do liquidation values affect financial contracts

DO LIQUIDATION VALUES AFFECT FINANCIALCONTRACTS EVIDENCE FROM COMMERCIAL LOAN

CONTRACTS AND ZONING REGULATION

EFRAIM BENMELECH

MARK J GARMAISE

TOBIAS J MOSKOWITZ

We examine the impact of asset liquidation value on debt contracting using aunique set of commercial property loan contracts We employ commercial zoningregulation to capture the flexibility of a propertyrsquos permitted uses as a measure of anassetrsquos redeployability or value in its next best use Within a census tract moreredeployable assets receive larger loans with longer maturities and durations lowerinterest rates and fewer creditors controlling for the propertyrsquos type sale price andearnings-to-price ratio These results are consistent with incomplete contracting andtransaction cost theories of liquidation value and financial structure

I INTRODUCTION

How do liquidation values affect financial contracts An ex-tensive theoretical literature [Williamson 1988 Harris and Raviv1990 Aghion and Bolton 1992 Shleifer and Vishny 1992 Hartand Moore 1994 Bolton and Scharfstein 1996 Diamond 2004]argues that optimal debt policy critically depends on how costly itis for creditors to seize and liquidate assets Empirical evidenceon this question is scarce however due to the difficulty in ob-taining a measure of an assetrsquos liquidation value or value in itsnext best use We provide empirical evidence on the link betweenliquidation value and debt contracts using a unique sample ofcommercial property loans and variation in property zoningordinances

Liquidation value is of central importance for financial deci-

We have benefited from the suggestions and comments of an anonymousreferee George Baker Antonio Bernardo Marianne Bertrand Patrick BoltonLauren Cohen Douglas Diamond Eugene Fama Edward Glaeser (the editor)Oliver Hart John Heaton Thomas Hubbard Eugene Kandel Steven KaplanRobert Novy-Marx Amil Petrin Michael Roberts Andrei Shleifer Jeremy SteinPer Stromberg Annette Vissing-Jorgensen Luigi Zingales and seminar partici-pants at Harvard University The University of Chicago Finance lunch IndianaUniversity University of Illinois at Urbana-Champaign Ohio State Universityand Princeton University Special thanks to Michael Arabe John Edkins andPeggy McNamara as well as COMPScom for providing commercial real estateand zoning regulation data to Robert Figlio David Ingeneri and CAP Index Incfor providing crime data and to Joseph Gyourko for supplying the Wharton LandUse Control Survey Moskowitz thanks the Center for Research in Security Pricesfor financial support

copy 2005 by the President and Fellows of Harvard College and the Massachusetts Institute ofTechnologyThe Quarterly Journal of Economics August 2005

1121

sions when contracts are incomplete and transaction costs existIn particular debt contracts allow the creditor to seize the debt-orrsquos assets when the latter fails to make a promised paymentSince the debtor cannot commit to not withdraw his humancapital from the project (as in Hart and Moore [1994]) or to notdivert cash flows to himself (as in Aghion and Bolton [1992])creditors will agree to lend only if the debt is secured by theprojectrsquos assets and default triggers its liquidation

Testing these theories requires that the econometrician ob-serve the liquidation value of the asset but it is difficult toascertain ex ante when the parties enter the debt contract whatthe proceeds from selling the asset to the next user might be Asa proxy for the ex ante value of the asset in its next best use weemploy property-specific zoning assignments to capture micro-level variation in liquidation values

The real estate market is a natural candidate for testingfinancial contracting from an incomplete contracting perspectiveFirst the loans are typically secured and nonrecourse thus pro-viding a set of project-specific financings and characteristics con-sistent with the inalienability of human capital described by Hartand Moore [1994] and other models Second debt levels in com-mercial real estate are typically very high at initiation (median of82 percent of value) Thus it is plausible that the financingprovided is closer to the maximal leverage or project debt capacitythe lender will tolerate which is closer to the underlying theoriesFinally the real estate market offers a potential measure of anassetrsquos liquidation value through zoning ordinances which governthe permitted uses of a property

This empirical approach is motivated by Shleifer and Vish-nyrsquos [1992] argument that a broader set of buyers can potentiallyraise the liquidation value of an asset Zoning regulations deter-mine the set of uses of a property Properties with more allowableuses should have a greater number of potential buyers all elseequal and therefore a higher value in the event of liquidation Apropertyrsquos zoning designation is thus a measure of its redeploy-ability in the sense of Williamson [1988]

We recognize that the current market price of the asset isalso likely to be affected by redeployability and more specificallyzoning which along with the financing environment may bejointly determined by local market unobservables [Glaeser andGyourko 2003 McMillen and McDonald 2002] Endogeneity con-

1122 QUARTERLY JOURNAL OF ECONOMICS

cerns of this type are less relevant for our study for severalreasons First the zoning code is set at the jurisdiction or citylevel We examine property-specific zoning assignments within acensus tract where census tract fixed effects difference out un-observable neighborhood effects such as local market conditionsquality or degree of bank redlining at a level finer than the localzoning code or the typical lending market (see Berger Demsetzand Strahan [1999] Petersen and Rajan [2002] and Garmaiseand Moskowitz [2004 2005]) Second to distinguish the impor-tance of collateral value from the current market value or prof-itability of the property we focus on the characteristics of thedebt contracts controlling for the sale price and capitalizationrate (income divided by price) of the property to attempt toisolate the component of redeployability related to the assetrsquossecondary or liquidation value The price and cap rate likely soakup other quality differences within a census tract that may beunrelated to collateral value

We find that controlling for the propertyrsquos price earnings-to-price ratio type general zoning year and census tractgreater redeployability is associated with larger loans lower in-terest rates longer maturity and duration debt and fewer cred-itors Moving from the least to the average (most) zoning flexibil-ity lowers the interest loan rate by 27 (58) basis points perannum increases the loanrsquos size relative to the value of theproperty by 19 (41) percentage points lengthens the loanrsquos ma-turity by 11 (23) years increases duration by 02 (05) years anddecreases the probability of borrowing from multiple lenders by40 (85) percentage points Including bank or buyer fixed effectsin addition to census tract fixed effects to further difference outunobservables related to the lender or borrower we find quanti-tatively similar effects

In addition our redeployability measure has a significantlylarger impact on loan contracts in states in which foreclosure isrelatively easy suggesting that it is the effect of zoning on theassetrsquos liquidation value that is driving our results since onlycollateral value is relevant in foreclosure We also find the effectof our zoning redeployability measure to be magnified in districtsin which survey evidence suggests that zoning rules are admin-istered more strictly and where local market liquidity is higher

We also employ another measure of liquidation value usingldquohistoricalrdquo zoning districts which are quite restrictive and in-

1123DO LIQUIDATION VALUES AFFECT FINANCIAL CONTRACTS

flexible We find that historic-zoned properties receive signifi-cantly fewer smaller and shorter duration loans are financed athigher interest rates and are more likely to be financed bymultiple creditors Since it is also more difficult to obtain zoningchanges within historic districts we interact our redeployabilitymeasure with the historic designation and find that redeployabil-ity has an even greater effect on loan terms in historic areas

Finally since the current price of the property should be afunction of liquidation value we also show that more redeploy-able properties enjoy higher market prices While we interpretthis result with caution due to greater endogeneity concerns it isconsistent with flexibility-of-zoning capturing liquidation value

Previous research has analyzed some of the implications ofincomplete contracting for financial structure but has not fo-cused on liquidation value which plays a prominent role in thetheory [Baker and Hubbard 2003 2004 Kaplan and Stromberg2003 Gilson 1997] The existence of inefficient liquidation or ldquofiresalesrdquo has been documented [Pulvino 1998 1999 Stromberg2000] but not the interplay between ex ante liquidation valueand financial structure at the time the contract is set Otherstudies examine the relation between balance-sheet figures suchas tangibility (eg the ratio of fixed assets to total assets) andcapital structure [Braun 2003 Harris and Raviv 1991 Rajan andZingales 1995] but it is not clear that such proxies either captureliquidation value or represent total debt capacity Benmelech[2005] analyzes the relation between asset salability and capitalstructure among nineteenth century American railroads findinga link to debt maturity but not leverage

In addition we provide novel micro-level evidence on therelation between liquidation value and number of creditors thatcomplements cross-country studies of lending relationships andcreditor protection [Ongena and Smith 2000 Esty and Megginson2003 Detragiache Garella and Guiso 2000]

The rest of the paper is organized as follows Section IIsummarizes theoretical predictions on the relation between liq-uidation value and financial contracting Section III describes thecommercial loan data local zoning regulations and our empiricalstrategy to measure changes in liquidation value through zoninglaws Section IV presents the empirical results and Section Vconcludes


II LIQUIDATION VALUE AND FINANCIAL CONTRACTS

The value of the creditorrsquos option to liquidate project assetsaffects both his willingness to provide financing and the terms onwhich financing is extended The concept of liquidation valueused in Harris and Raviv [1990] Hart and Moore [1994] andBolton and Scharfstein [1996] is fairly general an assetrsquos liqui-dation value is the amount that creditors can expect to receive ifthey seize the asset from managers and sell it on the open marketWilliamson [1988] and Shleifer and Vishny [1992] analyze twodifferent components of liquidation value Williamson in histransactions cost approach focuses on an assetrsquos redeployability(ie its value in alternative uses) Shleifer and Vishnyrsquos industry-equilibrium model suggests that assets with few potential buyersor with potential buyers who are likely to be financially con-strained when a firm attempts liquidation will be poor candi-dates for debt finance since liquidation is likely to yield a lowprice In these models project financing is highly influenced bythe value of the collateral in the creditorrsquos hands

The following are some of the central empirical predictionsarising from these models

PREDICTION 1 Debt levels increase in asset liquidation value

This general prediction emerges from Williamson [1988]Shleifer and Vishny [1992] Harris and Raviv [1990] and Hartand Moore [1994] Debt triggers liquidation in some states in allthese models and the benefits of debt are tied to the efficiency ofliquidation This prediction applies to the total debt capacity thelender is willing to supply Empirically the equilibrium debt levelis typically observed which all else equal is increasing in debtcapacity In the commercial real asset market debt levels atinitiation are quite high which suggests that they may be closerto the maximal leverage or debt capacity the lender will tolerate

PREDICTION 2 The promised debt yield decreases in asset liquida-tion value controlling for the debt level

Following Prediction 1 increased liquidation value lowersthe cost of liquidation In equilibrium lenders therefore chargelower interest rates on loans made on assets with higher liquida-


tion value controlling for the debt level This is in part whyoptimal debt levels also rise (Prediction 1)1

PREDICTION 3 Debt maturity increases in asset liquidation value

Prediction 3 emerges from Hart and Moore [1994] and fromShleifer and Vishny [1992] Hart and Moore argue that a higherprofile of liquidation values over time increases the assetrsquos dura-bility and makes longer maturity debt feasible Shleifer andVishny analyze the trade-off between the benefit of debt overhangin constraining management and liquidation costs Since as Ben-melech [2005] shows higher liquidation values make overhang(long-term) debt more attractive Shleifer and Vishny thus pre-dict an increase in debt maturity with liquidation value Al-though some of these theories only consider zero-coupon debt areasonable extrapolation yields the implication that debt dura-tion will also increase in liquidation value

PREDICTION 4 Firms borrow from multiple creditors when liqui-dation value is low and from a single creditor when liquida-tion value is high

This is a prediction of Bolton and Scharfstein [1996] andDiamond [2004] Multiple creditors provide discipline at the costof inefficient liquidation

PREDICTION 5 The current market value of the asset is increasingin its liquidation value

Since the liquidation value of the asset is a component of itsoverall value increasing the liquidation value increases currenttotal asset value [Harris and Raviv 1990]

IIA Application to Commercial Real Assets

In order to test these implications we employ a unique dataset of commercial property transactions and financial contractsand use property-specific zoning assignments to capture variation

1 Unconditionally an increase in the liquidation value of the asset raises theoptimal debt level but also provides a greater payment to creditors The net effecton promised debt yields is analytically ambiguous but in numerical results Harrisand Raviv [1990] show that firms with higher liquidation values consistently havehigher debt yields Controlling for the debt level of the firm by contrast higherliquidation values should be associated with lower promised yields since creditorscan expect a higher payment in the case of default


in liquidation value Some discussion of the relation between thedata and the models is in order

