INTERNATIONAL REAL ESTATE REVIEW 2003 Vol. 6 No. 1: pp Banking System, Real Estate Markets, and Nonperforming Loans

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1 Banking Sysem, Real Esae Markes, and Nonperforming Loans 43 INTERNATIONAL REAL ESTATE REVIEW 2003 Vol. 6 No. 1: pp Banking Sysem, Real Esae Markes, and Nonperforming Loans Wen-Chieh Wu Deparmen of Public Finance, Naional Chengchi Universiy, Taipei, Taiwan or jackwu@nccu.edu.w Chin-Oh Chang Deparmen of Land Economics, Naional Chengchi Universiy, Taipei, Taiwan or jachang@nccu.edu.w Zekiye Selvili Deparmen of Finance, California Sae Universiy Fulleron, Fulleron, CA., USA or zselvili@fulleron.edu This paper examines he link beween nonperforming loans, real esae prices, and he banking sysem. We found ha he level of nonperforming loans affecs bank profiabiliy as well as he price performance of real esae markes. We also analyzed he facors ha cause he raio of nonperforming loans o oal loans o flucuae. We observed ha a higher raio of corporae loans o individual loans resuls in a lower percenage of nonperforming loans. In conras, a lower real esae lending rae relaive o he primary lending rae leads o a higher percenage of nonperforming loans. These resuls sugges ha he percenage of nonperforming loans can be parially governed by he lending pracices of banks. Keywords Nonperforming Loans, Real Esae, Banking Sysem Inroducion Since he Asian Financial Crisis of 1997, he subjec of nonperforming loans has been revisied heavily by academics and praciioners alike (Kwack,

2 44 Wu, Chang and Selvili 2000, Quigley, 2001, Collyns, and Senhadji, 2002). Nonperforming loans (NPL) have been labeled one of he likely suspecs of he financial markes collapse in Asia. Their effecs on he financial and real esae markes and he facors ha cause hem are of ineres o researchers. Kwack s (2000) daa se of Asian economies beween he years 1994 and 1997 shows ha he percenage of nonperforming loans increased seadily in some counries such as Taiwan, and dramaically in oher counries such as Korea and Thailand. 1 The Goldman Sachs sudy of Asian bank porfolios conduced in Sepember 1998 repored even higher esimaes of NPL percenages beween 1997 and 1999: 11% for Singapore, 15% for Hong Kong, 20% for Malaysia, 29% for he Philippines, 34% for Korea, and 50% for Thailand. The NPL raio in Taiwan was repored as high as 9% a he beginning of Table 1 Real Esae Secor and Naional Economy Counry Real Esae Loans as a Percen of Toal Bank Loans Average Exposure o Real Esae as Percen of GNP Hong Kong Taiwan Malaysia Thailand Singapore Korea Philippines China Indonesia Table 1 represens saisics of Asian economies dependence on real esae markes. The source is Quigley (2001). Wha causes he percenage of nonperforming loans o rise in some of he emerging Asian economies? Alhough he resuls up o now have been raher indirec, he lending pracices of banks are viewed as likely candidaes. Reynolds, Raanakomu, and Gander (2000) suggesed ha he banks aggressive lending policies, coupled wih weak profiabiliy, led o he financial crisis. Kwack (2000) found ha a high corporae leverage raio 1 The percenage of nonperforming loans in 1994 for Taiwan, Korea, and Thailand was 1.85%, 7.80%, and 7.50%, respecively. By 1997, hese percenages had increased o 3.82%, 17.00%, and 18.00% for he same counries. 2 Taiwan Financial Saisical Absrac

3 Banking Sysem, Real Esae Markes, and Nonperforming Loans 45 affecs he level of nonperforming loans. A roubled real esae secor could be anoher conribuing force o a rising nonperforming loan raio. The poenial exposure of he naional economy o he real esae secor in hese economies is relaively large. Table 1 has saisics on he average exposure o real esae as a percenage of GNP, and he real esae loans as a percenage of oal bank loans (Quigley, 2001). The raio of real esae deb o GNP was more han half in Taiwan and Malaysia, and more han hree quarers in Hong Kong in Furhermore, he percenage of real esae loans is around 40% o 55% in Hong Kong, followed closely by Taiwan a 35% o 45%. Based on he same Goldman Sachs sudy, Quigley also repored real esae loans as a percenage of he oal nonperforming loans porfolio for Hong Kong. This figure was expeced o be as high as 60% in 1997, 52% in 1998, and 50% for The rise in nonperforming loan raios has serious consequences for he economy. Bernsein (1996) developed a model in which he showed ha he level of nonperforming loans is a significan deerminan of he level of bank coss, as well as he esimaes of scale economies in banking. Kwack (2000) is one of he numerous auhors who suggesed nonperforming loans as a cause of he Asian Financial Crisis. Several auhors have also linked real esae markes wih financial markes. Allen, Madura, and Wian (1995) and He, Myer, and Webb (1996) have found ha bank socks are very sensiive o changes in real esae marke reurns. King (2001) claimed ha he Asian Financial Crisis was riggered by Japanese commercial banks ha were considerably weakened by he collapse of real esae markes. Quigley (2001) suggesed ha he aciviies in he real esae marke conribued o he severiy of he crisis. Lu and So (2003) found ha Asian banks were significanly exposed o real esae risk in he pos-crisis era. The lending policy of banks is also affeced by nonperforming loan raios. Banks will adjus heir loan preference raios and he weigh of risky loans in order o avoid a crisis. This will lead o a more conservaive real esae lending policy, as suggesed by Shen and Chang (2002). However, he more resricive lending policy may lead o a poor performance in he real esae secor, hereby exacerbaing a possible crisis. In summary, real esae markes, nonperforming loans and he banking sysem are all closely relaed. Mos pas sudies have concenraed on wo ou of hese hree facors a a ime, bu far less has been documened on he ineracion of hese hree variables. We aemped o analyze he deerminans and consequences of nonperforming loans by focusing on heir abiliy o link real esae markes and he banking sysem. We also added

