Loss given default of residential mortgages in a low LTV regime: Role of foreclosure auction process and housing market cycles

Journal of Banking & Finance 39 (2014) 192–210

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Loss given default of residential mortgages in a low LTV regime: Role of foreclosure auction process and housing market cycles Yun W. Park a,⇑, Doo Won Bang b a b

Chung-Ang University, 221 Heukseok-dong, Dongjak-gu, Seoul, Republic of Korea Korea Housing Finance Corporation, Seoul, Republic of Korea

a r t i c l e

i n f o

Article history: Received 13 July 2013 Accepted 7 November 2013 Available online 21 November 2013 JEL classification: G21 G28 Keywords: Foreclosure auction process Residential mortgages Loss given default Low LTV regime Housing market cycle

a b s t r a c t Using loan-level foreclosure auction data we study the loss given default (LGD) of defaulted residential mortgages originated in Korea, a low LTV regime. We find that senior mortgages generate very low loss rates (5–10%) while losses of subordinated claims are in 30–50% range. We document the effects of housing market cycles on loss severity by showing that collateral characteristics that are overvalued during the boom increase loss severity during the market downturn. We also investigate how a broad set of time-of-origination and post-origination information on loan, collateral and borrower characteristics and foreclosure auction process influence the LGD of residential mortgages. Ó 2013 Published by Elsevier B.V.

1. Introduction The subprime mortgage loan crisis in the US that led to the global financial crisis highlights the impact of losses from residential mortgage loans on financial system stability. There is a growing consensus that monitoring credit risk of residential mortgages is an integral element of safeguarding financial system stability. The Basel II Capital Framework allows banks to calculate their minimum regulatory capital through the Advanced Internal Rating Based Approach (A-IRB). Under A-IRB, banks could use their own quantitative models to estimate probability of default (PD), exposure at default (EAD), loss given default (LGD) and other parameters required for calculating risk weighted assets (RWA).1 Therefore, banks need to develop valuation models of bank assets, for which the most important components are PD, EAD and LGD. Furthermore, empirical evidence on LGD needs to be collected by

⇑ Corresponding author. Tel.: +82 2 820 5793; fax: +82 2 813 8910. E-mail addresses: [email protected] (Y.W. Park), [email protected] (D.W. Bang). 1 LGD or loss severity is defined as 1-recovery rate. 0378-4266/$ - see front matter Ó 2013 Published by Elsevier B.V. http://dx.doi.org/10.1016/j.jbankfin.2013.11.015

banks and their supervisors.2 However, while there have been many studies on probability of default, the body of research on LGD, however, remains limited in the literature. Korean housing finance is characterized by a very low maximum LTV allowed (50% in Greater Seoul and 60% elsewhere).3 Since in most OECD countries the LTV limit is 80% or greater, Korea is unique in that its LTV limit has been between 50% and 60%.4 Korean housing market provides an ideal setting to study how very low LTV limit functions as a macro-prudential measure. By examining the loss severity of Korean residential mortgages we extend the study of the LGD of residential mortgages to the low LTV economy. There is an important difference between the foreclosure processes in the US and Korea. In the US, foreclosed properties are auctioned only once, and if there is no bidder willing to pay more than the foreclosing lender’s reservation price, the lender takes title. In Korea, on the other hand, foreclosed properties are 2 The total required capital is calculated as a fixed percentage of the estimated RWA. Under the capital accord of June 2004 (Basel Committee on Banking Supervision, 2004), financial institutions estimate one-year probability of default and expected LGD for the internal rating-based (IRB) approach. Basle III, which tightens Basel II, essentially follows the A-IRB of Basel II. 3 However, on April 1, 2013 Korean government announced its plan to raise the LTV limit to 70% for the first time homebuyers by the end of 2013. 4 See, for example, Table 3.2 of 2011 Global Financial Stability Report by IMF.

