The Political Economy of Green Office Buildings

Further Evidence on Political Risk in Industrial Property

Markets

By: David M. Harrison Associate and Jerry S. Rawls Professor of Finance Rawls College of Business Texas Tech University e-mail: [email protected] phone: (806) 742-3190

Acknowledgement: I wish to thank George Cashman, David Downs, John Kilpatrick, Brett Myers, Paul Goebel, Michael Seiler, and seminar participants at the 2013 American Real Estate Society annual meetings for their helpful and insightful comments on a previous version of this research. All remaining errors are, as always, my own. Further Evidence on Political Risk in Industrial Property Markets

Abstract:

Using a sample of 10,175 observations of transaction prices on industrial properties across the United States, this paper examines the extent to which industrial property prices are materially influenced by perceived political and/or regulatory risk. Specifically, using metrics constructed by the Brookings Institution and the U.S. Chamber of Commerce, as well as Wharton’s Residential Land Use Regulation Index to measure potential exposure to regulatory risk, the empirical results show industrial property transaction prices are significantly higher for properties located in geographic areas characterized by increased exposure to regulatory risk. These results clearly demonstrate market participants recognize their potential exposure to litigation risk, and incorporate such information into current market valuations. Further Evidence on Political Risk in Industrial Property Markets

I. Introduction

Recent empirical research suggests real estate market rents, vacancy rates, and transactions prices may well be significantly influenced by risk and uncertainty created through legal, political, and regulatory channels. For example, in capitalistic economies, secure property rights serve as a cornerstone tenet of economic value. To the extent legal delays, uncertainty, and/or corruption impair the market’s perception of the rights, privileges, and benefits (i.e., expected future cash flows) accruing to ownership or control of a given asset, corresponding values will be adversely affected.

Similarly, legitimate, yet politically unpopular business activities may be singled out for differential treatment by activists or other politically motivated parties. For example, within real estate markets, both existing and proposed industrial site locations are often heavily scrutinized and criticized with respect to their perceived environmental performance and impact.

Commercial property markets are also often highly localized in nature, due in large part to inherent and unavoidable issues surrounding property type and physical location specificity considerations. Thus, it is clearly plausible that geographic locations characterized by enhanced levels of environmentally conscious and/or politically active and motivated constituents will be less receptive and/or accommodating to industrial firms operating within their jurisdictions.

These relatively inhospitable operating environments could manifest themselves along a variety of dimensions, including both subtle institutionalized disadvantages (such as costly delays or denials in the permitting process or other enhanced difficulties in obtaining necessary planning and zoning variances) and more direct, public, or even confrontational manners (such as protests, boycotts, and/or lawsuits). Regardless of the form or nature of the political

1 engagement, the enhanced risk and uncertainty impose implicit costs on the firm. In an efficient market, such risks must be priced, and as such, property owners/investors may well alter rents and/or required returns in locations characterized by enhanced regulatory risk to offset their increased exposure.

The purpose of the current investigation is to examine multiple dimensions of political and regulatory risk, and further, to investigate their effect on firm value. More specifically, using a sample of 10,175 industrial property transactions from across the entire United States, the issue of whether inter-jurisdictional variation in regulatory land use and business process liability risk is embedded into market values is explored. Previewing the results, property investors and developers exposed to enhanced levels of regulatory land use risk appear to proactively recognize their exposure, raise rents, and thereby increase market prices to offset their higher expected costs and required returns. These regulatory risk premiums appear robust to a wide variety of alternative regulatory risk definitions.

The remainder of this paper is organized as follows. Section two further explores the fundamental reasons why litigation risk may materially influence market values, and reviews the limited existing literature exploring this phenomenon. Section three then motivates and describes the key, focal hypotheses examined by this study, outlines the sample data used throughout the empirical tests, and describes the methodological approaches employed. The results of the empirical analyses are presented in section four, while finally, section five summarizes the results and concludes.

II. Previous Literature

Regulatory Risk and Why it Matters

2 Regulatory risk may expose a real estate investor to the potential risk of lower than expected investment returns along multiple dimensions. For example, consider the owner of an existing industrial warehouse facility. Suppose a variety of potential tenants have been identified by the firm’s broker, who all stand ready, willing, and able to use the space at the current (ask) rent. Should this firm be indifferent between all potential tenants? The answer is clearly no.

More stable, financially secure tenants, characterized by relatively low default risk reduce the owner’s expected costs or losses associated with re-leasing the facility conditional upon default by the tenant. To the extent such enhanced business risk is attributable to the high political, judicial, or regulatory exposure of the predominant business lines of potential tenants, industrial warehouse owners (and investors) share in this litigation risk exposure via the enhanced tenant default risk.

Similarly, property developers catering to clients in highly volatile, heavily regulated, or controversial industries subject to high degrees of political activism – and thus an enhanced probability of being subject to legal action – also face derivative litigation risk due to their clientele base. The time, effort, and expense associated with securing necessary building permits and occupancy certificates may well be materially influenced by both the nature of the firm’s clientele (and hence development activity) and the political, regulatory, or judicial environment of the local market area in which the proposed development activity is set to take place. Profit maximizing developers will expect to be compensated, in some form, for this additional risk exposure and potential delays. Thus, rational property owners, developers, and investors may well adjust their required cost of capital, market asking rents, and/or property prices accordingly to compensate for this potential exposure to increased regulatory risk or uncertainty.