4 46 Wu, Chang and Selvili wo new explanaory variables ha have no received a lo of aenion before. The real esae lending rae relaive o he primary lending rae is he firs variable. We would expec his variable o shif he percenage of real esae loans, as well as change he characerisics of he pool of borrowers. We also conrolled for he amoun of corporae real esae loans wih respec o he amoun of individual real esae loans. Since corporae loans, on average, are viewed as being less risky han individual loans, his would have a direc impac on he percenage of nonperforming loans. Taiwan experienced a rise in nonperforming loans prior o he crisis. Furhermore, he Taiwanese economy has had a relaively high exposure o real esae boh in erms of GNP and oal bank loans. This makes Taiwan an ideal candidae for analyzing he issues menioned above. Using he Granger causaliy es (1996), we modeled he causal relaionships beween he performance of he real esae marke (as proxied by he housing prices), nonperforming loans, and reurns o he banking secor. We confirmed ha he percenage of nonperforming loans Granger-caused he profiabiliy of he banking secor. We hen used simulaneous equaions o esimae he deerminans of nonperforming loans and o analyze heir impac on he real esae markes and he banking sysem. We found ha lower real esae lending raes relaive o primary lending raes cause nonperforming loans o rise. In conras, a higher raio of corporae real esae loans o individual real esae loans leads o lower levels of nonperforming loans. Finally, we observed ha he nonperforming loan raio has negaive and significan effecs on boh real esae markes and he banking sysem. The remainder of his paper is organized as follows: Secion 2 builds he hypohesis and discusses exising lieraure. Secion 3 inroduces he daa and sample saisics. Secion 4 inroduces he economeric model and analyzes he empirical resuls. Secion 5 is he conclusion. Hypohesis Developmen Alhough he opics of real esae, nonperforming loans, and he banking sysem have always been of ineres o researchers, he relaed lieraure on hese opics experienced a surge during and afer he Asian Financial Crisis. One of he earlier sudies done on he opic of he banking and real esae secors is by Mei and Saunders (1995). They developed an asse pricing framework in which hey looked a he ineracion of ex-ane risk premiums on boh bank sock reurns and real esae reurns for he period 1970 o They also sudied he ime-varying componen of hese premiums wih respec o economic and real esae marke condiions. They found ha he

5 Banking Sysem, Real Esae Markes, and Nonperforming Loans 47 ime variaions in bank risk premiums are parially deermined by ineres raes and he real esae marke. They also discovered ha he real esae facor was imporan for banks during he 1980s. Similarly, Allen, Madura, and Wias (1995) used a seemingly unrelaed regression model o deermine wheher bank reurns are sysemaically affeced by real esae marke performance. Their daa se spans he years beween 1979 and They were able o documen a posiive and significan relaionship beween bank reurns and changing real esae values even afer accouning for he effecs of he financial markes and ineres raes. They also observed ha bank performance sensiiviy o he real esae secor increases over ime. He, Myer, and Webb (1996) used a hree-index model o examine he sensiiviies of sock reurns for differen bank groups. They found ha bank socks are quie sensiive o changes in real esae reurns. Specifically, hey observed ha banks wih high proporions of real esae loans are mos affeced by changes in real esae reurns. Mei and Saunders (1997) ook a slighly differen roue and examined bank performance in ligh of real esae as an invesmen ool. They found ha he sraegy of commercial banks and hrifs is o base heir decisions on pas real esae reurns raher han projeced ones. The auhors labeled his sraegy rend-chasing, and presened heir findings as an explanaion as o why hese real esae invesmens have performed poorly. Their daa se spans 1970 o 1989 for commercial banks, savings and loans, and life insurance companies, and hey used monhly REIT reurns as heir proxy for real esae asse reurns. They found ha real esae markes excess reurns are mean revering; herefore, buying afer posiive excess reurns and selling afer negaive excessive reurns will produce a money-losing sraegy. The relaive risk facor of real esae loans has been sudied from an agency cos sandpoin as well. Bernanke and Gerler (1995), Mishkin (1996), and Allen and Gale (2000) all discussed he moral hazard and adverse selecion issues ha are amplified in he real esae marke. Due o weaknesses in financial regulaions, banks ake on excessive levels of risk by lending o risky subjecs, such as real esae developers or buyers. In urn, he invesors who are he mos likely o defaul end o apply for hese loans, hereby generaing a emporary bubble. There has also been some work done on he effec of nonperforming loans on he banking sysem. Bernsein (1996) showed ha he level of nonperforming loans is a significan deerminan of he level of bank coss, as well as of he esimaes of scale economies in banking. He found ha he

