office employment occur as a result of changes in the local population, and the local, regional and national economies. It is hypothesized that because these changes are not homogeneous throughout the nation, each urban area should exhibit a unique cycle for office employment change.

From Noise to Cycles—Types of Office Employment Trends

In this research, three steps were used to discern whether the concept of the unique cycle effect applies or not. First, the quarterly percentage changes were computed for office employment and total employment for each of the fifty metropolitan areas. (Because of data problems, one metropolitan area, Colorado Springs was deleted and exhibits will list forty-nine metropolitan areas.) Second, these percentage changes were graphed to visualize patterns. Third, Auto-Regressive Integrated Moving Average (ARIMA) modeling was applied to the quarterly office employment changes. ARIMA modeling is commonly used to discern cyclic patterns (Nelson and Plosser, 1982). The data for the office employment trends covers nearly twenty years—from the first quarter of 1975 to the first quarter of 1994. Using quarterly data is sufficient for time series modeling to reveal the complexity of the seasonal and annual changes. In contrast, a set of annual data usually is not sufficient to reveal patterns.

The data set for office employment was provided by the Real Estate Consulting Alliance (RECA), Cambridge, MA. RECA prepares quarterly estimates of employment by three-digit SIC codes (see Exhibit 2) based on data prepared by AUS Consultants. This series incorporates data from the Bureau of Labor Statistics, the Bureau of Economics Analysis, the Annual Survey of Manufacturers, County Business Patterns, Current Population Survey, Dun and Bradstreet and ABI / Trinent. The result is the disaggregation of total industry employment into more specific employment categories at the county level, the basis for defining the metropolitan areas that are used here.

The proposed ‘‘model’’ employment cycle consists of a series of positive changes and then a series of negative changes—a sine wave (see Exhibit 1). This sinal cycle of office employment is analogous to the national business cycle of growth and recession.

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Exhibit 2

Two- and Three-Digit Office Related SIC Sectors

SIC Industry SIC Industry 271, 272, 273 Publishing 781, 782 Motion Pictures 483 Radio and TV 801 Doctor Offices 484 Cable TV 802 Dental Offices

60* Commercial Banking 804 Osteopaths 61* Nondeposit. Credit 811 Legal Services 62* Investment Banking 86 Membership Org. 63* Insurance Co. 871 Engineers, Arch. 64* Insurance Agents 872 Accountants 65* Real Estate 873 Research, Testing 67* Holding Co. 874 Management, PR 731 Advertising 89 Misc.

738 Misc. Bus. Srv.

*Only these sectors fall into the two-digit FIRE classification.

Exhibit 3

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An ARIMA model was applied to the changes in total and office employment for the U.S. and the fifty metropolitan areas to determine what types of patterns were generated by the RECA data series. An ARIMA model measures the degree of randomness between two observations in a series and whether there is a relationship between the two observations (Harvey, 1991). Autoregressive measures the correlation between two observations. Moving average measures the difference between two observations. Integration measures the lag effect of sets of changes between observations. Combined, the ARIMA model tests whether a pattern or trend exists for the data.

For this study, a two-level ARIMA model is used. The first level tests for patterns on a yearly basis, and the second level tests for seasonal differences, (patterns occurring from same season to same season). Major shifts in office employment occur in the fall and winter quarters coinciding with the planning horizon of major corporations. The changes in both total employment and office employment for the U.S. indicate that two complete business cycles occured during this study period: (1) Cycle One: from 1975 to mid-1982; and (2) Cycle Two: mid-1982 through mid-1991. The third current cycle is incomplete.

During the first cycle, office employment at the national level did not decline for any quarter (see Exhibit 3). However, the noticeable decrease in the rate of employment growth at the cycle’s trough in 1981–82 reinforces the growth image. In contrast, in the second cycle, a percentage decrease in office employment of 0.5% did occur; in 1991 the nation experienced a decline from one year to the next in overall office employment.