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auctioned again and again until they eventually sell following a schedule of minimum bid prices set by the court. Typically the first minimum bid price is at par with the appraisal value. The second is set typically at 80% of the appraisal value. The minimum bid price goes down successively in steps of 20% or so of the previous minimum bid price until the property is sold. It keeps lenders out of the position of becoming property owners and eliminates the category of REO (real estate owned), which lenders would have to carry on their balance sheets. As a result, the foreclosure process is efficient and auction variables (for example, duration of auction, number of failed auctions, and number of bidders) are well defined. We extend the study of the LGD of residential mortgages by investigating the relevance of this unique foreclosure process. Loss-given-defaults are measured for a sample of 2590 residential mortgages between January 2006 and January 2009. We find that the LGD for senior creditors is in the 5–10% range and the LGD for subordinated creditors, in the 30–50% range. The recovery performance indicates that the loss experienced by large commercial banks, which typically hold senior mortgages, is indeed limited consistent with the fact that residential mortgages in Korea are originated with very low LTV. Subordinated claimants suffer substantial losses suggesting that the credit risk of subordinated claims is much larger than that of the senior mortgages and that losses are likely to concentrate on savings banks and other smaller financial institutions which tend to carry substantial exposure to second liens. For senior mortgages, the frequency distribution of LGD has one sharp peak of 80.39% in the 0–10% interval. For subordinated claims the distribution is bimodal; the first mode is the 0–10% interval and the second mode is the 20–30% interval. The first peak is sharply defined in the 0–10% interval while the other is broadly defined over the 10–50% interval. Thereafter, the frequency distribution of LGD decreases monotonically with LGD. This is different from the frequency distribution of corporate bonds and loans, which are bimodal with peaks around 0% and 100%. Thus, even for subordinated claims, extreme losses are relatively infrequent consistent with the low LTV nature of mortgages. We find that the current loan-to-value ratio (CLTV) of defaulted loans rises sharply before the loan default. LGD rises sharply after CLTV rises above 80% reflecting the foreclosure auction costs as well as the fair return for investors in auctioned properties. Consistent with works reported elsewhere we find that CLTV matters the most for the loss severity of residential mortgages. We also document the influence of CLTV to LGD through the cumulative effect of past house price changes on CLTV. The study of the LGD of the residential mortgages has examined collateral, borrower and loan characteristics as well as macroeconomic factors. We extend the literature by examining the effect of foreclosure auction process on the LGD of residential mortgages explicitly. We find that foreclosure auction process is a critical determinant of LGD, which has not been documented elsewhere. The foreclosure auction variables account for about 10% of the variation of the LGD of the residential mortgage loans. The foreclosure auction-specific factors of importance are duration of auction, number of failed bids and the number of bidders. Studies have focused primarily on the LGD of senior mortgages. Losses may concentrate on subordinated claims, especially in a low LTV country. We extend the literature on the LGD of residential mortgages by examining the LGD of both senior mortgages and subordinated claims. Results on this issue will shed some light on the pricing of the credit risk of subordinated claims. Furthermore, the loss experienced by the entire class of subordinated claimants provides useful information on the loss of financial institutions holding subordinated mortgage claims. Some collateral characteristics may influence LGD as the housing market goes from hot to cold. Desirable collateral attributes can

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be overvalued during the booming market and undervalued during the subsequent market downturn. Specifically, we consider the effect of housing type, speculative submarket and property size on LGD. Apartments in Korea are known to show a greater volatility and liquidity compared to detached houses. If liquidity gets overpriced during booming housing markets then LGD would be higher for apartments than for detached houses during the market downturn. In addition, we study the effect of the speculative activity on LGD by examining the Gangnam district, which is the submarket affected by the recent housing bubble (from about year 2000 to year 2007) the most. We find that the LGD of the Gangnam submarket is smaller than elsewhere during the booming market, but larger than elsewhere during the market downturn consistent with the bubble hypothesis. We evaluate the effect of leverage on LGD by examining whether larger, therefore more expensive homes, which are financed using a greater leverage, lead to a larger LGD during the market correction. Consistent with the leverage hypothesis we find that larger homes show a lower LGD during the booming market, but a larger LGD during the market downturn. On the other hand, collateral characteristics may influence LGD indirectly via auction price. Collateral characteristics, which can influence the successful bidder’s ability to resell the auctioned unit, are likely to affect the auction price. We examine collateral characteristics such as proximity to subway, view, desirability of neighborhoods, size of the residential complex, possibility of redevelopment and reputation of the builder. There are two main types of housing stocks in Korea, namely, apartments and detached houses. Apartments are modern, highly standardized units in a big residential community while detached houses tend to be old and often remodeled all or in part into studios and rented out. A potentially large loss of subordinated claims may result if the owners of detached houses enter fake renters in the official resident registry maintained by the local government since fake renters can compete with mortgage lenders for seniority. We document some evidence of fake renters increasing the LGD of residential mortgage lenders. We examine the relevance of time-of-origination information about loans, collaterals and borrowers on LGD by constructing a foreclosure auction sample of defaulted mortgages purchased by Korea Housing Finance Corporation (KHFC) between 2004 and 2007. We find that of time-of-origination variables loan size, Gangnam district dummy and property size are significant; of post-origination variables CLTV, subordinated-claims-in-place dummy, the number of failed auctions and changes in collateral value are significant. Suggesting a link between housing market cycles and LGD, the effect of property size on LGD is negative for the 2004–2007 sample (booming market) while it is positive for the 2008–2009 sample (market downturn). The rest of the paper is organized as follows. In Section 2 we review the relevant literature. In Section 3 we describe the foreclosure auction data related to defaulted mortgage claims used in this research. Empirical findings are reported in Section 4. Conclusions are provided in Section 5.

2. Literature review The measurement of credit risk involves the estimation of three parameters: the probability of default on individual loans or a portfolio of loans, loss-given-defaults, and the correlation across defaults.5 Compared to the research on the modeling and estimation of PD there are far fewer studies on LGD. We will limit the review of 5 For a comprehensive review on credit risk models see, for example, Crouhy et al. (2000).