6 48 Wu, Chang and Selvili cos curve of banks wih high levels of nonperforming loans have he sandard U-Shape, wih he opimal poin residing beween five and en billion dollars. On he oher hand, he banks wih low levels of nonperforming loans do no exhibi he same characerisics. Their cos curve shows ha scale economies increase coninuously wih bank size. Reynolds, Raanakomu, and Gander (2000) also looked a bank size and furher examined he bank financial srucure in Asia prior o he financial crisis. They regressed financial performance raios such as loan preference, capial adequacy, liquidiy, and profiabiliy on srucural variables such as asses and income. They found ha during financial liberalizaion, loanpreference raios were lower, which hey inerpreed as increased levels of risk. They also found ha for some counries (Indonesia, Korea, and Thailand), he banks showed a sronger lending performance bu weaker profiabiliy, which may have led o he financial crisis. They also observed ha bank size maers, since profis and loan preferences increase wih size, and capial adequacy decreases wih size. The Asian Financial Crisis generaed a lo of quesions regarding he culpris and he underlying reasons for his phenomenon. Quigley (2001) is one of he many auhors who suggesed ha real esae markes were very significan in explaining he Asian Financial Crisis. His sudy bears similariies o ours. He poined ou he increasing supply of office space (as proxied by vacancy raes), he high raio of asse prices o marke rens, he high growh rae of bank credi, he high raio of nonperforming real esae loans, he relaive size of he real esae secor, and he relaive weigh of real esae among nonperforming asses as indicaors of an upcoming crisis. Quigley repored he percenage of real esae bank loans in Taiwan o be in he 35 o 45% range wih an average Moody s raing of D. He also repored a bank inermediaion raio of 1.46 and he average exposure o real esae as percenage of GNP o be 58%. He likened he price increase in real esae o a Ponzi scheme by saing ha when real esae is he only form of collaeral, here is a srong incenive for invesors o buy ino an appreciaing marke in order o borrow funds o expand. Collyns and Senhadji (2002) examined he link beween lending booms, asse price cycles, and financial crises across he Eas Asian counries. They found a srong relaionship beween bank loans and asse price inflaion. They saed ha he opimisic growh expecaions, heavy capial inflows, inadequae corporae governance, and dependence on inermediaion by under-regulaed banks all led o a rapid credi growh, especially in he real esae marke. Like earlier papers regarding agency cos issues, hey saed ha he real esae marke is paricularly vulnerable o he formaion of price bubbles because informaion asymmeries are larger, he supply is more rigid,

7 Banking Sysem, Real Esae Markes, and Nonperforming Loans 49 and he marke is herefore more imperfec. They documened he conemporaneous rise of non-performing loans, propery exposure, and real esae prices in Korea, Indonesia, Malaysia, he Philippines, Thailand, Hong Kong, and Singapore. Through a VAR analysis, hey concluded ha propery prices are srongly pro-cyclical, and bank loans conribued o he inflaion of propery prices prior o he crisis period. They also found ha he response of propery prices o credi is sronger during imes of rising prices han he response during imes of falling prices. The main policy implicaions of he paper are o srenghen credi assessmen while reducing reliance on collaeral, and o reduce he moral hazards in he banking sysem. In a relaed vein, Kallberg, Liu, and Pasquariello (2002) examined he impac of regime shifs on Asian equiy and real esae markes beween 1992 and They applied Granger causaliy and found ha equiy reurns cause real esae reurns, bu no vice versa. However, hey did observe wo-way causaliy for he volailiies of boh markes. They also found ha a counry s exposure o rade and firm leverage are imporan. They concluded ha regime shifs lead o higher relaive risk for real esae securiies. Anoher sudy ha is closely relaed o ours is ha of Kwack (2000), who looked a wheher here is a relaionship beween he Asian Financial Crisis and he weakness of financial insiuions, as well as he levels of inernaional ineres raes, shor-erm deb, excessive lending, and curren accoun deficis. The auhor conduced empirical analyses beween 1995 and 1997 in seven Asian counries: Indonesia, Korea, Malaysia, he Philippines, Singapore, Taiwan, and Thailand. He found ha he 3-monh LIBOR ineres rae, he nonperforming loan raes, and he corporae leverage raio are very significan in explaining he Asian Financial Crisis. He also modeled he level of nonperforming loans by including corporae leverage, LIBOR, he weigh of shor-erm deb wih respec o oal deb, he claims of bank deposis of he privae secor as a muliple of GDP, he curren accoun balance as a fracion of GDP, he corporae operaing margin, and he weigh of equiy wih respec o oal asses for banks. Wih he excepion of he corporae leverage raio, he did no find any significance for any of he variables using OLS. Our sudy aemps o link real esae, nonperforming loans, and he banking sysem. The deerminans of nonperforming loans are classified ino hree caegories: macroeconomic financial performance, real esae marke performance, and he lending policies of he banking secor. When boh he macroeconomy and real esae marke perform well, he level of

