incidence of the property tax on commercial real...

INCIDENCE OF THE PROPERTY TAX ON COMMERCIAL REAL ESTATE: THE CASE OF DOWNTOWN CHICAGO JOHN F. MCDONALD*

Abstract - This paper presents an empirical study of office rents and property taxes for individual buildings in downtown Chicago in 1991. The results show that 45 percent of property tax differentials are shifted forward to tenants as higher gross rents. Application of the Hausman (1978) test for specification error reveals that assessed value per square foot (the base for the property tax) is exogenous with respect to gross rent per square foot.

INTRODUCTION

The incidence of the local property tax on nonresidential real estate is an important empirical issue in local public finance. The conventional view, which stems from Mieszkowski (1972), is that interjurisdictional differences in nonresidential property taxes create excise tax effects, because capital and labor are mobile in the long run. Land is immobile, but its share of costs is too small to absorb the entire differential tax burden. The excise tax effect means that some of the burden of the tax is shifted forward to consumers, whose de-

*University of lllmols at Chlcago, Chicago, IL 60680

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mands must be less than perfectly elastic at the jurisdiction level.

The only direct empirical tests of the conventional view were conducted by Whea- ton (1984). Wheaton found that interjurisdictional differences in actual property taxes paid per square foot (as opposed to statutory rates) had no effect on gross rents in office buildings in metropolitan Boston. This finding means that, in con- trast to the conventional view, the burden of the tax was not being shifted from real estate capital and land onto labor or consumers. Wheaton’s results are consistent with the view that land and real estate capital are inelastically supplied to a jurisdiction for the time period under consider- ation. However, if the demand for offices is perfectly price elastic within a metropolitan area, interjurisdictional differences in property taxes create differences in after- tax returns to capital and therefore can lead to location shifts for capital in the long run to eliminate those after-tax return differentials. Another study of rent per square foot in office buildings by Clapp (1980) included a property tax variable. However, the variable included is the total property tax bill for the building, which is highly correlated with the size of the build-

ing. The variable has a positive and statistically significant coefficient, but the fact that Clapp (1980) did not use property tax per square foot makes the interpretation of his results difficult.

In this study, the focus is on office buildings located in downtown Chicago; the study is of intrajurisdictional differences in property tax rates. It is found that property tax rates per square foot vary enormously within this relatively small area. Indeed, so little of the variation in property taxes per square foot cart be explained by conventional measures’ of the economic success of thle office building (e.g., rent, vacancy rate, age of building, etc.) that the tax rate can be taken as an exogenous variable. The paper thus provides a strong test of whether variations in property taxes cannot be shifted forward. The finding in this paper is that 45%1 of an increase in the property tax per square foot applied to an office building was shifted forward to the tenants. The empirical results thus provide some support for the above-mentioned conventional view of property tax rnci- dence, albeit in the context of a single tax- ing jurisdiction. The paper does not ad- dress the issue of whether workers or consumers bear the burden of that portion of the property tax that is shifted forward.

Other empirical studies of the office market in downtown Chicago include Brennan et al. (1984), Hough and Kratz (1983), Mills (1992), and Posner (forthcomrng). These studies examined the effects on rent per square foot of various building characteristics and location within the downtown area. (Mills (1992) also included data on buildings in the rest of the metropolitan area.) Rents were found to vary a great deal within the downtown area, so location must be taken into account. Brennan et a/. (1984) used individual leases as the unit of observation and were primarily in- terested in discovering how rent varies with other terms in the lease. Hough and Kratz (1983) tested the most extensive set

of building characteristics and found that age of the building, number of floors in the building, availability of conference rooms, and recognized architectural distinc- tion were statistically significant determinants of average rent per square foot for the building. Mills (1992) found that asking rents were related to the age and size of the building and to the availability of retail services in the building. Mills employed a set of locatron dummies for the various parts of downtown Chicago that is vrrtually identical to the one used in this study. Pos- ner (forthcoming) used a similar set of variables and found that years since last major renovation and presence of security per- sonnel also were statistically significant determinants of average rent per square foot. However, none of these four studies included the property tax in its set of determinants of rent.

