urban industrial land redevelopment and contamination risk

Ž .Journal of Urban Economics 47, 414]442 2000doi:10.1006rjuec.1999.2147, available online at http:rrwww.idealibrary.com on

Urban Industrial Land Redevelopment andContamination Risk1

Daniel T. McGrath

Senior Fellow, Great Cities Institute, Uni ersity of Illinois at Chicago, 412 S. PeoriaStreet, Suite 400, Chicago, Illinois 60607-7067

E-mail: [email protected]

Received December 6, 1996; revised July 22, 1999

This study examines the role of contamination risk on urban industrial redevel-opment in the City of Chicago. The theoretical framework is the myopic optimalredevelopment rule which states that the redevelopment of an urban parcel willoccur when the parcel’s value through conversion to a new use, net of constructioncosts, exceeds the value of the same parcel continuing in its current use. Contami-nation liability is modeled as a land demolition cost that is capitalized into bidvalue. Assuming that the magnitude of this land demolition cost is a function of thea priori probability of contamination, the effects of contamination risk on landvalue and on the probability of redevelopment are estimated. Q 2000 Academic Press

1. INTRODUCTION

Over the past few years, intense discussion in the legal, urban planning,and economic policy literature has concerned the relationship betweenland contamination risk and the redevelopment of urban industrial prop-erty. The general view is that the current federal and state regulatoryrequirements regarding remediation of any discovered contaminationplaces substantial legal and financial barriers to the redevelopment ofurban industrial land. This is believed to be one of the forces contributingto employment deconcentration and to the acceleration of industrialdevelopment at the urban fringe of our metropolitan areas. This view hasprompted municipal officials to begin to take on the financial responsibil-ity for the remediation of contaminated industrial properties; the belief is

1This research was funded by the John D. and Catherine T. MacArthur Foundation andconducted for the City of Chicago’s Brownfields Forum. I express my thanks to CommissionerHenry Henderson of the City of Chicago’s Department of Environment for assistance inassembling the data for this research. I also express thanks to Joe Persky, John McDonald,and Gib Bassett of the University of Illinois at Chicago and to the anonymous referees fortheir helpful comments and suggestions.

4140094-1190r00 $35.00Copyright Q 2000 by Academic PressAll rights of reproduction in any form reserved.

REDEVELOPMENT AND CONTAMINATION RISK 415

that this public investment will make the central city more competitive inattracting industrial users for these properties. However, despite theimportance of this issue to the economic viability of our central cities, noempirical estimates utilizing current urban economic theory exist of theimpact of uncertain contamination liability on industrial redevelopment.The purpose of this paper is to begin to fill this gap by presenting anempirical estimation describing the effect of contamination risk on theindustrial land market in the City of Chicago}in terms of both the impacton land value and the effect on the probability of redevelopment.

The theoretical framework for this investigation is the myopic optimalw x w xredevelopment rule put forth by Brueckner 1 and Wheaton 13 in their

independent development of spatial growth models of metropolitan areas.This rule states that the redevelopment of a parcel will take place whenthe value of the parcel converted to a new use, net of construction costs,exceeds the value of the parcel remaining in its current use}that is, whenthere is a positive value differential on a given parcel. The myopic optimalredevelopment rule has been supported empirically by two recent studies.

w xThe first is Rosenthal and Helsley 12 who present empirical support forthis theory to explain residential redevelopment in the City of Vancouver,

w xBC. The second is Munneke 10 who finds support for this theory toexplain commercial and industrial redevelopment activity within the Cityof Chicago.

Following the econometric methodology utilized by Rosenthal and Hels-w x w xley 12 and Munneke 10 , this paper explores the question: Of the

industrial properties in the City of Chicago for which there is evidence ofredevelopment, what role has contamination risk played in their redevel-opment? This is accomplished by modeling contamination liability as ademolition cost, the magnitude of which is a function of the parcel’s apriori probability of contamination. Assuming that this demolition costwould be capitalized into a parcel bid value, an estimate of the extent towhich investors discount land value to account for contamination risk isdetermined. The determination of the effect of contamination risk on landvalue thus produces a way to identify the relationship between contamina-tion risk and the probability of redevelopment.

The principal dataset for this research is a group of 195 industrialproperties that were sold within Chicago during the 10-year period fromAugust 1983 through November 1993. Of the total, 95 properties representparcels that were sold for redevelopment. These observations were createdthrough matching industrial property sales records from the City of

Ž .Chicago’s land use database The Harris-REDI file with the City’s indus-trial building and demolition permit databases. The rest of the observa-tions, a control group of 100 properties, were chosen randomly from theHarris-REDI file. The probability of contamination was assigned according

DANIEL T. MCGRATH416

w xto a scale developed by Noonan and Vidich 11 and based on a land-useinvestigation for each of the 195 parcels in the dataset within the 1949 and1979 Sanborn Fire Insurance Maps.

The next section presents a more detailed discussion of the myopicoptimal redevelopment rule, identifies the assumptions used in this empiri-cal application of the theory, and then derives the functional forms for theland value and structural probit models. The third section discusses theeconometric methodology and identifies the specific econometric tech-nique utilized to correct for possible selection bias. The fourth section is adiscussion of the data used for this research, and the last two sectionspresent the empirical results and conclusions.

2. THE MODEL AND ASSUMPTIONS

2.A. Literature Re¨iew

The development of dynamic urban redevelopment models began withw x w xBrueckner 1 and Wheaton 13 in response to the inadequacies of static

equilibrium models of urban land-use for describing growth and change inurban structure. These authors derive an optimal redevelopment rule thatidentifies the economic conditions under which redevelopment will occur:that the present value of revenue obtainable from a parcel converted to anew use, net of capital development costs, must equal or exceed thepresent value of the gross revenue from the existing capital stock on theparcel. This is expressed as

r t , S*rL L r t , SrL LŽ . Ž .y c t S* G , 1Ž . Ž .

i i

Ž .where r t, S*rL is the revenue per acre obtainable from optimal capitalredevelopment, which depends on time t, and the ratio of optimal, new

Ž .capital S* to the amount of land L; i is the discount rate; c t is the unitcost of capital at time t; and S is the existing capital on the parcel. The left

Ž .side of Eq. 1 is the present value of the parcel in its redeveloped state,denoted V R. The right side of the equation is the present value of the

C Ž .parcel remaining in its current use, denoted V . Thus, Eq. 1 is rewrittenmost simply as

V R y V C G 0. 2Ž . Ž .

Restating, a parcel will be redeveloped when the present value of theparcel in its new use, which capitalizes the construction costs, minus thepresent value of the same parcel remaining in its current use}that is, the

Ž R C .value differential, V y V }is greater than or equal to zero.