8 50 Wu, Chang and Selvili nonperforming loans should be lower. 3 In conras, if he banks loan porfolios bear more risk, nonperforming loans are likely o be higher. Specifically, we sugges using he relaive cos of real esae borrowing o general borrowing as a deerminan of he level of moral hazard and adverse selecion issues ha have been discussed by oher auhors. If he relaive cos of real esae borrowing is low, hen we would expec more of he riskier candidaes o apply for loans. In oher words, a higher relaive real esae lending rae will resul in lower amouns of real esae loans. The credi risk and probabiliy of nonperforming loans would hus be reduced. We also predic he raio of corporae real esae loans wih respec o individual real esae loans o be imporan in explaining he percenage of nonperforming loans. The diversificaion principle suggess ha corporaions are no as risky as individuals due o heir size and he qualiy of heir asses. Therefore, if banks decrease he weigh of individual real esae loans in heir real esae loan porfolios, he level of nonperforming loans should go down. Anoher piece of he puzzle is analyzing he deerminans of banking reurn. We proxied he banking reurn wih several variables: banking profiabiliy (BP), 4 he reurn on asses (ROA), 5 he reurn on equiy (ROE), 6 he sock reurn o he banking secor, and a composie index of he las hree facors menioned (GBRI). 7 We classified he key deerminans as credi risk, macroeconomic performance, ineres raes, and size. The nonperforming loan raio is used o express credi risk. As he credi risk is higher, he banking reurn is expeced o be lower. As wih he nonperforming loans, he macroeconomic condiions should have an impac on he banking reurns. The ne difference beween he lending rae and he deposi rae is anoher imporan deerminan of performance, and should have a posiive impac on bank reurns. Consisen wih previous lieraure, he size of he loan porfolio is also expeced o affec bank reurns posiively. Finally, he las piece of he puzzle is he deerminans of he real esae marke performance, which we propose o proxy by he level of housing prices. We hypohesized ha he relevan facors are he nonperforming loan raio, macroeconomic performance, and he demand for housing. A higher nonperforming loan raio can indirecly affec he real esae marke, since higher nonperforming loan raios will push banks o be more conservaive, hereby causing igher lending policies and lower housing prices due o 3 In line wih prior sudies, we propose using GDP as a proxy for macroeconomic performance and housing price as a proxy for real esae marke performance. 4 BP = Ne Benefis/Ne Revenues 5 ROA = Ne Benefis/Asses 6 ROE = Ne Benefis/Equiy 7 GBRI = 50% sock reurn o he banking secor + 25% Reurn on Asses + 25% Reurn on Equiy.

9 Banking Sysem, Real Esae Markes, and Nonperforming Loans 51 lower demand. If he financial economy is performing well, hen housing prices should also be higher. The demand for housing can be proxied by he rae of change for vacan housing. When he demand for housing is lower, prices will ineviably go down. The hree specificaions of nonperforming loans, banking reurns, and real esae reurns help us look a all of he facors a once and close he feedback loop. Daa and Descripive Saisics The sock reurn o he banking secor (SRB), GDP growh rae (GDP), he real esae lending rae (R r ), he primary lending rae (R l ), he deposi ineres rae (R d ), he corporae real esae loans (Corp), he individual real esae loans (Ind), he oal loans (Loan), and he nonperforming loan raio (NPL) are all obained from he Taiwan Financial Saisical Absracs. The housing price (P) is defined as he hedonic housing price measured by he Taiwan Real Esae Research Cener in Taiwanese Dollars/Ping. The number of vacan houses (Vacq) is repored as a descripive saisic and derived from he daa provided by Tai-Power Company. 8 Due o he availabiliy of monhly nonperforming loan daa, 9 he sample period sars in January 1996 and ends in April 2003, which produced 88 observaions. The descripive saisics are available in Table 2. The bank reurn figures were all less han 1.5 percen, wih he lowes indicaor being he bank profiabiliy (BP) a % and he highes indicaor being he reurn on equiy (ROE) %. The raio of corporae o individual real esae loans was less han ¼ a The average GDP growh rae (g) during he sample period was close o 4 percen. The nominal housing price in NT dollars per ping (P) was 176,313, and he real housing price in NT dollars per ping was 169,051. The nonperforming loan raio (NPL) hovered around 5 percen, which confirms he saisics found in oher sudies like Quigley s (2001). The difference beween he lending rae and he deposi rae averaged around 3 percenage poins. The quaniy of vacan housing (Vacq) was under 2 million a 1,577,681, and he rae of change (Vac) was less han 1 percen a percen. The raio of he real esae lending rae o he primary lending rae was slighly less han uniy a The real esae loans comprised more han one hird of oal loans a approximaely 37 percen. 8 If he elecriciy use of a house is lower han a lower limi hreshold value, he house is defined as vacan. 9 Before 1996, he nonperforming loan raio daa was only available on a yearly basis.