THEORETICAL CONSIDERATIONS

Mieszkowski (1972) argued that a uniform real estate tax across all jurisdictions will have no effect on the allocation of re- sources and will simply reduce the Ireturns to capital (and land). Rather, it is real estate tax differentials that alter real estate prices and resource allocation. Mieszkowski (1972, pp. 90-93), as part of his more general analysis, discusses the incidence of a relatively high real estate tax in a central business district. In this model, the market for downtown real estate is assumed to be competitive with demand and supply elasticities greater than zero and less than in- finity. The model leads to the standard result that an Increase in the real estate tax (compared to that prevailing elsewhere) will change the rent on real estate according to

dR/dt = 1 /(l -- Ed/E,)

where R is rent, t is the tax per unit of real estate, Ed is the market elasticity of demand for real estate (defined as a positive

I PROPERTY TAX INCIDENCE IN CHICAGO

number), and Es is the market elasticity of supply. The tax t is assumed to be a specific tax on floor space (dollars per square foot), because this is how it is regarded by the participants in the market for office space in downtown Chicago. Obviously, if Ed is infinite, dR/dt = 0; and if Es is zero (infinite), dR/dt = 0 (dR/dt = 1).

This paper is concerned with individual office buildings within a downtown area. What happens if the real estate tax im- posed upon an individual building is higher than that prevailing elsewhere in that same downtown area? One’s first reaction might be simply to say that the individual building faces a demand of infinite elasticity and therefore there will be no impact on the rent charged tenants. But there are reasons to doubt that demand is infinitely elastic. Moving is costly. Suitable quarters must be found, and moving expenses must be paid. If suitable quarters cannot be found in the immediate vicinity, then the tenant’s customers, suppliers, and employees must all make adjustments. Some customers may be lost, relationships with suppliers may be changed, and some valuable employees may be lost as well. In short, the owner of the office building very likely has some market power over the tenants. However, that market power is limited by the fact that landlords do not wish to lose good tenants; landlords and tenants normally form a long-term relationship.

Consider a simple model of pricing for a firm with market power. Rent is determined according to

0 R = (1 + m)(C + t)

where m is the markup over marginal cost, C is marginal cost excluding the real estate tax, and t is (as earlier) the tax per unit of real estate. The effect changing the tax is

q dR/dt = (1 + m) + t(dm/dt)

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In the standard C ham berlinian firm maximizes profits, or

case the

(1 + m) = ed/(ed - 1)

where ed is the elasticity of demand faced by the firm. Because a profit-maximizing firm with local monopoly power will have marginal revenue greater than zero, e,, must be greater than one. If demand elasticity is a constant, dm/dt = 0 and dR/dt must exceed one; the firm with local monopoly power will increase rent by an amount greater than the increase in the tax. Such a result seems implausible and is not consistent with the empirical findings presented subsequently. However, if demand becomes more elastic as price increases, then dm/dt < 0 and it is possible that dR/dt < 1. For example, in the text- book case of a linear demand curve, ex- actly one-half of a tax increase is passed forward to the tenants. To see this, assume a linear demand curve R = a - bq, where q is quantity. Setting marginal revenue equal to marginal cost (C + t) leads to the profit-maximizing price of R = (a + c + t)/2.

Consider another possible model. Following Gordon (1967), Cauley and Sandler (1974), and Sebold (1970), suppose that the firm does not strictly maximize profits. Office building owners normally engage building managers who are responsible for setting rents, marketing the building, etc. There may be some separation of ownership and control which permits building managers to engage in activity that does not lead to profit maximrzation. Building managers may adjust the markup downward (dm/dt < 0) in response to an increase in the tax rate, and rent may adjust by an amount that is less than the increase in the tax.