The first empirical exploration of the optimal redevelopment rule wasw xundertaken by Rosenthal and Helsley 12 in an examination of the

redevelopment of single family dwellings in Vancouver, BC. In addition tofinding strong empirical support for this theory in residential redevelop-ment, Rosenthal and Helsley argue that if net demolition costs are zero,then a new method for determining the value of vacant land emerges thatavoids some of the problems of hedonic estimation methods. These co-authors also present the most generalized expression of the optimalredevelopment rule: that the marginal costs of delaying redevelopmentmust equal or exceed the marginal benefits of delaying redevelopment.Incorporating the assumptions that:

Ž .1 landowners are myopic; that is, they act as if the level of incomeobtainable from redevelopment will remain constant into the future,

Ž .2 capital costs are constant,Ž .3 no structural depreciation occurs, andŽ .4 demolition costs are zero,

the optimal redevelopment rule reduces to the form derived by Bruecknerand Wheaton.

w xMunneke 10 presents empirical support that the optimal redevelop-ment rule describes the redevelopment of commercial and industrialproperties in the City of Chicago during the decade of the 1980s. Addition-ally, Munneke asserts that the assumption of a zero demolition cost mightnot be appropriate for industrial properties and makes an argument that ifdemolition costs are a significant component of the redevelopment deci-sion, then these costs must be incorporated into the redevelopment rule.In Munneke’s analysis, the structural demolition costs are incorporated

Ž R C .outside the value differential term V y V , and the parcel’s capital-to-land ratio SrL is found to be a statistically significant proxy variable forthe structural demolition costs.

2.B. Contamination Liability as Demolition Costs

Assume that the contamination liability for a given industrial parcel canbe modeled as two separate demolition costs. The first is associated withthe existing structure, as would be the case for asbestos or lead contamina-tion. The second is associated with the land, as would be the case for PCB

w xor heavy metal soil contamination. As identified by Munneke 10 , thestructural demolition cost DS would be a function of the existing capital-to-land ratio for the parcel, SrL. However, it makes intuitive sense thatstructures of a certain vintage andror prior industrial use would have ahigher likelihood of contamination and thus have a higher structuraldemolition cost. Increases in demolition cost associated with any possible


structural contamination would be readily apparent to a redeveloperthrough inspection of the existing structure. Therefore, in addition tobeing a function of the parcel’s capital-to-land ratio, the structural demoli-tion cost would also be a function of the conditionrdepreciation level d, or

S SŽ .D s D SrL, d .The estimation of remediation costs associated with land contamination

is more uncertain for a redeveloper, and this fact is at the center of thebrownfields debate. However, over the 19 years since the passage ofCERCLA,2 as industrial redevelopers have gained more experience withthe legal requirements and subsequent financial implications of this act,one might reasonably expect that contamination liability would, over time,become to some extent predictable and capitalized into parcel bid value.This paper takes the view that there exists an observable environmentalvariable E that can accurately determine a priori the land contaminationliability associated with a given parcel to be redeveloped. Thus, the landdemolition costs would become a function of the environmental variable,

L LŽ .or D s D E .

2.C. Value Equations and Functional Forms

Every industrial location can be represented by a quantity of industrialreal estate R, which is a function of the land area L and the amount of

w xcapital on the site, S 8 . The value of any given parcel of industrial realŽestate would be expressed as the quantity of industrial real estate units of

. Ž .unknown scale , R L , S , times the unit price of industrial real estate, P.iw xConsistent with the functional form proposed by Mills 9 , the per real

estate unit price P is a function of the temporal market conditions and ofthe locational and neighborhood characteristics that distinguish the differ-ent spatial real estate markets within an urban area. Thus, for an ithindustrial parcel in its current use, the value V C is written as

C CP X = R L , SŽ . Ž .i i iCV si i

P Ce b C0 qb C X C

iŽ .s = R L , S , 3Ž .Ž .i ii

where P C is the price per real estate unit of industrial real estate in itscurrent use, R is the units of industrial real estate, L is the amount of landarea, S is the measure of existing capital in units of floor space, i is theinterest rate, and XC is the vector of explanatory variables other than landi

2 The Comprehensive Environmental Response, Compensation, and Liability Act, other-wise known to as Superfund.


and capital for the ith current-use parcel that determines the temporaland spatial variations in the unit real estate price within the urban area.Another simplifying assumption is that the elasticity of substitution of Lfor S is equal to 1, which allows the use of a Cobb]Douglas functionalform, or R s LaSl. Dividing this expression by the land area of thei i iparcel, L , would produce an expression for the observed per-square-footivalue. Taking logs, the functional form for the current-use value equationis written as

CVi C C Cln s A q a y 1 ln L q l ln S q b X , 4Ž . Ž .i i iLi

C w C xwhere A is a constant which equals ln P q b , a is the elasticity of0land value to land area, l is the elasticity of land value to floor space, andthe other variables are as previously defined.

Accounting for the two demolition variables produces a functional formfor redeveloped parcels that is fundamentally different from the above-de-

Ž .scribed current-use functional form, Eq. 4 . The value function of an ithoptimally redeveloped parcel is written as

R R RR b qb X0 iP e SŽ . iU UR SV s = R L , S y c t S y D , dŽ . Ž .i i i i iž /i Li

? L y D L E ? L , 5Ž . Ž .i i i

where S* is the optimal level of structural capital following parcel redevel-opment, which is not directly observable at the time of sale and where DS

and D L are the respective demolition costs defined on a per-square-footbasis. Assume that the optimal capital for an ith parcel of industrial land isa function of the amount of land, L , and its location within the urbaniarea, X R, and is written asi

SU s s L , X R . 6Ž .Ž .i i i

Thus, for redeveloped parcels, the per-square-foot present value equationbecomes

R R R RV P X ? R L , s L , XŽ . Ž .i i i i i Rs y c t ? s L , XŽ . Ž .i iL ii

SiS Ly D , d y D E . 7Ž . Ž .i iž /Li


It would be convenient if the value equations for V C and V R were ofsimilar form, that is, if the left side of the estimating equations for both

C R w xV and V was expressed as ln V rL . Therefore, in its most generalizedi iw R xform, an expression for ln V rL can be written asi i

RV Si iRln s g X , L , , d , E , 8Ž .i i i iL Li i

where X R is the vector of variables determining the spatial and temporaliŽvariations in the parcel’s unit price and is not necessarily the same vector

C .of variables that determine V , L is the land area of the parcel, S rL isi i ithe existing capital-to-land ratio, d is some measure of the depreciation ofithe existing capital on the parcel, and E is the a priori measure of landicontamination for the parcel. Assuming a log-linear specification for thefunction g gives

RVi R Rln s b q b Y , 9Ž .0 iLi

where Y R is defined as the full vector of independent variables expressediŽ .on the left side of Eq. 8 . This approach is justified as a Taylor series

Ž . Ž .expansion of Eq. 7 . In the actual estimation of Eq. 9 , the expansion iscarried out as far as it has explanatory power. The rationale here is tolinearize and produce an easily estimatable and comparable functionalexpression for V R that can be utilized in the two-stage method to correctfor possible selection bias between the redeveloped and current-use prop-erties and thus obtain consistent estimates for the value differential,Ž R C .V y V , for each parcel in the dataset.

