STRUCTURING EQUITY INVESTMENT IN PPP PROJECTS Deepak. K. Sharma 1 and Qingbin Cui 2

ABSTRACT STRUCTURING EQUITY INVESTMENT IN PPP PROJECTS Deepak. K. Sharma 1 and Qingbin Cui 2 Earlier sudies have esablished guidelines o opimize he capial srucure of a privaized projec. However, in he US, many Public-Privae Parnership (PPP) projecs may no be fully self-financed hrough oll or oher operaing revenues due o insufficien revenue sreams. Wih he limied deb capaciy secured by oll revenues, mos PPP projecs mus be backed by boh privae equiy invesmen and public funds. The equiy srucure is of essence in a PPP deal because i implies risk and profi sharing. Therefore i provides a mechanism of privae incenive and public ineres proecion. Afer idenifying he limied upfron analysis of he financing srucure, he Governmen Accounabiliy Office has called for academic research and applicaion of solid ools o proec public ineres in PPP projecs. This paper presens a srucured approach for deermining equiy invesmen in PPP projecs. Scenarios are generaed using a linear programming model o reach he opimal equiy srucure under risk and uncerainy. The model divides equiy invesmens ino privae equiy and public funds and reaches he opimal equiy allocaion by maximizing he benefis from PPP financing. The I-10 Connecor projec is used as a case sudy o illusrae how equiy invesmen is srucured, given he limied bonding capaciy from oll revenues. KEYWORDS: Public Privae Parnerships, Equiy Financing, Opimizaion. INTRODUCTION Alhough he US governmen has impressively managed o provide essenial ransporaion infrasrucure for economic developmen and naional securiy, he counry sill needs o increase infrasrucure faciliies o mee he demands from is people. Typically, he developmen of ransporaion infrasrucure needs a significan upfron invesmen, which used o be funded by gasoline ax revenues. Due o he shrinkage of ax revenues and he recen financial crisis, he Federal and Sae governmens find hemselves in a disressed condiion and canno fund enough projecs for he mainenance and upkeep of he exising infrasrucure. Moreover, ransporaion infrasrucure projecs are more complex and involve many eniies. As a resul, managemen of infrasrucural projecs have become more challenging for public agencies. Since he early 1990s an increasing rend ha has been observed is ha many projecs were being delivered hrough Public Privae Parnerships (PPPs) o address he funding shorage and o improve projec performance. A Public-Privae Parnership can be broadly defined as a long erm agreemen beween public and privae secors for muual benefi (HM Treasury, 2000). This agreemen seeks o involve he privae secor in he nonradiional areas of a projec wih he risks and rewards being shared in new ways (USDOT 2004). For example, a public agency may provide righ-of-way and he righ o collec user fees, while a privae firm provides financing, echnological innovaion, and on-going service. Researchers and praciioners idenify many conracual arrangemens as 1 PhD Suden, Deparmen of Civil & Environmenal Engineering, Universiy of Maryland, College Park, MD, 20742, USA; dsharma@umd.edu 2 Assisan Professor, Deparmen of Civil & Environmenal Engineering, Universiy of Maryland, College Park, MD, 20742, USA; cui@umd.edu 1

PPPs, such as: fee-based conrac services; Design-Build (DB); Design-Build-Operae-Mainain (DBOM); Design-Build-Finance-Operae (DBFO); Build-Own-Operae (BOO), and long-erm leases (Malle, 2008; USDOT, 2007; Abdel, 2007). In he Unied Saes, mos parnerships require he privae secor o be responsible for acquiring he majoriy of he necessary financing (FHWA 2009). Unied Kingdom and Ausralia are widely recognized as forerunners in PPPs which have been used in various secors of faciliy developmen since he 1980s (Abdel, 2007). As repored by he Public Privae Infrasrucure Advisor Faciliy (PPIAF) and he World Bank, PPP programs in he UK and Ausralia have been very successful and few PPP projecs performed inefficienly or failed o mee heir objecives (Sanghi 2007). Early sudies indicae ha he successful delivery of PPP projecs depend upon a properly formulaed PPP agreemen ha boh aracs privae capial as well as and preserves public ineress (Zhang e al 2001, FHWA 2009). However, PPPs are sill new in he US. Many sae ransporaion