10 52 Wu, Chang and Selvili Table 2 Descripive Saisics Variables Mean Sd Dev Bank Asses (Nominal Terms) (NT$ million) 11,691,763 2,226,386 Bank Asses (Real Terms) (NT$ million) 11,175,330 1,996,757 Bank Benefis (Nominal Terms) (NT$ million) 11,463 26,221 Bank Benefis (Real Terms) (NT$ million) 11,116 24,945 Bank Equiy (Nominal Terms) (NT$ million) 914, ,717 Bank Equiy (Real Terms) (NT$ million) 874, ,998 Bank Profiabiliy (Ne Benefis/Ne Revenues) (BP) (%) Corporae Real Esae Loans (Nominal Terms) (NT$ 644,568 93,996 million) Corporae Real Esae Loans (Real Terms) (Corp) (NT$ 613,756 82,651 million) Corporae R.E. Loans/Individual R.E. Loans (Corp/Ind) Deposi Rae (R d ) (%) GDP Growh Rae (Nominal Terms) (g) (%) GDP Growh Rae (Real Terms) (%) General Banking Reurn Index (GBRI) (%) Housing Price (Nominal Terms) (NT$/ping) 176,313 5,790 Housing Price (Real Terms) (P) (NT$/ping) 169,051 9,486 Individual Real Esae Loans (Nominal Terms) (NT$ 2,775, ,088 million) Individual Real Esae Loans (Real Terms) (Ind) (NT$ 2,643, ,872 million) Nonperforming Loan Raio (NPL) (%) Primary Lending Rae (R l ) (%) Primary Lending Rae Deposi Rae (R l R d ) (%) Quaniy of Vacan Housing (Vacq) (unis) 1,577, ,335 Rae of Change of Vacq (Vac) (%) Real Esae Lending Rae (R r ) (%) Real Esae Lending Rae/Primary Lending Rae (R r /R l ) Real Esae Loans (Nominal Terms) (NT$ Million) 3,841, ,093 Real Esae Loans (Real Terms) (LoanR) (NT$ Million) 3,659, ,565 Real Esae Loans/Toal Loans (LoanR/Loan) (%) Reurn on Bank Asses (ROA) (%) Reurn on Bank Equiy (ROE) (%) Sock Reurn o he Banking Secor (SRB) (%) Toal Loans (Nominal Terms) (NT$ million) 10,365,820 1,521,093 Toal Loans (Real Terms) (Loan) (NT$ million) 9,867,669 1,328,217 Sample Size Noe: This able shows he descripive saisics for he sample from January 1996 o April The unis and, if applicable, he variable acronyms and wheher he variables are shown in real or nominal erms are presened in parenheses. The general banking reurn index is calculaed as follows: GBRI = 25% ROA + 25% ROE + 50% SRB

11 Banking Sysem, Real Esae Markes, and Nonperforming Loans 53 The Economeric Model and Empirical Analyses Since all of hree main variable groups, namely, real esae valuaion, banking profiabiliy, and nonperforming loans, are uni roo processes; we applied Granger causaliy ess (Granger, 1969). We firs applied he Johanson (1988) co-inegraion ess as shown in Table 3. Using he opimal lag lengh of 12, we found he following: The nonperforming loan raio (NPL) was co-inegraed wih he housing price (P), he sock reurn o he banking secor (SRB), and he general banking reurn index (GBRI). The housing price (P) was also co-inegraed wih he sock reurn o he banking secor (SRB) and he general banking reurn index (GBRI). We used vecor auoregression models (VAR) for he ses of variables ha were no co-inegraed. The resuls repored in Table 4 closely follow our predicions. We observed ha he reurn on asses Granger-causes he nonperforming loan raio and he housing price a he 5 percen level of significance. In urn, he nonperforming loan raio Granger-causes boh he reurn on bank asses, as well as he reurn on bank equiy a he 1 percen level of significance. These findings sugges ha here is a wo-way causal relaionship beween he nonperforming loan raio and bank reurns measures by he reurn on asses. Furhermore, here is evidence ha bank reurns (ROA) also affec housing prices. For hose ses of variables ha are co-inegraed, we ran he Granger causaliy ess wih he vecor error correcion model (VEC). The resuls are presened in Table 5. This analysis confirms ha he nonperforming loan raio Granger-causes he sock reurns o he banking secor and he general banking reurn index a he 1 percen level of significance, and housing prices a he 10 percen level of significance. We found ha he housing price Granger-causes he sock reurns o he banking secor a he 5 percen level and he general banking reurn index a he 10 percen level. Furhermore, we observed ha he sock reurn o he banking secor Granger-causes he nonperforming loan percenage and he housing price a he 10 percen level. Boh Tables 4 and 5 indicae ha he nonperforming loan raio is a very imporan deerminan of boh he banking secor s profiabiliy and he real esae marke s performance.