3. ECONOMETRIC METHODOLOGY

The industrial redevelopment decision can be tested empirically throughthe use of a structural probit model. Following closely the estimation

w x w xprocedure used by Rosenthal and Helsley 12 and by Munneke 10 , thismodel is written as

C R s d V R y V C q g i s 1, 2, . . . , N , 10Ž .Ž .i i i i

where C R represents the criterion function or index which indicates wheniŽ R C .redevelopment will take place upon sale, V y V is the parcel’s valuei i

Ž 2 .differential, and g is the error term which is assumed to be N 0, s . Ai g

parcel is redeveloped upon sale if C R ) 0 and remains in its current use ifiC R F 0. In the probit estimation over the entire sample, C R is assignedi ithe value of 1 for parcels that were redeveloped upon sale and 0 for


parcels remaining in their current use. If the criterion for redevelopment isdetermined by a parcel’s value differential, the probit estimation proce-dure will identify the coefficient, d , as significant and of a positive value.

Ž . RTo accomplish the probit estimation of Eq. 10 , estimates for both Vand V C are required to calculate the value differential for each parcel.However, only one value, either V R or V C, is ever observed at the time ofa parcel’s sale. It is assumed that in a competitive land market, the saleprices of industrial parcels sold for redevelopment will reveal V R and thatthe sale prices for current-use parcels will reveal V C. The estimation ofboth V R and V C for each parcel is accomplished by separating the datasetinto these two groups and then regressing the observed sale prices on thevectors of observable spatial and temporal variables that determine valuefor each group respectively. Using the estimated coefficients from eachvalue function, an estimate for the value differential is thus obtained foreach parcel in the combined dataset.

If one assumes that these estimated value equations take on a linearform, then the value differential can be written as

V R y V C s b R Y R y b C XC q y y v , 11Ž . Ž .Ž .i i i i i i

where Y R and XC are the vectors of the observable spatial and temporali icharacteristics determining redeveloped and current-use value, respec-tively, b R and b C are the respective coefficients of the redeveloped andcurrent-use value equations, and the error terms y and v are assumed toi i

Ž 2 . Ž 2 .be y ; N 0, s and v ; N 0, s .i y i v

Ž . Ž .Substituting Eq. 11 into the original structural probit model 10 , thereduced-form probit model is derived as

C s u Z y h , 12Ž .i i i

where u is the vector of all coefficients, Z is the vector of the union of alliŽ .explanatory variables, and the error term h s d y y v q g .i i i i

Ž .The estimation of the structural probit equation 10 follows the two-w x w xstage procedure developed by Lee 5 and outlined by Maddala 7 . This

method involves the substitution of estimated endogenous variables intothe criterion function prior to its estimation via a probit analysis. The firststep in this procedure is to use the probit method to estimate the reduced

Ž .form probit equation 12 to obtain estimates for u based on the dichoto-mous observations of R}that is, whether or not the ith parcel wasredeveloped.

The second step is to estimate the respective redeveloped and current-usevalue functions in a way that produces unbiased estimates for the coeffi-cients b R and b C. As discussed above, only one value, either V R or V C,


will ever be observed when a parcel is sold. However, the original criterionŽ .function, Eq. 10 , requires the calculation of the value differential for all

observations in the dataset. The individual value functions for V R and V C

are estimated separately by regressing parcel sale prices on the vectors ofobservable spatial and temporal characteristics for each parcel Y R and XC,i irespectively. Since the data have been divided into two separate groups,unobserved characteristics favorable to each group may exist, and selectionbias becomes a possibility. Expressed more formally, when selection bias ispresent, the conditional expectation of the error terms of the valueequations will not be zero. Consistent and unbiased estimates of the valueequation parameters are obtained by defining new error terms, yU andivU , and by the introduction of new estimated explanatory variables W R

i iand W C, the Mills ratios, into their respective equations. By including theiMills ratios into the value functions, the conditional expectation of both yU

iand vU is now zero. The values of W R and W C are computed for eachi i iobservation in the combined dataset using the estimates of u obtained

Ž .from the initial probit estimation of the reduced form probit equation 12identified in step 1. The respective redeveloped and current-use valueequations to be estimated are written as

f u zŽ .i UR R RV s b Y y s q yi i yh iž /F u zŽ .is b R Y R y s W R q yU 13Ž .i yh i i

and

f u zŽ .i UC C CV s b X q s q vi i vh iž /1 y F u zŽ .is b CX C q s W C q vU , 14Ž .i vh i i

where yU s y q s W R, vU s v y s W C, s is the covariance be-i i yh i i i vh i yh

tween y and h, s is the is the covariance between v and h, f is thevh

standard normal density function, and F is the cumulative normal densityŽ . Ž .function. Estimation of Eqs. 13 and 14 using OLS produces the re-

quired consistent and unbiased estimates for the value function coeffi-cients b R and b C. Additionally, statistical significance of the Mills ratio inthe OLS estimation signals the presence of selection bias between the datasubsets.

One problem resulting from the insertion of the Mills ratios into thevalue functions is that it results in a downward bias in the estimatedstandard errors of the coefficients. This downward bias could result in


variables erroneously being identified as significant. To correct for thisw xbias in the standard errors, a procedure developed by Lee et al. 6 must be

implemented to produce the correct asymptotic covariance matrix. In thisanalysis, all models utilizing the two-stage method have utilized thisprocedure, built into the econometrics software, to obtain unbiased esti-mated standard errors for the value function coefficients.

Obtaining consistent and unbiased estimates for b R and b C in step 2allows the estimation of the value differential for each observation in thedataset. Thus, the structural probit equation to be estimated becomes

R ˆDiffC s a V q g ,Ž .i i i

ˆDiff ˆR ˆC ˆR R ˆC Cwhere V s V y V s b Y y b X . 15Ž .i i i i i

4. DISCUSSION OF DATA

The principal dataset is a group of 195 redeveloped and current-useproperty sale transactions that occurred within the City of Chicago fromAugust 1983 through November 1993. These records were obtained fromthe City of Chicago’s Harris-REDI database maintained by the City’sDepartment of Planning and Development. The Harris-REDI database isa combination of the City’s Harris Land-use Data with land sale transac-

Ž .tions compiled by Real Estate Data, Inc. REDI , which have beengathered from all real estate transfer declarations filed with Cook County.

The redeveloped property data subset is a group of 95 redevelopedindustrial properties. This subset was created by matching the 8043 indus-trial land sale transactions with 1867 industrial building permit records andthe City’s 881 industrial demolition permit records that were issued by theCity of Chicago Department of Buildings from January 1984 throughDecember 1993. Information concerning the neighborhood racial composi-tion for the parcels in the database was obtained by matching the recordswith 1990 census information.