agencies have no esablished bes pracices and guidelines for PPP projecs, causing srong public resisance due o serious concerns regarding he proecion of public ineress in PPP deals. In 2008 he Governmen Accounabiliy Office (GAO) conduced a sudy o evaluae PPP projecs in erms of proecing public ineress. As GAO has poined ou, since he public secor gives up conrol over a fuure sream of oll revenues in exchange for upfron paymen concession, PPPs migh no be warraned due o he uncerainies of raffic on hese oll roads. I may happen ha he ne presen worh of he exchanged fuure sream of oll revenues will become much larger han he up-fron concession received (GAO, 2008). GAO recommended ha ransporaion agencies develop and conduc upfron financial analysis o deermine he benefis and coss of PPP agreemens and o beer deliver ransporaion infrasrucure projecs. LITERATURE REVIEW PPPs sill relaively new in he ransporaion secor, are believed o bring maximum benefis compared o oher projec delivery sysems (Pakkala, 2002, Koppinen 2007, Abdel 2007). In he US, PPPs originaed from educaional programs and became increasingly used in urban renewal projecs in he 1960s (Yescombe, 2007). Since he 1990s, an increasing rend of PPP applicaion o he ransporaion secor has been observed due o he funding shorage in many saes. Wihou srong poliical and public suppor, he use of PPPs is limied in he US ransporaion secor as compared o such forerunners such as he UK and Ausralia. Lack of well esablished procedures, guidelines, and analysis ools for PPP projecs hinder ransporaion agencies from delivering ransporaion infrasrucure wih PPP conracs. Recenly, he Texas legislaure paused privae invesmens for oll roads (Linderberger 2009). Zhang (2005a) invesigaed PPP pracices in European and Asian counies and idenified barriers o he successful implemenaion of PPPs in ransporaion infrasrucure developmen. He recommended ha he bes value procuremen should be used on PPP projecs, which would improve he efficiency of projec delivery. The bes value approach requires public agencies o evaluae bids wih a se of predeermined crieria in a wo-sage procuremen process. During he firs sage, privae parners are required o submi heir applicaion for pre-qualificaion. Then, selec privae firms send heir bids. The sponsoring agency awards he work o he bid ha offers he bes value, leaving aside he bid wih he lowes cos. This process ensures ha he public agency ges he bes value which covers cos, ime, qualiy, safey, ec (FHWA 2007b). The bes value procuremen could also incorporae value for money analysis ha is ypically conduced in 2

Ausralia and UK. The value for money analysis compares he PPP procuremen o alernaive radiional procuremen mehods under uncerain condiions. Since projecs would proceed hrough PPP projecs only when PPP procuremen provides he beer value compared o a more radiional procuremen mehod, he value for money analysis ensures ha PPP procuremen achieves he bes value for public agencies (Akinoye e al 2003, Myhbusers 2007). Researchers also invesigaed he financial aspecs of PPP projecs. Gross (2009) pu forward an approach o srucuring concession lenghs and oll raes. Zhang (2005b) used an opimizaion model o faciliae privae and public secor for conducing financial viabiliy analysis in order o deermine he opimal deb and equiy srucure. Chiara and Garvin (2008) used he Maringale variance model and he general variance model as alernaive modeling ools for BOT risk evaluaion. Brandao and Saraiva (2008) viewed he minimum raffic guaranee (MTG) as an opion and developed a model o evaluae governmen oulays in PPPs. Similarly, Lui and Cheah (2009) used he real opions heory o model he PPP srucure in wase waer reamen plans. Abdel (2007) described implemenaion principles in PPP projecs based on he analysis of concession agreemens and he successful experience in he UK and Briish Columbia. Zhang (2005c) repored he primary financial crieria for selecing he righ privae parners in PPP deals. These crieria include ne presen value, inernal rae of reurn, and oal invesmen schedule. Though a grea number of sudies have been conduced in oher counies, no convincing work has been conduced in he US. The Governmen Accounabiliy Office has called for he developmen of upfron analysis ools for PPP projecs in