12 54 Wu, Chang and Selvili Table 3 Johanson Co-inegraion Tess Variables Included Opimal Lag Lengh LR Tes Saisics for No Co-inegraion Inferred Number of Co-inegraing Relaionship NPL and P NPL and SRB NPL and ROA NPL and ROE NPL and GBRI P and SRB P and ROA P and ROE P and GBRI Noe: This able shows he resuls of he Johanson co-inegraion ess. NPL is he nonperforming loan raio, P is he housing price in NT$ per ping in real erms, ROA is he reurn on bank asses, ROE is he reurn on bank equiy, SRB is he sock reurn o he banking secor, and GBRI is he general banking reurn index. All series are in real erms wih respec o he firs quarer of If he log likelihood raio is greaer han he 5% criical value, he null hypohesis of no co-inegraion is rejeced. The 5% criical value is for all ess repored. The inferred number of co-inegraing relaionship is shown in he las column. To furher our analysis and o perform a robusness check, we developed a se of simulaneous equaions semming from our hypoheses discussed in Secion 2. The firs specificaion expresses he relaionship beween he nonperforming loan raio (NPL) and is possible deerminans: NPL = α R Corp + r, α g + α P + α + α ε (1) R 4 l Ind, 1 1 where NPL is he nonperforming loan raio, P is he change in he housing price in nominal erms, R r is he real esae lending rae, R l is he is he primary lending rae, Corp is he amoun of corporae real esae loans, and Ind is he amoun of individual real esae loans. We expeced α 1 < 0, α 2 < 0, α 3 < 0, and α 4 < 0, according o our hypohesis.

13 Banking Sysem, Real Esae Markes, and Nonperforming Loans 55 Table 4 Granger Causaliy Tess of he Ineracion of Nonperforming Loans, Banking Secor Profiabiliy, and Real Esae Performance Using Vecor Auocorrelaion (VAR) Null Hypohesis F-Saisic P-value ROA does no Granger-cause NPL 1.91 ** 0.05 NPL does no Granger-cause ROA 3.24 *** 0.00 ROE does no Granger-cause NPL NPL does no Granger-cause ROE 3.13 *** 0.00 ROA does no Granger-cause P 2.00 ** 0.04 P does no Granger-cause ROA ROE does no Granger-cause P P does no Granger-cause ROE Noe: This able shows he resuls of he Granger causaliy ess. NPL is he nonperforming loan raio, P is he housing price in NT$ per ping in real erms, ROA is he reurn on bank asses, and ROE is he reurn on bank equiy. All series are in real erms wih respec o he firs quarer of The lag lengh of 12 quarers is from he co-inegraion analysis repored in Table 3. The second column of he able shows he F saisics of he null hypohesis ha he lag coefficien of he causal variable is equal o zero. The aserisks indicae rejecion of he null hypohesis a he 1 percen or beer (***) and 5 percen (**) levels of significance. Table 5 Granger Causaliy Tess of he Ineracion of Nonperforming Loans, Banking Secor Profiabiliy, and Real Esae Valuaion Using Vecor Error Correcion (VEC) ECM 1 D(NPL) D(P) (-0.52) (-2.75) * ECM 2 D(NPL) D(SRB) (-1.86) * (-3.15) *** ECM 3 D(NPL) D(GBRI) (-0.52) (-2.75) *** ECM 4 D(P) D(SRB) (-1.86) * (-2.47) ** ECM 5 D(P) D(GBRI) (0.00) (-2.61) *** Noe: This able shows he Granger causaliy ess using he vecor error auocorrelaion model (VEC). NPL is he nonperforming loan raio, P is he housing price in NT$ per ping in real erms, SRB is he sock reurn o he banking secor, and GBRI is he general banking reurn index. The firs column of he able indicaes he five error correcion models (ECM), which correspond o he five pairs of variables ha are consisen wih he co-inegraion vecors found in Table 3. The dependen variables, which are he firs differences (as indicaed by he leer D ) of he quaniies in parenheses, are shown nex o he model number. The numbers below he dependen variables are he coefficiens of he error correcion erms, which indicae he long-erm influence of he causal variable. The numbers in parenheses are he -