The cyclical form for the U.S. employment change data was an ARIMA (1,0,0) (1,1,1) of a positive quarterly autoregressive parameter, a positive seasonal autoregressive parameter and a negative seasonal moving average parameter.

For the period 1975–94, national office employment showed an average quarterly growth of 1%. No statistical difference in this average quarterly growth was found between the first cycle and the second cycle because the series is stationary. For the metropolitan areas, the ARIMA modeling (see Exhibit 4) produced three clusters: (1) Cyclical—periods of expansion and then contraction in office employment, similar to that of the national model. At least one model parameter was positive and one negative. A cycle was defined as that period from most negative change to most negative change. (2) Structured—office employment only showed short-term growth trends, and did not exhibit the trough to peak pattern. No systematic downturn signal (parameter) appeared in the model. (3) Random Walk—office employment changes are random; the model is defined as noise.

In the cases of structured models and random walk, an approximation was made of a possible cycle. This ‘‘approximate’’ cycle was the time from lowest percentage change to the next lowest percentage change in employment. (Spurious one-period, lower change shocks were ignored.)

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Exhibit 4

ARIMA Time Series Models of Quarterly Change in Office Employment

Major Model Form (ARIMA) Seasonal Model Component Major Model Form (ARIMA) Seasonal Model Component Cyclical Model Cities Random Walk Cities

Albuquerque 1,0,0 1,1,0 Baltimore 1,0,0 0,0,0 Cleveland 1,0,0 0,1,1 Charlotte 1,0,0 0,0,0 Columbus 1,0,0 1,1,0 Cincinnati 1,0,0 0,0,0 Denver 1,0,0 1,1,1 Houston 1,0,0 0,0,0 Hartford 1,0,0 1,1,1 Jacksonville 1,0,0 0,0,0 Memphis 1,0,0 1,1,1 Norfolk 0,0,0 0,0,0 Milwaukee 1,0,0 1,1,0 Philadelphia 1,0,0 0,0,0 Orlando 1,0,0 1,1,1 Pittsburgh 1,0,0 0,0,0 Phoenix 1,0,0 1,1,1 Oakland 1,0,0 0,0,0 Sacramento 1,0,0 1,1,0 San Antonio 1,0,0 0,0,0 San Diego 1,0,0 1,1,1 San Francisco 1,0,0 0,0,0 St Louis 1,0,0 1,0,0 San Jose 1,0,0 0,0,0 Toledo 1,0,0 1,1,1 Tucson 1,0,0 0,0,0 USA 1,0,0 1,1,1 Wichita 1,0,0 0,0,0 Structured Model Cities

Atlanta 1,0,0 0,0,1 Birmingham 1,0,0 0,0,1 Boston 1,0,0 0,1,1 Chicago 1,0,0 0,1,1 Dallas 1,0,0 0,1,1 Detroit 1,0,0 0,1,1 El Paso 1,0,0 0,1,1 Greensboro 1,0,0 0,1,1 Indianapolis 1,0,0 0,1,1 Kansas City 1,0,0 1,1,1 Los Angeles 1,0,0 0,1,1 Louisville 1,0,0 0,1,1 Miami 1,0,0 0,1,1 Minneapolis 1,0,0 1,1,1 Nashville 1,0,0 0,1,1 New Orleans 1,0,0 0,1,1 Omaha 1,0,0 0,1,1 Rochester 1,0,0 0,1,1 Salt Lake 1,0,0 1,1,0 Tampa 1,0,0 0,1,1 Tulsa 1,0,0 0,1,1 Washington 1,0,0 0,1,1

The majority of cities exhibited one complete cycle during the 1980s (from a low point, usually the early 1980s, to the subsequently scattered low points in the later 1980s). These cycle varied, see Exhibit 5. Noticeably, the early 1980s recession struck rather uniformly across most cities with the low points occurring between 1981 and

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Exhibit 5

Length of Office Employment Cycles

1982. The exceptions or late starters (1984 and beyond) were Los Angeles, St. Louis, San Antonio and Houston.

The three groups of metro areas, cyclical, structured or random walk, differ in: (1) the length of their cycles; (2) the average of the quarterly change; and (3) the volatility (standard deviation) of the quarterly change (see Exhibit 6). Generally cyclical metropolitan areas experienced the longest period of time from trough to trough and the highest quarterly average growth, 1.2% per quarter. The other two groups, structured model metros and the random walk model areas had lower growth rates with the random noisy areas exhibiting the highest volatility.