order o beer proec public ineress (GAP 2008). This paper provides a promp for he allocaion of capial, paricularly for equiy invesmens beween public and privae parners. FINANCING MECHANISM PPP projecs are financed based on expeced revenues from projec operaions. If a projec is expeced o yield a large amoun of revenues, sufficien deb financing from he financial marke can be obained. This is called deb financing. The Federal Governmen provides financial suppor for infrasrucural developmen wih credi assisance such as TIFIA, GARVEE, privae aciviy bond, ec. Addiionally, sae governmens may also use general revenues o secure general obligaion bonds for infrasrucural developmen. However, if he expeced revenues fall shor, deb financing may no cover oal projec coss which creaes a financial gap. The financial gap needs o be closed wih funds from eiher public or privae secors. While debs mus be paid a a pre-deermined rae and wihin a pre-deermined period, projec funds from public and privae secors, ypically known as equiy financing, ake high risks and ge repaid afer deb service. The equiy componen is of essence in PPP projec financing and needs careful aenion and a full evaluaion. Firs, deb capaciy is deermined by he projec revenue sream and evaluaed by financial insiues. Second, privae parners are willing o inves in PPP projecs only when hey anicipae a high rae of reurn, or a minimal inernal rae of reurn (MIRR) from he invesmens. If he projec is no profiable enough, no privae parners will ake he risk o inves. Therefore public agencies may have o give away a significan share from he oal profi o arac privae invesmens, even if equiy invesmens may jus be a small percenage of he financial gap. Third, public agencies mus proec heir ineress and ensure ha privae parners do no abandon projecs when privae parners obain enough profis from PPP projecs earlier han expeced. Earlier exi from PPP projecs may benefi privae parners because hey could reduce heir operaional and mainenance coss. Privae parners are hus required o guaranee a 3

minimum amoun of invesmen o reduce he risk o public agencies. Furhermore, srong public resisance o high privae profi in PPP projecs pushes many public agencies o limi he rae of reurn for privae invesmens. Therefore, he amoun of privae equiy, or he allocaion of privae equiy and public funds in PPP deals, remain a major subjec of PPP financing. MODELING FOR EQUITY FINANCING Division of equiy financing beween privae parners and public agencies deermines he sharing of projec profi sreams and affecs he successful delivery of PPP projecs. A Linear Programming (LP) model is developed o help public agencies accomplish heir objecives while remaining aracive o privae invesmens. I is assumed ha a PPP projec spans T years. Funding is secured and projec sars a ime poin =0. The following noaions are used hroughou he paper. C = Consrucion cos D = Deb E 1 = Privae Equiy E 2 = Public Funds i A =Rae of reurn for public agency i B =Rae of reurn for deb holders i P =Rae of reurn for privae parner γ = Public Opporuniy Loss Coefficen R = Revenue a ime DS = Deb Service a ime OM = Operaion & Mainenance coss a DSR = Deb Service Reserve paymen a P 1() = Profi Sharing for public parner a P 2() = NPW of Profi for public agency a DSCR = Deb Service Coverage Raio Objecive Funcion: Max s.. T DS D*DSCR - (1 i = 0 + B) T DS( ) (D - (1 i ) <= 0 = 0 + A T P1( ) ) +(E 1 - (1 i ) = 0 + A ) γ* E 2 DS * DSCR ( R + DSR OM ) <= 0 C- (D + E 1 + E 2 ) <= 0 T P1( ) E 1 - <= 0 (ELP) = 0 (1+ ip(min) ) T P1( ) - E 1 <= 0 = 0 (1+ ip(max) ) P 1() <= R - OM - DS D, DS, E 1, E 2, P 1, P 2, >= 0 The objecive of he opimizaion is o maximize he benefis for he public agency from PPP financing. The hree benefis and coss componens included in he objecive funcion are deb financing benefis (coss), privae equiy financing benefis (coss), and opporuniy coss associaed wih public funds. The model mus saisfy several consrains. Firs, he deb capaciy consrain defines he maximal amoun of deb ha a PPP projec could suppor. Financial raing companies, like Fich and S&P rae he projec in accordance o he profiabiliy. The projec raing deermines he Deb Service Coverage Raio (DSCR), which, along wih he projec revenue sream, is used o calculae he deb capaciy. Second, he deb holders require he deb 4