3 Related Empirical Evidence

To date, the academic literature on the impact of political risk, and more specifically regulatory risk, has received only scant attention with respect to industrial property markets.

That said, a wealth of evidence from related markets and contexts suggests the potential for significant, value relevant wealth effects accruing to firms and investors proactively assessing and managing this source of potential risk exposure. Consider first the evidence on judicial procedures and economic growth provided by international markets. More specifically, Feld and

Voigt (2003 & 2006) document a systematically significant and positive relationship between real growth in per capita gross domestic product (GDP) and their country specific measure of “de facto” judicial independence. Simiarly, Djankov et al. (2003) find that government mandates for enhanced procedural formalism within the judicial process increase the expected time to complete such processes. Moreover, such formalism also increases corruption within the system, which further results in reduced consistency of outcomes, honesty, fairness, and access to justice.

The authors conclude the combined result of these effects is that jurisdictions characterized by high levels of judicial, procedural formalism will also be characterized by relatively low levels of economic growth. On the other hand, Hayo and Voight (2008) report mixed evidence with respect to the impact of judicial procedure formalism on economic growth, with both written procedures and the right to counsel exhibiting positive relations to economic growth. Taken together, these studies suggest that efficiently functioning, transparent legal and regulatory systems may help foster economic growth.

Turning to U.S. markets, further evidence that legal and regulatory risk dimensions materially influence firm decision making can be garnered by examining firm location decisions.

For example, Bebchuk and Cohen (2002) report that nearly 60% of publicly traded firms in the

4 United States, including 59% of Fortune 500 firms and 68% of recent IPO’s, are incorporated in

Delaware. Such geographic clustering in firm location decisions may be driven by either a

“Race to the Top,” in which states proactively attract businesses by developing, investing in, or providing innovative legal and/or regulatory structures designed to enhanced shareholder wealth; or by a “Race to the Bottom,” in which inter-jurisdictional competition incentivizes states to create and foster rules which are favorable to corporate managers and decision makers, rather than shareholders.1 In general, the market appears to view the Delaware legal system as investor friendly, as both Romano (1985) and Heron and Lewellen (1998) document positive abnormal returns accruing to firms reincorporating in Delaware, while Daines (2001) reports a positive association between Delaware locations and enhanced values of Tobin’s Q.

Turning specifically to real estate markets, dramatic geographic clustering in the incorporation of real estate firms is also evident, as Venable (2012) reports that “more than 80% of all publicly registered REITs are formed in Maryland.” While conventional wisdom posits that Delaware incorporation decisions by non-REIT firms enhance shareholder wealth, Hartzell,

Kallberg, and Liu (2008) find managers of REITs incorporated in Maryland to be relatively more entrenched, and insulated from both market driven and shareholder governance pressures and concerns. While Delaware and Maryland have been uniquely successful in attracting business

(re)incorporations, on the other end of the spectrum, Bebchuk and Cohen (2002) report that

Illinois (11%) and California (22%) have experienced tremendous difficulties in retaining incorporations of successful in-state businesses. Taken together, these findings suggest inter-

1 Evidence consistent with a “Race to the Top” driving firm location decisions may be found in, among others, Winter (1977 & 1989), Fischel (1982), Easterbrook and Fischel (1991), and Romano (1993a, 1993b, & 1998). Evidence suggesting a “Race to the Bottom” effect is at work may be found in, among others, Cary (1974), Bebchuk (1992), and Bar-Gill, Barzuza, and Bebchuk (2001). See Subramanian (2002) for further evidence that state laws dramatically influence in-state firm retention and out-of-state firm attractions. 5 state variation in state legal and regulatory procedures may well materially influence firm location decision making.

Finally, with respect to contract terms and conditions, the residential mortgage market provides additional empirical evidence that inter-state differences in legal processes materially influence both firm and product market outcomes. For example, a broad literature on mortgage default definitively demonstrates that inter-state variation in foreclosure processes influences outcomes for both borrowers and lenders. More specifically, Clauretie (1987) finds foreclosure rates are higher in states allowing “Power of Sale” foreclosure as opposed to those requiring

“Judicial” foreclosure, while both Clauretie (1989) and Clauretie and Herzog (1990) find losses to mortgage insurers vary directly with the length of time required to complete the foreclosure action within a given state. Examining individual loan contract terms, Ambrose and Sanders

(2005) show that mortgage interest rates in states prohibiting deficiency judgments are higher than those in states which allow creditors access to the non-housing wealth of defaulting mortgagors, while Pence (2003 & 2006) documents that relative loan sizes are smaller in jurisdictions where it is more costly and/or time consuming to complete foreclosure actions. In sum, while little direct, empirical evidence has been published examining linkages between regulatory risk and industrial property market outcomes, the above studies provide a compelling backdrop to suggest such linkages are likely to exist.