The criterion used to identify whether a parcel was purchased forredevelopment is whether the sold parcel had an industrial demolitionpermit andror an industrial building permit filed with the Department ofBuildings within 24 months of the sale date and is currently zoned forindustrial use. If so, then the property is considered to have been pur-chased for redevelopment. Including industrial building permits in additionto industrial demolition permits produces a ‘‘softer’’ redevelopment rule.Often, existing structures are not completely removed by the new user, butthrough additional construction, the existing structure is substantiallymodified for a new use. This situation falls somewhere in between the


‘‘hard’’ redevelopment criteria in which all existing capital is removed andthe case where a parcel is purchased for current use and remains essen-tially unchanged. The intention here is to identify those industrial parcelsthat show evidence of investment by a redeveloper, whether throughcomplete demolition or through modification of the existing structure.

The salerpermit address matching was verified by visual investigation ofeach parcel in the Sanborn Fire Insurance Maps of the City of Chicago.Additionally, to ensure that the identified address match represents aproperty that is currently in industrial use and was not purchased forconversion to either residential or commercial use, the current zoning ofeach property was verified as industrial by site identification in the 1994Chicago Zoning Ordinance zoning maps.

The current-use group of sales is a set of 100 observations chosenrandomly from the industrial sale record. These properties were verified tohave no building or demolition permits associated with them any time aftertheir sale and are also currently zoned for manufacturing.

The environmental variable E, which has been hypothesized to be asignificant determinant of the land contamination liability faced by abuyerrredeveloper and subsequently a determinant of the land demolitioncosts, is represented by the a priori probability of the parcel’s contamina-tion. This continuous variable was identified for specific land uses by

w xNoonan and Vidich 11 in a survey of completed cleanups by 17 environ-mental engineering firms in the northeast U.S. Table 1 outlines theindividual a priori probabilities of contamination for 25 specific categoriesof land use. Denoted as PROBCON in the redeveloped and current-usevaluation models, this variable is used to signal higher liability costsbecause, at present, there are no publicly available remediation costdatabases which might be used to produce an accurate remediation costestimate or to produce statistically significant contamination risk factors.

In an effort to duplicate the process a buyer would have undertaken in aPhase I environmental investigation, a historical land-use investigation wasundertaken for each of the 195 properties in the database. To determinethis land use, each property was researched in both the 1949 and 1975versions of the Sanborn Fire Insurance Maps. These maps, available onmicrofilm, provide a wealth of land-use information, identifying the spe-cific company using the site at the time and often the specific nature of theindustrial activity. From this investigation, each property has been assignedan SIC-code interpretation which best represents the historical land use.From this SIC-code interpretation, a value of the a priori probability ofcontamination has been identified from Noonan and Vidich’s classifica-tion. The definitions and the summary of the statistics of the variablesused in this analysis are presented in Table 2.


TABLE 1The A-priori Probability of Contamination Based on Historical Land Use

Probability ofCommercial and Industrial Land Use Categories Contamination

1. Former coal gas plants, fuel distributors, chemical distributors, .99airports, incinerators

2. Auto salvage yards, plastic manufacture, electric utility, refining, .95hazardous waster storagertransfer

3. Oil and other petroleum storage .924. Metal plating, landfills, chemical manufacture, metal finishingr .90

tool & dye, laboratories5. Heavy industrial manufacturing, power plants, paper manufacturing, .88

gas stations6. Tanneries .877. Urban vacantrabandoned land, furniture repair and stripping, .85

circuit board manufacturers, tank farms, waste treatment plants8. Metal working and fabrication .839. Railroad yards and right of ways, vehicle maintenance facilities .82

10. Refuse recycling facilities, machine shops, electronics assembly .80facilities, agricultural mixersrformulators, high technologymanufacturing

11. Junkyards, electronics manufacture .7912. Industrial parks, automotive assembly facility, light industrial .75

manufacturing13. Dry cleaners .7414. Auto repair shops .7215. Chemical research facility .7016. Trucking terminal, textile printing and finishing .6517. Resource recovery facilities, electricalrplumbingrHVAC service .6018. Photographic .5319. Auto dealerships, fabric dyeing establishments, pharmaceutical .50

establishments20. Highways, research facilities .4021. Warehouses .3522. Gas utilities .3523. Retail property .2524. Residential, rural vacant property, hospitals .20

Ž .25. Offices non-manufacturing .13

w xSource: Noonan and Vidich 11 .


TABLE 2Statistical Summary of the Land Use Data

Full Redeveloped Current-UseSample Parcels Parcels

Ž . Ž . Ž .N s 150 N s 95 N s 100

Std. Std. Std.Mean Dev. Mean Dev. Mean Dev.

LNUP 2.5811 1.1494 2.5150 1.2315 2.6438 1.068Natural Log of theParcel per sq. ft.sale price

LNA 10.3329 1.1841 10.5885 1.1977 10.09 1.1239Natural Log of theland area in sq. ft.

LNSPACE 9.9283 2.9283 9.6997 3.9631 10.1455 1.3391Natural Log of thebuilding area insq. ft.

LAND 69,341 142,584 82,905 158,991 56,546 124,471Parcel land area insquare feet

CBD 5.6384 2.8056 5.7687 2.8966 5.5146 2.7251Distance in milesfrom the centralbusiness districtŽintersection ofLaSalle and

.Jackson StreetsLNORTHD 0.5538 0.4984 0.6316 0.4849 0.4800 0.5021

1 if the parcel isnorth of Lake St.,0 otherwise

AGE 54.33 23.74 54.29 23.98 54.37 23.65Age of the buildingin years

COND 1.5897 0.6255 1.5158 0.5809 1.6600 0.6547Condition Code ofthe building basedon external surveyby City of Chicago:1 s excellent,2 s good, 3 s fair,4 s poor

DAYS 2018.9 966.9 1877.3 862.2 2153.3 1043.4Number of daysfrom the initialsaledate inthe dataset,8r1r83


TABLE 2}Continued

Full Redeveloped Current-UseSample Parcels Parcels

Ž . Ž . Ž .N s 150 N s 95 N s 100

Std. Std. Std.Mean Dev. Mean Dev. Mean Dev.