service is secured wih higher ne revenue during he projec operaion phase. A reserve fund could also be used o pay deb service. The reserve fund is eiher from iniial public or privae invesmens, or operaion profis reserves from earlier years. Third, PPP financing mus be able o cover projec coss. Fourh, he rae of reurn for privae parners mus be larger enough o arac privae invesmens, ye small enough o proec public ineress. i P(min) and i P((max) indicae he low and high boundaries of he rae of reurn for privae parners. Furhermore, profis o privae parners mus be paid afer deb services are paid. In mos cases, he proposed model ELP involves a grea amoun of variables and equaions. To simplify he calculaion, an alernaive model SLP is developed and presened below. The objecive funcion is changed o minimize coss so ha he resuls will be on he posiive side. In he SLP model, all values are discouned back o ime 0. R, DS, P 1, and OM are he presen worh of cash flows R, DS, P 1(), and OM. The wo coefficiens α and β are used o conver values a he discoun rae i D o i B and i PI. These consans are easily obained by dividing he presen worh of cash flows a i D by he presen worh of he same cash flows a i B or i P.. D, DS, E 1, P 1 and E 2 remains as he decision variables in he SLP model. Min (DS - D) + (P 1 E 1 ) + γ *E 2 s.. D * DSCR R <=0 DS = α * D D + E 1 + E 2 = C P 1 >= β min * E 1 P 1 <= β max * E 1 P 1 <= R OM - DS DS, D, P1, E1 and E2 >=0 D, E1,E2 <= C (SLP) The objecive funcion in he SLP model is defined as minimizaion of PPP financing coss o public agencies. The hree ypes of financing mechanism in PPP projecs are deb, privae equiy, and public funds. The difference beween Deb Service and Deb represens he public coss hrough deb financing. If he expeced revenue is less, hen he deb available from banks and governmen agencies will decrease. This happens because he banks who give deb ake Deb Service Coverage Raio (DSCR) ino consideraion when calculaing he amoun of deb. The DSCR is calculaed as Revenue/Deb. In such cases he finance gap is arranged hrough equiy finance which is coslier han he debs. In reurn for equiy invesmens, privae parners ake a large share of projec profis which ranslaes ino high raes of reurn. Hence public agencies need o fill he financial gap wih privae capial in he meanime o ensure ha he reurn o privae parners is no unexpecedly high. (P 1 -E 1 ) represens he cos of privae equiy financing. A reducion of upfron public invesmens may be beneficial o public agencies. These reduced upfron invesmens leave more money-in-hand o be used for oher new or renovaing jobs. By using public funds in a PPP projec, he public agency essenially gives up he opporuniy o build oher infrasrucure ha could bring economic and social benefis o he public. In he ELP and SLP models, a public opporuniy loss coefficien γ is used o calculae he opporuniy loss due o he use of public funds in PPP projecs. One mus noice ha profi sharing for he public agency should also be incorporaed ino he coefficien γ. When γ=1, 5

amoun of benefi from PPP projec operaion derived from funds invesed in he PPP projec by he public will equal he cos of opporuniy loss from alernaive infrasrucure developmen. γ<1 indicaes ha opporuniy cos is less han he benefis from he PPP projecs. The opposie is rue when γ>1. The higher he γ, he larger he opporuniy loss. In boh models, γ *E 2 represens he oal opporuniy cos of public funds in a PPP projec. CASE STUDY The Alabama Deparmen of Transporaion (ALDOT) received an unsolicied proposal o build a 23 mile highway named US 231/I-10 Connecor which will run beween Alabama border o Dohan. This highway was proposed o provide a safer and a more efficien road nework o relieve raffic congesion. Dohan, also known as Hub of he Wiregrass, is locaed a a disance of abou 100 miles from Mongomery and a abou 200 miles from Birmingham and Mobile. This proposed highway will connec Dohan wih hese major populaion ceners, which are currenly served by nework of Inersae Sysem. The preliminary Traffic and Revenue Sudy repor esimaed he cos of consrucion of he connecor highway o be $100 million (he numbers are adjused wihin reasonable limis o mainain he secrecy of acual numbers associaed wih he projec). I also esimaed he