III. Data and Methodology

Why Examine Industrial Property Markets?

Industrial property markets provide a uniquely compelling laboratory for the examination of potential regulatory risk exposure for a number of reasons. First, industrial warehouse space

6 is, compared to many other property types, a relatively homogenous space market product.

While facility attributes such as size, clear heights, and building class vary from location to location, more idiosyncratic dimensions of taste and style preferences exert relatively little influence within this property type sector.2 Thus, traditional valuation of industrial property locations has been relatively systematic in nature. Additionally, users of industrial space exhibit tremendous variation in the intensity of their industrial activity across locations, and with respect to the environmental footprint they impose on the physical locations they occupy. Warehouses storing non-perishable consumer products or catalogs may well be non-controversial, and viewed as relatively benign neighbors or co-tenants, while chemical plants or other hazardous material storage facilities may well be politically unpopular and expose the developer, tenants, owners, and investors to enhanced political or regulatory risk. Thus, industrial property markets provide an environment characterized by relatively straightforward valuation processes and a relatively high degree of potential regulatory risk for many market participants. As such, we begin the construction of our sample by collecting information on all industrial property transactions tracked by CoStar, occurring between January 1st, 2012 and December 31st, 2012, for the entire

United States. This results in a total sample of 10,175 industrial property transactions.

What Drives Industrial Property Prices?

In examining the influence of regulatory risk on industrial property markets, we must first identify what additional factors may drive, or influence, observable market prices. Consistent with the existing literature on industrial property valuation, the current investigation posits that values are driven primarily by the physical attributes of each facility and the location specific

2 See, for example, Vandell and Lane (1989) or Jaffee, Stanton, and Wallace (2010), for further discussion of intangible dimensions of commercial real estate valuation risk such as reputational capital and/or “plaque-in-the- lobby” effects. 7 demand for those amenities. The political and regulatory environment in which each firm operates has also been shown to exert an influence on market rents and vacancy rates. As such, one innovation of the current investigation is to extend this paradigm and explore the impact of multiple dimensions of regulatory risk on the selling prices of industrial property facilities. Each of these attributes is explored more fully below.

Facility Attributes, Building Class, and Property Type Focus

As with most commercial properties, the primary determinants of observable market values on industrial facilities are presumed to be individual facility attributes. In general, larger and newer facilities are expected to command higher prices, while market values should be inversely related to vacancy rates. Similarly, properties with more desirable amenities (e.g., increased clear height, enhanced building class, etc.) should likewise command higher market values.

Consistent with the previous literature, observable transaction prices are also expected to be inversely related to the intensity of the industrial production activity occurring at a given site.

Facilities designed to accommodate and house relatively large numbers of employees or showcase finished products, rather than simply providing a defined space for storing inert material, require developers, landlords, or building owners to invest in additional, costly amenities. Thus, in the empirical model specifications below, explicit controls are included to capture the effects of the primary focus, or business usage activity, occurring at each facility.

Once again, location values are expected to vary directly with the degree of public interaction with the space, and inversely with the intensity of onsite industrial production.

8 Regulatory Risk Metrics

Finally, as the focal point of the current investigation is the relationship between regulatory risk and industrial property market values, empirical proxies for regulatory risk must be identified. The current investigation employs three primary measures of regulatory risk: 1) the Wharton Residential Land Use Regulatory Index (WRLURI), 2) the Brookings Institution

Land Use Regulatory Risk Sub-Indices, and 3) the U.S. Chamber of Commerce State Liability

Rankings. More specifically, the Wharton Residential Land Use Regulatory Index is designed to provide “a summary measure of the stringency of the local regulatory environment.”3 While these values were originally designed to facilitate the analysis of housing supply inelasticity, the broad based nature of the content analysis used to construct these index values suggests they may well be applicable in addressing a wide variety of regulatory risk questions. Higher index values correspond to a more stringent regulatory environment, which effectively limits, or increases the costs of, additional development activities. Such policies would therefore be expected to curtail supply, create additional barriers to entry for new properties within a given market, and thereby increase market values on existing structures.

Second, to augment state-level WRLURI values, Brookings Institution Land Use

Regulatory Indices from the nation’s 50 largest metropolitan areas are employed. These indices examine specific components of land use regulations such as zoning policies, planning requirements, and growth restrictions such as impact fees, moratoria, and permit caps. As with the WRLURI data, higher index values suggest enhanced regulatory oversight and control, which should thus be associated with constrained supply, increased barriers to entry, and increased market values for existing structures which have already successfully navigated their way through the development process.

3 See Gyourko, Saiz, and Summers (2007), page 3. 9 The third measure of regulatory risk employed throughout this investigation relies on information obtained the U.S. Chamber of Commerce. More specifically, the U.S. Chamber of

Commerce Institute for Legal Reform periodically surveys senior executives, litigators/attorneys, and in-house general counsel of U.S. corporations with annual revenues in excess of $100 million on their perceptions of individual state liability legal systems.4 Overall judicial perception scores, on a scale of 0 to 100, are then assigned to each state based upon survey responses, with higher scores indicating a more favorable business climate with respect to litigation risk. These Chamber of Commerce (COC) judicial scores serve as an alternative measure of regulatory risk in the empirical specifications which follow. As higher scores suggest a more business friendly environment, ex-ante COC scores should be negatively related to expected operating costs and current market values, as market supply should be better able to readily adjust to changing market conditions. Sub-indices of these COC rankings, crafted along individual dimensions of regulatory/litigation risk, are also employed for robustness tests.