PCNTAFAM 0.2953 0.3804 0.2184 0.3197 0.3683 0.4188Percentage ofAfrican-Americanpopulation withincensus tract ofparcel

CAPINT 1.4526 2.1187 1.2632 2.0079 1.6324 2.2139Capital Intensity asmeasured by thebuilding floor areadivided by parcelland area

PROBCON 0.6778 0.2240 0.6785 0.2248 0.6770 0.2243The a-prioriprobability ofparcelcontaminationbased onhistoricalland use

VALDIFF y24.493 634.603 190.511 569.232 y228.747 628.497The estimated valuedifferential

R CŽ .V y Vof the parcel in000’s of 1995$

5. EMPIRICAL RESULTS

5.A. The Reduced Form Probit Results

As previously discussed in Section 3, the two-stage method requires theŽ .initial estimation of the reduced form probit equation 12 . Although the

purpose of this initial step is to facilitate the calculation of the Mills ratiosW R and W C, which are then included as estimated variables in theirrespective value functions, the estimation results are by themselves ofparticular interest. The estimation results, presented in Table 3, identifytwo separate estimations, Model 1 and Model 2. Model 2 includes thestructural and land demolition variables, CAPINT, the capital intensity ofthe parcel, and PROBCON, the a priori probability of contamination


TABLE 3Estimation Results of the Reduced Form Probit

Model 1 Model 2

Intercept y3.616 y3.650Ž . Ž .2.533 2.540

LNA 0.652 0.778Ž . Ž .Natural Log of land area in sq. ft. 3.876 3.805

LNSPACE y0.287 y0.414Ž . Ž .Natural Log of the building area in sq. ft. 2.499 2.567

LAND y1.743E-06 y1.841E-06Ž . Ž .Parcel land area in square feet 1.830 1.915

CBD y0.345 y0.038Ž . Ž .Distance in miles from the central business 8.745 0.949

Ždistrict intersection of LaSalle and Jackson.Streets

LNORTHD 0.385 0.400Ž . Ž .1 if the parcel is north of Lake St., 1.940 1.962

0 otherwiseAGE 0.009 0.009

Ž . Ž .Age of the building in years 1.889 1.881COND y0.099 y0.112

Ž . Ž .Condition Code of the building based on 0.586 0.658external survey by City of Chicago

DAYS y1.556E-04 y1.483E-04Ž . Ž .Number of days from the initial saledate in 1.468 1.386

the dataset, 8r1r83PCNTAFAM y0.587 y0.626

Ž . Ž .Percentage of African-American population 2.155 2.260within census tract of parcel

CAPINT } 0.079Ž .Capital Intensity as measured by the building 1.158

floor area divided by parcel land areaPROBCON } y0.076

Ž .The a-priori probability of parcel 0.167contamination based on historical industrialland use of the parcel

LLF y116.653 y116.640LRI 0.1365 0.1366

Note: The absolute values of the t statistics are presented n parentheses. The dependentvariable in the reduced-form probit is REDEV which takes on the value of 1 if the parcel wasredeveloped and 0 otherwise. The LRI is analogous to the R2 statistic in an OLS model. See

w xGreene 2 , p. 651.


TABLE 4OLS Estimation Results of Redeveloped Value Equations

Model VR1 Model VR2

Intercept 2.350 4.114Ž . Ž .2.379 3.903

LAND y1.296E-05 y1.525E-05Ž . Ž .Parcel land area in square feet 2.577 3.197

LANDSQ 3.083E-11 3.779E-112Ž . Ž . Ž .LAND 1.654 2.142

LANDCB y1.635E-17 y1.994E-173Ž . Ž . Ž .LAND 1.508 1.947

CBD y0.984 y0.773Ž . Ž .Distance from the central business district 2.506 2.080

Ž .intersection of LaSalle and Jackson StreetsCBDSQ 0.178 0.150

2Ž . Ž . Ž .CBD 2.877 2.527CBDCD y0.009 y0.008

3Ž . Ž . Ž .CBD 3.053 2.795LNORTHD 0.499 0.385

Ž . Ž .1 if the parcel is north of Lake St., 2.293 1.7140 otherwise

DAYS 1.722E-03 1.859E-03Ž . Ž .Number of days from the initial saledate 4.064 4.740

dataset, 8r1r83DAYSQ y4.400E-07 y4.430E-07

2Ž . Ž . Ž .DAYS 4.120 4.522PCNTAFAM 2.760 3.116

Ž . Ž .Percentage of African-American 2.151 2.661population within census tractof parcel

PAFAMSQ y3.526 y3.713Ž .2 Ž . Ž .PCNTAFAM 2.574 2.969

Demolition variablesCOND } y0.520

Ž .Condition code of the building based 3.623on external survey by City of Chicago

CAPINT } 0.293Ž .Capital intensity as measured by the building 1.991

floor area divided by parcel land areaCAPINTSQ } y0.017

2Ž . Ž .CAPINT 1.668PROBCON } y4.593

Ž .The a priori probability of parcel 2.005contamination based on historical land use

PCONSQ } 3.2422Ž . Ž .PROBCON 1.667

RW y0.679 y0.175R Ž . Ž .Mills ratio for V 1.614 0.359

2Adj. R 0.542 0.623

Note: The absolute values of the t statistics are presented in parentheses. N s 95. Thedependent variable is LNUP, the natural log of the per square foot sale price in real 1995$.


TABLE 5OLS Estimation Results of Current-Use Value Equations

Model VC1 Model VC2

Intercept 7.418 7.667Ž . Ž .7.617 7.928

LNA y0.669 y0.731Ž . Ž .Natural log of the land area in sq. ft. 4.204 4.991

LNSPACE 0.397 0.426Ž . Ž .Natural log of the bulding area in sq. ft. 3.848 4.331

CBD y0.350 y0.349Ž . Ž .Distance from the central business district 2.986 2.969

Ž .intersection of LaSalle and Jackson StreetsCBDSQ 0.023 0.023

2Ž . Ž . Ž .CBD 2.441 2.497LNORTHD 0.405 0.374

Ž . Ž .1 if the parcel is north of Lake St., 2.060 1.9680 otherwise

AGE y0.006 y0.008Ž . Ž .Age of the building in years 1.254 1.704

COND y0.321 y0.273Ž . Ž .Condition code of the building based on external 2.153 1.864

survey by City of ChicagoPCNTAFAM y0.687 y0.565

Ž . Ž .Percentage of African-American population 2.676 2.287within census tract of parcel

PROBCON } y0.281Ž .The a priori probability of parcel 0.799

contamination based on historical land useCW 0.076 0.273

C Ž . Ž .Mills ratio for V 0.128 0.5112Adj. R 0.5086 0.5080

Note: The absolute values of the t statistics are presented in parentheses. The dependentvariable is LNUP, the natural log of the per square foot sale price in real 1995$.

based on historical land use, for the estimation of Mills ratios for the valueequations, VR2 and VC2, presented in Tables 4 and 5, respectively. Model1 excludes these variables, as they are not included in the specifications ofeither VR1 or VC1.3

The results of these two initial probit estimations can be interpreted asidentifying the contribution of the explanatory variables to the probabilityof redevelopment outside their role in determining the level of value

3Note that while the variable COND, the condition code of the existing structure, is alsoconsidered a determinant of the structural demolition variable DS, it is included in bothModel 1 and Model 2 because the variable COND is one of the variables in vector XC whichidetermines current-use value V C.


differential. Of particular interest is the contribution of PROBCON. Itsinsignificance in Model 2 identifies that there is no systematic contributionof the a priori probability of contamination to the probability of redevelop-ment between the redeveloped and current-use data groups. If the redevel-oped and current-use data subsets are indeed representative samples, thisinitial result suggests that there is not a fundamental investor bias againstcontaminated properties within Chicago due to contamination risk.