Commercial property loans are secured highlighting the po-tential importance of liquidation value and are typically nonre-course [Stein 1997]2 The lender may only pursue the collateralin this case the property and not any other assets of the borrowerin case of default3 Examining variation in financial contractswithin a particular asset class also helps by reducing heteroge-neity in control issues cash flow rights risk or industry competi-tiveness that may arise when examining contracts across vastlydifferent assets projects or investments Finally we argue in thenext section that property-specific zoning assignments within acensus tract can capture micro-level variation in liquidation val-ues used to test the predictions of the models

III DATA AND EMPIRICAL STRATEGY

We briefly describe the data sources used in the paper andour identification strategy for capturing asset liquidation value

IIIA Transaction and Financing Level Data of CommercialReal Assets

Our sample consists of commercial real asset transactionsdrawn from across the United States over the period January 11992 to March 30 1999 from COMPScom a leading provider ofcommercial real estate sales data Garmaise and Moskowitz[2003 2004] provide an extensive description of the COMPSdatabase and detailed summary statistics There are 14159 com-mercial transactions that meet our data requirements over oursample period where the data span eleven states CaliforniaNevada Oregon Massachusetts Maryland Virginia Texas

2 While most commercial real estate loans are nonrecourse our data do notspecify the recourse status of individual loans To the extent that the recoursefeature is related to property type and region our use of property type and censustract fixed effects should account for recourse discrepancies Furthermore weverify that all of our main findings are robust to the exclusion of properties withgreater than 95 percent leverage where recourse is more likely to be usedFinally in California and Oregon pursuing recourse against a defaulting borroweris statutorily prohibited under the preferred and most common form of foreclosure[National Mortgage Servicerrsquos Reference Directory 2001] All the main results inthe paper are robust to using data from only these states

3 In addition although very few repeat buyers exist in our sample includingborrower fixed effects to difference out borrower attributes has little effect on thecoefficient estimates but reduces power considerably


Georgia New York Illinois and Colorado plus the District ofColumbia

COMPS records for each property transaction the sale pricespecific zoning designation (described below) and terms of theloan contract at the time of sale As documented by Garmaise andMoskowitz [2003 2004] debt financing dominates the financialstructure of commercial properties comprising 71 percent of thepropertyrsquos value on average These magnitudes suggest that theloans are likely closer to the maximal debt capacity of the assetCOMPS also provides eight digit latitude and longitude coordi-nates of the propertyrsquos location which we link to Census datasurvey data from the Wharton Land Use Control Survey andcrime rate data from Cap Index Inc

Table I reports summary statistics on the properties in oursample Panel A shows that the average sale price is $24

TABLE ISUMMARY STATISTICS OF ZONING DESIGNATIONS COMMERCIAL REAL ESTATE

TRANSACTIONS AND PROPERTY TYPES

PANEL A MEAN CHARACTERISTICS OF PROPERTIES ACROSS GENERAL ZONING CATEGORY

Zoning category NumberDebt

frequency Leverage PriceMaturity(duration)

Loanrate

Multiplecreditors

Zoningcodes

ALL PROPERTIES 14159 071 071 2386767 15 (68) 828 012 161Organizations

(O) 311 063 072 3495907 10 (79) 825 010 5Waterfront (W) 6 067 085 4887500 15 (86) 700 025 3Manufacturing

(M) 3188 068 072 1807378 10 (68) 873 013 25Residential (R) 7917 081 074 1404530 25 (100) 784 013 36Business (B) 1827 067 072 3478963 7 (64) 865 007 21Commercial (C) 4878 068 067 3138222 10 (69) 864 012 53CommManu

(CM) 252 074 074 1003192 10 (66) 874 019 4Historic (H) 258 068 066 3581531 10 (79) 908 013 4

PANEL B DISTRIBUTION OF ZONING CATEGORY ACROSS PROPERTY TYPE

General zoning type (abbreviated) number of propertiesProperty type O W M R B C CM H

Retail 94 2 227 247 837 1898 87 45Commercial 35 0 107 127 218 749 31 68Industrial 20 0 1953 44 78 230 68 25Apartment 28 0 253 5860 110 383 12 65Mobile home

park 1 0 1 19 0 2 0 1Special 10 0 5 176 18 47 3 2Residential land 38 0 37 1160 14 57 1 6Industrial land 5 0 362 16 3 16 4 2Office 74 4 227 233 520 1396 38 27Hotel 6 0 16 35 29 100 8 17


million though values range from $20000 to $750 millionRecorded details of the loan contract include loan-to-valueratio number of creditors maturity interest rate whether theloan rate is floating or fixed the length of amortization andwhether the loan was backed by the Small Business Adminis-tration (occurring only 13 percent of the time) Using thereported interest rate (r) loan maturity (m) and amortizationperiod (a) we estimate the duration D of the loan assumingthat the debt coupons are paid annually and that there is onefinal balloon payment at maturity

(1) D r 1 m 1r r 11 r1m

r1 1 ra

m 1 r1m 1 ra

1 1 ra

The mean age of our properties is just under 29 years but rangesfrom zero to 200 years Overall the properties in the data set arerelatively small and old and are financed with relatively long-term debt compared with institutional quality real estate (See

TABLE I(CONTINUED)

PANEL C MEAN CHARACTERISTICS OF PROPERTIES ACROSS PROPERTY TYPE

Property type NumberDebt


Loanrate

Multiplecreditors

Caprate

Retail 3949 074 072 1610357 10 (66) 880 010 1033Commercial 1650 040 068 1670517 4 (49) 897 007 1038Industrial 3784 070 073 1589490 10 (67) 872 012 997Apartment 6997 090 074 1529293 25 (100) 777 013 1004Mobile home

park 41 076 071 5087748 10 (68) 846 019 919Special 290 070 077 2109284 10 (62) 888 020 1100Residential

land 1713 041 075 1004216 7 (41) 891 011 NAIndustrial land 568 037 074 921757 8 (48) 906 008 NAOffice 3380 067 068 6595045 10 (66) 860 010 1017Hotel 270 063 069 10574474 14 (66) 882 022 1221

Panel A reports the average loan frequency loan-to-value (LTV) ratio sale price median loanmaturity and duration (in parentheses) in years loan rate (percent per year) frequency of multiplelenders (secondsubordinated loans) and number of unique zoning code designations for all propertiesand for each general zoning category Panel B reports the distribution of general zoning categories acrossten property types The number of properties under each of the eight broad zoning categories for eachproperty type are reported Panel C reports the average loan frequency loan-to-value (LTV) ratio saleprice loan maturity (in years) loan rate (percent per year) frequency of multiple lenders (secondsubordinated loans) and capitalization rate (net income on the property in the previous year divided bythe sale price in percent) across the property types Data are from COMPScom covering the periodJanuary 1 1992 to March 30 1999


for example Titman Tompaidis and Tsyplakov [2004])4 Theseproperties are particularly appropriate for tests of the role ofliquidation value since the real option to liquidate the asset (forexample by knocking it down and constructing something new) ismore important for older lower quality buildings

IIIB Zoning Designations

Our sample consists of properties that are located in a varietyof urban and suburban locations 387 percent of the propertiesare located in the 20 most populated United States cities 623percent are in the top 50 cities and 838 percent are located in oneof these major cities or have a population density of at least100000 residents per three-mile radius We match our sampleto the zoning codes of the corresponding urban or suburban lo-cality We observe 161 unique zoning designations among ourproperties

Zoning regulations are controlled by local units of the gov-ernment and are designed to manage the physical development ofland and the uses to which each individual property may be putZoning definitions are typically nested and classified along twofacets The first dimension spans the breadth of permitted usesThe most common categories of this dimension in urban areas arebusiness commercial manufacturing residential organizationsand historic The second dimension of zoning determines theintensity and scope of the allowable use of the property within itsbroad category It may limit the permitted size of the buildingrelative to the size of the lot the number of individual unitspermitted on the lot or the maximum height or number of storiesAn alphabetic modifier typically describes the zoning category(first dimension) while the second dimension is denoted by anumeric scale Appendix 1 provides an example of the residentialzoning codes in New York City We term the numerical intensitythe ldquowithin zoning valuerdquo Higher values indicate broader scopesof allowable uses within the zoning general category

Since zoning is a local affair set at the county city ormunicipality level its ordinances and classifications vary fromplace to place Variation in zoning across cities or neighborhoods

4 The length of loan maturity is in part driven by the large fraction ofapartment buildings in our sample that carry very long-term loans perhaps dueto the involvement of Fannie Mae and Freddie Mac in this market Althoughpower is reduced considerably the magnitudes of our results including maturityand duration are robust to the exclusion of apartments


can be driven by political considerations esthetic or historic pres-ervation efforts and motives for controlling growth in an areaSome of these are endogenous and possibly related to an under-lying effect that also determines the financing environment Forexample Glaeser and Gyourko [2003] discuss the determinationof zoning in an area and its conformity to local market conditionsHowever by employing census tract fixed effects which are muchfiner than the level at which zoning codes were set or lendingmarkets operate (see Berger Demsetz and Strahan [1999] Pe-tersen and Rajan [2002] and Garmaise and Moskowitz [20042005]) we difference out local market conditions potentially af-fecting the zoning code and financing environment Variation inzoning within census tracts is a planning tool that provides for avariety of land uses in a given neighborhood while regulating theeffects of externalities Many zoning designations are quite oldand reflect historical planning agendas [McMillen and McDonald2002] For example Swope [2003] reports that as of 2003 zoninglaws in many major cities in the United States (eg Boston) dateback to the 1950s and 1960s and thus are less likely to be drivenby an omitted variable that affects loan provision today Even incities in which the zoning ordinance has been amended repeat-edly zoning laws can yield different micro-level zoning designa-tions within a census tract For example the Chicago zoningordinance has been criticized as being unpredictable at the microlevel In the next section we confirm that our within census tractmeasure exhibits no correlation with local financing characteris-tics Table I Panels A and B report summary statistics on zoningcodes and categories across properties

IIIC Using Zoning Regulations to Measure Liquidation Values

Using the zoning designation of each property at the time ofsale we exploit variation within an area and zoning category interms of the flexibility of permitted uses of the property Ourproxy for liquidation value is a measure of the propertyrsquos rede-ployability or zoning flexibility within its general zoning categoryProperties with more flexible zoning designations admit morepotential uses Creditors who seize a property subject to restric-tive zoning will find it difficult to pursue alternative uses for thestructure or land whereas creditors who foreclose on a propertythat is loosely zoned can redeploy the asset in many differentways

To illustrate the dimensions of zoning and how we compute


our measure of redeployability consider the case of residentialzoning districts in New York City According to the NYC Zoninghandbook there are eighteen different zoning districts within theresidential category Appendix 1 provides a detailed descriptionof each of the residential zoning districts in NYC and a summaryof their permitted uses The allowable uses within the generalresidential zoning category are increasing with the zoning districtnumeric scale For example the R-2 zoning district allows for aminimum lot area of 3800 square feet allows only detachedsingle- or two-family residences and allows a maximum numberof dwelling units per acre of eleven whereas R-4 allows a mini-mum lot area of 970 square feet semidetached structures as wellas single- or two-family residences and allows up to 45 dwellingunits per acre Moving down the code the higher the numericvalue the fewer constraints placed on property uses

To construct our redeployability measure we extract thenumeric ldquowithin valuerdquo to capture redeployability within eachbroad zoning category For comparison across locales and zoningcategories we then scale the within zoning numeric value by thenumeric value of the zoning designation with maximum allow-able uses within its broad category in the local area For examplea zoning district of M-1 is first coded by a manufacturing dummyvariable that is set equal to 1 and a redeployability variablewithin this category If the manufacturing zoning designationsfor a particular locale are M-1 M-2 M-3 and M-4 then thewithin redeployability value is 0255 Scaling the raw withinzoning value for the range of allowable uses in a given areanormalizes the local zoning assignments across jurisdictions Forproperty p with zoning designation A-n in jurisdiction j thismeasure is nmax(n P( A j)) where P( A j) is the set of propertieswithin jurisdiction j that have the same general zoning categoryA We use the empirically observed maximum value in jurisdic-tion j for scale where results are robust to defining j to be the zipcode two-mile radius five-mile radius county or MSA For con-venience and uniformity we report results defining locales forscale at the zip code level