IV. Empirical Analysis

Descriptive and Univariate Analysis

The empirical portion of this analysis begins with an examination of the basic sample characteristics, both independently and across alternative regulatory risk environments. It then proceeds to a more rigorous examination of the determinants of industrial property prices, employing OLS regressions of the following general form:

4 Most recently, these surveys were administered and completed in January of 2010 by The Harris Poll, with response data available from 1,482 relevant parties. See Harris (2010) for additional, detailed information on these state liability scores and rankings. 10 Ln(Market Price) = f(facility attributes, building class, property type focus, regulatory risk, error)

Finally, robustness tests examining the impact of individual aspects of regulatory risk across both land use and business process risk dimensions are evaluated.

To begin, descriptive statistics for all variables employed throughout the empirical portion of this analysis are provided in Table #1. Examining the data, it becomes readily apparent that sample industrial property transactions consist of a wide range of properties, with a diverse set of amenities and operating characteristics. More specifically, sample transaction prices range from only a few thousand dollars to over $64 million, with standardized values per square foot exhibiting a similarly wide distribution from less than $1 to over $7,000 per square foot. The average transaction exhibits a market value of approximately $1.4 million, and was completed at a standardized rate of slightly under $70 per square foot.5 The typical industrial facility within this sample is slightly over 30,000 square feet in area, and is located on a land parcel approximately four acres in size. Over 90% of sample facilities are one story in height, while similarly over 92% of transacted units report no vacancies. The typical sample facility is approximately 36 years old, and exhibits a maximum reported clear height of slightly under 20 feet. The vast majority (97.3%) of sample properties possess mid-range amenities and/or locations, with approximately 30% of the facilities categorized as Class “B” properties and over two-thirds categorized as Class “C” units. Turning to property type focus, over ½ (54.9%) of all sample units are classified as warehouses. Other identifiable property types which are well represented across the sample include manufacturing facilities (16.1%), distribution centers

(4.3%), self-storage complexes (3.6%), and service facilities (3.6%).

5 These mean values are skewed slightly upward by high value transactions, with median values of only $685,000 or $48.29 per square foot. 11 Turning to the regulatory risk metrics, both the Wharton and Brookings Institution land use regulation metrics are constructed so that aggregate index values exhibit a mean of zero. As such, sample transactions appear to be slightly more common in areas characterized by enhanced levels of land use regulation. On the other hand, the Chamber of Commerce regulatory risk metrics are designed to capture business process and/or litigation risk, with higher values (scale

= 0 to 100) denoting a more business friendly operating environment. As such, along this dimension of regulatory risk, sample transactions appear to be over-represented in areas characterized by reduced regulatory risk. These seemingly inconsistent results highlight the need to analyze regulatory risk from a variety of different perspectives.

Continuing, Table #2 begins the comparative analysis by segmenting sample transactions into those occurring in “High Regulatory Risk” versus “Low Regulatory Risk” jurisdictions.

High (Low) risk areas are defined as those with WRLURI values greater (less) than zero.

Univariate comparisons across these sample cuts provide supportive and insightful, though by no means conclusive, evidence that regulatory risk materially influences market value. For example, consistent with the above arguments that increased regulation imposes costs and constrains supply, both raw transaction prices and values per square foot are significantly greater in “High Risk” jurisdictions than in their “Low Risk” counterparts. This result holds even though industrial facilities in “Low Risk” jurisdictions appear to be significantly larger (in terms of both building sizes and lot sizes), newer (age), and possess better amenities (clear heights and

Building Class values). Interestingly, manufacturing facilities appear to be substantively more common in “High Risk” jurisdictions, while distribution centers and self-storage complexes are more frequently observed in “Low Risk” jurisdictions.6

6 These findings with respect to manufacturing facilities are, in part, a legacy of the historic concentration of manufacturing facilities across the Rust Belt. 12 Multi-variate Analysis

The results of the cornerstone, multi-variate analysis of this investigation are presented in

Table #3. More specifically, each column presents the results of an OLS regression model designed to explain the determinants of the natural log of sample industrial property transaction prices. In column 1, log prices are regressed exclusively against a set of facility attributes and indicator variables for Building Class and Property Type focus. These base case results are designed to provide a benchmark with which to compare the impact of alternative dimensions of regulatory risk. Consistent with both economic theory and ex-ante expectations, these column 1 results confirm larger facilities command higher prices, while increased age and vacancy rates detract from value. Similarly, enhanced amenities appear to add value, as increased clear heights are associated with higher prices and building classes monotonically align with value expectations. Finally, and consistent with the notion that industrial property values are inversely related to the intensity of on-site industrial activity, service centers and showroom facilities both exhibit higher market values than warehouse facilities of similar size, vintage, and quality.