14 56 Wu, Chang and Selvili saisics of he coefficiens. All series are in real erms wih respec o he firs quarer of The lag lengh of 12 quarers is from he co-inegraion analysis repored in Table 3. The coefficiens for he 12 lagged variables of he dependen variables, as well as hose for he causal variables, are no shown in he able. One aserisk indicaes a 10 percen level of significance; wo aserisks indicae a 5 percen level of significance; and hree aserisks indicae a 1 percen level of significance or beer. The second specificaion arises from he deerminans of he bank profiabiliy (BP) and is expressed as follows: ( Rl, Rd, ) + β Loan BP β + µ = 0 + β1npl + β2g + β3 4 (2) where BP is he bank profiabiliy, NPL is he nonperforming loan raio, g is he GDP growh rae, R l is he is he primary lending rae, R d is he deposi rae, and Loan is he amoun of oal loans in nominal erms. We expeced β 1 < 0, β 2 > 0, β 3 > 0, and β 4 > 0. The hird specificaion explains he housing price level in nominal erms: P = γ 0 + γ 1NPL + γ 2 g + γ 3Vac + ξ (3) where P is he housing price in nominal erms, NPL is he nonperforming loan raio, g is he GDP growh rae, and Vac is he rae of change of vacan housing. We prediced γ 1 < 0, γ 2 > 0, and γ 3 < 0. Equaion (1) can be incorporaed ino Equaions (2) and (3); herefore, we can rewrie he srucural form model as a reduced form model consising only of he banking profiabiliy and he housing price deerminans. This reduced form model is shown as follows: BP = A + A g + A Loan+λ R + A g A P A R r, 1 l, 1 Corp + A 6 Ind A7 ( R R ) l, d, + AVac 8 (4) P = B R Corp r, B2 g 1 + B3 g + B4 P 1 + B5 + B + BVac + η R 6 l Ind 7 (5), 1 1 where BP is he bank profiabiliy, g is he GDP growh rae, P is he housing price in nominal erms, R r is he real esae lending rae, R l is he is he primary lending rae, Corp is he amoun of corporae real esae loans, Ind is he amoun of individual real esae loans, R d is he is he deposi rae, and

15 Banking Sysem, Real Esae Markes, and Nonperforming Loans 57 Vac is he rae of change of vacan housing. We expeced he following relaionships o hold: A 2 > 0, A 3 > 0, A 4 > 0, A 5 > 0, A 6 > 0, A 7 > 0, A 8 < 0, A 9 > 0, B 2 > 0, B 3 > 0, B 4 > 0, B 5 > 0, B 6 > 0, and B 7 > 0. In oher words, he banking profiabiliy depends on curren and previous GDP growh raes, he housing price, he relaive cos of real esae borrowing, he raio of corporae o individual real esae loans, he spread beween he lending rae and he deposi rae, he rae of change in vacan housing, and he oal amoun of loans. Similarly, he housing price depends on he curren and previous GDP growh raes, he previous level of housing price, he relaive cos of real esae borrowing, he raio of corporae o individual real esae loans, and he rae of change in vacan housing. Table 6 Regression Resuls - Srucural Form Model 1 (NPL) 2(BP) 3 (P) Consan *** (13.86) g *** (0.45) *** (68.16) (-6.28) g *** (-1.36) (-4.26) P P (1.03) R r, 1 / R *** l, 1 (-16.11) R l, R d, (0.42) Corp 1 / Ind ** 1 (-2.06) Loan (1.15) NPL *** *** (-2.77) (-8.88) Vac (-1.235) Sample Size R Noe: This able shows he resuls of Equaions (1), (2), and (3), respecively. The firs model has he nonperforming loan raio (NPL) as he dependen variable, he second model has he bank profiabiliy (BP) as he dependen variable, and he hird model has he housing price (P) as he dependen variable. All variable definiions remain he same as in Table 2. The figures in parenheses represen he -values. One aserisk (*) represens significance a he 10 percen level, wo aserisks (**) represen significance a he 5 percen level, and hree aserisks (***) represen significance a he 1 percen level or beer.

16 58 Wu, Chang and Selvili In order o avoid he biases caused by insiuional changes, we excluded he recen observaions. The sampling period hus spans from January 1996 o Sepember 2002, which resuled in 81 observaions. Table 6 shows he resuls of Equaions (1), (2), and (3). Wih he excepion of one resul, he significan oucomes are consisen wih our hypoheses. We found ha he raio of corporae o individual real esae loans is negaively significan a he 5 percen level in deermining he nonperforming loan raio. We observed ha he relaive cos of real esae borrowing is negaively significan a he 1 percen level in deermining he nonperforming loan raio. We also observed ha he GDP growh rae is negaively significan in deermining he nonperforming loan raio. Table 7 Regression Resuls - Reduced Form Model BP P Consan (-1.26) (1.32) g (-1.09) (0.93) g (0.96) (-0.86) P *** 1 (0.55) (22.82) R r, 1 / R 0.150*** l, 1 (2.92) (1.69) R l, R d, (-0.88) Corp 1 / Ind (-0.81) (1.69) Loan 0.006** (2.05) Vac 0.120** (2.07) (0.19) Sample Size R This able shows he resuls of Equaions (4) and (5), respecively. The firs model has he bank profiabiliy (BP) as he dependen variable and he second model has he housing price (P) as he dependen variable. All variable definiions remain he same as in Table 2. The figures in parenheses represen he -values. One aserisk (*) represens significance a he 10 percen level, wo aserisks (**) represen significance a he 5 percen level, and hree aserisks (***) represen significance a he 1 percen level or beer.