Our measure of redeployability treats each within numeric

5 When modifiers are used in zoning districts we refine the within numericvalues further such that they account for this subdivision For example given thefollowing residential zoning designations within an area R-1 R-2A R-2B R-2Cand R-3 the within numeric value of R-2C will be 267 and its scaled value whichis our measure of redeployability will equal 26730 089


value equally for simplicity and to avoid imposing an arbitrarynonlinear structure We see no reason to expect any bias in thelinear specification that would have any relation to loan contractterms Moreover we formally test and reject a nonlinear specifi-cation in favor of a linear model6

A natural question arises about whether zoning laws areactually enforced and how easy it is to acquire a zoning varianceThis issue is essentially an empirical one The evidence we de-scribe in Section IV in support of the effects of zoning on debtcontracts suggests that zoning restrictions certainly do some-times bind Rezoning or obtaining a variance is typically difficultand costly (in terms of time uncertainty and expense) andtherefore zoning remains quite stable However we also exploitthe variation in zoning enforcement across regions and find thatthe effects on contracts are magnified in districts where zoningrules are administered more strictly

Figure I plots the distribution of our redeployability measureacross all properties in our sample The mean (median) scaledflexibility measure is 051 (050) with a standard deviation of 024and ranges from 008 to 1

IV EMPIRICAL RESULTS OF REDEPLOYABILITY (THROUGH ZONING)

Using zoning flexibility to measure ex ante liquidation valuewe test the predictions of the models from Section II

IVA Econometric Model

Our econometric model considers the effect of our redeploy-ability variables on the following loan characteristics annualinterest rate frequency (ie whether or not a loan is granted abinary variable) leverage (loan size divided by the sale price)loan maturity in years loan duration in years and presence ofmultiple creditors (a binary variable) The equation estimated is

6 We check for the presence of nonlinearities associated with our redeploy-ability measure by regressing each of our loan characteristics as well as the saleprice on dummy variables for every redeployability value (there are 427 uniquevalues) We then take the estimated dummy coefficients from this regressionrepresenting the effect each redeployability value has on the particular loan termsor price and regress them on the continuous redeployability measure its squaredterm and cubed term For all dependent variables the nonlinear terms arerejected in favor of a linear specification for describing the data


(2) loan characteristici

Fredeployabilityi pricei cap ratei controlsi i

where cap rate is the most recent earnings on the property di-vided by the sale price and controlsi is a vector of controlscontaining a set of property and neighborhood attributes for asseti including census tract year property type and zoning category

Summary statistics of the liquidation value measure standard

Mean MedianStandarddeviation Minimum Maximum

Redeployability 051 050 024 008 1

FIGURE IDistribution of Redeployability (Zoning Flexibility)

The distribution of a measure of real asset liquidation value determined by aproxy for the assetrsquos redeployability measured by its zoning classification isplotted below The allowable use of the property within its broad zoning categoryand local zoning jurisdiction scaled by the maximum allowable uses within anarea and zoning category is the measure of redeployability Higher values indi-cate broader scopes of allowable uses within a general category and jurisdiction


fixed effects and i is an error term The sale price and cap rateare included as regressors to control for value in current use andcurrent profitability thereby isolating the component of redeploy-ability related to secondary or collateral value We mainly esti-mate linear models though other functional forms are consideredfor the binary dependent variables

In advance of our discussion of the empirical results it isworthwhile to consider the econometric issues raised by our speci-fication in equation (2) The first point is that the sale price itselfmay be a function of the redeployability variable we would expectmore redeployable properties to realize higher prices and indeedwe provide evidence in favor of this hypothesis in subsection IVIThis relation presents no special econometric problem

The second and more serious concern is that some unob-servable variable (such as bank redlining) has a simultaneouseffect on loan provision sale prices and zoning regulations ren-dering all of our variables endogenous and difficult to interpretThis issue is taken up in the real estate literature (eg McMillenand McDonald [1991] Quigley and Rosenthal [2004] and Wallace[1988]) and there is evidence that local market conditions canaffect the general zoning of an area7 Therefore we employ censustract fixed effects to difference out unobservables at a level muchfiner than the level at which zoning is being set or local financialmarkets operate A census tract typically covers between 2500and 8000 persons or about a four-square block area in most citiesand is designed to be homogeneous with respect to populationcharacteristics economic status and living conditions (sourceUnited States Census Bureau) In our loan sample we have 2090census tracts (about four properties per tract) of which 1296contain more than one property transaction 485 have at least fivetransactions and 170 contain more than ten transactions

Local debt market conditions are clearly highly uniformwithin a census tract so the financing environment is unlikely tobe driving the micro-level zoning variation we study The stan-dard definition of the local banking market in the literature (egBerger Demsetz and Strahan [1999]) is the local MetropolitanStatistical Area (MSA) or non-MSA county We explicitly testwhether zoning and the financing environment within a census

7 Some useful references on the relationship between zoning and prices arePogodzinski and Sass [1991] Pollakowski and Wachter [1990] Glaeser and Gy-ourko [2003] and McMillen and McDonald [2002]


tract are related by regressing various lending bank characteris-tics on our redeployability measure and census tract fixed effectsWe find no significant relation between redeployability and aver-age bank deposit size (t 074) bank asset size (t 061)bank fraction of deposits within the county (t 001) city (t 001) or zip code (t 147) nor the frequency of thrifts (t 078) Thus it is not the case that zoning flexibility within acensus tract is correlated with the financial environment

In addition we also show that the inclusion of bank fixedeffects (with census tract fixed effects) does not materiallyweaken our results This result indicates that our findings are notdriven by different types of banks making loans to more or lessredeployable properties

We also control for the sale price and earnings-to-price ratioof the property in an attempt to isolate the component of ourredeployability measure related to liquidation value Variablesaffecting market value and zoning simultaneously should be cap-tured by the sale price and cap rate and may in fact understatethe effect of our zoning variable on loan terms Potential omittedvariables affecting zoning and financing on a specific propertywithin a census tract type year and zoning category and con-trolling for sale price and cap rate are difficult to envisionMoreover previous empirical work shows that higher ldquoqualityrdquoareas are associated with restrictive zoning [Quigley andRosenthal 2004] while we find by contrast that it is flexiblezoning that predicts greater loan provision Thus it is difficult toargue that ldquoqualityrdquo effects are driving our results

Alternatively unobservable variables may be property-spe-cific for example a characteristic of the buyer It is highly un-likely however given the stability of zoning classifications thatany buyer characteristic could affect the zoning of a property atthe time of sale Moreover because census tracts are designed tocapture population and economic homogeneity using tract fixedeffects helps control for characteristics of buyers and sellers Inaddition despite having only a few multiple borrowers andtherefore very low power we find that our results are robust tothe inclusion of borrower fixed effects in the sense that our pointestimates are similar Borrower fixed effects effectively differenceout any quality differences across borrowers

We are essentially estimating reduced-form equations for theprice quantity and terms of the debt supplied which is reason-able since we are only interested in testing the equilibrium out-


comes and implications proposed by the theories in Section II Asargued earlier these effects may be closer to supply-side con-straints The similarity of the coefficients under the borrowerfixed effects specification also indicate that we are likely captur-ing supply-side effects However while it would be interesting todifferentiate among the theories our data are insufficiently richfor us to do so Therefore we can only say whether the results areconsistent with these theories in general

IVB Asset Redeployability (Flexibility of Zoning)

The first column of Table II Panel A reports results for theregression of the loan interest rate on our redeployability mea-sure the log of the sale price and the capitalization rate of theproperty and a set of controls including census tract fixed effectsIn addition to fixed effects for year property type census tractand zoning category we include the Herfindahl index of bankingconcentration within a fifteen-mile radius of the property (a mea-sure of local bank competition for commercial loans) the log ofproperty age and the 1995 crime risk and growth in crime riskfrom 1990 to 19958 In addition we also include attributes of theloan such as maturity amortization leverage and dummies forfloating rate loans and Small-Business-Administration-backedloans

We find that redeployability significantly decreases the in-terest rate charged controlling for the debt level Moving fromthe least flexibly zoned designation to the average (most) flexiblyzoned within an area and zoning category translates into a 27 (58)basis point drop in loan interest rates This result is consistentwith Prediction 29

The second and third columns of Table II Panel A examinethe relation between leverage and redeployability Column 2 em-ploys a binary dependent variable for whether debt is used Weestimate a linear probability model to avoid making functionalform assumptions but a conditional logit model yields similarresults We find that properties with greater redeployability do

8 Crime risk data come from CAP Index Inc who compute the crime scoreindex for a particular location by combining geographic economic and populationdata with local police FBI Uniform Crime Reports victim and loss reports SeeGarmaise and Moskowitz [2005] for further discussion

9 Harris and Raviv [1990] claim that when not conditioning on loan size thepromised yield should increase with liquidation value This numerical result oftheir model is not borne out by the data however as unconditional interest ratesare also decreasing in redeployability in unreported results


TABLE IIASSET REDEPLOYABILITY (MEASURED BY ZONING INTENSITY OF USE)

AND DEBT CONTRACTS

PANEL A CENSUS TRACT FIXED EFFECTS

Dependentvariable

Interestrate

Debtfrequency Leverage

Debtmaturity

Loanduration

Multiplecreditors

Redeployability 06311 00078 00447 24821 04892 00926(259) (013) (212) (194) (250) (236)

log(price) 00850 00235 07173 00678 00091(385) (467) (594) (365) (261)

Cap rate 00081 00077 00042 02292 00393 00027(198) (801) (260) (1011) (1124) (416)

Fixed effectsCensus tract yes yes yes yes yes yesGeneral zoning yes yes yes yes yes yesProperty type yes yes yes yes yes yesYear yes yes yes yes yes yes

R2 064 035 034 051 046 027R2 (no FE) 026 008 006 016 010 004 Observations 3536 9365 7733 7733 1971 7733

PANEL B CENSUS TRACT AND BANK FIXED EFFECTS

Dependentvariable

Interestrate


Debtmaturity

Loanduration

Multiplecreditors


log(price) 00963 00321 04951 00489 00320(386) (704) (281) (190) (441)

Cap rate 00280 00051 00024 01111 00327 00002(585) (599) (157) (360) (762) (015)

Fixed effectsBank yes yes yes yes yes yesCensus tract yes yes yes yes yes yesGeneral zoning yes yes yes yes yes yesProperty type yes yes yes yes yes yesYear yes yes yes yes yes yes

R2 086 042 059 067 073 086

Panel A reports regression results of the loan interest rate frequency of debt total leverage debtmaturity loan duration and the frequency of multiple creditors on a measure of real asset redeployabilityusing the allowable use of the property given by its zoning classification Additional regressors include the logof the sale price of the property (excluded from the loan-to-value regression) the capitalization rate of theproperty (the current earnings on the property divided by the sale price) the Herfindahl index of bankingconcentration within a fifteen-mile radius of the property the log of property age and the current crime risklevel and recent growth rate in crime risk for the propertyrsquos location (obtained from CAP Index Inc) Theinterest rate regressions also include the leverage ratio an indicator for floating rates an indicator forwhether the loan is backed by the Small Business Administration and the loan maturity and amortizationas regressors Regressions include fixed effects for general zoning category property type year and censustract Regressions are run under OLS with robust standard errors Coefficient estimates and their associatedt-statistics (in parentheses) are reported along with adjusted R2s including and excluding the fixed effectsand the number of observations Panel B adds bank fixed effects to the regressions


not receive loans significantly more frequently However debtfrequency is apparently the only loan characteristic that is notaffected by a propertyrsquos redeployability As column 3 indicatesleverage or the size of the loan as a fraction of the sale priceconditional on a loan being present increases with redeployabil-ity Moving from the least to average (maximum) zoning flexibil-ity results in a 19 (41) percentage point increase in leverage10

This result provides support for Prediction 1 assets with greaterliquidation values have higher debt levels If as argued earlierdebt levels are more likely driven by supply-side constraints thenthis result indicates higher debt capacity with liquidation valuesas well

Column 4 of Panel A details results in support of Prediction3 that loan maturities significantly increase with liquidation val-ues A move from the least to the average (most) flexible zoningdesignation within a neighborhood and zoning category results inapproximately 11 (23) more years of maturity on the loan Giventhat the mean loan maturity in the sample is roughly fifteenyears this is a 73 (153) percent increase Column 5 also showsthat loan duration increases with redeployability A move fromthe least to the average (most) redeployable property leads to anincrease in duration of approximately 02 (05) years This resultprovides further support for Prediction 3