Alternatively, consider a modified version of the model of taxation under oligopoly developed by Stern (1987) and Delipalla and Keen (1992), for example. Firms maxi-

mize profits but set marginal cost equal to perceived marginal revenue which depends partly upon thle conjectured reactions of other firms’ output to changes in its own output. Assume that the price the firm charges is a function of its own output and of the output of the industry, or R = R(Q, o), where R is rent, Q is industry out- pl-lt (floor space), and g is the firm’s output. The total (differential of total revenue is

dl:Rq) = Rd9 +- (<sR/d9)9 d9

+ (aR/aQ)(dQ/d(;i)q dq.

The firm’s perceived marginal revenue can be written as

191 d(Rq)/dq = RI1 - (l/cd) -- (a/Ed)1

where cx is the conjectured elasticity of industry output vvith respect to the output of the firm. The case of (Y == 0 corresponds to the case in which industry output is un- affected by chaInges in the firm’s output, while CY = 1 is the assumption that the other firms match any change in the firm’s output. Normally, it IS assumed that 0 < (Y < 1, but Stern (1987, p. 136) raises the possibility that t[~ < 0, which means that thle other firms increase output in response to a decrease in the firm’s output. Indeed, this is likely in the case under consider- atron, because only the building (firm) in question is subject to an increase in the real estate tax. In this model

Q (1 -t m) = c,/(e, -- 1 .-- (cYe&)d)l.

The value of dR/dt is greater than one if all three of the elasticities in equation 6 are constant, but changes in any or all of these elasticities as price rises may make drn/dt < 0.

In summary, this section has discussed three basic models of the firm to examine the effect of a specific tax on office floor space on office rent. The standard model of a profit-maximizing local monopolist, a model of a nonprofit rnaximizing firm, and an oligopoly model with conjectural variation all lead to the conclusion that the ex- tent to whch an increase in the tax will be shifted forward is indeterminate.

THE DATA

The data for the study are a sample of 259 office buildings in downtown Chicago. The data on the characteristics of individual buildings were reported to the Building Owners and Managers Association of Chr- cage (BOMA/Chicago), and the property tax data were provided by the Office of the Assessor of Cook County. The data were gathered on October 1, 1991, and the property tax figures are for taxes paid in 1991. The second (and final) installment of the property tax was paid in September, 1991. Property taxes paid in 1991 were determined by assessed values set In 1990, which are largely based on assessed values set at the previous comprehensive reassessment of downtown property in late 1988. It is thus likely that some of the assessments were inaccurate in 1990. However, it is also likely that the assessments1 con- tained substantial inequities in 198r3. The assessment of large, complex buildings that are bought and sold infrequently is a very difficult task. Furthermore, Cook County provides a mechanism for appealing the assigned assessed value at the time of the comprehensive reassessment. Building owners engage the services of lawyers who specialize in such appeals, and it is an open question whether the appeals process leads to more or less inequity in assessed values. These considerations suggest that the property tax paid in 1991 per square foot of rentable space rnay be an exogenous variable. Empirical evidence reported subsequently supports this conclusion,

I - - _ -~ --~-_ ~ __ _

PROPERTY TAX INCIDENCE IN CHICAGO

which is also consistent with Wheaton’s (1984) empirical results for the Boston metropolitan area.

Table 1 displays the data collected by the Illinois Department of Revenue (1992) on the accuracy of property tax assessments in Cook County in 1990, the same period upon which this study is based. These data provide strong indirect evidence that the property tax bill faced by commercial property owners may be an exogenous variable. Public records of property transfers for the year 1990 were combined with assessed values as of 1990 to produce the ratio of assessed value to market value. Property transfers are excluded by a process of manual checking to determine whether the transfer is a bona fide “arm’s length” transaction. Excluded transactions include transfers between relatives, transfers under compulsion, transfers to governmental units or nonprofit organizations, and deeds of convenience or correction of errors. The data are reported for four types of property; residential properties containing six housing units or less (primarily single-family

homes), residential properties containing more than six units, commercial properties (including office buildings, retail properties, etc.), and industrial properties. The data are displayed for all of Cook County, the City of Chicago, and the assessment district inside Chicago that includes the downtown area.