Over time, the variation in quarterly office employment growth among cities is declining (see Exhibit 7). As the variation in growth rates declines, the F-Statistic (for the discriminant testing) for a period, became less significant over time. The first period, from 1975 to mid-1982, had the greatest spread because metropolitan areas in the Sunbelt grew more rapidly than in the Northeast. Since 1983, the differences

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Exhibit 6

Office Employment Cycles—Growth and Volatility

Cycle Length Years Average Growth Std. Dev. Growth Cycle Length Years Average Growth Std. Dev. Growth Cyclical Cities Random Walk Model Cities

Albuquerque 8.50 0.015 0.016 Baltimore 8.00 0.008 0.013 Cleveland 8.00 0.007 0.010 Charlotte 8.75 0.015 0.019 Columbus 5.50 0.011 0.011 Cincinnati 8.75 0.011 0.012 Denver 8.20 0.011 0.014 Houston 4.75 0.011 0.014 Hartford 9.75 0.008 0.011 Jacksonville 7.00 0.011 0.009 Memphis 7.00 0.010 0.014 Norfolk 6.75 0.017 0.039 Milwaukee 7.75 0.009 0.010 Oakland 8.50 0.012 0.012 Orlando 8.75 0.018 0.013 Philadelphia 8.50 0.008 0.011 Phoenix 9.25 0.017 0.014 Pittsburgh 8.75 0.008 0.010 Sacramento 9.75 0.015 0.013 San Antonio 3.25 0.013 0.011 San Diego 8.75 0.015 0.015 San Francisco 6.25 0.006 0.010 St Louis 7.25 0.008 0.010 San Jose 6.75 0.013 0.014 Toledo 8.25 0.007 0.014 Tucson 7.00 0.014 0.015 United States 8.25 0.010 0.009 Wichita 8.25 0.010 0.015 Average 8.21 0.012 0.013 Average 7.23 0.011 0.015 Structured Model Cities

Atlanta 9.25 0.015 0.011 Birmingham 6.25 0.011 0.011 Boston 6.00 0.009 0.012 Chicago 7.75 0.008 0.012 Dallas 4.25 0.015 0.013 Detroit 8.00 0.009 0.012 El Paso 3.75 0.009 0.010 Greensboro 8.75 0.010 0.010 Indianapolis 8.00 0.011 0.014 Kansas City 7.00 0.009 0.012 Los Angeles 7.00 0.008 0.013 Louisville 6.75 0.012 0.014 Miami 7.00 0.010 0.012 Minneapolis 6.75 0.012 0.012 Nashville 6.00 0.012 0.013 New Orleans 5.00 0.005 0.013 Omaha 7.75 0.010 0.013 Rochester 8.50 0.009 0.009 Salt Lake 9.75 0.013 0.011 Tampa 8.75 0.016 0.012 Tulsa 3.50 0.012 0.014 Washington 9.25 0.012 0.011 Average 7.05 0.011 0.012

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Exhibit 7

Divergence, Office Employment Growth

in the quarterly changes across metropolitan areas have dampened. Throughout this period, the stable cyclical cities maintained their average growth rate, while growth moderated in the cities that experienced high growth rates in the early 1980s.

Lag, Lead or Neutral—Relationship of Total Employment Trends

to Office Employment Trends

The quarterly changes in total employment and office employment were tabulated and plotted for the 1975–94. As illustrated in the Exhibit 8, office employment grew faster than total employment in every metro area. While total employment grew at an average rate of 0.2%–1%, office employment grew from 0.8%–1.5%.