Finally Prediction 4 states that firms will borrow from onecreditor when liquidation value is high and from multiple credi-tors when liquidation value is low To test this prediction weregress the presence of a second creditor on our redeployabilitymeasure Column 6 of Table II Panel A shows that assets withhigher redeployability are significantly less likely to be financedby multiple creditors supporting this prediction The differencebetween the least and average (most) redeployable assets trans-lates into a 40 (85) percentage point decline in the probability ofmultiple creditors being present which is a 33 (71) percent de-cline from the 12 percent frequency of multiple creditors in thesample

In terms of the dollar benefit from these loan terms for theaverage (median) property sale price of $24 ($06) million andaverage (median) leverage ratio of 071 (082) the maximuminterest rate savings from more redeployable assets is $10700

10 We report OLS results The truncated regression models of Cragg [1971]and Powell [1986] yield similar findings


($3100) per year Over the fifteen-year average length of theloan the present value of these savings is $90041 ($27000 at themedian) assuming a discount rate equal to the average loan rate(828 percent) Taking into account that more redeployable assetshave greater leverage (45 percent) and longer maturity (25years) the present value of savings increases to $104360 or$11353 per year on average and $31308 or $3406 per year at themedian These are the maximum effects from redeployabilitymoving from the least to most flexibly zoned in an area Movingfrom least to average flexibility results in values of about halfthose above

IVC Bank Fixed Effects

In Table II Panel B we repeat the regressions in Panel Aadding bank fixed effects We analyze how the loan terms offeredby a given bank in a census tract vary with the redeployability ofa property Bank fixed effects eliminate any bank-specific lendingpolicies or specialization that might be related to zoning provid-ing another control for the financing environment As Panel Bshows the point estimates are remarkably similar to those inPanel A and despite losing power the results remain statisticallysignificant (except for debt maturity) This result suggests thatour findings do not arise from the matching of redeployable prop-erties with certain types of banks

IVD Robustness

An alternative hypothesis for our results is that lenderssimply base their decisions on the current price or earnings ofthe property having nothing to do with collateral or secondaryvalue If zoning is related to the value of the property and itsfuture earnings and the log of the sale price and cap rate(current earnings over price) do not fully capture these effectsthen our results may have nothing to do with collateral valuewhich is the basis of the theories we propose to test Thisalternative story seems particularly relevant for interest ratesand leverage but it is more difficult to see why maturity andmultiple creditors would be affected if collateral were unim-portant Nevertheless we attempt to address this alternativehypothesis directly First we test the robustness of our find-ings to alternative specifications that control for sale price andearnings-to-price by including interactions of the cap rate and


sale price with zoning category and property type dummies aswell as adding squared and cubed terms of log(sale price) andcap rate to the regression In all these specifications the coeffi-cients on redeployability are virtually unchanged (statisticallyand economically) across the loan characteristics (results notreported) having little impact on the effect of our zoningredeployability measure

IVE Ease of Foreclosure

A more direct test of whether more flexible zoning capturesliquidation value or is correlated with property attributeshaving little to do with liquidation value is to consider theimpact of our redeployability measure when the probability ofliquidation is ex ante higher or lower If our measure is unre-lated to liquidation value then the likelihood of the liquidationstate should have little impact on the effect of zoning on loanterms

To analyze this question we consider state-level variationin the ease of foreclosure There is substantial variation acrossstates in the time and cost required to seize a property from adefaulted debtor [Pence 2003] If as we argue redeployabilityaffects the value of a property in the hands of a creditor thenit should be much less important in states in which foreclosureis very slow and costly since the discounted value of seizing aproperty in such states is low irrespective of its potentialfuture uses (redeployability) However if the alternative the-ory holds that collateral is unimportant or our measure fails tocapture it then redeployability would not be more important instates in which foreclosure is easy since foreclosure affectsonly the bankrsquos access to the collateral Indeed under thealternative hypothesis one might argue that expected futureoperating cash flows are more important for loan terms inhard-to-foreclose jurisdictions since the creditor really wantsto avoid default in those states This alternative would implyour measure having a larger rather than smaller effect on loanterms in hard-to-foreclose states

To measure the cost of foreclosure we make use of data onFannie Maersquos optimum time frame within which it expects aforeclosure to be completed in various states This time frameis highly correlated with other estimates of the average lengthof time required to accomplish a foreclosure [National Mort-


gage Servicerrsquos Reference Directory 2001] We construct a mea-sure of foreclosure times that takes the value of three for timeframes more than 200 days two for time frames between 120and 200 days one for time frames between 60 and 120 daysand zero otherwise We also consider whether the state allowsnonjudicial foreclosures which are substantially less costlythan judicial foreclosures [Pence 2003] Our measure for thecost of foreclosure is the sum of a dummy for judicial foreclo-sures and the foreclosure time variable above described inAppendix 2 for reference

In Table III Panel A we report results from regressing loanterms on redeployability the interaction between redeployabilityand cost of foreclosure and the controls from the previous regres-sions (Note that census tract fixed effects account for the level ofthe state-level foreclosure variable but not its interaction) Theresults indicate that differences in redeployability across proper-ties are less important for loan terms where the costs of foreclo-sure are high Specifically the interaction between redeployabil-ity and foreclosure costs is significantly positive for interest ratesand multiple creditors and significantly negative for debt matu-rity and loan duration These results suggest redeployabilityproxies for the value of collateral rather than unobserved futureearnings or property quality

IVF Zoning Strictness

In Panel B of Table III we further examine whether theimpact of zoning regulations differs across jurisdictions In par-ticular we expect that zoning regulations should matter more inareas with stricter application of zoning rules To test this pre-diction we interact our redeployability measure with variablesdesigned to capture strictness of zoning regulation

We capture the strictness of zoning using two measuresfrom the Wharton Land Use Control Survey (see Glaeser andGyourko [2003]) The first variable is an index of zoning strict-ness for an area created by taking the average of the percent-age of applications for zoning changes that were approved inthe local MSA during 1989 (coded as follows 5 0 to 10percent 4 11 to 29 percent 3 30 to 59 percent 2 60 to89 percent 1 90 to 100 percent) and the estimated number ofmonths between application for rezoning and issuance of abuilding permit for the development of a property in the MSA


taking the average for single family units and office buildings(coded as follows 1 Less than 3 months 2 3 to 6 months3 7 to 12 months 4 13 to 24 months 5 More than 24months) Interacting the zoning strictness index with redeploy-

TABLE IIICROSS-SECTIONAL EVIDENCE ON REDEPLOYABILITY AFFECTING DEBT CONTRACTS

Dependent variable Interest

rateDebt

frequency LeverageDebt

maturityLoan

durationMultiplecreditors

PANEL A INTERACTIONS WITH FORECLOSURE COSTS


Redeployability 08076 00146 00080 19448 01099 06226foreclosure cost (322) (030) (042) (175) (168) (288)

PANEL B INTERACTIONS WITH STRICTNESS OF ZONING


Redeployability 28903 03669 02270 47521 16350 01862zoning strictness (239) (308) (539) (159) (375) (239)


Redeployability 04061 00797 00369 40564 09022 00512growth management (189) (181) (202) (174) (260) (087)

PANEL C INTERACTIONS WITH LOCAL MARKET LIQUIDITY


Redeployability 12878 02108 01364 44819 11029 00843demand-to-supply (164) (334) (579) (279) (473) (201)

Regression results of the loan interest rate frequency of debt total leverage debt maturity loanduration and frequency of multiple creditors on asset redeployability (measured by the intensity of allowableuse from the propertyrsquos zoning designation) and its interaction with characteristics of the local market inwhich the property resides are reported Panel A considers the interaction with ease of foreclosure at the statelevel This variable is the sum of a judicial-foreclosure-only dummy and an index for the 1998 Fannie Maeoptimum foreclosure time frame Panel B examines interactions with variables designed to capture thestrictness of zoning The first is an index of zoning strictness which is the average of the following twomeasures from the Wharton Land Use Control Survey the percentage of applications for zoning changes thatwere approved in the local MSA during 1989 and the estimated number of months between application forrezoning and issuance of a building permit for the development of a property in the MSA (average for singlefamily units and office buildings) The second measure is an index of the effectiveness of growth managementtechniques employed in the MSA obtained from the Wharton Land Use Control Survey Specifically surveyrespondentsrsquo assessment of the effectiveness of ordinances building permits and zoning ordinances incontrolling growth are provided on a scale of 1 (not important) to 5 (very important) and the average acrossthe three categories is the growth management index Panel C examines the interaction of redeployabilitywith a measure of local market liquidity namely the average of the quantitative ratings of survey respon-dents from the Wharton Land Use Control Survey on the ratio of demand for land uses relative to the acreageof land zoned for those uses across single family multifamily commercial and industrial uses and acrossvarious lot sizes All regressions include the regressors from Table II Panel A including census tract generalzoning category property type and year fixed effects with robust standard errors Appendix 2 details thesources and computations of all relevant variables


ability and rerunning the loan term regressions (includingcensus tract fixed effects)11 Table III Panel B shows thatproperty-specific redeployability has a stronger effect on loancharacteristics in jurisdictions with strict zoning rules In re-gions with the lowest level of zoning strictness redeployabilitydoes not have statistically or economically significant effects

The second zoning rigor measure we use is an index of theeffectiveness of growth management techniques employed inthe MSA through zoning ordinances and permits Specificallysurvey respondentsrsquo assessment of the effectiveness of ordi-nances building permits and zoning ordinances in controllinggrowth are provided on a scale of 1 (not important) to 5 (veryimportant) and the average across the three categories is thegrowth management index we employ As Panel B showsredeployability has a greater effect on all loan characteristicsin jurisdictions that use zoning ordinances and permits mosteffectively to control and manage growth in the area The twosets of results in Panel B indicate that zoning flexibility is abetter measure of redeployability in areas where zoning mat-ters more and is adhered to more tightly Appendix 2 describesin more detail the construction of these variables and theirsource

IVG Market Liquidity

Panel C of Table III examines interactions with measures oflocal market liquidity The measure we employ is the average ofthe qualitative ratings by the Wharton Land Use Control Surveyrespondents comparing the acreage of land zoned versus de-manded across single family multifamily commercial and in-dustrial uses and across various lot sizes (coded on a 1ndash5 ratingscale 1 Far more than demanded 5 Far less than de-manded) Appendix 2 details the construction of this measureWhen demand for a type of property is high relative to supply weexpect the redeployment option to be of greater use and to beexploited more frequently If by contrast land is plentifullyavailable then there should be little incentive to redeploy aproperty The results displayed in Panel C show that redeploy-ability has the predicted stronger effect on all loan characteristics

11 Since this variable is measured at a level greater than a census tract(MSA) including census tract fixed effects accounts for the level of this variable


(though it is insignificant for duration) when relative demand isstrong

IVH Historic Zoning

Historic zoning regulations tend to be especially conserva-tive inflexible and well-enforced so a historic designation cansubstantially reduce a propertyrsquos redeployability and liquidityAs another measure of liquidation value we therefore examinea historic zoning designationrsquos effect on loan contracts in TableIV We include the usual controls and census tract fixed effectsWe find that properties zoned historic receive significantlyfewer smaller and shorter duration loans are financed athigher interest rates and are more likely to be financed bymultiple creditors The economic magnitudes of these effectsare large A historic designation is associated with an interestrate that is 59 basis points higher a 108 percentage pointreduction in the probability of a loan a 49 percentage pointsmaller loan-to-value ratio a loan duration that is 011 yearsshorter and an 119 percentage point increase in the probabil-ity of multiple creditors The effect on debt maturity is statis-tically insignificant

Moreover it is also generally quite difficult to changezoning classifications for historically zoned properties There-fore the current zoning designation should be more bindingfor historic properties and should enhance the impact of our

TABLE IVHISTORIC ZONING DESIGNATIONS

Dependentvariable

Interestrate


Debtmaturity

Loanduration

Multiplecreditors

Historic 05913 01085 00490 04942 01109 01187(317) (227) (217) (039) (193) (249)


Redeployability 19869 02219 03050 112079 03075 03126historic (384) (203) (226) (217) (059) (202)

Regression results of the loan interest rate frequency of debt total leverage debt maturity loanduration and frequency of multiple creditors on an indicator variable for properties with a historic zoningdesignation are reported Results from interacting the redeployability measure with the historic dummy arealso reported The regressions include the control variables from Table II Panel A including fixed effects forcensus tract general zoning category property type and year with robust standard errors Coefficientestimates and their associated t-statistics (in parentheses) are reported


zoning redeployability measure Consistent with this predic-tion the interaction term between redeployability and historicdesignation provides even greater effects on all loan terms(other than duration which has the right sign but is insignifi-cant) in Table IV