The remaining columns in Table #3 add alternative measures of regulatory risk to the regression specification. More specifically, column 2 adds the Wharton Residential Land Use

Regulation Index to the valuation model. The addition of this regulatory risk metric does not alter the directionality or significance of any significant parameter estimate previously reported in model 1. That said, with the inclusion of this risk measure, the indicator variables for manufacturing facilities and distribution centers now attain statistical significance at conventionally accepted levels. Comfortingly, both attributes are again consistent with the notion that industrial intensity is inversely related to value. Turning to our (WRLURI)

13 regulatory risk metric, the significant positive coefficient is entirely consistent with the focal hypothesis of this investigation. More precisely, higher index values connote increased local regulatory oversight and control of land use outcomes, thus increasing compliance costs, erecting barriers to new development, thereby constraining potential future supply, and thus increasing the market values of existing properties which have already successfully navigated the construction and development process.

Column three provides a similar analysis, but replaces the Wharton land use regulation metric with the business process/litigation risk metric derived from the U.S. Chamber of

Commerce survey discussed above. Once again, the results for facility attributes, building class, and property type focus are broadly consistent with those previously reported. Turning to the business process regulatory risk metric, recall that higher values are associated with more business friendly operating environments. As such, the significantly negative coefficient estimate on the Chamber of Commerce risk index is once again consistent with the notion that regulatory intervention increases compliance costs and constrains supply. In this instance, states with more business friendly regulatory environments would be expected to be more receptive to growth and development activities. Thus, regulatory compliance costs should be reduced, supply constraints alleviated, and regulatory risk premiums accruing to completed (industrial) facilities minimized.

Finally, column 4 includes both the land use and business process risk metrics simultaneously. As before, all facility attributes, building class, and property type focus metrics continue to exhibit their previously observed significance and directionality. Within this fully specified model, both regulatory risk metrics retain their previously documented importance.

14 This latter finding suggests multiple dimensions of regulatory risk may well be value relevant for industrial property facilities.

Robustness Check #1: Land Use Regulation Sub-Index Evaluation

Given the previous findings that land use regulation may well be value relevant for industrial properties, an obvious related question arises as to which specific dimensions of such regulatory risk are priced. To investigate this issue, individual components of the Brookings

Institution Index of Land Use Regulation are next evaluated to assess their degree of correlation.

These sub-indices are available for the 50 largest metropolitan areas across the United States, as well as for 23 sub-metropolitan level markets. Given the consistent scaling, for industrial properties located outside of this geographical coverage area index values are imputed using state-level values of the broader WRLURI. These sub-indices evaluate the nature and degree of regulatory oversight and intervention with respect to specific aspects of local land use and development activities. The nine specific dimensions evaluated by the current investigation include: 1) the presence and degree of exclusionary zoning which limits particular development activities, 2) the presence and extent of zoning which prohibits particular development activities,

3) the presence or absence of a comprehensive plan designed to manage local urban growth, 4) the presence or absence of one or more growth containment devices,7 5) utilization of impact fees to offset negative consequences of development activities, 6) the presence of adequate public facilities ordinances restricting growth and development activities to those areas which may be effectively served by existing, or planned expansions to, infrastructure and/or other public services, 7) the presence and utilization of restrictive growth or building moratoria, 8) the

7 Containment devices include such items as pre-specified service or boundary areas for the provision of municipal services such as sewer, water, and fire protection. 15 presence of permit caps on new construction, and 9) the presence of an affordable housing program to ensure the benefits of development activities are accessible to community members across the socio-economic spectrum. As with the aforementioned, broad-based Wharton Index, these Brookings metrics are scaled to a mean of zero with higher values indicative of more intensive regulatory oversight and involvement. Not surprisingly, jurisdictions with high (low) levels of regulatory oversight along a given dimension frequently exhibit high (low) regulatory risk scores along multiple dimensions. As evidenced in Table #4, there is a strong degree of correlation across these alternative land use regulation metrics, with an average correlation across the 36 possible index pairs equal to 0.6411. That said, there is far from perfect uniformity with 14 pairs of index values exhibiting correlations less than or equal to 0.6, while only two index pairs exhibit positive correlations in excess of 0.8. As such, to evaluate which specific dimensions of local land use regulation may be value relevant to industrial property markets,

Table #5 presents the condensed results from a series of nine OLS regressions, each substituting an individual Brookings Institution derived land use regulation sub-index for the broader

WRLURI values previously employed. In each instance (9 out of 9), regardless of the sub-index employed, higher levels of governmental oversight and involvement are associated with higher prices for existing industrial facilities. Once again, this is consistent with the notion that government regulations increase compliance costs, constrain supply, and allow owners of existing structures to capture regulatory risk premiums.