Cook County has a classification system for property assessment; the ratio of assessed value to market value that is specified by county ordinance is different for the four classes of property. (Cook County also has assessment classifications that are used to grant formally reductions in property taxes for the purpose of stimulating local economic development. Properties included in these programs are not included in Table 1.) The smaller residential properties are supposed to be assessed at 16 percent of market value. As shown in Table 1, the actual median assessment ratio was 9.06 percent for the entire county. The coefficient of dispersion, as defined by the Illinois De- partment of Revenue (1992) is the average absolute deviation in the assessment ratio

TABLE 1 MEDIAN ASSESSMENT RATIOS FOR COOK COUNTY: 1990

Geographic Area Category

Median Coefficient Assessment of

Ratio Dispersion

Quartiles

First Third n

Cook County residential 9.06 (16) 0.24 (<7 unit) residential 17.37 (33) 0.60 (>6 unit) commercial 23.41 (38) 0.62 industrial 27.51 (36) 0.46

City of residential 8.68 (16) 0.30 Chicago (<7 unit)

residential 16.69 (33) 0.57 (>6 unit) commercial 20.59 (38) 0.68 industrial 20.35 (36) 0.55

Assessment residential 8.51 (16) 0.39 district w/downtown (<7 unit)

residential 15.84 (33) 0.66 (>6 unit) commercial 20.43 (38) 0.58 industrial 23.33 (36) 0.56

aFigure in parentheses is the assessment ratio specified by Cook County ordinance. Source: Illinois Department of Revenue (1992).

7.93 10.30 49K

10.70 24.59 465

16.40 31.43 334 18.58 35.37 90

7.28 10.24 2OK

10.08 24.12 415

14.70 30.59 213 14.13 31.90 44

6.99 10.18 9K

8.81 23.78 290

14.55 30.71 131 13.15 36.55 31

113

divided by the median assessment ratio. For smaller residential properties, this coefficient of dispersion was 0.24, which means that the average deviation in assessment ratio was 24 percent of the 9.06 percent median. The assessment ratios at the first and third quartiles were 7.93 and 10.30 percent, and the number of property transfers used in these calculations was 48,890. Such assessment performance is perhaps not as uniform as one would wish, but there was far less uniformity in the assessment of the other types of property.

Commercial property In Cook County is supposed to be assessed at 38 percent of market value, but the actual median for the entire county in 1990 was 23.41 percent. The coeffcient of dispersion was 0.62, meaning that the average deviation tn assessment ratio was 62 percent of the median. The first and third quartiles were 16.40 and 31.43 percent, which means that half of the properties in the data base of 334 transfers fell outside this range. The assessment ratios for larger residential and industrial properties for the entire county also had low median values relative to le- gal requirements and were nonuniform.

Table 1 shows that assessments in the City of Chicago were generally less uniform than for the county as a whole. The median assessment ratio for commercial property in the city was 20 59 percent with a coefficient of dispersion of 0.68. Commer- cial property in the assessment district that includes the downtown area was assessed a brt more uniformly; the median assessment ratio was 20.43 percent, and the coefficient of dispersion was 0.58. The first and third quartile assessment ratios were 14 135 and 30.71 percent, which means in effect that a piec:e of commercial property could easily have faced a property tax bill that was double (or more) than that faced by an equivalent property. These results were based on data for 13 1 commercial property ‘transfers.

The sample of 259 buildings for the pres- ent study Includes nearly all of the famous office structures in down town C hrcago, but most of the buildings are less well known. Table 2 displays the variable deflni- tions and descriptive statistics for the data used in this study. The mean age for buildings in the sample is 5’7.7 years, and the mean height IS 19.9 floors. Using buildings as observations, the mean gross rent per year is $19.06 per square foot, and the mean vacancy rate is 17.4 percent (as of October 1, 1991). The mean assessed value for the 259 buildings is $19.43 per square foot of rentable space. Average rent and assessed value per square foot have mean values that are nearly equal, but note that the standard deviation of rent is 6.92 and the standard deviation (of assessed value is 14.11. Average rent varies from $3.90 to $44.00, but assessed value varies from $0.20 to $114.90. (These extreme values were double-checked with the Office of the Assessor of Cook County.)