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the literature to the studies on loss severity. Within the LGD literature there are relatively more studies on the LGD of wholesale exposures such as corporate bonds and corporate loans than on retail exposures such as residential mortgage loans and credit card loans. We review existing studies on corporate bonds first, then corporate loans, and finally residential mortgage loans. Since Altman (1989) applied actuarial analysis to study default rates of US corporate bonds, extensive empirical literature on credit risk in the bond market developed. For example, Carey (1998) reports default rates, loss severity, and average loss rates for a large sample of privately placed bonds. Altman et al. (2005) conduct a literature survey on the empirical evidence of default recovery rates of corporate bonds and loans. They report an average loss given default of 63% (recovery rate of 37%) for the US corporate bond market over the period 1982–2001. They note that the aggregate recovery rate on defaulted bonds is affected negatively by the supply of defaulted bonds and the LGD appears to be positively correlated with probability of default. Acharya et al. (2003) compare the loss severity of secured bonds vs. unsecured bonds. They report an average loss-given-default of 52% (an average recovery rate of 48%) for senior secured bonds, and 49% (51%) for senior unsecured bonds for the period 1982–1999. They conclude that recovery on individual bonds is affected not only by seniority and security, but also by the industry conditions at the time of default supporting the theoretical study by Shleifer and Vishny (1992), who examine the impact of industry conditions on liquidation values. Using corporate bond data from Moody’s Default Risk Service Database, Schuermann (2004) reports that the recovery distribution of corporate bonds is bimodal, with recoveries lower in recessions than in expansions. Fewer studies have focused on bank loan markets because of the private nature of these transactions. Asarnow and Edwards (1995) examine 831 defaulted loans at Citibank over the period 1970–1993. They report an average loss-given-default of 35% (an average recovery rate of 65%). Similar to corporate bonds (Schuermann, 2004) the distribution of recovery rates is bi-modal, with a concentration of recovery rates on either the low or the high end of the distribution. Using the present value of cash flows, Carty and Lieberman (1996) measure the recovery rate on a sample of 229 small and medium-size loans in the US. They report an average loss rate of 21%. Unlike Asarnow and Edwards (1995) they find that the distribution of loss severity is skewed toward the low end of the scale. Grossman et al. (1998) analyze the recovery rate on 60 syndicated bank loans over the period 1991–1997. Based on secondary market prices after the credit event, they report an average loss rate of 18%, which is in line with the findings of Carty and Lieberman. More recently, Altman and Suggit (2000) study the credit performance of US syndicated bank loans with a size at least US$100 million over the period 1970–1996. They report on their default rates, but do not report on the loss severity of defaulted loans. Hurt and Felsovalyi (1998) and La Porta et al. (2003) investigate the loss severity of wholesale loans in Latin American markets. Hurt and Felsovalyi analyze 1149 bank loan losses in Latin America for the 1970–1996 period. They report an average LGD of 31.8% (an average recovery rate of 68.2%). They show that loan size is a contributing factor to loss rates, with large loan default exhibiting lower recovery rates. They attribute this to the fact that large loans, often not secured, were made to family owned business groups. As in Asarnow and Edwards’ study, they report a bi-modal distribution. Analyzing loan losses in Mexico in the context of related lending La Porta et al. (2003) report an average LGD of 54% (an average recovery rate of 46%) for unrelated loans, and an average LGD of 73% (an average recovery rate of 27%) for related loans over the period 1995–1999. Evidence of skewness toward the high end of the distribution is also reported.

A few studies examine wholesale credits extended by European banks. Salas and Saurina (2002) and Acharya et al. (2006) report on the determinants of the aggregate level of banks’ loan losses. Dermine and Neto de Carvalho (2006) analyze the LGD of individual loans. Using SME (small to medium enterprise) loans of a Portuguese bank they report an average LGD of 32.7% (a cumulative recovery rate of 67.3%) after 36 months and an average LGD of 30% (a cumulative recovery rate of 70%) after 48 months. Allen et al. (2004) survey the credit risk measurement and modeling of retail loan products and note that although several models exist to guide the providers of wholesale loans, the research on retail risk measurement is quite sparse. They point out that both PD and LGD are critical inputs for the credit risk measurement of retail credits in A-IRB. However, the models they identify (proprietary credit scoring models, option-theoretical structural models and reduced-form models) are all PD models, which tend to treat LGD as a point estimate. Using information from 11 US and Canadian banks Risk Management Association (2000) examines how banks assign two important risk characteristics: expected default frequency and LGD along product lines. They find that overall LGD tends to be higher for retail loans (except first mortgages) than wholesale loans. The body of research on the LGD of residential mortgage loans is also sparse, but growing.6 Clauretie and Herzog (1990) study the effect of state foreclosure laws on loan losses for mortgages insured privately and by government (e.g., Federal Housing Administration). They find that judicial procedure and the statutory right of redemption extend the foreclosure and liquidation processes and thus are associated with larger loan losses. Using the option theoretical approach to price default and foreclosure delay, Ambrose et al. (1997) investigate the relevance of the foreclosure regime on the loss severity experienced by the lenders. Lekkas et al. (1993) study the LGD of mortgage loans that Freddie Mac purchased from 1975 to 1990. They report that LGD increases with the default probability in the region and deceases with the age of the mortgage loan. Pennington-Cross (2003) and Calem and LaCour-Little (2004) report that the initial LTV, CLTV, the loan age and loan size influence LGD. Using the loan-level default and recovery data of high loan-to-value residential mortgages from several private mortgage insurance companies, Qi and Yang (2009) find that characteristics of mortgage loans and mortgage collaterals influence loan loss severity. In particular, they show that CLTV is the most influential of these explanatory factors.7 They also report that LGD is higher in states with judicial foreclosure process and statutory rights of redemption suggesting that the default, foreclosure and settlement process matters. Furthermore, they report that LGD is higher during the period of market downturn. Zhang et al. (2010) investigate the effect of housing market cycles on LGD using a subprime residential mortgage loss dataset from 1998 to 2009. They report that house price changes before the default show a long lagged effect on LGD. They argue that since CLTV is driven by historical house price changes a series of the lagged house price changes reflect the effect of housing market cycles on LGD. In this paper, we extend the literature on LGD in a number of dimensions. The studies reported to date are primarily based on the US housing markets, a high LTV regime. We extend the study of the LGD of residential mortgage loans to a low LTV regime by studying the LGD using loan-level foreclosure auction data of defaulted residential mortgages from a wide range of financial institutions in Korea. We extend the literature by examining the effect 6 Note, however, that they are covered more extensively in policy oriented literature. 7 Bang and Park (2012) also report some evidence that CLTV is an important factor of loss rates of Korean mortgages.