Robustness Check #2: Business Process Risk Sub-Index Evaluation

As with the just evaluated land use regulation metrics, finer detail is also available with respect to the Chamber of Commerce litigation risk index. More specifically, information is

16 collected and publicly available along ten unique dimensions of business process liability risk, with each designed to measure one dimension of the business friendly nature of an individual state’s legal liability system. These dimensions are: 1) the overall treatment of torts and contract litigation, 2) the existence and enforcement of meaningful venue requirements, 3) the treatment of mass consolidation and class action lawsuits, 4) damages, 5) timeliness of summary judgements and dismissals, 6) discovery procedures, 7), scientific and technical evidence standards, 8) judicial impartiality, 9) judicial competence, and 10) judicial fairness. As with the aggregate index, more business friendly jurisdictions should be associated with lower costs of regulatory compliance, foster enhanced development activity, and thereby reduce regulatory risk premiums embedded in existing facility prices.

While index scores are estimated along each dimension, only a listing of the five best performing and five worst performing states along each dimension are available for this analysis.

As such, to investigate these individual dimensions of business, liability, or litigation risk a series of ten OLS regressions are estimated, with each model replacing the raw, aggregate COC

Regulatory Risk Index with a pair of binary, indicator variables identifying both the five best and five worst performing states along each specific dimension. In general, for each regulatory risk component, the best performing states should be associated with reduced risk premiums, and hence lower property values and negative coefficient estimates. Examining the results presented in Table #6, this prediction is indeed confirmed across nine of the ten model specifications. Only in model 7, where regulatory risk and business liability exposure is measured with respect to the efficiency of standards and procedures governing scientific and technical evidence do we see this relationship reversed.

17 Conversely, the preceding analysis would also predict increased regulatory risk premiums, and hence higher property values and positive coefficient estimates for the five worst performing state indicator variables. Somewhat limited support is found for this contention, as six of the ten coefficient estimates along this dimension do indeed exhibit (significantly) positive signs. Interestingly, the scientific and technical evidence flag once again produces a result exactly in opposition to expectations, perhaps suggesting extant theory along this dimension needs to be reconsidered. Similarly, three additional worst state identifiers exhibit unexpected negative signs. These metrics are related to: 1) meaningful venue requirements, 2) judicial impartiality, and 3) judicial competence. While the current investigation leaves the underlying, root cause of these seemingly unexpected results as an open empirical question, it has been suggested that deep pocketed businesses and corporations may well be better equipped to take advantage of limitations, loopholes, and inconsistencies within the legal system than their more financially constrained counterparties. While this appears to be an interesting hypothesis which is somewhat consistent with the observed results, further analysis of this issue remains beyond the scope of the current investigation. Nonetheless, taken together, the preponderance of the empirical evidence provided throughout this investigation provides strong support for the notion that regulatory risk is a significant, value relevant component of industrial property valuation decisions.

V. Summary and Conclusions

This paper examined whether industrial property transaction prices are materially influenced by various dimensions of regulatory risk. Using a sample of 10,175 transactions of industrial property facilities from across the United States, between January 1st and December

18 31st 2012, empirical estimation results indicate market values are significantly higher for properties located in jurisdictions characterized by enhanced exposure to regulatory risk. More specifically, using regulatory land use risk metrics drawn from both Wharton and the Brookings

Institution, as well as Chamber of Commerce State Liability Scores designed to measure business process liability litigation risk, estimation results indicate modest enhancements in a jurisidiction’s regulatory environment significantly alter market values on industrial properties.

As such, these results suggest market participants on multiple sides of industrial property transactions must proactively recognize their potential exposure to regulatory risk, and take meaningful steps to avoid, or receive compensation for, bearing it.

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22 Table #1 Descriptive Statistics This table presents descriptive statistics on the selling prices and facility attributes for a sample of 10,175 industrial property transactions between January 1st and December 31st, 2012. Information on the regulatory environment of the local market area is also reported.

Variable Mean Std. Dev. Minimum Maximum Transaction Values: Selling Price ($) 1,396,845 2,556,410 4,004 64,300,000 Price per ft2 68.428 105.417 0.11 7,041.67

Facility Attributes: Building Size (ft2) 33,138 73,624 216 2,753,195 Lot Size (ft2) 169,204 586,429 1,150 33,700,000 # of Stories 1.108 0.409 1 10 Vacancy Rate (%) 4.734 19.414 0 100 Age (years) 36.041 21.098 0 184 Maximum Clear Height (ft) 18.768 4.285 0 60

Building Class: Class “A” Property (yes=1) 0.010 0.101 0 1 Class “B” Property (yes=1) 0.299 0.458 0 1 Class “C” Property (yes=1) 0.674 0.469 0 1 Class “F” Property (yes=1) 0.017 0.128 0 1

Property Type Focus: Warehouse (yes=1) 0.549 0.498 0 1 Manufacturing (yes=1) 0.161 0.368 0 1 Distribution (yes=1) 0.043 0.203 0 1 Self-Storage (yes=1) 0.036 0.187 0 1 Service (yes=1) 0.036 0.186 0 1 Show Room (yes=1) 0.013 0.115 0 1 Other (yes=1) 0.161 0.368 0 1