IVI Liquidation Value and Current Market Price

Finally although the primary focus of our analysis is on thefeatures of the debt contract we also analyze the relation be-tween redeployability and prices We recognize that this regres-sion is more open to endogeneity concerns and we interpret theresult with caution Nevertheless this analysis provides a test ofPrediction 5 that an assetrsquos market price increases with liquida-tion value

We regress the log of the property sale price on our rede-ployability measure and current earnings as a measure ofproperty size and profitability and include the census tractand other fixed effects The coefficient on redeployability ispositive 075 and statistically significant (t 892) indicatingthat higher liquidation value is associated with higher marketprice though we note that the direction of causality is notindisputable12

V CONCLUSION

Despite the breadth of theory on incomplete contracting forfinancial structure supporting evidence is sparse The lack ofempirical evidence is in part due to the difficulty in obtainingex ante measures of asset liquidation value and observingasset-specific contracts We provide novel evidence linking as-set liquidation value measured through regulation of zoningflexibility and debt structure using asset-specific commercialloan contracts Greater asset redeployability and higher liqui-

12 Interestingly this result contrasts with the general finding in the resi-dential real estate market that tighter zoning is associated with higher prices[Quigley and Rosenthal 2004] This discrepancy may arise largely from between-versus within-neighborhood comparisons Our result that zoning flexibility in-creases property values is property-specific relative to properties within a censustract whereas Quigley and Rosenthalrsquos results are between neighborhoods Itmay well be that zoning flexibility is valuable for each property owner but thatthe negative externalities of zoning flexibility reduce property values at theneighborhood level


dation values significantly alter the terms of loan contracts ina manner consistent with theories of incomplete contractingand transaction costs More redeployable assets are financed atlower interest rates receive larger longer maturity andlonger duration loans and are less likely to face multiplecreditors Extending these results to nondebt contracts andloans without the nonrecourse feature may shed more light onthe importance of contractual incompleteness and transactioncosts in determining the boundaries of the firm

In addition to incomplete contracting theories of capitalstructure our results also emphasize the importance of collat-eral in financial contracting and credit market rationing Whilemost of the literature analyzes collateral requirements ratherthan collateral quality (eg Stiglitz and Weiss [1981] andWette [1983]) the effect of collateral quality on credit rationingis a potentially important question that has not received de-tailed empirical study For instance we show that higher liq-uidation values and interest rates are negatively correlated(predicted by Bester [1985]) yet we also find that higher liq-uidation values imply larger loans Thus better collateral de-creases the amount of credit rationing as well as the cost ofborrowing

APPENDIX 1 AN EXAMPLE OF THE ZONING CODE FROM NYC ZONING

OF RESIDENTIAL DISTRICTS

This appendix presents a detailed description of each ofthe residential zoning districts in New York City as an exampleof the variation in zoning laws employed to capture liquidationvalues across real assets A summary of the zoning code andthe associated permitted uses of the property as defined by thecode are reported for residential districts in NYC only Similarmeasures are applied for the districts within the other eightbroad zoning categories organizations waterfront manufac-turing business commercial commercialmanufacturing his-toric and residential and across all other zoning districts andcities in our sample from 1992 to 1999 covering twelve states(including the District of Columbia) and roughly 850 differentzip codes


Zon

ing

desi

gnat

ion

Use

sM

axim

um

floo

rar

eara

tio

Min

imu

mre

quir

edop

ensp

ace

rati

oM

axim

um

lot

cove

rage

Min

imu

mre

quir

edlo

tar

ea(s

qft

)

Max

imu

mn

um

ber

ofdw

elli

ng

un

its

orro

oms

per

acre

Per

dwel

lin

gu

nit

Per

zon

ing

room

Un

its

Roo

ms

R1-

1S

ingl

e-fa

mil

yde

tach

edre

side

nce

050

150

mdash95

00mdash

4mdash

R1-

2S

ingl

e-fa

mil

yde

tach

edre

side

nce

050

150

mdash57

00mdash

7mdash

R2

Sin

gle-

fam

ily

deta

ched

resi

den

ce0

5015

0mdash

3800

mdash11

mdashR

2XS

ingl

e-fa

mil

yde

tach

edre

side

nce

050

150

mdash38

00mdash

11mdash

R3-

1S

ingl

etw

o-fa

mil

yde

tach

ed

sem

idet

ach

edre

side

nce

050

mdash35

1040

145

0mdash

304

2mdash

R3-

2G

ener

alre

side

nce

050

mdash35

1040

145

0mdash

304

2mdash

R3A

Sin

gle

two-

fam

ily

deta

ched

ze

rolo

tli

ne

resi

den

ce0

50mdash

3510

401

450

mdash30

42

mdash

R4

Gen

eral

resi

den

ce0

75mdash

4597

0mdash

45mdash

R4-

1S

ingl

etw

o-fa

mil

yde

tach

ed

sem

idet

ach

ed

zero

lin

ere

side

nce

075

mdash45

686ndash

970

mdash45

65

mdash

R4A

Sin

gle

two-

fam

ily

deta

ched

resi

den

ce0

75mdash

4568

697

0mdash

456

5mdash

R4B

Sin

gle

two-

fam

ily

deta

ched

resi

den

ces

ofal

lty

pes

075

mdash45

686

970

mdash45

65

mdash

R5

Gen

eral

resi

den

ce1

25mdash

5560

5mdash

72mdash

R5B

Gen

eral

resi

den

ce1

6555

545

605

mdash80

mdashR

6G

ener

alre

side

nce

078

ndash24

327

5to

395

mdashmdash

109

to99

160

176

400

460

R7

Gen

eral

resi

den

ce0

87ndash3

44

155

to22

0mdash

mdash84

to77

207

226

519

566

R8

Gen

eral

resi

den

ce0

94ndash6

02

59

to10

7mdash

mdash59

to45

295

387

738

968

R9

Gen

eral

resi

den

ce0

99ndash7

52

10

to6

2mdash

mdash45

to41

387

425

968

1062

R10

Gen

eral

resi

den

ce10

Non

emdash

mdash30

581

1452

Sou

rce

NY

CZ

onin

gH

andb

ook

Ade

tail

edde

scri

ptio

nof

each

ofth

ere

side

nti

alzo

nin

gdi

stri

cts

inN

ewY

ork

Cit

yas

anex

ampl

eof

the

vari

atio

nin

zon

ing

law

sem

ploy

edto

capt

ure

liqu

idat

ion

valu

esac

ross

real

asse

tsA

sum

mar

yof

the

zon

ing

code

and

the

asso

ciat

edpe

rmit

ted

use

sof

the

prop

erty

asde

fin

edby

the

code

are

repo

rted

for

resi

den

tial

dist

rict

sin

NY

Con

lyS

imil

arm

easu

res

are

appl

ied

for

the

dist

rict

sw

ith

inth

eot

her

eigh

tbr

oad

zon

ing

cate

gori

eso

rgan

izat

ion

sw

ater

fron

tm

anu

fact

uri

ng

busi

nes

sco

mm

erci

alc

omm

erci

alm

anu

fact

uri

ng

his

tori

can

dre

side

nti

alan

dac

ross

all

oth

erzo

nin

gdi

stri

cts

and

citi

esin

our

sam

ple


APPENDIX 2 VARIABLE DESCRIPTION AND CONSTRUCTION

For reference a list of the construction of the variables usedin the paper and their sources is presented

Redeployability (flexibility of use) the scaled within zoningcategory and jurisdiction numeric value associated with agiven propertyrsquos zoning ordinance For property p with zoningordinance An in jurisdiction j this measure is nmax(n P(A j)) where P(A j) is the set of properties within jurisdiction jthat have the same general zoning category A Redeployabil-ity is the numeric value indicating flexibility of use n rela-tive to the maximum flexibility within a given property type Aand local jurisdiction j which sets the zoning code (sourceCOMPS)

Zoning category dummy variables for the broad zoning des-ignation of a property For property p with zoning designationAn this is A There are eight broad zoning categories in thesample organizations waterfront manufacturing residentialbusiness commercial commercial-manufacturing and historic(source COMPS)

Debt frequency a binary variable for whether the propertywas financed with bank debt (occurring 71 percent of the time)(source COMPS)

Leverage the ratio of total value of bank debt borrowed onthe property to the sale price (source COMPS)

Debt maturity the maturity of the bank loan contract inyears (source COMPS)

Loan interest rate the annual percentage interest rate on thebank loan contract and whether it is floating or fixed (sourceCOMPS)

Multiple creditors a binary variable for the presence of morethan one creditor making a loan on the property Commercialproperties have first and second trust deeds (mortgages) wherethe latter has lower priority claim on the real asset These occurabout 12 percent of the time and indicate the presence of morethan one creditor (source COMPS)

Capitalization rate the current or most recent annual earn-ings on the property divided by the sale price (source COMPS)

Property type dummy variables indicating ten mutually ex-clusive types retail commercial industrial apartment mobilehome park special residential land industrial land office andhotel (source COMPS)


Crime risk A crime score index comprising the seven partone offenses of the FBI homicide rape aggravated assaultrobbery burglary larceny and motor vehicle theft Thecrime risks measure the probability that a certain crime will becommitted in a given location relative to the county level ofcrime Hence this variable is a relative (within county) crimerisk measure Crime scores are provided at three points intime 1990 1995 and 2000 Each property is matched withthe crime score index for its latitude and longitude coordi-nates obtaining a property specific crime score Both thelevel of crime risk (relative to the county level) and thegrowth in crime risk (change in relative crime risk from 1990to 1995) are employed as control variables (source CAPIndex Inc)

Zoning strictness index the average of the following twomeasures from the Wharton Land Use Control Survey(WLUCS) Wharton Urban Decentralization Project the per-centage of applications for zoning changes that were approvedin the local MSA during 1989 and the estimated number ofmonths between application for rezoning and issuance of abuilding permit for the development of a property in the MSAThe first variable is ZONAPPR from the WLUCSmdashthe esti-mated percentage of applications for zoning changes approvedduring the past twelve-month period in the MSA coded from1ndash5 as follows [1 0 to 10 percent 2 11 to 29 percent 3 30 to 59 percent 4 60 to 89 percent 5 90 ndash100 percent]The second variable is the average of the following threevariables

1 PERMLT50mdashthe estimated number of months betweenapplication for rezoning and issuance of building permitfor the development of a subdivision of less than 50 singlefamily units coded as follows [1 Less than 3 months2 3 to 6 months 3 7 to 12 months 4 13 to 24months 5 More than 24 months 6 NA]

2 PERMGT50 mdashthe estimated number of months betweenapplication for rezoning and issuance of building per-mit for the development of a subdivision of more than50 single family units coded as follows [1 lessthan 3 months 2 3 to 6 months 3 7 to 12months 4 13 to 24 months 5 more than 24 months6 NA]

3 PERMOFFmdashthe estimated number of months between


application for rezoning and issuance of building permitfor the development of an office building of under 100000square feet coded as follows [1 less than 3 months 2 3 to 6 months 3 7 to 12 months 4 13 to 24 months5 more than 24 months 6 NA]

The zoning strictness index is computed as (5 ZONAPPR) (PERMLT50 PERMGT50 PERMOFF)3)excluding MSArsquos with 6 NA (source Wharton Land Use Con-trol Survey Wharton Urban Decentralization Project Develop-ment Regulation Survey Questionnaire 1989 Also see Glaeserand Gyourko [2003])

Growth management index The effectiveness of growthmanagement techniques through ordinances zoning ordi-nances and permits employed in the MSA obtained from theWharton Land Use Control Survey The average of the vari-ables GROMAN2 GROMAN3 and GROMAN8 are employedas the growth management index GROMAN2 3 and 8 arequantitative ratings by survey respondents of the effective-ness of growth management techniques in controlling growthin their community using ordinances building permits andzoning ordinances respectively The rating is on a scale of 1ndash5and is coded as follows [1 Not important 5 Very impor-tant] (source Wharton Land Use Control Survey WhartonUrban Decentralization Project Development Regulation Sur-vey Questionnaire 1989 Also see Glaeser and Gyourko[2003])