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Y.W. Park, D.W. Bang / Journal of Banking & Finance 39 (2014) 192–210 Table 1 Construction of the 2006–2009 Gangnam–Gangbuk foreclosure auction sample. Year

All property types

Apartments and detached houses Nationwide

Final sample

Nationwide

Seoul

2006 2007 2008 2009

116,029 89,313 83,880 93,162

10,789 6962 4778 6206

40,828 28,252 30,011 33,655

Seoul 2993 1923 1569 2754

Gangnam 350 298 309 630

Gangbuk 389 262 206 270

Gangnam 350 268 298 623

Gangbuk 389 233 164 265

Total

382,384

28,735

132,746

9239

1587

1127

1539

1051

We start with the universe of all auctions with court case numbers between 2006 and 2009, which are cross-checked against Good Auction database. We isolate apartments and detached houses. Then, of these cases we draw auctions involving collaterals located in three districts of Gangnam and four districts of Gangbuk. This is to study the effect of the housing market cycle in a hot market, which turns cold (Gangnam) and a market, which remains relatively calm through the period (Gangbuk).

of the foreclosure auction process on the LGD of the residential mortgage loans explicitly. The study of the LGD of the residential mortgage loans has examined loan, collateral and borrower characteristics as well as macro-economic factors. No study to our knowledge has modeled the foreclosure auction process explicitly and carefully while the overall performance of all parties concerned – especially buyers and lenders – are affected profoundly by the foreclosure auction process. Studies to-date tend to examine only the LGD of senior mortgages. Losses may concentrate on subordinated claims. We extend the study by examining the LGD of both senior and subordinated claims. Results on this issue will shine some light on the credit risk of subordinated claims.

320 Nationwide Seoul Busan Gangnam Gangbuk

280

240

200

160

120

80

3. Data We use auction data related to foreclosed residential mortgages from Good Auction, a foreclosure auction information company.8 We construct the sample using foreclosure auctions completed between January 2006 and January 2009 involving collaterals located in Seoul. The sample size is 2590. Specifically, collaterals are located either in four districts in North Seoul (Gangbuk, Seongbuk, Joongrang and Jongro) also known as Gangbuk or in three districts in South Seoul (Gangnam, Seocho and Songpa) also known as Gangnam. The three Gangnam districts are the most affluent residential markets in Korea. Table 1 shows how the sample is constructed. There are 382,384 foreclosure auctions recorded in the court registry between 2006 and 2009 in Korea covering all property types. As we restrict ourselves to apartments and detached houses only, the number reduces to 132,746 cases. As we further restrict the sample to three districts of Gangnam and four districts of Gangbuk, the sample reduces to 2714 cases. After we remove 124 cases with incomplete data, the final sample becomes 2590 cases: 1539 cases for Gangnam submarket and 1051 cases for Gangbuk submarket. Faced with a rapid price escalation from 2000 to 2006, Korean government declared Gangnam a speculative real estate market and imposed a series of restrictions aimed at preventing further price escalation and speculative activity in the Gangnam residential market.9 Fig. 1 shows the Kookmin Bank house price index of the country as a whole as well as Seoul, Gangnam and Gangbuk.10 Gangnam shows the most rapid house price escalation between 8 There is a very active and liquid foreclosure auction market in Korea. The industry that provides foreclosure auction information is highly competitive. There are a number of reliable foreclosure auction information providers. Good Auction provides detailed information on all court supervised real estate foreclosure auctions in Korea. Their information delivery is online-based (www.goodauction.co.kr). Crosscheck with the court documents and bank records authors conducted shows that their information is highly accurate. 9 The unprecedented housing market boom for the 2000–2006 period and the subsequent policy interventions are discussed in detail by Park et al. (2010). 10 Kookmin Bank House Price Index is the official house price index in Korea. We calibrate the index to 100 in January, 2000 to show the average price changes during the sample period more clearly.

2000:01

2002:01

2004:01

2006:01

2008:01

Fig. 1. Trends in average house prices in Korea and major submarkets. For the figure we use Kookmin Bank House Price Index calibrated to 100 in January, 2000. Shaded area, which is between January 2008 and December 2009, is considered as the period of housing market correction in the paper.