Regulatory Risk Metrics: WRLURI Index 0.086 0.597 -1.13 2.32 COC: Regulatory Risk Index 57.644 7.134 35.1 77.2 BI: Exclusionary Zoning Index 0.027 0.649 -2.01 1.51 BI: “No” Zoning Index 0.078 0.715 -1.54 4.71 BI: Planning Index 0.091 0.658 -2.55 1.51 BI: Containment Index 0.115 0.692 -1.13 2.52 BI: Impact Fee Index 0.163 0.722 -1.34 2.42 BI: Adequate (P) Facility Index 0.087 0.648 -2.67 2.75 BI: Growth Moratoria Index 0.097 0.619 -2.98 1.51 BI: Permit Cap Index 0.113 0.721 -1.13 3.96 BI: Affordable Housing Index 0.153 0.702 -1.25 3.00

23 Table #2 Descriptive Statistics by “Regulatory Risk” This table presents descriptive statistics for sample properties segregated by regulatory risk. “High Risk” areas are defined as those with a Wharton Residential Land Use Regulatory Index (WRLURI) greater than or equal to 0, while “Low Risk” areas are correspondingly defined as those areas in which the index is negative. #High Risk=5,638; #Low Risk=4,537.

“High Risk” “Low Risk” T-stat of Variable Mean Mean Difference Transaction Values: Selling Price ($) 1,503,914 1,263,794 4.71*** Price per ft2 78.78 55.57 11.10***

Facility Attributes: Building Size (ft2) 28,360 39,073 -7.31*** Lot Size (ft2) 143,881 201,075 -4.84*** # of Stories 1.11 1.10 1.19 Vacancy Rate (%) 4.56 4.95 -1.01 Age (years) 36.57 35.39 2.80*** Maximum Clear Height (ft) 18.66 18.91 -2.96***

Building Class: Class “A” Property (yes=100) 0.80 1.32 -2.60*** Class “B” Property (yes=100) 28.56 31.61 -3.34*** Class “C” Property (yes=100) 69.14 65.20 4.22*** Class “F” Property (yes=100) 1.51 1.87 -1.43

Property Type Focus: Warehouse (yes=1) 55.25 54.42 0.84 Manufacturing (yes=1) 18.75 12.81 8.13*** Distribution (yes=1) 3.64 5.18 -3.84*** Self-Storage (yes=1) 3.19 4.19 -2.67*** Service (yes=1) 3.69 3.50 0.50 Show Room (yes=1) 1.49 1.17 1.40 Other (yes=1) 13.99 18.73 -6.48***

Regulatory Risk Metrics: WRLURI Index 0.520 -0.452 140.0*** COC: Regulatory Risk Index 56.45 59.13 -19.2***

24 Table #3 Determinants of Industrial Property Selling Prices This table presents results from a series of four OLS regressions investigating the determinants of transaction prices on industrial properties. All transactions occurred between January 1st and December 31st, 2012. The natural log of transaction prices are modeled as a function of each facility’s attributes, building class, property type focus, and the regulatory environment of the local market area. All models employ robust standard errors using White’s correction.

Variables I II III IV Facility Attributes: Log Building Size (ft2) 0.738 0.709 0.718 0.694 (58.7***) (60.0***) (57.5***) (59.0***) Log Lot Size (ft2) -0.113 -0.067 -0.083 -0.046 (-10.8***) (-6.68***) (-7.82***) (-4.51***) # of Stories 0.091 0.069 0.102 0.079 (3.04***) (2.29**) (3.41***) (2.62***) Vacancy Rate (%) -0.002 -0.002 -0.174 -0.002 (-4.29***) (-4.16***) (-4.23***) (-4.09***) Age (years) -0.005 -0.005 -0.004 -0.005 (-9.32***) (-11.3***) (-8.47***) (-10.5***) Maximum Clear Height (ft.) 0.014 0.013 0.013 0.013 (5.55***) (5.76***) (5.56***) (5.77***)

Building Class: Class “A” Property (omitted/base) ------Class “B” Property (yes=1) -0.401 -0.385 -0.360 -0.354 (-5.31***) (-5.26***) (-4.78***) (-4.86***) Class “C” Property (yes=1) -0.559 -0.539 -0.525 -0.514 (-7.29***) (-7.27***) (-6.86***) (-6.95***) Class “F” Property (yes=1) -1.023 -0.808 -0.937 -0.752 (-3.65***) (-2.88***) (-3.37***) (-2.71***)

Property Type Focus: Warehouse (omitted/base) ------Manufacturing (yes=1) -0.020 -0.064 -0.245 -0.066 (-0.86) (-3.04***) (-1.11) (-3.18***) Distribution (yes=1) 0.044 0.075 0.063 0.089 (1.13) (2.08**) (1.67*) (2.48**) Self-Storage (yes=1) 0.036 0.077 0.043 0.081 (0.72) (1.61) (0.87) (1.69*) Service (yes=1) 0.205 0.211 0.199 0.206 (4.88***) (5.37***) (4.80***) (5.28***) Show Room (yes=1) 0.147 0.129 0.147 0.130 (2.22**) (2.03**) (2.29**) (2.10**) Other (yes=1) 0.058 0.069 0.054 0.065 (2.21**) (2.88***) (2.07**) (2.74***)