Demand-to-supply the average of the quantitative ratings ofsurvey respondents from the Wharton Land Use Control Surveyon the ratio of demand for land uses relative to the acreage of landzoned for those uses across single family multifamily commer-cial and industrial uses and across various lot sizes Specificallythe measure of demand to supply is the average of the followingvariables

1 DLANDUS1ndash4mdashquantitative rating by survey respon-dent comparing the acreage of land zoned versus demandfor the following land uses Single family MultifamilyCommercial and Industrial [1ndash5 rating scale 1 Farmore than demanded 5 Far less than demanded 0 No opinion or No reply]

2 DLOTSIZ1ndash5mdashquantitative rating by survey respondentcomparing the availability of land zoned versus demandfor the following single family residential lot sizes Less


than 4000 square feet 4000 to 8000 square feet 8000ndash10000 square feet 10000ndash20000 square feet and Morethan 20000 square feet [1ndash5 rating scale 1 Far morethan demanded 5 Far less than demanded 0 Noopinion or No reply]

We exclude zeros or no replies (source Wharton Land Use Con-trol Survey Wharton Urban Decentralization Project Develop-ment Regulation Survey Questionnaire 1989 Also see Glaeserand Gyourko [2003])

Foreclosure costs the sum of two variables time frame forforeclosure and judicial foreclosure dummy The time frame forforeclosure is based on the 1998 Fannie Mae optimum timewithin which it expects a foreclosure to be completed in a givenstate The time frame variable takes the value of three for timeframes more than 200 days two for time frames between 120 and200 days one for time frames between 60 and 120 days and zerootherwise The judicial foreclosure variable is zero if the statepermits nonjudicial foreclosures and one otherwise (source Na-tional Mortgage Servicerrsquos Reference Directory [2001])

HARVARD UNIVERSITY

UNIVERSITY OF CALIFORNIA LOS ANGELES

UNIVERSITY OF CHICAGO AND NBER

REFERENCES

Aghion Philippe and Patrick Bolton ldquoAn lsquoIncomplete Contractsrsquo Approach toFinancial Contractingrdquo Review of Economic Studies LIX (1992) 473ndash494

Baker George P and Thomas N Hubbard ldquoMake versus Buy in Trucking AssetOwnership Job Design and Informationrdquo American Economic Review XCIII(2003) 551ndash572

Baker George P and Thomas N Hubbard ldquoContractibility and Asset Owner-ship On-Board Computers and Governance in United States TruckingrdquoQuarterly Journal of Economics CXIX (2004) 1443ndash1479

Benmelech Efraim ldquoAsset Salability and Debt Maturity Evidence from 19thCentury American Railroadsrdquo Working paper Graduate School of BusinessUniversity of Chicago 2005

Berger Allen N Rebecca S Demsetz and Philip E Strahan ldquoThe Consolidationof the the Financial Services Industry Causes Consequences and Implica-tions for the Futurerdquo Journal of Banking and Finance XXIII (1999)135ndash194

Bester Helmut ldquoScreening vs Rationing in Credit Markets with Imperfect In-formationrdquo American Economic Review LXXV (1985) 850ndash855

Bolton Patrick and David Scharfstein ldquoOptimal Debt Structure and the Numberof Creditorsrdquo Journal of Political Economy CIV (1996) 1ndash26

Braun Matias ldquoFinancial Contractibility and Assetsrsquo Hardness Industrial Com-position and Growthrdquo Working paper Harvard University 2003

Cragg John ldquoSome Statistical Models for Limited Dependent Variables withApplication to the Demand for Durable Goodsrdquo Econometrica XXXIX (1971)829ndash844

Detragiache Enrica Paolo Garella and Luigi Guiso ldquoMultiple versus Single


Banking Relationships Theory and Evidencerdquo Journal of Finance LV (2000)1133ndash1161

Diamond Douglas ldquoPresidential Address Committing to Commit Short-TermDebt When Enforcement Is Costlyrdquo Journal of Finance LIX (2004)1447ndash1479

Esty Benjamin C and William L Meginson ldquoCreditor Rights Enforcement andDebt Ownership Structure Evidence from the Global Syndicated Loan Mar-ketrdquo Journal of Financial and Quantitative Analysis XXXVIII (2003) 37ndash59

Garmaise Mark J and Tobias J Moskowitz ldquoInformal Financial NetworksTheory and Evidencerdquo Review of Financial Studies XVI (2003) 1007ndash1040

Garmaise Mark J and Tobias J Moskowitz ldquoConfronting Information Asymme-tries Evidence from Real Estate Marketsrdquo Review of Financial Studies XVII(2004) 405ndash437

Garmaise Mark J and Tobias J Moskowitz ldquoBank Mergers and Crime The Realand Social Effects of Bank Competitionrdquo forthcoming Journal of Finance(2005)

Gilson Stuart C ldquoTransaction Costs and Capital Structure Choice Evidencefrom Financially Distressed Firmsrdquo Journal of Finance LII (1997) 161ndash196

Glaeser Edward and Joseph Gyourko ldquoThe Impact of Building Restrictions onAffordable Housingrdquo Federal Reserve Bank of New YorkmdashEconomic PolicyReview (2003) 21ndash39

Harris Milton and Artur Raviv ldquoCapital Structure and the Informational Role ofDebtrdquo Journal of Finance XLV (1990) 321ndash349

Harris Milton and Artur Raviv ldquoThe Theory of Capital Structurerdquo Journal ofFinance XLVI (1991) 297ndash355

Hart Oliver and John Moore ldquoA Theory of Debt Based on the Inalienability ofHuman Capitalrdquo Quarterly Journal of Economics CIX (1994) 841ndash879

Kaplan Steven N and Per Stromberg ldquoFinancial Contracting Theory Meets theReal World Evidence from Venture Capital Contractsrdquo Review of EconomicStudies LXX (2003) 281ndash316

McMillen Daniel P and John F McDonald ldquoA Simultaneous Equations Model ofZoning and Land Valuesrdquo Regional Science and Urban Economics XXI(1991) 55ndash72

McMillen Daniel P and John F McDonald ldquoLand Values in a Newly ZonedCityrdquo Review of Economics and Statistics LXXXIV (2002) 62ndash72

The National Mortgage Servicerrsquos Reference Directory 18th ed (Tustin CAUSFN) 2001

Ongena Steven and David C Smith ldquoWhat Determines the Number of BankRelationships Cross-Country Evidencerdquo Journal of Financial Intermedia-tion IX (2000) 26ndash56

Pence Karen ldquoForeclosing on Opportunity State Laws and Mortgage CreditrdquoWorking Paper Board of Governors of the Federal Reserve May 2003

Petersen Mitchell and Raghuram G Rajan ldquoDoes Distance Still Matter TheInformation Revolution in Small Business Lendingrdquo Journal of Finance LVII(2002) 2533ndash2570

Pogodzinski Michael J and Tim Sass ldquoMeasuring the Effects of MunicipalZoning Regulations A Surveyrdquo Urban Studies XXVIII (1991) 597ndash621

Pollakowski Henry O and Susan Wachter ldquoThe Effect of Land Use Constraintson Housing Pricesrdquo Land Economics LXVI (1990) 315ndash324

Powell James L ldquoSymmetrically Trimmed Least Squares Estimation for TobitModelsrdquo Econometrica LIV (1986) 1435ndash1460

Pulvino Todd C ldquoDo Fire-Sales Existrdquo An Empirical Investigation of Commer-cial Aircraft Transactionsrdquo Journal of Finance LIII (1998) 939ndash978

mdashmdash ldquoEffects of bankruptcy Court Protection on Asset Salesrdquo Journal of Finan-cial Economics LII (1999) 151ndash186

Quigley John M and Larry A Rosenthal ldquoThe Effects of Land-Use Regulation onthe Price of Housing What Do We Know What Can We Learnrdquo WorkingPaper University of California Berkeley 2004

Rajan Raghuram G and Luigi Zingales ldquoWhat Do We Know about CapitalStructure Some Evidence from International Datardquo Journal of Finance L(1995) 1421ndash1460

Shleifer Andrei and Robert W Vishny ldquoLiquidation Values and Debt Capacity


A Market Equilibrium Approachrdquo Journal of Finance XLVII (1992)1343ndash1366

Stein Joshua ldquoNonrecourse Carveouts How Far Is Far Enoughrdquo Real EstateReview XXVII (1997) 3ndash11

Stiglitz Joseph and Andrew Weiss ldquoCredit Rationing in Markets with ImperfectInformationrdquo American Economic Review LXXI (1981) 393ndash410

Stromberg Per ldquoConflicts of Interest and Market Illiquidity in Bankruptcy Auc-tions Theory and Testsrdquo Journal of Finance LV (2000) 2641ndash2691

Swope Christopher ldquoUnscrambling the Cityrdquo Congressional Quarterly (2003)Titman Sheridan Stathis Tompaidis and Sergey Tsyplakov ldquoDeterminants of

Credit Spreads in Commercial Mortgagesrdquo Working Paper University ofTexas at Austin 2004

Wallace Nancy ldquoThe Market Effects of Zoning Undeveloped Land Does ZoningFollow the Marketrdquo Journal of Urban Economics XXIII (1988) 307ndash326

Wette Hildegard C ldquoCollateral in Credit Rationing in Markets with ImperfectInformation A Noterdquo American Economic Review LXXIII (1983) 442ndash445

Williamson Oliver E ldquoCorporate Finance and Corporate Governancerdquo Journal ofFinance XLIII (1988) 567ndash591


sions when contracts are incomplete and transaction costs existIn particular debt contracts allow the creditor to seize the debt-orrsquos assets when the latter fails to make a promised paymentSince the debtor cannot commit to not withdraw his humancapital from the project (as in Hart and Moore [1994]) or to notdivert cash flows to himself (as in Aghion and Bolton [1992])creditors will agree to lend only if the debt is secured by theprojectrsquos assets and default triggers its liquidation

Testing these theories requires that the econometrician ob-serve the liquidation value of the asset but it is difficult toascertain ex ante when the parties enter the debt contract whatthe proceeds from selling the asset to the next user might be Asa proxy for the ex ante value of the asset in its next best use weemploy property-specific zoning assignments to capture micro-level variation in liquidation values

The real estate market is a natural candidate for testingfinancial contracting from an incomplete contracting perspectiveFirst the loans are typically secured and nonrecourse thus pro-viding a set of project-specific financings and characteristics con-sistent with the inalienability of human capital described by Hartand Moore [1994] and other models Second debt levels in com-mercial real estate are typically very high at initiation (median of82 percent of value) Thus it is plausible that the financingprovided is closer to the maximal leverage or project debt capacitythe lender will tolerate which is closer to the underlying theoriesFinally the real estate market offers a potential measure of anassetrsquos liquidation value through zoning ordinances which governthe permitted uses of a property

This empirical approach is motivated by Shleifer and Vish-nyrsquos [1992] argument that a broader set of buyers can potentiallyraise the liquidation value of an asset Zoning regulations deter-mine the set of uses of a property Properties with more allowableuses should have a greater number of potential buyers all elseequal and therefore a higher value in the event of liquidation Apropertyrsquos zoning designation is thus a measure of its redeploy-ability in the sense of Williamson [1988]

We recognize that the current market price of the asset isalso likely to be affected by redeployability and more specificallyzoning which along with the financing environment may bejointly determined by local market unobservables [Glaeser andGyourko 2003 McMillen and McDonald 2002] Endogeneity con-

















































Loanrate

Multiplecreditors

Zoningcodes




(CM) 252 074 074 1003192 10 (66) 874 019 4Historic (H) 258 068 066 3581531 10 (79) 908 013 4







(1) D r 1 m 1r r 11 r1m

r1 1 ra

m 1 r1m 1 ra

1 1 ra


TABLE I(CONTINUED)




Loanrate

Multiplecreditors

Caprate






























































AND DEBT CONTRACTS


Dependentvariable

Interestrate


Debtmaturity

Loanduration

Multiplecreditors


log(price) 00850 00235 07173 00678 00091(385) (467) (594) (365) (261)

Cap rate 00081 00077 00042 02292 00393 00027(198) (801) (260) (1011) (1124) (416)




Dependentvariable

Interestrate


Debtmaturity

Loanduration

Multiplecreditors


log(price) 00963 00321 04951 00489 00320(386) (704) (281) (190) (441)

Cap rate 00280 00051 00024 01111 00327 00002(585) (599) (157) (360) (762) (015)