2000 and 2007. Gangbuk along with Busan, the second largest city of Korea, shows much more moderate price escalation during the same period. For this reason we sample the Gangnam submarket, which is ‘hot’ during the boom and turns ‘cold’ during the correction, and the Gangbuk submarket, which is relatively calm through the cycle, to study the effects of the housing market cycle on LGD. Table 2 shows the variables used to study. The dependent variable is LGD. LGD is estimated by dividing the loan loss by the unpaid loan balance (UPB). The loss of senior mortgages (LGD1) is the unpaid loan balance net of distributions to senior mortgages. The distributions to senior mortgages are the auction price net of auction expenses, distributions to all claims more senior than senior mortgages as well as any accrued interests before the auction. We estimate the loss of subordinated claims (LGD2) as the unpaid loan balance of the subordinated claims minus the distributions to subordinated claims. The distributions to subordinated claims are in turn estimated as the proceeds from auction net of distributions to all claims more senior than subordinated claims. CLTV is estimated by dividing the UPB of the senior mortgages by the appraisal value of the collateral established at the beginning of the auction process. We examine the following explanatory variables as factors that can influence the LGD: characteristics of collaterals, characteristics of loans, characteristics of borrowers, macro variables and existing claims on collaterals. Characteristics of collaterals are housing unit type, property floor area and submarket dummy; for apartments we add large apartment complex dummy, floor level, distance from the nearest subway station, redevelopment option dummy and the largest construction firm dummy. Characteristics of loans are CLTV level dummies and subordinated-claims-in-place dummy. Foreclosure auction variables are duration of auction, number of failed

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Table 2 Variable definitions. Variables

Constant

Variable definitions

Dependent variable

LGD1 LGD2

(Unpaid balance-distributions to senior mortgage loans)/unpaid balance (Unpaid balance-distributions to subordinated claims)/unpaid balance

Three subdistricts of Gangnam Housing type Floor area Large apartment complex dummy Floor level First floor dummy Redevelopment option dummy Largest construction firm dummy Proximity to subway station

Three subdistricts of Gangnam are 1; 0, otherwise Apartment is 1; 0, otherwise Floor area of the housing unit in m2 If the number of apartments in the apartment complex >1000, it is 1; 0, otherwise Floor level of the apartment First floor is 1; 0, otherwise If the apartment was constructed before 1980, it is 1; 0, otherwise If the construction firm is one of the top 4 construction firms, it is 1; 0, otherwise If time to walk to subway station <5 min, it is 1; 0, otherwise

Loan characteristics

CLTV CLTV below 50% CLTV50–60% CLTV60–70% CLTV70–80% CLTV80–100% CLTV100–120% CLTV over 120% CLTV – 80 Subordinated-claims-in-place dummy

Current LTV CLTV < 50% 50% 6 CLTV < 60% 60% 6 CLTV < 70% 70% 6 CLTV < 80% 80% 6 CLTV < 100% 100% 6 CLTV < 120% CLTV P 120% CLTV-80% If one or more subordinated claims against the property are in place, it is 1; 0, otherwise

Auction characteristics

Duration of auction (month) No. of failed auctions No. of bidders

End of auction – start of auction No. of auctions held before sale No. of auction bidders

Existing claims

No. of tenants

No. of tenants, who typically pay a combination of a large sum of lease deposit and monthly rents

Macroeconomics variables

DHPI Interest rate DGDP Unemployment

Rate of change in the Kookmin Bank HPI (house price index) 3-year government bond yield Rate of change in GDP Unemployment rate in Seoul

Explanatory variables Housing characteristics

Panel A. Senior mortgages

60.0% 50.0%

Senior claims Subordinated claims

50.21

47.37

8.0% 7.0%

39.04

40.0%

6.0%

Apartments Detached Houses

7.17

6.77

5.78

32.36

5.0%

30.0%

4.36

4.0%

20.0%

4.25

3.53

3.0% 10.0%

2.30 5.41

4.08

6.14

6.93

2.0%

1.33

1.0%

0.0% 2006

2007

2008

2009

0.0% 2006

2007

2008

2009

Fig. 2. LGD of residential mortgages by seniority.

Panel B. Subordinated claims

bids and the number of bidders. Macro variables are the change in the housing price, the change in GDP, interest rates and unemployment rates. For the detached houses we add the number of tenants as a measure of existing claims. Fig. 2 shows trends of the average LGDs of senior mortgages as well as subordinated claims. The average LGD of senior mortgages is between 5% and 10%; the average LGD of subordinated claims is between 30% and 50%. The average LGD during the market correction (2008–2009 financial crisis) is higher than during the tail end of the housing market boom (2006–2007) consistent with Qi and Yang, who find that LGD in distressed housing markets is significantly higher than under normal housing conditions. The difference in loan losses between senior mortgages and subordinated claims is fairly consistent during the entire sample period. We find that loss severity is typically minor (less than 10%) for the senior claimants while the loss severity is between 30% and 50% for subordinated claimants, which is not excessively large, showing the relative safety of residential mortgages originated with low LTV. This is in contrast to the nation-wide mean LGD of 25.5% of senior high LTV mortgages in the US during the sample period of 1990–2003 as reported by Qi and Yang.

60.0% 50.0% 40.0% 30.0%

Apartments Detached Houses

50.18

50.07

51.97

41.17 35.10 28.25

20.0%

25.66 16.30

10.0% 0.0% 2006

2007

2008

2009

Fig. 3. Trends of the LGDs of residential mortgages.