5.B. Rede eloped Value Equations

The OLS estimation results of the two specifications of the redevelopedvalue equations are presented in Table 4. Model VR1 excludes thestructural and land demolition variables, CAPINT, COND, and PROB-CON, whereas Model VR2 is specified with these variables.

The log-linear functional form is justified as a Taylor series expansion ofŽ .Eq. 7 . Explanatory variables were expanded out as far as they were

significant in the OLS estimations. A number of variables exhibit complex,Ž R .non-linear relationships to ln V rL . The redeveloped value function

exhibits statistically significant cubic relationships with respect to bothŽ . Ž .land area LAND and distance from the central business district CBD ,

w xwhich is consistent with the results presented by Munneke 10 . The cubicrelationship with respect to CBD identifies a rapid decline in value asparcel distance from the CBD increases with a local minimum at 3.7 milesŽrepresenting a 70% decline in average unit bid value from a CBD

. Žlocation and with a local maximum at 8.7 miles representing a 50%.decline in average unit bid value from a CBD location . Also significant is

the dummy variable, LNORTHD, which identifies whether a parcel has anorth or south location within the City. North-side parcels command onaverage a unit price about 47% higher than similar south-side parcels. Thespatial pattern described by these spatial variables identifies the lowestindustrial land values to be the near southern locations between 2.5 and 5miles from the CBD}locations generally regarded as the City’s mostsocially and economically distressed}and the highest to be on the northside between 7 and 10 miles from the CBD, areas close to O’Hare Airport.

While land contamination exists in all the industrial areas of the City ofChicago, there are many contaminated sites on the predominantlyAfrican-American south side, and there is a general perception that theCity’s African-American neighborhoods bear more environmental contam-

w xination risk, despite empirical evidence to the contrary 2 . Thus, it wasimportant to control for race in determining the effect of contaminationrisk on land value, in order to eliminate the possibility that the reductionsin land value are in fact due to the impact of distressed social andeconomic conditions associated with African-American neighborhoods inChicago. The variable PCNTAFAM identifies the percentage of African-


American population within the census tract of the parcel. Both ModelsVR1 and VR2 exhibit statistically significant quadratic relationships withrespect to neighborhood African-American population percentages. Parcelunit bid value declines from a central maximum of about 40% African-American population. In other words, parcels in census tracts of about40% African-American population have the highest bid values. One expla-nation for this result is that the quadratic form for the variable PC-NTAFAM may be capturing a value discount effect associated with areasof high Hispanic population. It is well documented that in the City ofChicago African-American neighborhoods are typically segregated from

w xHispanic neighborhoods 2 , and areas of high Hispanic population tend tohave low African-American population.4

Both redeveloped value models, VR1 and VR2, exhibit a stronglysignificant quadratic temporal trend in unit bid value. The quadraticexpression of the variable DAYS, which is the number of days from the

Ž .first sale-date observation 8r1r83 , identifies a trend of substantialincrease in unit land bid value that peaked around the end of April 1989,followed by a decline which brought unit bid value back to a level onlyslightly above their average level in 1983. On average, the April 1989 unitbid value is about 600% above the unit bid value in August 1983. This dateis consistent with the peak of real estate speculation that was occurring inland markets nationwide and the high regional and national economicgrowth period of the late 1980s. Interestingly, by the last date in thedataset, November 1993, average unit prices had returned to approxi-mately their original level.

The vector of structural and land demolition variables, CAPINT, COND,and PROBCON, respectively, all exhibit significance in the redeveloped

Ž .value equation VR2. Capital intensity CAPINT , as measured by thestructure floor space divided by building land area, exhibits a significantquadratic relationship to parcel unit bid value. Parcel unit bid valueincreases with increasing capital intensity, with a local maximum at acapital intensity value of about 9, and exhibits declines in unit value athigher capital intensities. The variable COND represents the conditioncode of the building as identified by visual inspection of the structure bythe Chicago Department of Buildings. COND is a whole number from 1 to4, with 1 representing a sound structure with no obvious repair needed, 2

4 Further analysis supports this hypothesis. An analysis of the parcels in the dataset shows asignificant inverse relationship between African-American and Hispanic population percent-ages. Additionally, when the quadratic form for the variable PCNTAFAM is substituted witha dummy variable that takes on the value of 1 when the census tract is greater than 75%African-American or greater than 75% Hispanic or greater than 95% African-American and

Ž .Hispanic combined 0 otherwise , statistical significance of the dummy variable and all othervariables are maintained in both the redeveloped and current-use value models.


representing a structure needing minor repair, 3 representing a structureneeding major repair, and 4 identifying an uninhabitable, dilapidatedstructure. In both the redeveloped and current-use data groups, theCOND variable ranged from 1 to 3 with no structures of condition code 4being represented in the data. As expected, the estimation results ofModel VR2 show that a unitary increase in COND results in a 40%decrease in a parcel’s unit value. Clearly, if a high quality structure existson a parcel purchased for redevelopment, the redeveloper will have to paythe salvage value of the existing structure. Also, given that this study uses asofter redevelopment rule, it is possible that the existing structures wereincorporated into the redevelopment of the site.

Of most importance is the impact of the environmental variable PROB-CON, which represents the a priori probability of contamination based onhistorical land-use. Model VR2 exhibits a significant quadratic relationshipwith respect to the contamination risk associated with the parcel. Therelationship between the probability of contamination and parcel unit bidvalue is presented in Fig. 1. There is a rapid decline in parcel unit bidvalue as contamination risk rises, which comes to a local minimum atcontamination probability of about 75%, with a very slight increase athigher probabilities of contamination. This slight rise in value effect couldbe an artifact of the data, and perhaps a better functional form for

FIG. 1. Impact of the probability of contamination on average unit bid value.


PROBCON would be a negative exponential form rather than a quadratic.5

However, parcels of greatest public concern have tended to be those withhighest contamination risk. The slight increase in value at high levels ofcontamination risk could be interpreted as evidence of redevelopmentfollowing more intense governmental scrutiny and response on the mostcontaminated of sites and thus perhaps identifies the capture of scaleeconomies in remediation technology for the most contaminated sites byresponsible parties.

Figure 2 presents the distributions of the estimated sale price discountsper parcel associated with the structural and land demolition variables.The estimated structural demolition discounts were calculated for each ofthe 95 parcels in the redeveloped group by identifying the total combineddiscount associated with the variables COND, CAPINT, and CAPINTSQ.Similarly, the land demolition discounts were calculated for each parcel byidentifying the combined discount associated with the variables PROB-CON and PCONSQ. The respective structural and land demolition dis-

5Statistical significance is maintained in the model with the contamination variable speci-fied in natural log form.