Almost half of the buildings (117 out of 259) are located in the central downtown (Loop) area. Sixty-three buildings are located north of the Loop area (and north of the Chicago River), 31 are east of the Loop area, 14 are just west of the Loop .area along Wacker Drive, 27 are located farther to the West, and seven are south of the Loop area. The mean rent in the central downtown area is $19.82 per square foot per year. Compared to central downtown, mean rent is $5.06 higher along Wacker Drive, $5.30 lower in the area farther to the West, and $6.65 lower south of the Loop. These rent differentials are statistically significant. Mean rent is $0.72 lower north of the Loop and $1.03 lower east of the Loop, but these differences are not statistically significant. This delineation of the subareas of downtown Chicago is conventional, and the mean rents follow the pattern found by Brennan et al. (1984), Mills (1992), and Posner (forthcomrng).

The data gathered by BOMA/Chicago

114

r PROPERIY TAX INCIDENCE IN CHICAGO

OFFICE TABLE 2

BUILDINGS IN DOWNTOWN CHICAGO: VARIABLE DEFINITIONS AND DESCRIPTIVE STATISTICS (SAMPLE SIZE IS 259.)

Average annual gross rent per square foot (10/91)

Assessed value per square foot

Age of building (years) Number of floors Percentage of office space

vacant (10/91) Percentage of building in of-

fice space Total rentable space (1000

square feet) Building built before 1950,

not rehabbed since 1979

Mean

$19.06

$19.43 57.66 19.88

17.36

87.29

374.92

0.301

Standard Deviation

6.92

14.11 30.60 14.69

17.09

71.76

444.69

Minimum Maximum

$3.90 $44.00

$0.20 $114.90 1 .oo 120.00 3.00 100.00

0.00 90.36

8.48 100.00

10.00 3152.00

Location of building

North of Chicago River 0.243 Wacker Drive (West Loop) 0.054 East of Loop 0.120 West of Chicago River 0.104 South of Loop 0.027 Central Loop 0.451

Sources: BOMA/Chicago and Office of the Cook County Assessor.

(1991) provide a comprehensive description of the downtown office market. The data base includes 342 private and public buildings that provide a total of 115.6 million square feet of office space. (The sample of 259 includes privately owned buildings that provide information on gross rent.) The vacancy rate as of October 1, 1991 was 17.2 percent of the 115.6 million square feet. The mean building age of 57.7 years needs to be clarified. Of the 342 buildings, 63 were completed since 1980. This group of newer buildings contains 36.4 million square feet of rentable space. Fifty-six buildings were completed during the 1960-1979 period, and these buildings supply 33.7 million square feet of space. Only 28 buildings were completed from 1930 to 1959, and this group of buildings contains only 7.5 million square feet of space. Downtown Chicago includes 195 buildings that were completed before 1930, and these buildings supply 37.9 million square feet of space. This large group of old buildings includes 67 buildings (with

9.1 million square feet of rentable space) that were built before 1910.

EMPIRICAL RESULTS

The regression model estimated is a re- duced-form hedonic function, as derived by Clapp (1980) for office rent. Average gross rent per square foot in the building is a function of the building’s location in downtown Chicago, selected building attributes as suggested by the previous studies, and assessed value per square foot. The building attributes included are the age of the building, number of floors, percentage of the office space that is vacant, and whether an older building (built before 1950) had not been renovated since 1979. Assessed value per square foot is defined as the total assessed value of the building divided by total rentable space, which includes office space and other types of rentable space.