Fig. 3 shows trends of the LGDs by collateral types. Panel A shows trends of the LGDs of senior mortgages for apartments as well as detached houses. We find that the LGD of the apartment subsample is consistently higher than that of the detached house subsample. Panel B shows trends of the LGDs of subordinated claims for apartments as well as detached houses. We find that the LGD of the apartment subsample is about 40% while the LGD

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Y.W. Park, D.W. Bang / Journal of Banking & Finance 39 (2014) 192–210 Table 3 Descriptive statistics. Variables

N

Dependent variable Loan characteristic

LGD1 (%) LGD2 (%) CLTV (%)

Existing claims

No. of tenants

Collateral characteristic

Gangnam district dummy Apartment dummy Floor area (m2) Large housing complex dummy Floor level First floor dummy Redevelopment dummy Largest construction firm dummy Subordinated-claims-in-place dummy Proximity to subway station dummy

2590 2590 2590 2590 2275 2275 2275 2275 2590 2590

Auction characteristic

Duration of auction (month) No. of failed auctions No. of bidders

2590 2590 2590

Macroeconomic variables

3-year treasury rate (%) DGDP (%) Unemployment (%)

2590 2590 2590

N with a given characteristic (proportion)

2590 1699 2590 315 1539 (59.42%) 2275 (87.84%) 331 (12.78%) 256 (11.25%) 350 (15.38 %) 216 (9.49%) 1699 (65.59%) 604 (23.32%)

Mean

Median

Max

Min

SD

5.8 43.6 62.8

0.0 38.9 60.0

93.1 100.0 195.0

0.0 0.0 0.3

12.5 39.1 36.2

1.6

1.0

12.0

0.0

1.8

0.2 0.9 110.5 0.1 7.1 0.1 0.2 0.1 0.6 0.3

0.0 1.0 84.9 0.0 5.0 0.0 0.0 0.0 1.0 0.0

1.0 1.0 3143.6 1.0 50.0 1.0 1.0 1.0 1.0 1.0

0.0 0.0 5.8 0.0 1.0 0.0 0.0 0.0 0.0 0.0

0.4 0.3 87.0 0.4 5.7 0.3 0.4 0.3 0.5 0.4

7.5 1.2 6.32

6.0 1.0 4.0

31.0 17.0 85.0

1.0 0.0 1.0

4.0 1.0 7.37

4.8 2.7 4.3

4.9 2.6 4.3

8.0 4.9 5.4

3.3 1.8 3.6

0.7 0.5 0.5

LGD1: LGD of senior mortgages, LGD2: LGD of subordinated claims.

of the detached house subsample is about 30% showing that loan loss severity is higher for apartments than for detached houses. Furthermore, we find that LGD is somewhat higher during the market correction (2008–2009) than before the market correction (2006–2007) for both property types. The overall LGD is closer to the LGD of the apartment subsample than the LGD of the detached house subsample since there are far more apartments than detached houses in the sample. We show summary statistics of key variables in Table 3. The median CLTV is 60.0% showing that the CLTV rises above the LTV limit for most foreclosed mortgages. This is consistent with the finding in the PD literature that the high CLTV, which is caused by the drop in house price or increase in debt, makes default more likely. The mean and median numbers of tenants are 1.6 and 1.0, respectively.11 The mean and median property floor areas are 110.5 m2 and 84.9 m2, respectively. The mean and median times in auction are 7.5 months and 6.0 months, respectively, while the median number of auctions held is 1.0 times and the mean number of auctions held is 1.2 times. Table 4 shows the LGD by key variables. Panel A shows the LGD of senior mortgages by key attributes. Of 2590 observations there are 811 cases (31.3%) with CLTV greater than 80%. The average LGD is negligible up to CLTV of 80%; it rises to 6.9% for CLTV between 80% and 100%. The average LGD is 20.9% for CLTV between 100% and 120% and 35.2% for CLTV larger than 120%. As CLTV rises, LGD rises. The LGD of apartments is 6.2% being larger than that of detached houses. The collateral is sold in the first auction in 19.73% of cases, where the loss is negligible (LGD of 1.9%). In about half of the cases two auctions are held before the auction is successful with the LGD of 4.6%. Panel B shows the LGD of all subordinated claims by key attributes. Of 1699 observations there are 1329 cases (78.2%) with CLTV greater than 80%, 112 cases (6.59%) with CLTV less than 50%. The average LGD is about 25% up to CLTV of 100%; it rises to 50.11% for CLTV between 100% and 120%, and to 73.83% for CLTV over 120%. The LGD of apartments is 45.29%, which is larger than that of detached houses (30.56%). The average LGD is 26.94% if the collateral is sold in the first auction and rises to 39.38% if it is sold in the second auction. 11 As shown in Table 13, there are 77 cases (24.9%) with three or more tenants for the detached houses subsample (310 cases).