FIG. 2. Frequency distribution of estimated sale price discounts per parcel to account forstructural and land demolition costs.


counts were then assembled into normalized distributions. As expected,the structural demolition discount has a median value of about y$287,000per parcel and exhibits only a 4% probability of actually being positive.Further investigation of the four observations that exhibit positive struc-tural demolition discounts shows that their average discount was substan-

Ž .tially positive q$342,000 and all four parcels have a condition code equalto 1, identifying a high quality structure on the parcel. This is consistentwith the view that when high quality structures exist on parcel, investorsseeking to redevelop the parcel have to pay the salvage value of thebuilding, whether it is utilized or razed for redevelopment.

The distribution of estimated land demolition exhibits a somewhatskewed distribution, and the land demolition or contamination discountsare quite high on a per parcel basis. The median land demolition discountis approximately y$1.9 million dollars per parcel or about y$1 million peracre, although discounts between y$400,000 and y$800,000 occurred

w xwith the most frequency. For comparison, Noonan and Vidich 11 calcu-late from their survey of site remediations that the total cost of remedia-tion, which includes Phase I, Phase II, and cleanup, is on average about$290,000, with a minimum and maximum cost identified at $111,000 and$914,000, respectively.6

The contribution of the structural and land demolition variables to theexplanatory power of the redeveloped value function is significant, improv-ing the adjusted R2 by six percentage points, from 0.542 to 0.623. Lastly,the coefficient of the Mills ratio in the redeveloped value function VR1 isnearly significant, which could be interpreted as indicating a model mispec-ification without the inclusion of the demolition variables. More impor-tantly, however, the Mills ratio is insignificant in Model VR2, identifyingthat, with the inclusion of the demolition variables, model specification isimproved and selection bias is likely not present between the redevelopedand current-use data groups.

5.C. Current-Use Value Equations

The results of the two specifications of the current-use equations, VC1and VC2, are presented in Table 5. Model VC1 is specified without theland demolition variable PROBCON, and Model VC2 is specified with thisvariable to test if the probability of contamination is a significant determi-nant of current-use value as well as for redeveloped value. The variableCOND, the condition code of the structure, is included in both VC1 andVC2. This is because the condition of the structure is relevant to purchaseof a parcel remaining in its current use, and it is not intended as a proxy

6 Dollar values converted to 1995$. It is not clear if these dollar figures represent cleanupcosts per site or per acre.


for any structural demolition costs as is the case for the redeveloped valueequation. The variable PROBCON in Model VC2 is insignificant, al-though, consistent with expectations, the sign of the coefficient is negative.

C Ž .The functional form for V , Eq. 4 , resulted from the imposition of aCobb]Douglas functional form for the real estate variable, or R s LaSl.Focusing on the results for Model VC2, the coefficient of LNA is negativeand significant, indicating that, for current-use parcels, the per-square-footunit value declines with increasing land area. The coefficient of LNA is

Ž .equal to a y 1 , where a represents the elasticity of land value to landarea in a Cobb]Douglas functional form. a is estimated within thecurrent-use data group to be equal to 0.269. The coefficient of LNSPACEin the model is equal to l, which represents the elasticity of land value tofloor space. In Model VC2, the coefficient of LNSPACE is positive andsignificant and identifies that the elasticity of land value to building spaceis equal to 0.426.

Of the locational variables, the influence of CBD and LNORTHD onunit bid value are significant for current-use parcels, with unit bid valueexhibiting a quadratic relationship with respect to CBD. The average unitbid value declines to a minimum reduction of about 70% at 7.5 miles fromthe CBD, rising thereafter. A north location relative to a south locationgains 35% in unit bid value. Similar to the redeveloped group of parcels,the sector with highest average unit value is the northwest sector of thecity, outside nine miles from the central business district.

Both of the site-specific explanatory variables, COND and AGE, arestatistically significant. Each unit increase in condition code decreases unitbid value by 39% and each additional 10 years to the structure’s agedecreases unit value by about 8%. The racial population variable PC-NTAFAM, which identifies the percentage of African-American popula-tion within the census tract of the parcel, also has a large and significanteffect on current-use unit value. A 10% increase in census tract African-American population reduces parcel unit bid value about 6%. Lastly, thecoefficient of the Mills ratio is not significant, again identifying no selec-tion bias between the redeveloped and current-use data groups.

5.D. Structural Probit Results

The redeveloped and current-use value equations estimated above areused to calculate the value differential for each property in the fulldataset. The value differential for each property is the difference in theestimated value identified by the redeveloped value equation minus the

Ž R C .predicted value identified by the current-use value equation, or V y V .This measurement of estimated parcel value differential is then used in the

Ž .structural probit model, Eq. 15 , to test the hypothesis that redevelopment


occurs when the value of parcel converted to a new use exceeds theparcel’s current-use value.

Similar to the previously mentioned situation caused by the inclusion ofŽ .an estimated variable the Mills ratio in the value equations, since the

probit model is estimated using a variable that is itself estimated from thevalue equations, there may be bias in the standard errors of the probitmodel. In this probit analysis, the standard errors shown are uncorrected,so there is a possibility that they may be biased downwards.7

The results of the various specifications of the structural probit equa-tions are presented in Table 6. The dependent variable of the probit modelis REDEV, which takes on the value of 1 if a parcel is among theredeveloped properties and 0 if it is among the current-use properties. Theestimated values of the dependent variable REDEV are interpreted as theprobability of redevelopment occurring contingent upon a sale. The ex-planatory variable in the structural probit model is VALDIFF, which is theestimated value differential calculated as the difference between thepredicted redeveloped value and the predicted current-use value for allobservations.

For Probit Model 1 in Table 6, which uses as a measure for VALDIFFŽthe difference between VR1 and VC1 model specifications that exclude

7Recognizing the possibility of standard error bias but leaving the standard errors uncor-rected is the standard approach to this problem, due to the complexity of the correction

w x w xprocedure. This approach is the same as used by Munneke 10 . See also Lee 4 .

TABLE 6Estimation Results of the Structural Probit

ValuedifferentialŽ .Constant VALDIFF LLF LRI

Model 1 } 0.000698 y127.748 0.054Ž .Excluding demolition variables 3.342

VALDIFF s VR1 y VC1 y0.119778 0.000809 y127.039 0.060Ž . Ž .1.185 3.398

Model 2 } 0.001470 y118.076 0.126Ž .Including demolition variables 4.426

VALDIFF s VR2 y VC2 y0.027682 0.001469 y118.034 0.126Ž . Ž .0.290 4.437

Note: The absolute values of the t statistics are presented in parentheses. The dependentvariable in the structural probit is REDEV which takes on the value of 1 if the parcel wasredeveloped and 0 otherwise. The LRI is analogous to the R2 statistic in an OLS model. See

w xGreene 2, p. 651 .