25 Regulatory Risk Metrics: WRLURI Index ----- 0.518 ----- 0.494 (39.0***) (38.1***) COC: Regulatory Risk Index ------0.022 -0.017 (-17.9***) (-15.6***)

Intercept 7.900 7.666 8.962 8.495 (59.5***) (60.4***) (63.5***) (63.6***) Observations 10,175 10,175 10,175 10,175 F(k,N-k-1) 554.30 702.80 556.87 698.14 Prob>F 0.0000 0.0000 0.0000 0.0000 R-Squared 0.4947 0.5613 0.5117 0.5713

26 Table #4 Correlation Coefficients for Regulatory Risk Sub-Indices This table presents correlation coefficients between each pair of regulatory risk sub-indices. Each sub-index represents the Brookings Institution Index of Land Use Regulation values along a given dimension. These indices are tracked for the largest 50 Metropolitan areas in America, as well as 23 sub-metropolitan level markets. For industrial properties located outside of this geographical coverage area, index values are imputed using state-level values of the Wharton Residential Land Use Regulation Index (WRLURI).

Adequate Exclusionary “No” Impact Public Growth Permit Affordable Zoning Zoning Planning Containment Fees Facilities Moratoria Cap Housing Exclusionary Zoning 1.0000 “No” Zoning 0.7870 1.0000 Planning 0.5370 0.3967 1.0000 Containment 0.5052 0.4388 0.7993 1.0000 Impact Fees 0.4196 0.5207 0.7561 0.7785 1.0000 Adequate Public Facilities 0.7766 0.6438 0.7195 0.6761 0.6689 1.0000 Growth Moratoria 0.8256 0.7508 0.7042 0.5891 0.6417 0.7730 1.0000 Permit Cap 0.5344 0.5301 0.7080 0.7455 0.6000 0.5252 0.5247 1.0000 Affordable Housing 0.4153 0.5204 0.7858 0.7080 0.8980 0.6110 0.6336 0.6301 1.0000

27 Table #5 Industrial Property Values and Land Use Regulation Risk This table presents the results of nine OLS regressions examining the determinants of transaction prices on industrial properties. Specifically, the natural logs of transaction prices are modeled as a function of the facility’s attributes, building class, property type focus, and nine differing dimensions of the regulatory environment corresponding to the local market area where each property is located. Each row represents output of an OLS regression employing 10,175 observations and the complete set of controls employed in Table #3. All models employ robust standard errors using White’s correction.

Variables β F-test Adjusted (t-stat) (p-value) R2 Regulatory Risk Dimension: 0.265 573.70 Exclusionary Zoning 0.5156 (20.1***) (0.0000) 0.286 591.82 “No” Zoning 0.5245 (21.45***) (0.0000) 0.410 634.76 Planning 0.5461 (30.1***) (0.0000) 0.400 649.51 Containment 0.5484 (34.4***) (0.0000) 0.424 685.80 Impact Fees 0.5599 (37.7***) (0.0000) 0.317 587.89 Adequate Public Facilities 0.5247 (22.7***) (0.0000) 0.374 613.17 Growth Moratoria 0.5324 (24.9***) (0.0000) 0.368 635.93 Permit Cap 0.5437 (28.8***) (0.0000) 0.476 725.61 Affordable Housing 0.5721 (43.5***) (0.0000)

28 Table #6 Industrial Property Values and Business Process Risk This table presents the results of ten OLS regressions examining the determinants of transaction prices on industrial properties. Specifically, the natural logs of transaction prices are modeled as a function of the facility’s attributes, building class, property type focus, and ten differing dimensions of the regulatory environment corresponding to the local market area where each property is located. Each row represents output of an OLS regression employing 10,175 observations and the complete set of controls employed in Table #3. All models employ robust standard errors using White’s correction.

Variables Best Worst F-test Adjusted States States (p-value) R2 Regulatory Risk Dimension: -0.276 0.647 592.20 Tort & Contract Litigation 0.5392 (-4.96***) (32.2***) (0.0000) -0.276 -0.572 512.16 Meaningful Venue Requirements 0.5040 (-8.79***) (-12.8***) (0.0000) -0.221 0.547 576.30 Treatment of Class Action Suits 0.5329 (-5.84***) (27.5***) (0.0000) -0.504 0.632 606.58 Damages 0.5449 (-11.8***) (31.6***) (0.0000) -0.241 0.547 577.43 Timeliness of Judgments/Dismissals 0.5331 (-6.05***) (27.6***) (0.0000) -0.458 0.535 586.18 Discovery 0.5374 (-11.3***) (27.0***) (0.0000) 0.201 -0.534 510.65 Scientific & Technical Evidence 0.5022 (7.71***) (-11.9***) (0.0000) -0.511 -0.270 497.97 Judge’s Impartiality 0.5014 (-8.13***) (-8.38***) (0.0000) -0.107 -0.553 506.50 Judge’s Competence 0.5004 (-2.17**) (-12.4***) (0.0000) -0.504 0.632 606.58 Judge’s Fairness 0.5449 (-11.8***) (31.6***) (0.0000)