The ordinary least-squares regression results are shown in column 1 of Table 3. All

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TABLE 3 REGRESSION ANALYSIS OF GROSS RENT PER SQUARE FOOT: OFFICE BUILDII’JGS IN DOWNTOWN

CHICAGO IN 1991 (T-RATIOS IN PARENTHESES)

Constant

Age of building (years)

Number of floors

Percentage vacant

Huilding built before 1950, not rehabbed since 1979

Assessed value per square foot

Predicted assessed value per square foot (Hausman test)a

21.103 (13.87) -- 0.069 (5.08) 0.118

(4.43) - 0.047 (2.48)

-- 2.185 (2.92) 0.090

(3.64)

-

20.390 22.962 (4.14) (10.52)

- 0.066 -0.077 (2.81) (5.06) 0.119 0.119

(4.42) (4.44) -.0.044 -0.050 (1.79) (2.65)

- 2.172 -2.271 (2.87) (3.02) 0.090 0.104

(3.56) (3.80)

0.025 (0.15)

-0.077 (1.19)


North of Chicago River 0.946 -- 0.898 - 1.020 (1.14) (1 .OO) (1.22)

Wacker Drive (West Loop) 0.551 0.264 1.098 (0.37) (0.11) (0.70)

East of Loop -- 1.024 -- 0.941 -1.167 (1.01) (0.82) (1.14)

\fVest of Chicago River -- 3.154 - 2.969 -3.613 (2.83) (1.80) (3.06)

South of Loop -- 2.740 ---2.417 -3.577 (1.36) (0.82) (1.68)

R2 0.496 0.496 0.499 Sample size 259 259 259

;- ----- .------__-_____--- The results in column 2 (column 3) use predicted property taxes based on regression results reported in column 1 (col-

umn 2) of Table 4.

of the building attribute variables enter vvith hilghly statistically significant coefficients with the expected signs. The effect of building age is to reduce rent by 6.9 cents per square foot per year Kent per square foot increase:; by 11.8 cents as the number of floors is increased by one. An added point on the vacancy rate reduces rent by 4.7 cents per square foot, and an old building (built before 19150) that has not been rehabbed since 1979 commands a rent that is $2.18 per square foot less than in other buildings. Most importantly for this study, the coefficient of assessed value per square foot is 0.09 (with a t ratio of 3.64). Property tax payments for the year are calculated simply as 0.20 times the assessed value. An increase in assessed value of $1.00 Increases taxes by $0.20

which, in Iurn, increases rent by ‘$0.09. Therefore, an increase in the tax of $1 .OO per square foot increases rents by $0.4!5 (0.09/0.20). The coefficients of the location dummies have the expected ‘signs relative to the central downtown area (the omitted category), but, with the exception of location West of the Chicago River, these effects do not attain statistical signifi- cance That R2 for the OLS regression is 0.50.

The pnnclpal potential1 difficulty with the OLS resultsz is a possible simultaneity bias involving the assessed value variable. As discussed rlarlier, assessed value by law is supposed to be based upon the market value of the building. Commercial property in Chicago is supposed to be assessed at a

I PROPERTY TAX INCIDENCE IN CHICAGO

rate of 38 percent of market value, so the property tax is supposed to be the same percentage of market value for all buildings. (Table 1 shows that such is not the case.) Consider the following simple model of building value and rent. Gross rent R that the tenant pays is a function of vector of building and location features a. A building of infinite life and given gross rent R(a) has a value V:

V = [(l - e)R(a) - 71/l/r

where e is the percentage of gross rent devoted to other expenses, T is the property tax rate as a proportion of building value, and r is the discount rate. For sim- plicity, assume that the building contains one square foot of rentable space, so equation 7 is value per square foot. Equa- tion 7 can be rewritten V = [(l - e)R(a)l/ (r + T), which means that the tax bill t per square foot is

t = TV = [( 1 - e)TR(a)]/(r + 7).