R2 086 042 059 067 073 086













IVD Robustness


















rateDebt


maturityLoan






















IVH Historic Zoning




Dependentvariable

Interestrate


Debtmaturity

Loanduration

Multiplecreditors

Historic 05913 01085 00490 04942 01109 01187(317) (227) (217) (039) (193) (249)









V CONCLUSION










Zon

ing

desi

gnat

ion

Use

sM

axim

um

floo

rar

eara

tio

Min

imu

mre

quir

edop

ensp

ace

rati

oM

axim

um

lot

cove

rage

Min

imu

mre

quir

edlo

tar

ea(s

qft

)

Max

imu

mn

um

ber

ofdw

elli

ng

un

its

orro

oms

per

acre

Per

dwel

lin

gu

nit

Per

zon

ing

room

Un

its

Roo

ms

R1-

1S

ingl

e-fa

mil

yde

tach

edre

side

nce

050

150

mdash95

00mdash

4mdash

R1-

2S

ingl

e-fa

mil

yde

tach

edre

side

nce

050

150

mdash57

00mdash

7mdash

R2

Sin

gle-

fam

ily

deta

ched

resi

den

ce0

5015

0mdash

3800

mdash11

mdashR

2XS

ingl

e-fa

mil

yde

tach

edre

side

nce

050

150

mdash38

00mdash

11mdash

R3-

1S

ingl

etw

o-fa

mil

yde

tach

ed

sem

idet

ach

edre

side

nce

050

mdash35

1040

145

0mdash

304

2mdash

R3-

2G

ener

alre

side

nce

050

mdash35

1040

145

0mdash

304

2mdash

R3A

Sin

gle

two-

fam

ily

deta

ched

ze

rolo

tli

ne

resi

den

ce0

50mdash

3510

401

450

mdash30

42

mdash

R4

Gen

eral

resi

den

ce0

75mdash

4597

0mdash

45mdash

R4-

1S

ingl

etw

o-fa

mil

yde

tach

ed

sem

idet

ach

ed

zero

lin

ere

side

nce

075

mdash45

686ndash

970

mdash45

65

mdash

R4A

Sin

gle

two-

fam

ily

deta

ched

resi

den

ce0

75mdash

4568

697

0mdash

456

5mdash

R4B

Sin

gle

two-

fam

ily

deta

ched

resi

den

ces

ofal

lty

pes

075

mdash45

686

970

mdash45

65

mdash

R5

Gen

eral

resi

den

ce1

25mdash

5560

5mdash

72mdash

R5B

Gen

eral

resi

den

ce1

6555

545

605

mdash80

mdashR

6G

ener

alre

side

nce

078

ndash24

327

5to

395

mdashmdash

109

to99

160

176

400

460

R7

Gen

eral

resi

den

ce0

87ndash3

44

155

to22

0mdash

mdash84

to77

207

226

519

566

R8

Gen

eral

resi

den

ce0

94ndash6

02

59

to10

7mdash

mdash59

to45

295

387

738

968

R9

Gen

eral

resi

den

ce0

99ndash7

52

10

to6

2mdash

mdash45

to41

387

425

968

1062

R10

Gen

eral

resi

den

ce10

Non

emdash

mdash30

581

1452

Sou

rce

NY

CZ

onin

gH

andb

ook

Ade

tail

edde

scri

ptio

nof

each

ofth

ere

side

nti

alzo

nin

gdi

stri

cts

inN

ewY

ork

Cit

yas

anex

ampl

eof

the

vari

atio

nin

zon

ing

law

sem

ploy

edto

capt

ure

liqu

idat

ion

valu

esac

ross

real

asse

tsA

sum

mar

yof

the

zon

ing

code

and

the

asso

ciat

edpe

rmit

ted

use

sof

the

prop

erty

asde

fin

edby

the

code

are

repo

rted

for

resi

den

tial

dist

rict

sin

NY

Con

lyS

imil

arm

easu

res

are

appl

ied

for

the

dist

rict

sw

ith

inth

eot

her

eigh

tbr

oad

zon

ing

cate

gori

eso

rgan

izat

ion

sw

ater

fron

tm

anu

fact

uri

ng

busi

nes

sco

mm

erci

alc

omm

erci

alm

anu

fact

uri

ng

his

tori

can

dre

side

nti

alan

dac

ross

all

oth

erzo

nin

gdi

stri

cts

and

citi

esin

our

sam

ple






























HARVARD UNIVERSITY



REFERENCES
































































































Loanrate

Multiplecreditors

Zoningcodes




(CM) 252 074 074 1003192 10 (66) 874 019 4Historic (H) 258 068 066 3581531 10 (79) 908 013 4







(1) D r 1 m 1r r 11 r1m

r1 1 ra

m 1 r1m 1 ra

1 1 ra


TABLE I(CONTINUED)




Loanrate

Multiplecreditors

Caprate






























































AND DEBT CONTRACTS


Dependentvariable

Interestrate


Debtmaturity

Loanduration

Multiplecreditors


log(price) 00850 00235 07173 00678 00091(385) (467) (594) (365) (261)

Cap rate 00081 00077 00042 02292 00393 00027(198) (801) (260) (1011) (1124) (416)




Dependentvariable

Interestrate


Debtmaturity

Loanduration

Multiplecreditors


log(price) 00963 00321 04951 00489 00320(386) (704) (281) (190) (441)

Cap rate 00280 00051 00024 01111 00327 00002(585) (599) (157) (360) (762) (015)


R2 086 042 059 067 073 086













IVD Robustness


















rateDebt


maturityLoan






















IVH Historic Zoning




Dependentvariable

Interestrate


Debtmaturity

Loanduration

Multiplecreditors

Historic 05913 01085 00490 04942 01109 01187(317) (227) (217) (039) (193) (249)









V CONCLUSION










Zon

ing

desi

gnat

ion

Use

sM

axim

um

floo

rar

eara

tio

Min

imu

mre

quir

edop

ensp

ace

rati

oM

axim

um

lot

cove

rage

Min

imu

mre

quir

edlo

tar

ea(s

qft

)

Max

imu

mn

um

ber

ofdw

elli

ng

un

its

orro

oms

per

acre

Per

dwel

lin

gu

nit

Per

zon

ing

room

Un

its

Roo

ms

R1-

1S

ingl

e-fa

mil

yde

tach

edre

side

nce

050

150

mdash95

00mdash

4mdash

R1-

2S

ingl

e-fa

mil

yde

tach

edre

side

nce

050

150

mdash57

00mdash

7mdash

R2

Sin

gle-

fam

ily

deta

ched

resi

den

ce0

5015

0mdash

3800

mdash11

mdashR

2XS

ingl

e-fa

mil

yde

tach

edre

side

nce

050

150

mdash38

00mdash

11mdash

R3-

1S

ingl

etw

o-fa

mil

yde

tach

ed

sem

idet

ach

edre

side

nce

050

mdash35

1040

145

0mdash

304

2mdash

R3-

2G

ener

alre

side

nce

050

mdash35

1040

145

0mdash

304

2mdash

R3A

Sin

gle

two-

fam

ily

deta

ched

ze

rolo

tli

ne

resi

den

ce0

50mdash

3510

401

450

mdash30

42

mdash

R4

Gen

eral

resi

den

ce0

75mdash

4597

0mdash

45mdash

R4-

1S

ingl

etw

o-fa

mil

yde

tach

ed

sem

idet

ach

ed

zero

lin

ere

side

nce

075

mdash45

686ndash

970

mdash45

65

mdash

R4A

Sin

gle

two-

fam

ily

deta

ched

resi

den

ce0

75mdash

4568

697

0mdash

456

5mdash

R4B

Sin

gle

two-

fam

ily

deta

ched

resi

den

ces

ofal

lty

pes

075

mdash45

686

970

mdash45

65

mdash

R5

Gen

eral

resi

den

ce1

25mdash

5560

5mdash

72mdash

R5B

Gen

eral

resi

den

ce1

6555

545

605

mdash80

mdashR

6G

ener

alre

side

nce

078

ndash24

327

5to

395

mdashmdash

109

to99

160

176

400

460

R7

Gen

eral

resi

den

ce0

87ndash3

44

155

to22

0mdash

mdash84

to77

207

226

519

566

R8

Gen

eral

resi

den

ce0

94ndash6

02

59

to10

7mdash

mdash59

to45

295

387

738

968

R9

Gen

eral

resi

den

ce0

99ndash7

52

10

to6

2mdash

mdash45

to41

387

425

968

1062

R10

Gen

eral

resi

den

ce10

Non

emdash

mdash30

581

1452

Sou

rce

NY

CZ

onin

gH

andb

ook

Ade

tail

edde

scri

ptio

nof

each

ofth

ere

side

nti

alzo

nin

gdi

stri

cts

inN

ewY

ork

Cit

yas

anex

ampl

eof

the

vari

atio

nin

zon

ing

law

sem

ploy

edto

capt

ure

liqu

idat

ion

valu

esac

ross

real

asse

tsA

sum

mar

yof

the

zon

ing

code

and

the

asso

ciat

edpe

rmit

ted

use

sof

the

prop

erty

asde

fin

edby

the

code

are

repo

rted

for

resi

den

tial

dist

rict

sin

NY

Con

lyS

imil

arm

easu

res

are

appl

ied

for

the

dist

rict

sw

ith

inth

eot

her

eigh

tbr

oad

zon

ing

cate

gori

eso

rgan

izat

ion

sw

ater

fron

tm

anu

fact

uri

ng

busi

nes

sco

mm

erci

alc

omm

erci

alm

anu

fact

uri

ng

his

tori

can

dre

side

nti

alan

dac

ross

all

oth

erzo

nin

gdi

stri

cts

and

citi

esin

our

sam

ple






























HARVARD UNIVERSITY



REFERENCES


















































(1) D r 1 m 1r r 11 r1m

r1 1 ra

m 1 r1m 1 ra

1 1 ra


TABLE I(CONTINUED)




Loanrate

Multiplecreditors

Caprate






























































AND DEBT CONTRACTS


Dependentvariable

Interestrate


Debtmaturity

Loanduration

Multiplecreditors


log(price) 00850 00235 07173 00678 00091(385) (467) (594) (365) (261)

Cap rate 00081 00077 00042 02292 00393 00027(198) (801) (260) (1011) (1124) (416)




Dependentvariable

Interestrate


Debtmaturity

Loanduration

Multiplecreditors


log(price) 00963 00321 04951 00489 00320(386) (704) (281) (190) (441)

Cap rate 00280 00051 00024 01111 00327 00002(585) (599) (157) (360) (762) (015)


R2 086 042 059 067 073 086













IVD Robustness


















rateDebt


maturityLoan






















IVH Historic Zoning




Dependentvariable

Interestrate


Debtmaturity

Loanduration

Multiplecreditors

Historic 05913 01085 00490 04942 01109 01187(317) (227) (217) (039) (193) (249)









V CONCLUSION










Zon

ing

desi

gnat

ion

Use

sM

axim

um

floo

rar

eara

tio

Min

imu

mre

quir

edop

ensp

ace

rati

oM

axim

um

lot

cove

rage

Min

imu

mre

quir

edlo

tar

ea(s

qft

)

Max

imu

mn

um

ber

ofdw

elli

ng

un

its

orro

oms

per

acre

Per

dwel

lin

gu

nit

Per

zon

ing

room

Un

its

Roo

ms

R1-

1S

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e-fa

mil

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tach

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nce

050

150

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edby

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rted

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den

tial

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rict

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lyS

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are

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ied

for

the

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rict

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ith

inth

eot

her

eigh

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oad

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ing

cate

gori

eso

rgan

izat

ion

sw

ater

fron

tm

anu

fact

uri

ng

busi

nes

sco

mm

erci

alc

omm

erci

alm

anu

fact

uri

ng

his

tori

can

dre

side

nti

alan

dac

ross

all

oth

erzo

nin

gdi

stri

cts

and

citi

esin

our

sam

ple






























HARVARD UNIVERSITY



REFERENCES









































































































AND DEBT CONTRACTS


Dependentvariable

Interestrate


Debtmaturity

Loanduration

Multiplecreditors


log(price) 00850 00235 07173 00678 00091(385) (467) (594) (365) (261)

Cap rate 00081 00077 00042 02292 00393 00027(198) (801) (260) (1011) (1124) (416)




Dependentvariable

Interestrate


Debtmaturity

Loanduration

Multiplecreditors


log(price) 00963 00321 04951 00489 00320(386) (704) (281) (190) (441)