However, greater volatility accompanied this higher growth. A unique set of twelve cities located in the Sunbelt were found in which the growth rate was statistically higher than the rest when both growth and the volatility of this growth were considered (see Exhibits 9 and 10).

Across metropolitan areas, the total employment and the office employment generally marched together. Same period cross-correlations and lagged cross-correlations (up to twelve periods) were taken for total and office employment changes for each metropolitan area for 1975–94. In only two cases, Boston and St. Louis, were the

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Exhibit 8

Quarterly Growth Rate and Volatility

Exhibit 9

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Exhibit 10

City Ranking by Growth

City Average Office Growth Std. Dev. Office Growth City Average Office Growth Std. Dev. Office Growth New Orleans 0.005 0.013 Portland 0.011 0.013 San Francisco 0.006 0.010 Indianapolis 0.011 0.014 Cleveland 0.007 0.010 Denver 0.011 0.014 Toledo 0.007 0.014 Houston 0.011 0.014 St Louis 0.008 0.010 Cincinnati 0.011 0.012 Hartford 0.008 0.011 Oakland 0.012 0.012 Pittsburgh 0.008 0.010 Louisville 0.012 0.014 Chicago 0.008 0.012 Washington 0.012 0.011 Baltimore 0.008 0.013 Minneapolis 0.012 0.012 Philadelphia 0.008 0.011 Tulsa 0.012 0.014 Los Angeles 0.008 0.013 Nashville 0.012 0.013 Rochester 0.009 0.009 San Antonio 0.013 0.011 Detroit 0.009 0.012 San Jose 0.013 0.014 Boston 0.009 0.012 Salt Lake City 0.013 0.011 Milwaukee 0.009 0.010 Tucson* 0.014 0.015 Kansas City 0.009 0.012 Dallas* 0.015 0.013 El Paso 0.009 0.010 Albuquerque* 0.015 0.016 Memphis 0.010 0.014 Atlanta* 0.015 0.011 Miami 0.010 0.012 Sacramento* 0.015 0.013 Omaha 0.010 0.013 San Diego* 0.015 0.015 Greensboro 0.010 0.010 Charlotte* 0.015 0.019 Wichita 0.010 0.015 Tampa* 0.016 0.012 USA 0.010 0.009 Norfolk* 0.017 0.039 Birmingham 0.011 0.011 Phoenix* 0.017 0.014 Columbus 0.011 0.011 Orlando* 0.018 0.013 Jacksonville 0.011 0.009 *Satistically different.

changes negatively correlated. For other metropolitan areas, neither office nor total employment lead nor lagged the other (see Exhibit 11). There was no relationship between the type of model for a metropolitan area and the correlation between total and office employment. The type of model could not be used as a predictor of growth or risk.

The Links between Economic Base Components and Office

Employment

No statistically significant difference could be found among the economic bases of the three model groups. Neither age nor size of the metropolitan areas could explain the office employment patterns.

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Exhibit 11

Correlation between Total Employment Cycle and Office Employment Cycle

City Cycle Type Corr. Coeff. City Cycle Type Corr. Coeff. Negative Relationship High Correlation

Boston 2 20.42 Albequerque 1 0.66 St Louis 1 20.41 Atlanta 2 0.66 Columbus 1 0.69 Low Correlation Los Angeles 2 0.66 Baltimore 3 0.32 Milwaukee 1 0.63 Cleveland 1 0.25 Minneapolis 2 0.62 Detroit 2 0.39 New Orleans 2 0.66 Greensboro 2 0.38 Oakland 3 0.64 Hartford 1 0.38 San Francisco 3 0.68 Houston 3 0.39 San Jose 3 0.64 Kansas City 2 0.27 Tampa 2 0.71 Orlando 1 0.34 Toledo 1 0.67 Tulsa 2 0.39 Wichita 3 0.68 Medium Correlation Very High Correlation