.the demolition variables , the impact of VALDIFF on the probability ofredevelopment is positive and significant. The significance of VALDIFF inProbit Model 1 is maintained for probit model specifications that bothinclude and exclude a constant term, although the constant is statisticallyinsignificant. The insignificance of the constant term is an interestingresult, given the fact that the demolition variables have been excluded

R w xfrom the redeveloped value function V . Munneke 10 argues that if thevalue differential is the only determinant in the criterion function, then asignificant constant term would represent a fixed demolition cost forindustrial property. However, Munneke finds in his probit analysis ofindustrial properties that the inclusion of a constant term, while statisti-cally significant, eliminates the significance of the value differential vari-able for industrial properties.8 In this analysis, the insignificance of theconstant term in the Probit Model 1 supports the view that demolitioncosts are likely to be systematically related to some characteristic of theparcel that would certainly be capitalized into land value and thus becomepart of the parcel’s value differential.

Model 2 uses the value equations specified with the demolition variablesVR2 and VC2 to calculate VALDIFF. The mean value for VALDIFF is,as expected, positive for the redeveloped subset of parcels, and, on

Žaverage, parcels in the redeveloped group have realized about $190,500 in.1995$ gain in value through conversion to a new use. The standard

deviation of VALDIFF is quite large, however, at $558,000. Also, asexpected for the current-use group, the predicted value differential isnegative with a value of y$229,000 and a standard deviation of $635,000.

In Probit Model 2, the significance of the VALDIFF variable is substan-tially enhanced, and, as in Probit Model 1, the constant term is insignifi-cant. Probit Model 2 identifies that, for industrial properties in the City ofChicago, a value differential on the order of $957,000 will result in a 90%probability of redevelopment by the private market. The estimated rela-tionship between the magnitude of the value differential and the predictedprobability of redevelopment, as estimated by Probit Model 2, is presentedin Fig. 3.

The significance of the coefficient for the VALDIFF variable in thestructural probit model allows identification of an estimated relationshipbetween the a priori probability of land contamination and the probabilityof industrial redevelopment. This relationship, presented in Fig. 4, isproduced by calculating the value differential at the sample means of the

8 w xAlso in Munneke’s 10 analysis, he finds the inclusion of a capital intensity variable intothe probit model is statistically significant. From this result, he argues that demolition costsare systematically related to the amount of capital on an industrial parcel and that this is asignificant determinant of redevelopment.


FIG. 3. Estimated relationship between value differential and probability of redevelop-ment for industrial real estate in Chicago for Model 2: Valdiff s VR2 y VC2.

redeveloped parcels for all variables other than PROBCON. The value ofPROBCON is allowed to vary between 0 and 1. The change in the value

Ž .differential VALDIFF associated with the change in PROBCON is thentranslated into a change in the probability of redevelopment, as specifiedby the structural probit model. In a separate evaluation, hypothetical

FIG. 4. Estimated relationship between the a priori probability of contamination and theprobability of redevelopment evaluated at the sample means.


Žreductions of contamination risk to zero i.e., setting the variable PROB-.CON to zero for each parcel in the redeveloped group produced an

average increase of 35 percentage points in the probability of redevelop-ment for each parcel, to an average probability of redevelopment of 92%.

6. CONCLUSION

The results of this study support two major conclusions. First, thisanalysis provides strong support for the view that investors who arepurchasing land for industrial redevelopment are discounting their bidvalue to account for contamination risk in a systematic fashion, and withinthe City of Chicago contamination risk appears to have been fully capital-ized into industrial land values. The magnitude of this discount}a median76% unit value discount which translates to about a median $1.9 millionper parcel discount or about a $1.0 million per acre discount in 1995$}issomewhat higher in comparison to the limited information we have for themagnitude of private voluntary cleanup costs, including Phase I, Phase II,and remediation expenditures. Therefore, it is possible that investors areperhaps either overestimating the financial liability or that the discountsincorporate the present value of required legal costs certain to be part ofany site redevelopment. However, the results suggest that contaminationrisk is not, per se, a detriment to redevelopment. Contamination riskreduces the value of land, which in the short term reduces the valuedifferential available to an investor and increases the scale of financialcapital required for redevelopment. The evidence here suggests that in-vestors, at least those in Chicago, could expect to recoup the expendituresrequired to remove contamination liability through increase in land valueafter site remediation.

Second, this study provides additional empirical evidence to support themyopic redevelopment rule in explaining industrial redevelopment activityin an urban area, and this analysis is the first to utilize the myopic optimalredevelopment rule to estimate the relationship between land contamina-tion risk and the probability of urban industrial redevelopment. On aver-age, a hypothetical cleanup of the average parcel in the dataset, with a 0.67probability of contamination and a value differential on the order of$190,000, improves the probability of redevelopment about thirty-fivepercentage points, from 0.57 to 0.92.

The evidence presented here concerning the capitalization of contami-nation risk shows that, while there is not a fundamental bias againstcontaminated industrial parcels within the urban core, the financial bur-den, estimated at approximately $1 million per acre, without questionincreases the scale of financial capital required for private market redevel-opment. The evidence here also suggests that a publicly funded cleanupwould substantially increase land value and thus increase the parcel’s value


differential. This could substantially increase the probability of a parcel’sredevelopment by the private market, as well as increase the property taxrevenue from the parcel. For these reasons, combined with the reductionin risk to human health and ecosystems, more aggressive municipal gov-ernmental intervention in site remediation might indeed be justified. Theevidence here suggests that it quite possible for a publicly funded remedia-tion of a contaminated site to increase, in the short term, the probability ofredevelopment by the private market, but only after some serious scrutiny

Žof the other systematic determinants of land value and thus value differ-.ential for the parcel in question. Intervention would be optimal on

marginal properties that have the locational and other site characteristicswhich, but for the existence of contamination risk, would clearly bedesirable to an industrial user. For such properties, the gain in land valueand the subsequent increase in value differential resulting from a siteremediation might indeed bring the parcel into a more competitive posi-tion in the private industrial real estate market and could be viewed as aviable strategy to attract jobs to a central-city location. Therefore, under-standing the spatial patterns of value differential for industrial parcelswithin the urban core could be a very important strategy for local govern-ments to optimize their efforts, specifically publicly funded site cleanups,to attract new industrial users for brownfield properties within the urbancore.

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4. L. F. Lee, Unionism and wage rates: A simultaneous equations model with qualitativeŽ .and limited dependent variables, International Economic Re¨iew, 19, 415]433 1978 .

Ž .5. L. F. Lee, Identification and estimation in binary choice models with limited censoredŽ .dependent variables, Econometrica, 47, 977]995 1979 .

6. L. F. Lee, G. S. Maddala, and R. P. Trost, Asymptotic covariance matrices of two stageprobit and two state tobit methods for simultaneous models with selectivity, Econo-

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10. H. J. Munneke, Redevelopment decisions for commercial and industrial properties,Ž .Journal of Urban Economics, 39, 229]253 1996 .

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urban industrial land redevelopment and contamination risk

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