The property tax per square foot increases with gross rent according to

(dt/dR)R, = [(l - e)T/(r + T)IR,

where R, is the marginal effect of attribute 1 On gross rent. For example, if 7 = 0.04 (a typical value for 7 in Chicago), e = 0.4, and r = 0.06, then dt/dR = 0.24. In short, variables that increase gross rent are also supposed to increase property taxes. This means that the disturbance term in the gross rent equation in column 1 of Ta- ble 3, which depends upon a collection of omitted building and/or location attributes, may be (positively) correlated with the property tax variable.

The actual method used to determine as-

117

sessed values is not known completely, but it is known that the assessor uses a formula to assign a total assessed value to a building (rather than assessed value per square foot). This formula can be approxi- mated as

AV = /3S + OC + g[f(age, location)] + 9u

where AV is total assessed value, 9 is total rentable floor space, 5 is office space, C is other rentable space (so 5 + C = 9) /3 and 8 are parameters, and u is a random error term. It is known that the assessor applies a higher coefficient to office space (p > f3), and it is also known that adjustments of some sort are made for the age of the building and its location. It is also obvious that the formula for AV includes a sizable error term, and it is reasonable to assume that this error is larger for larger buildings. This equation for AV implies that assessed value per square foot of rentable space, the vanable used in this study, is

av = AV/9 = 8 + (/3 - @(S/q)

+ f(age, location) + u.

As such, this variable is exogenous in the rent equation.

The Hausman (1978) specification test is used to check for possible simultaneity bias. Under the null hypothesis in large samples, the assessed value and disturbance term are uncorrelated. The alterna- tive hypothesis is that these two variables are correlated and that OLS does not yield consistent estimates of the coefficients. The Hausman test involves estimation of a re- duced-form equation for assessed value. The predicted values from this estimated equation are then included in an expanded OLS regression for gross rents. The null hypothesis is not rejected if the hypothesis that the coefficient of the predicted as-

sessed value is zero cannot be rejected. The coefficient of the predicted assessed value is the difference between the OLS and the two-stage least-squares coeffl- clents of assessed value.

Tvvo alternativle versions of the Hausman test were run. In the first version, stage one of the Hausman test is an OLS regression of assessed value per square foot on all of the independent variables listed in column 1 of Table 3 plus the total square feet of rentable space and the proportion of rentable space devoted to offices (as opposed to retailing and other uses). Pre- liminary regression results (available upon request) indicated that these two variables are not determinants of office rents, and equation 11 indicates that the proportion of rentable space devoted to offices is an irnportant determinant of the assessment. As shown in column 1 of Table 4, the R2 for this regression is 0.22. The predicted values from this regression are then en- tered in the gross rent regression, and the results are shown in column 2 of Table 1. The coefficient of predicted assessed value is small (0.025) and statistically insignificant (1 = 0.115). This version of the Hausman test thus leads to the conclusion that assessed value per square foot can be con- sidered an exogenous variable.

SlDrne readers of an earlier version of this paper expressed concern that the results of the Hausman test can be sensitive to the choice of exogenous variables included in the first-stage regression. A second version of the Hausman test has been run to ex- atlline this issue. In this case, it is hypothe- sized that, in addition to the variables included in column 1 of Table 4, assessed value per square foot is determined by an “unofficial” program of granting property tax breaks to individual buildings Cook County has an official program that lowers assessed value to 42% of its normal level, bdt this program does not apply to the dl2wnto’wn area. Nevertheless, individual

building owners have the opportunity to contact the assessor’s office in advance of a reassessment in order to point out finan- cial problems. Also, the appeal process IS available once the reassessment is issued. It IS obvious that some building owners have used these opportunities to their advan- tage, but which ones?