Cap rate 00280 00051 00024 01111 00327 00002(585) (599) (157) (360) (762) (015)


R2 086 042 059 067 073 086













IVD Robustness


















rateDebt


maturityLoan






















IVH Historic Zoning




Dependentvariable

Interestrate


Debtmaturity

Loanduration

Multiplecreditors

Historic 05913 01085 00490 04942 01109 01187(317) (227) (217) (039) (193) (249)









V CONCLUSION










Zon

ing

desi

gnat

ion

Use

sM

axim

um

floo

rar

eara

tio

Min

imu

mre

quir

edop

ensp

ace

rati

oM

axim

um

lot

cove

rage

Min

imu

mre

quir

edlo

tar

ea(s

qft

)

Max

imu

mn

um

ber

ofdw

elli

ng

un

its

orro

oms

per

acre

Per

dwel

lin

gu

nit

Per

zon

ing

room

Un

its

Roo

ms

R1-

1S

ingl

e-fa

mil

yde

tach

edre

side

nce

050

150

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150

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den

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ploy

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ted

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lyS

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are

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ied

for

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rict

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her

eigh

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ing

cate

gori

eso

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izat

ion

sw

ater

fron

tm

anu

fact

uri

ng

busi

nes

sco

mm

erci

alc

omm

erci

alm

anu

fact

uri

ng

his

tori

can

dre

side

nti

alan

dac

ross

all

oth

erzo

nin

gdi

stri

cts

and

citi

esin

our

sam

ple






























HARVARD UNIVERSITY



REFERENCES



























































IVD Robustness


















rateDebt


maturityLoan






















IVH Historic Zoning




Dependentvariable

Interestrate


Debtmaturity

Loanduration

Multiplecreditors

Historic 05913 01085 00490 04942 01109 01187(317) (227) (217) (039) (193) (249)









V CONCLUSION










Zon

ing

desi

gnat

ion

Use

sM

axim

um

floo

rar

eara

tio

Min

imu

mre

quir

edop

ensp

ace

rati

oM

axim

um

lot

cove

rage

Min

imu

mre

quir

edlo

tar

ea(s

qft

)

Max

imu

mn

um

ber

ofdw

elli

ng

un

its

orro

oms

per

acre

Per

dwel

lin

gu

nit

Per

zon

ing

room

Un

its

Roo

ms

R1-

1S

ingl

e-fa

mil

yde

tach

edre

side

nce

050

150

mdash95

00mdash

4mdash

R1-

2S

ingl

e-fa

mil

yde

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edre

side

nce

050

150

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7mdash

R2

Sin

gle-

fam

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ched

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den

ce0

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3800

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150

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gle

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Gen

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gle

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Gen

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den

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Gen

eral

resi

den

ce1

6555

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605

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alre

side

nce

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327

5to

395

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109

to99

160

176

400

460

R7

Gen

eral

resi

den

ce0

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44

155

to22

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mdash84

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207

226

519

566

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Gen

eral

resi

den

ce0

94ndash6

02

59

to10

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387

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eral

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den

ce0

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52

10

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387

425

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Gen

eral

resi

den

ce10

Non

emdash

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581

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Sou

rce

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onin

gH

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Ade

tail

edde

scri

ptio

nof

each

ofth

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side

nti

alzo

nin

gdi

stri

cts

inN

ewY

ork

Cit

yas

anex

ampl

eof

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vari

atio

nin

zon

ing

law

sem

ploy

edto

capt

ure

liqu

idat

ion

valu

esac

ross

real

asse

tsA

sum

mar

yof

the

zon

ing

code

and

the

asso

ciat

edpe

rmit

ted

use

sof

the

prop

erty

asde

fin

edby

the

code

are

repo

rted

for

resi

den

tial

dist

rict

sin

NY

Con

lyS

imil

arm

easu

res

are

appl

ied

for

the

dist

rict

sw

ith

inth

eot

her

eigh

tbr

oad

zon

ing

cate

gori

eso

rgan

izat

ion

sw

ater

fron

tm

anu

fact

uri

ng

busi

nes

sco

mm

erci

alc

omm

erci

alm

anu

fact

uri

ng

his

tori

can

dre

side

nti

alan

dac

ross

all

oth

erzo

nin

gdi

stri

cts

and

citi

esin

our

sam

ple






























HARVARD UNIVERSITY



REFERENCES
































































rateDebt


maturityLoan






















IVH Historic Zoning




Dependentvariable

Interestrate


Debtmaturity

Loanduration

Multiplecreditors

Historic 05913 01085 00490 04942 01109 01187(317) (227) (217) (039) (193) (249)









V CONCLUSION










Zon

ing

desi

gnat

ion

Use

sM

axim

um

floo

rar

eara

tio

Min

imu

mre

quir

edop

ensp

ace

rati

oM

axim

um

lot

cove

rage

Min

imu

mre

quir

edlo

tar

ea(s

qft

)

Max

imu

mn

um

ber

ofdw

elli

ng

un

its

orro

oms

per

acre

Per

dwel

lin

gu

nit

Per

zon

ing

room

Un

its

Roo

ms

R1-

1S

ingl

e-fa

mil

yde

tach

edre

side

nce

050

150

mdash95

00mdash

4mdash

R1-

2S

ingl

e-fa

mil

yde

tach

edre

side

nce

050

150

mdash57

00mdash

7mdash

R2

Sin

gle-

fam

ily

deta

ched

resi

den

ce0

5015

0mdash

3800

mdash11

mdashR

2XS

ingl

e-fa

mil

yde

tach

edre

side

nce

050

150

mdash38

00mdash

11mdash

R3-

1S

ingl

etw

o-fa

mil

yde

tach

ed

sem

idet

ach

edre

side

nce

050

mdash35

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145

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304

2mdash

R3-

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ener

alre

side

nce

050

mdash35

1040

145

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304

2mdash

R3A

Sin

gle

two-

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ched

ze

rolo

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ne

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den

ce0

50mdash

3510

401

450

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42

mdash

R4

Gen

eral

resi

den

ce0

75mdash

4597

0mdash

45mdash

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1S

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mil

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ed

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zero

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ere

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075

mdash45

686ndash

970

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65

mdash

R4A

Sin

gle

two-

fam

ily

deta

ched

resi

den

ce0

75mdash

4568

697

0mdash

456

5mdash

R4B

Sin

gle

two-

fam

ily

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ched

resi

den

ces

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lty

pes

075

mdash45

686

970

mdash45

65

mdash

R5

Gen

eral

resi

den

ce1

25mdash

5560

5mdash

72mdash

R5B

Gen

eral

resi

den

ce1

6555

545

605

mdash80

mdashR

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ener

alre

side

nce

078

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327

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395

mdashmdash

109

to99

160

176

400

460

R7

Gen

eral

resi

den

ce0

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44

155

to22

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mdash84

to77

207

226

519

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R8

Gen

eral

resi

den

ce0

94ndash6

02

59

to10

7mdash

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to45

295

387

738

968

R9

Gen

eral

resi

den

ce0

99ndash7

52

10

to6

2mdash

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to41

387

425

968

1062

R10

Gen

eral

resi

den

ce10

Non

emdash

mdash30

581

1452

Sou

rce

NY

CZ

onin

gH

andb

ook

Ade

tail

edde

scri

ptio

nof

each

ofth

ere

side

nti

alzo

nin

gdi

stri

cts

inN

ewY

ork

Cit

yas

anex

ampl

eof

the

vari

atio

nin

zon

ing

law

sem

ploy

edto

capt

ure

liqu

idat

ion

valu

esac

ross

real

asse

tsA

sum

mar

yof

the

zon

ing

code

and

the

asso

ciat

edpe

rmit

ted

use

sof

the

prop

erty

asde

fin

edby

the

code

are

repo

rted

for

resi

den

tial

dist

rict

sin

NY

Con

lyS

imil

arm

easu

res

are

appl

ied

for

the

dist

rict

sw

ith

inth

eot

her

eigh

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oad

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ing

cate

gori

eso

rgan

izat

ion

sw

ater

fron

tm

anu

fact

uri

ng

busi

nes

sco

mm

erci

alc

omm

erci

alm

anu

fact

uri

ng

his

tori

can

dre

side

nti

alan

dac

ross

all

oth

erzo

nin

gdi

stri

cts

and

citi

esin

our

sam

ple






























HARVARD UNIVERSITY



REFERENCES
























































IVH Historic Zoning




Dependentvariable

Interestrate


Debtmaturity

Loanduration

Multiplecreditors

Historic 05913 01085 00490 04942 01109 01187(317) (227) (217) (039) (193) (249)









V CONCLUSION










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Use

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its

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gori

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ng

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nes

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alc

omm

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alm

anu

fact

uri

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tori

can

dre

side

nti

alan

dac

ross

all

oth

erzo

nin

gdi

stri

cts

and

citi

esin

our

sam

ple






























HARVARD UNIVERSITY



REFERENCES





















































V CONCLUSION










Zon

ing

desi

gnat

ion

Use

sM

axim

um

floo

rar

eara

tio

Min

imu

mre

quir

edop

ensp

ace

rati

oM

axim

um

lot

cove

rage

Min

imu

mre

quir

edlo

tar

ea(s

qft

)

Max

imu

mn

um

ber

ofdw

elli

ng

un

its

orro

oms

per

acre

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dwel

lin

gu

nit

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zon

ing

room

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its

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ms

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1S

ingl

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mil

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nce

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150

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150

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gle-

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nin

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ing

law

sem

ploy

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ure

liqu

idat

ion

valu

esac

ross

real

asse

tsA

sum

mar

yof

the

zon

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code

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the

asso

ciat

edpe

rmit

ted

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edby

the

code

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rted

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den

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rict

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lyS

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are

appl

ied

for

the

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inth

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oad

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ing

cate

gori

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ion

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ater

fron

tm

anu

fact

uri

ng

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nes

sco

mm

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alc

omm

erci

alm

anu

fact

uri

ng

his

tori

can

dre

side

nti

alan

dac

ross

all

oth

erzo

nin

gdi

stri

cts

and

citi

esin

our

sam

ple






























HARVARD UNIVERSITY



REFERENCES























































Zon

ing

desi

gnat

ion

Use

sM

axim

um

floo

rar

eara

tio

Min

imu

mre

quir

edop

ensp

ace

rati

oM

axim

um

lot

cove

rage

Min

imu

mre

quir

edlo

tar

ea(s

qft

)

Max

imu

mn

um

ber

ofdw

elli

ng

un

its

orro

oms

per

acre

Per

dwel

lin

gu

nit

Per

zon

ing

room

Un

its

Roo

ms

R1-

1S

ingl

e-fa

mil

yde

tach

edre

side

nce

050

150

mdash95

00mdash

4mdash

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2S

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mil

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edre

side

nce

050

150

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gle-

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eral

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Sin

gle

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gle

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Gen

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176

400

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eral

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to22

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207

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eral

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den

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02

59

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eral

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den

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52

10

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425

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eral

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den

ce10

Non

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581

1452

Sou

rce

NY

CZ

onin

gH

andb

ook

Ade

tail

edde

scri

ptio

nof

each

ofth

ere

side

nti

alzo

nin

gdi

stri

cts

inN

ewY

ork

Cit

yas

anex

ampl

eof

the

vari

atio

nin

zon

ing

law

sem

ploy

edto

capt

ure

liqu

idat

ion

valu

esac

ross

real

asse

tsA

sum

mar

yof

the

zon

ing

code

and

the

asso

ciat

edpe

rmit

ted

use

sof

the

prop

erty

asde

fin

edby

the

code

are

repo

rted

for

resi

den

tial

dist

rict

sin

NY

Con

lyS

imil

arm

easu

res

are

appl

ied

for

the

dist

rict

sw

ith

inth

eot

her

eigh

tbr

oad

zon

ing

cate

gori

eso

rgan

izat

ion

sw

ater

fron

tm

anu

fact

uri

ng

busi

nes

sco

mm

erci

alc

omm

erci

alm

anu

fact

uri

ng

his

tori

can

dre

side

nti

alan

dac

ross

all

oth

erzo

nin

gdi

stri

cts

and

citi

esin

our

sam

ple






























HARVARD UNIVERSITY



REFERENCES













































































HARVARD UNIVERSITY



REFERENCES

















































do liquidation values affect financial contracts

Documents