In Table 5 the average LGDs by CLTV level are shown for senior mortgages as well as subordinated claims in each year. Panel A shows the average LGDs by CLTV level for senior mortgages. The average LGD of the senior mortgages is 5.8%. There is little loss up to about 80% CLTV rising quickly after 80% CLTV. When CLTV is between 100% and 120%, loss is about 20%. The average LGD by CLTV level is similar from year to year. In Panel B we show the LGD of subordinated claims by the CLTV level where the CLTV is based on the unpaid loan balance of senior mortgages. The average LGD of all subordinated claims is 43.6%. When the senior mortgage CLTV is between 70% and 80% loss is 58.9%, loan loss is almost complete when the senior mortgage CLTV is over 80% as expected. The average LGD of subordinated claims by CLTV level is similar from year to year as well. The frequency distribution as well as the Kernel density of LGDs is shown in Fig. 4. The frequency distribution of the LGDs of the senior mortgages is shown in Panel A. Unlike the bi-modal frequency distribution of the LGDs of corporate bonds (Schuermann, 2004) and commercial loans (Asarnow and Edwards, 1995) the frequency distribution of Korean residential mortgages is unimodal with 80.39% concentration between 0% and 10% LGD. The frequency falls to 6.53% between 10% and 20%. Thereafter LGD falls gradually to zero. The Kernel density of the LGDs of senior mortgages is shown in Panel B. The frequency distribution of the LGDs of subordinated claims is shown in Panel C. The percentage frequency of the LGDs of subordinated claims tends to decline with LGD. In more than two thirds of cases losses are less than 50%. In about a quarter of cases losses are between 0% and 10%. The Kernel density of the LGD of the subordinated claims shown in Panel D is bimodal. However, unlike the frequency distribution of the LGD of corporate bonds and loans where the frequency distribution is bimodal with the highest concentrations around 0% and 100% LGD, the first peak of the subordinated claims of residential mortgage loans is around 3% LGD and the second peak is around 30%. That is, unlike corporate bonds and loans where losses are either none or complete, losses tend to be minor or moderate for subordinated claims. Fig. 5 shows the average LGDs by CLTV intervals. Panel A shows the average LGDs of senior mortgages by CLTV intervals. For senior mortgages the average LGD is low and flat up to about CLTV of 80% rising quickly after 80% CLTV for both apartments and detached houses. Note also that, when CLTV rises above 80%, loan losses start

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Table 4 Comparison of the LGD by key attributes. Variables

N (%)

Mean

Min

SD

50% below 50–60% 60–70% 70–80% 80–100% 100–120% 120% over All CLTV

1013(39.1) 261(10.1) 289(11.2) 216(8.3) 378(14.5) 271(10.5) 162(6.3) 2590(100)

0.2 0.3 0.6 2.6 6.9 20.9 35.2 5.8

82.5 58.2 25.0 93.1 91.1 86.8 76.5 93.1

0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

3.9 3.6 3.0 9.7 10.7 11.9 13.2 12.5

Housing type

Apartments Detached houses

2275(87.84) 315(12.16)

6.2 3.1

93.1 82.3

0.0 0.0

12.7 10.8

No. of failed auctions

0 1 Over 1

511(19.73) 1286(49.65) 793(30.62)

1.9 4.6 10.2

67.1 66.7 93.1

0.0 0.0 0.0

7.1 10.1 16.7

Region

Gangnam Non-Gangnam

1051(40.58) 1539(59.42)

3.6 7.3

82.3 93.1

0.0 0.0

10.1 13.7

50% below 50–60% 60–70% 70–80% 80–100% 100–120% 120% over All CLTV

112(6.59) 67(3.94) 91(5.36) 100(5.89) 333(19.60) 388(22.84) 608(35.79) 1699(100)

1.81 0.00 2.03 6.11 26.08 50.11 73.83 43.57

100.00 100.00 100.00 100.00 100.00 100.00 100.00 100.00

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

11.60 0.00 8.96 20.07 31.90 35.37 25.94 39.05

Housing type

Apartments Detached houses

1500(88.29) 199(11.71)

45.29 30.56

100.00 100.00

0.00 0.00

39.12 35.97

No. of failed auctions

0 1 Over 1

344(20.25) 843(49.62) 512(30.14)

26.94 39.38 61.62

100.00 100.00 100.00

0.00 0.00 0.00

34.30 37.57 37.50

Region

Gangnam Non-Gangnam

1066(62.74) 633(37.26)

47.96 36.16

100.00 100.00

0.00 0.00

38.94 38.12

Panel A. Senior mortgages CLTV

Panel B. Subordinated claims CLTV

Max

Table 5 Average LGDs of residential mortgages by CLTV bands, year and seniority. Year

Average

Below 50%

50–60%

60–70%

70–80%

Panel A. Average LGDs by CLTV bands and year for senior mortgages 2006 5.4 0.2 2007 4.1 0.4 2008 6.1 0.0 2009 6.9 0.3

0.9 0.1 0.0 0.0

0.8 0.5 0.5 0.7

3.2 1.3 2.6 2.7

2006–2009

0.3

0.6

2.6

5.8

0.2

Incremental LGD relative to CLTV < 70%

80–100% 6.2 5.2 8.9 7.5

100–120% 19.7 16.7 26.8 20.6

Over 120% 39.5 37.8 40.4 31.0

6.9

20.9

35.2

4.3

18.3

32.6

Panel B. Average LGDs by CLTV bands and year for subordinated claims 2006 39.0 15.5 2007 32.4 14.0 2008 47.4 20.0 2009 50.2 25.3