The available data can be used to determine which buildings lhave been granted a tax break that is equal to (or greater than) the official program that exists in other parts of the ctty. The “tax break” variable IS defined (3s follows. According to Table 1, commercial buildings in the assessment area that includes downtown had a median assessment that is 20.4 percent of market value. A tax break that reduces the assessment to 42 percent of its normal level would mean an assessment of 8.6 percent of market value. Data on the market values of buildings are not available, but recall that market value V = (1 - e)R/ (r -t +I. Suppose that the ratio of assessed to market value of 0.086 indicates the awardtng of a tax break. From the equation for V, the ratio of assessed value to rent per square foot for such buildings is

w/R == O.OSC,[(l - e)/(r + T)]

If e = 0.4, r = 0.06, and 7 = 0.04, then w/R q = 0.52. A dummy variable for the awarding of a tax break has been defined for bulldings with av/R less than 0.50. Thirty-four buildings (13 percent) fall into thts category. A probit analysis (not reported) using the tax break dummy as the dependent variable reveals that the tax break IS not related to some obvious crite- ria, such as age of building, vacancy rate, or building size. The tax break variable is providing new information.

The first stage regressi’on for the Hausman test has been run with the tax break vari-

PROPERTY TAX INCIDENCE IN CHICAGO

TABLE 4 REGRESSION ANALYSIS OF ASSESSED VALUE PER SQUARE FOOT: OFFICE BUILDINGS IN DOWNTOWN

CHICAGO IN 1991 (T-RATIOS IN PARENTHESES)

(1) (2) Constant

Age of building (years)

Number of floors

Percentage vacant

Building built before 1950, not rehabbed since 1979

Percentage of rentable space devoted to offices

Total rentable space (1000 square feet)

Tax break granted


North of Chicago River

Wacker Drive (West Loop)

East of Loop

West of Chicago River

South of Loop

R2 Sample size

18.124 (3.12)

-0.125 (3.73) 0.001

(0.00) -0.045 (0.94)

-1.611 (0.84) 0.121

(2.41) 0.000

(0.12) -

0.324 (0.15) 8.206

(2.20) -2.195 (0.85)

-7.935 (2.76)

-12.443 (2.46) 0.225

259

24.755 (4.56)

-0.136 (4.43)

-0.095 (1.13)

-0.049 (1.13)

- 1.304 (0.74) 0.090

(1.95) 0.002

(0.93) - 15.242

(6.83)

-0.568 (0.28) 6.309

(1.83) -2.094 (0.89)

-7.678 (2.91)

-8.896 (1.91) 0.348

259

able included, and the results are shown in column 2 of Table 4. As one would ex- pect, the tax break variable is highly statistically significant and the ti is 0.35 (compared to 0.22 without the tax break variable). The predicted assessed value from this new regression equation is en- tered into the regression equation for gross rent, and the results are shown in column 3 of Table 3. Once again, the predicted value of the assessment is not statistically significant, although the t ratio is 1.19 (compared to 0.15 for the earlier Hausman test in column 2 of Table 3). The entire Hausman test was then repeated using a critical value of av/R of 0.65 rather than 0.50, and the predicted value of the assessment once again was not statistically significant. In short, these additional Haus-

119

man tests support the conclusion that the assessed value per square foot is an exogenous variable in the equation for gross rent.

Conclusions

This study of 259 private office buildings in downtown Chicago has shown that assessed value per square foot strongly influ- ences a building’s average gross rent per square foot. In 1991, 45 percent of property tax differences across buildings were shifted forward to tenants. This result means, of course, that 55 percent of property tax differentials were absorbed by the owners of the building and land.

The effect of the property tax on gross rent can be estimated with considerable

precision, because assessed value per square foot turns out to be an exogenous variable with respect to gross rent. The property tax is supposed to be determined by the market value of the building, which clepends upon gross rent and expenses. However, the apparent difficulty with es- tablishing accurate estimates of market values tums the property tax into an exogenous-some rnight say arbitrary---vanable.

This study pertains only to the office market in downtown ChIcago in 1991. Further cross-sectional and time-series studies of other commercial real estate mark.ets are needed to determine the generality of the results presented here.

ENDNOTE

*The author thanks Mike Thorn of BOMA/CtlIcago

asststarlce and the refereer for hcllpful comments

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incidence of the property tax on commercial real...

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