expeced revenue sreams which were obained from wo differen raffic growh cases a Base Case and an Exernal-Exernal (EE) Boosed Trip Table case. Three scenarios were developed from he Base Case. The wors case scenario assumes ha he oll revenue growh (which incorporaes raffic growh and oll growh wih inflaion) would be 4.6% for 30 years. Under he average scenario, he oll revenue growh rae is expeced o be 4.6% for he firs en years, 8% for he nex en years, and 4.6% for he las en years. Under he bes case scenario, he oll revenue will grow a 4.6% for he firs en years and 8% for he nex weny years. Three more scenarios were similarly developed using EE Boosed revenue sreams. Under some of hese scenarios, however, he oll revenue could no secure enough deb o cover all projec coss. Therefore, equiy financing mus be used in his projec. A reasonable disribuion of privae equiy and public funds remains he major concern o he sae agency because he equiy allocaion balances aracing he privae secor and proecing public ineress. Based on he SLP model, an excel opimizaion model was developed o deermine he opimal allocaion of equiy invesmen for he I-10 Connecor projec. Bea disribuion was used o calculae he presen worh of expeced revenue under each scenario. Furhermore, sensiiviy analysis was conduced using risk analysis ool @Risk 4.0 o evaluae he impac of uncerainy in he oll revenue and he opporuniy loss coefficien. Daa ses used on he base run are lised in Table 1. The opimal privae equiy invesmens under base case and EE boosed case are $9.55 and $11.76 million, respecively. Table 1 Daa Used In LP Analysis and Resuls C ($M) R ($M) DSCR α β (min) β (max) γ Opimal Privae Equiy Base Case 100 65 1.50 1.20 1.36 2.10 2.00 $ 9.55 million EE Boosed 100 80 1.50 1.20 1.36 2.10 2.00 $ 11.76 million 6

DSCR was seleced a a value of 1.5. Afer geing he opimal resuls, sensiiviy analysis was conduced o es he impac of various expeced revenues which range from 0 o $180M. This enabled in geing a se of values of all he decision variables by varying he expeced revenue from 0 o $180M. Similar sensiiviy analysis was conduced by changing DSCR from 1.35 o 1.75 in he model. Figure 1 shows he impac of expeced revenues on agency cos, deb capaciy, privae equiy, profi sharing o privae parner, and public funds when DSCR is 1.5. Figure 1 Impacs of Expeced Revenue I can be observed ha as he expeced revenue increases, oal financing coss o he sae agency will decrease. This can be explained by he increasing deb capaciy due o high oll revenue. Deb financing is ypically cheaper han equiy financing. When high oll revenue is expeced, ne profi is expeced o be high. Therefore, he projec would be more aracive o he privae secor, decreasing he need for public funds. This rend coninues unil he projec is compleely self-financed hrough deb. Equiy financing becomes cosly in he PPP projec. The daa obained hrough solver sensiiviy analysis was used o plo he graph beween he expeced revenue and he equiy srucure raio of E2 and E1. Each poin on his graph represens an opimal value obained by sensiiviy analysis. Revenue was seleced o range from 0 o 180. The resuls are shown in figure 2. These curves, named as Opimal Curves, were hen used o obain he values of E2 and E1 by projecing he values of expeced revenue from he X axis o he opimal value curves and hen projecing hem o he Y axis. Given he DSCR, he opimal equiy srucure, described as public fund over privae equiy (E2/E1), can be seleced. When he DSCR is uncerain and wihin a range, he public agency could define he opimal range of equiy srucure. This is called equiy srucure efficien space. I should be noed here ha a value of 0 for he raio E2 and E1 indicaes ha he opimal soluion should have no invesmen from DOT bu i should no be misinerpreed ha E1 should also be zero. We can no know abou he amoun of privae equiy invesmen from he graphs shown in figure 2. The informaion abou privae invesmen can be obained using he sensiiviy analysis repor. In he I-10 Connecor projec, he opimal equiy srucure is 3.0 under base case, 5.8 under EE boosed case for an assumed value of DSCR 1.5 and E1+E2 should be equal o 7