17 Banking Sysem, Real Esae Markes, and Nonperforming Loans 59 The only significance for he banking profiabiliy came from he nonperforming raio percenage, which has a negaive and significan effec a he 1 percen level. The puzzling oucome is he negaive and significan relaionship beween he housing price and GDP growh rae. We prediced he coefficien o be posiive, since we would expec he real esae marke o perform beer when he overall economy is doing well. One possible reason is ha Taiwan s housing prices display a cerain degree of rigidiy while economic growh flucuaes. Therefore, he concurren changes do no necessarily represen a reacion o he same variables. The nonperforming loan raio also has a negaive and significan (1 percen level) impac on he housing price. The reduced form equaions are regressed in Table 7. We observed ha he banking profiabiliy is posiively affeced a he 1 percen level by he relaive cos of real esae borrowing, which is consisen wih our hypohesis. We also found ha he oal amoun of loans is a posiive and significan deerminan of bank profiabiliy a he 5 percen level. The rae of change in vacan housing is expeced o have a negaive impac on profiabiliy, bu insead he coefficien is posiive and significan a he 5 percen level. Again, his is presumably due o he real esae marke behaving fairly rigidly when compared o he banking secor or he general economy. Finally, we observed ha he pas level of housing prices is a very significan and posiive deerminan of he curren level of housing prices. The analysis was also conduced wih variables in real erms and he resuls, which were no repored in he ables, are qualiaively similar. Where here is indicaion of auocorrelaion as measured by Durbin-Wason ess, Cochrane-Orcu generalized differencing procedures were used. The resuls (no shown) are qualiaively idenical. Conclusions The risky lending behavior of banks and he recessive real esae secor can cause increasing levels of nonperforming loans. In urn, a high percenage of nonperforming loans can push banks o adop more resricive real esae lending policies, causing he real esae marke o slump. This paper analyzed he ineracions of he nonperforming loan raio, he reurns o he banking secor, and he real esae marke. Using Granger causaliy ess, we found ha he nonperforming loan raio can indeed have an effec on bank profiabiliy. The converse is also rue: bank profiabiliy has an impac on he nonperforming loan raio. We also found ha he bank profiabiliy and he housing price have causal relaionships. To beer analyze he deerminans of each of hese hree facors, we modeled a se of simulaneous equaions. We prediced ha he nonperforming loan raio can be explained by he GDP growh rae, he change in housing price, he

18 60 Wu, Chang and Selvili relaive cos of real esae borrowing, and he raio of corporae o individual real esae loans. We found ha all bu he change in housing price are imporan deerminans of he nonperforming loan raio. We also modeled he banking profiabiliy by including he GDP growh rae, he spread beween he lending rae and he deposi rae, he oal amoun of loans, and he nonperforming loan raio. The resuls suggesed ha he nonperforming loan raio affecs banking profiabiliy negaively, as expeced. Furhermore, we prediced he explanaory variables for he housing price. We expeced he GDP growh rae, he nonperforming loan raio, and he rae of change in vacan housing o be imporan in explaining he housing price. We found ha he growh rae was significan, bu wih he wrong sign, which could be due o he inflexibiliy of he real esae marke in he shor run. In conras, we observed ha he nonperforming loan raio affecs he housing price negaively. Finally, we modeled wo reduced form equaions and found ha he relaive cos of real esae borrowing and he oal amoun of loans affec he banking profiabiliy. Anoher possible resul, perhaps due o he inflexibiliy of he real esae marke, is he posiive relaionship beween he rae of change in vacan housing and he banking profiabiliy. We observed ha he pas level of he housing price is he single mos imporan deerminan of he curren level of he housing price. The findings of his sudy provide policy insighs. Reducing he nonperforming loan raio has a posiive influence on boh he real esae secor as well as he banking sysem. Adjusing he real esae lending policies is a possible way of reducing he nonperforming loan raio. However, banks mus perform his adjusmen wih care. If hey make heir policies oo conservaive, real esae lending could drasically conrac, exering a downward pressure in he real esae marke. References Allen, F., and D. Gale. (2000), Bubbles Crises, The Economic Journal, 110, Allen, T.M., J. Madura, and K.J. Wian, (1995), Commercial Bank Exposure and Sensiiviy o he Real Esae Marke, The Journal of Real Esae Research, 10, Bernanke, B., and M. Gerler, (1995), Inside of a Black Box: The Credi Channel of Moneary Policy Transmission, Journal of Economic Perspecives, 9,

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20 62 Wu, Chang and Selvili Reynolds, S.E., S. Raanakomu, and J. Gander, (2000), Bank Financial Srucure in Pre-crisis Eas and Souheas Asia, Journal of Asian Economics, 11, Shen, C.H., and Y.H. Chang, (2000), Do Regulaion and Ownership Affec Bank Performance? Governance May Maer, Academia Economic Papers (in Chinese), 30,