Birmingham 2 0.45 Charlotte 3 0.93 Chicago 2 0.42 Denver 1 0.87 Cincinnati 3 0.46 Indianapolis 2 0.76 Dallas 2 0.54 Norfolk 3 0.97 El Paso 2 0.55 Jacksonville 3 0.45 Louisville 2 0.49 Memphis 1 0.59 Miami 2 0.56 Nashville 2 0.54 Omaha 2 0.57 Philadelphia 3 0.45 Phoenix 1 0.58 Pittsburgh 3 0.41 Rochester 2 0.41 Sacramento 1 0.48 Salt Lake City 2 0.51 San Antonio 3 0.56 San Diego 1 0.48 Tucson 3 0.59 USA 1 0.48 Washington 2 0.54

Model Type: 15 cyclical, 2 5 structured model and 3 5 noise.

A regression analysis generated Exhibit 12 in which the dependent variable was quarterly office employment growth and the independent variables were the ratios of the employment of each industry over all employment in 1975. Across the board, the nonagricultural groupings of wholesale trade, the military sector, government employment and business services positively affected the office employment growth. Surprisingly, the communications industry did not drive office employment growth.

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Exhibit 12

Quarterly Growth and Economic Base

Variable b SeB t Signif.t Wholesale 83.79 31.47 2.66 .011 Military 33.71 6.85 4.92 .000 Agriculture 76.56 31.30 2.45 .018 Government 25.63 11.16 2.30 .026 Business Services 102.42 49.33 2.08 .045 Communications 262.66 32.45 21.93 .060 Note: Multiple R5 .9711, R25 .943, adjusted R25 .936, std. err. 5 2.91, F-Statistic 5 126.85 and

Signif.F5 .00.

Regression: DF5 6, sum of squares 5 6452.03 and mean square 5 1075.34. Residual: DF5 46, sum of squares 5 389.97 and mean square 5 8.48.

Exhibit 13

Economic Base Differences between High Office Employment Growth and Other Cities

Ratio of Economic Base Component in 1975 as

Variable F Signif.

Mean for Higher Growth Cities Agriculture 5.452 .024 Higher Business 0.979 .327

Communications 1.615 .210 Declining Industry 0.243E-1 .877 Defense 0.102 .751 Drugs 2.066 .157 Education 7.033 .011 Lower Energy 2.035 .160 Finance 1.657 .204 Government 3.689 .061 Higher Health 1.442 .236 High Technology 1.101 .300 Military 11.301 .002 Higher Real Estate 3.033 .088 Resources 2.031 .160 Tourism 7.144 .010 Higher Traditional Manufacturing 12.431 .001 Lower Wholesale 1.051 .313

Note: Between groups: Chi square5 36.52, df 5 18 and Signif. 5 .006.

In the high growth areas, government employment, military and tourism accentuated the higher than average growth in the office employment (see Exhibit 13). The high office growth areas also showed lower than average concentrations in education and traditional manufacturing, but higher than average concentrations in agricultural activities.

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Conclusion

Given that office employment is a driver of office demand, the purpose of this study was to determine whether there was a generic office employment cycle or whether each metropolitan area experienced its own unique cycle. The result of opening this window on the vista of office employment cycles reveals a blurred Monet-like landscape. Hints of substantive images may be discerned from afar, but close-up the images disintegrate into a jumble of impressionistic dabs. Only thirteen metropolitan areas showed distinct sine-wave cycle characteristics from which the optimal development period could be determined empirically.

Office employment changes have been emerging into a series of three patterns over time. Not as crisp and delineated as the national business cycle, office employment trends are grouped by metropolitan areas into the following approximate thirds: (1) cyclical metros with peaks of highs and lows; (2) metros with structured trends of alternating steps of growth; and (3) noisy cities, in which office employment staggers down the path in a random walk. Neither the office employment profile nor the metropolitan economic base profile provided clues as to the trend (or lack thereof) in office employment.