oal equiy requiremen in boh cases. Wih a DSCR range of 1.35-1.75, he equiy srucure raio ranges from 3.74 o 10.3 under he base case scenario. Figure 2 Equiy Srucure Using Curve Obained By Opimal Soluion Sensiiviy of he objecive funcion was esed by varying he value of γ. The sensiiviy repor indicaed ha afer a paricular value of γ, he model reducing he public funds o 0 covered he equiy requiremen fully from privae equiy. This indicaes ha he value of γ helps o esablish a cu off poin for public funds in he projec. Lasly, i may happen ha informaion abou he expeced revenue is no available. In such a case he decisions of disribuion of equiy can be made by using he exreme corners of he area conained by he opimal curves and he expeced range of he expeced revenue. This is demonsraed in figure 3. Figure 3 Decision for E1 and E2 During Higher Levels of Uncerainy The area of uncerainy for equiy in figure 3 is proporional o he uncerainy. If he uncerainy is less, he curves of opimal soluions will be much closer and he range of expeced revenue can also be replaced by a poin value of expeced revenue. In any case, linear 8

programming can be used for he disribuion of equiy for opimizing desired oucome. I is reasonable o say ha he SLP model describes he PPP finance srucure on a very primiive level. This model can be exended furher o model muliple bank loans and muliple privae equiy invesmens a differen rae of reurns. The use of public opporuniy loss coefficien γ enables he weighing of he social and exernal benefis of public funds, bu he selecion of γ requires a careful consideraion. CONCLUSION Equiy srucure is of essence o PPP projec financing. In an effor o successfully deliver PPP projecs, ransporaion agencies mus carefully design he equiy srucure o simulaneously arac privae capial and proec public ineress. This paper presens a model o help he agencies maximize he benefis from PPP financing. The model includes he benefis and coss from deb and equiy financing and allows users o incorporae opporuniy loss ino he evaluaion. The case sudy discussed in his paper shows ha opimal equiy srucure depends significanly upon hree facors: expeced oll revenue, deb service coverage raio, and public opporuniy loss coefficien. The research suggess ha an opimal equiy srucure space could be defined under uncerainy. However, careful aenion should be given o he selecion of hese imporan parameers in PPP financing design. REFERENCES Abdel, A. M. (2007). Successful Delivery of Public-Privae Parnerships for Infrasrucure Developmen. Journal of Consrucion Engineering and Managemen. ASCE. 133(12), 918-931. Akinoye, A., Hardcasle, C., Beck, M., Chinyio, E. and Asenova, D. (2003). Achieving Bes Value In Privae Finance Iniiaive Projec Procuremen. Journal of Consrucion Managemen and Economics. (July 2003) 21, 461-470 Brandao, L. E. T. and Saraiva, E. (2008). The Opion Value of Governmen Guaranees in Infrasrucure Projecs. Journal of Consrucion Managemen and Economics. (November 2008), 26, 1171-1180. Chiara, N. and Garvin, M. (2008). Variance models for projec financial risk analysis wih applicaions o Greenfield BOT highway projecs. Journal of Consrucion Managemen and Economics. (Sepember 2008) 26, 925-939 FHWA (2007a). Inernaional PPP Case Sudies Repor Prepared by AECOM TEAM, Task Order 05-002. FHWA (2007b), User Guidebook on Implemening Public Privae Parnerships for Transporaion Infrasrucure Projecs in he Unied Saes Prepared by AECOM TEAM, Task Order 05-002. FHWA (2009) Public-Privae Parnerships for Highway Infrasrucure: Capializing on Inernaional Experience. Repor No. FHWA-PL-09-010. Finnery, J. D. (2007). Projec Financing. Second Ediion. John Wiley & sons Inc. GAO (2008). Securing Poenial Benefis and Proecing he Public Ineres Could Resul from More Rigorous Up-Fron Analysis. Unied Saes Governmen Accounabiliy Office. GAO-08-1052T Gross, M. E. and Garvin M. J. (2009). Approaches For Srucuring Concession Lenghs and Toll Raes For Transporaion Infrasrucure PPPs. Consrucion Research Congress, ASCE, 191-200 HM Treasury (2000) Public Privae Parnerships: The Governmen s Approach, The Saionery Office, Crown Copyrigh 2000, London. HM Treasury (2006) Value for Money Assessmen Guidance. London Koppinen, T. and Lahdenpera, P. (2007). Realized Economic Efficiency of Road Projec Delivery Sysems. Journal of Infrasrucure Sysems. ASCE 13(4), 321-329. 9

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