Based upon this review of a two-cycle period from 1975 to 1994 for the metropolitan areas, the evidence suggests that office space demand cycles are or will be dampening for the majority of the major metropolitan areas.

Answering the original set of questions, this research determines the following:

n

Cycles: Is there a generic office employment cycle? The evidence suggests that the majority of the metro areas are dampening towards a common cycle for which the cycle for the total office employment in the U.S. provides the basic pattern.

n

Latitudinal: Does the length of cycle vary across cities? Yes, but not much for most of the metropolitan areas. The Biblical cycle of seven years may prove, on average, to be a realistic benchmark for the average length of the office employment cycle.

n

Longitudinal: How has the length of cycles changed over time? The period of this research appears to be too short for any statistically significant conclusions about the cycle lengths. Summarily it appears that were was one cycle from 1974 to 1982 and another from 1982 to 1991. The U.S. economy is in its third cycle since 1975.

n

Composition: What structural changes in the economic base affect the cycle? The metropolitan economic base seems to provide clues about growth rates, but cannot be used to identify office employment cycle types.

n

Lag: Does office employment in a metropolitan area lag or lead changes in total employment? Refuting the theory that office employment is a residual of total employment, this research finds that office employment and total employment march together. No significant timing differences

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can be found. Office employment grew faster, an observation commonly made. But the volatility of that growth is greater than the volatility of the growth in total employment, which is contrary to comments made about the stability of service-oriented employment growth.

The need to open the window wider is evident from this research which has framed the major cyclical characteristics of the office employment as a determinant of demand for space. The next steps for future research are to introduce other factors and interpretive variables in order to identify the nature of the office development and leasing cycle.

References

Born, W. and S. A. Pyhrr, Real Estate Valuation: The Effect of Market and Property Cycles,

Journal of Real Estate Research, 1994, 9, 455–86.

Carn, N., J. Rabianski, R. Racster and M. Seldin, Real Estate Market Analysis, Englewood Cliffs, NJ: Prentice Hall, 1988.

Harvey, A., The Econometric Analysis of Time Series, Cambridge, MA: MIT Press, 1991. Kelly, H., Forecasting Office Space Demand in Urban Areas, Real Estate Review, 1983, 13,

87–95.

Nelson, C. and C. Plosser, Trends and Random Walks in Macroecnomic Time Series: Some Evidence and Implications, Journal of Monetary Economics, 1982, 10, 130–62.

Noyelle, T. J. and T. M. Stanback, The Economic Transformation of American Cities, Totowa, NJ: Rowan and Allanheld, 1984.

Shilling, J. D., C. F. Sirmans and J. B. Corgel, Price Adjustment Process for Rental Office Space, Journal of Urban Economics, 1987, 22, 90–00.

Shilton, L. G., The Manhattan Office Industries, New York: New York University, Sylvan Lawrence Center, 1985.

——, The Changing Demand for Office Space, Real Estate Review, 1995, 25:2, 89–94. Shilton, L. and J. K. Tandy, The Information Precision of CBD Office Vacancy Rates, Journal

of Real Estate Research, 1993, 8:3, 421–40.

Shilton, L. G. and J. R. Webb, Office Employment Growth and the Changing Function of Cities,

Journal of Real Estate Research, 1991, 7, 73–90.

Wheaton, W. C., The Cyclic Behavior of the National Office Market, Journal of the American

Real Estate and Urban Economics Association, 1987, 15, 281–99.

Wheaton, W. C. and R. G. Torto, Vacancy Rates and the Future of Office Rents, Journal of the

American Real Estate and Urban Economics Association, 1988, 16, 430–55.

The author thanks the Urban Land Institute, the Real Estate Research Institute, Cornerstone Realty Advisers and Fordham GBA for providing research-funding support. An earlier draft received the BOMA award for the best paper on office markets presented at the 1994 American Real Estate Society meeting. Also acknowledged are the comments by participants at the 1994 meeting, those of two anonymous reviewers and the technical support of Janet Tandy.

References

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