31.2 32.2 33.6 37.3

53.9 33.4 48.5 46.8

56.1 39.1 67.1 66.5

78.7 70.3 87.2 87.0

97.4 95.1 100.0 97.9

100.0 100.0 100.0 100.0

2006–2009

34.6

46.4

58.9

82.3

97.8

100.0

15.2

27.0

39.5

62.9

78.4

80.6

Incremental LGD relative to CLTV < 50%

43.6

19.4

LGDs are in percent. CLTV is the current LTV.

to rise linearly with CLTV. It suggests that the buyers, who are the investors of auctioned properties, get about 20% discounts on properties and lenders give up about 20% of the value of the properties as discounts to investors in the foreclosure auction. This result shows that foreclosure auction costs represent an important source of loan losses to lenders. Panel B shows the average LGD of subordinated claims by CLTV intervals. For subordinated claims the average LGD is 21.20% for apartments and 32.17% for detached houses for CLTV below 50%; it remains more or less flat up to about CLTV

of 120%; and it rises to 42.78% for apartments and 56.08% for detached houses with CLTV greater than 120%. 4. Empirical models and results The dependent variable is LGD. The explanatory variables are characteristics of loans and collaterals, foreclosure auction process variables and macro variables. Specifically, the LGD regression models are as follows:

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Panel A. Senior mortgages

Panel A. Frequency distribution of the LGDs of senior mortgages 50.0% 90.0%

Apartments Detached houses

80.39

80.0%

40.0%

36.97

70.0% 60.0%

30.67

30.0%

50.0% 22.12

40.0%

20.0%

30.0%

11.72

10.0%

20.0%

6.48

10.0%

6.53

6.37

3.71

1.66

0.0%

0.77

0.35

0.12

0.04

0.08

0.0%

0.23

0.39 1.14

0.03

CLTV Below 50 CLTV 50-60

0-10% 10-20% 20-30% 30-40% 40-50% 50-60% 60-70% 70-80% 80-90% 90-100% LGD

CLTV 60-70

8.20

2.62

CLTV 70-80

CLTV 80-100 CLTV 100-120 CLTV Over 120

Panel B. Subordinated claims

Panel B. Kernel density of the LGDs of senior mortgages

60.0%

80 50.0%

70

56.08

Apartments Detached houses 41.69

60

40.0%

50

30.0%

32.20

Density

32.17

20.0%

30

32.03 28.64

24.82

40

42.78

37.91

21.20

22.93

24.51

24.42

CLTV 60-70

CLTV 70-80

24.41

10.0%

20 0.0% 10

CLTV Below 50 CLTV 50-60

0 0

10

20

30

40

50

60

70

80

90

10 0

Panel C. Frequency distribution of the LGDs of subordinated claims 25.0% 22.39

20.0% 17.26 15.44

15.0%

13.51 10.54

10.0% 7.45 5.14

5.0%

4.05 2.24

1.97

0.0% 0-10% 10-20% 20-30% 30-40% 40-50% 50-60% 60-70% 70-80% 80-90% 90-100% LGD

Panel D. Kernel density of the LGDs of subordinated claims 2.0

Density

1.6

1.2

0.8

0.4

0.0 0

10

20

30

40

50

60

70

80

90

10 0

Fig. 4. Frequency distribution and Kernel density of the LGDs.

LGDi ¼ a þ

J K L X X X bj Loanij þ ck Collateralik þ bl Auctionil j¼1

þ

k¼1

l¼1

M X

cm Macroim þ ei

m¼1

Here, LGDi is the loss given default of case i. Xij is the value of the explanatory variable Xj for case i where Loan is loan characteristics; Collateral is collateral characteristics; Auction is auction process variables, and Macro is macro variables. Of loan characteristics

CLTV 80-100

CLTV 100-120 CLTV Over 120

Fig. 5. Average LGD as a function of CLTV by collateral types.

variables we use CLTV at the time of auction and subordinatedclaims-in-place dummy for the defaulted loan i. Due to lack of time-of-origination data for the 2006–2009 Gangnam-Gangbuk foreclosure auction sample we do not examine the effects of borrower characteristics as well as loan characteristics such as initial LTV, loan age and loan size, which are discussed in PenningtonCross (2003), Calem and LaCour-Little (2004) and Qi and Yang (2009). Instead, we investigate the effect of time-of-origination variables on LGD by constructing a separate sample at the end of Section 4. Of collateral characteristics we first consider apartment dummy, Gangnam dummy and floor size, which are attributes that may get overpriced during the booming housing markets leading to a greater LGD when mortgages are foreclosed and collaterals are auctioned off. We create apartment dummy to capture price volatility and liquidity effect; Gangnam dummy to capture speculative activity; and large property floor area to capture leverage effect. We expect that, because of larger price volatility, losses on apartments will be larger during the contracting market just as capital gains on apartments will be larger during the booming market compared to detached houses. Indeed, we find that the volatility of apartment prices is greater than detached houses. Using the entire time series (1986–2012) of the KB House Price Index, we find that the standard deviation of the percentage changes in apartment prices is 9.82%, which is considerably larger than that of detached houses, which is 5.41%. Apartments are far more liquid than detached houses in Korea since they are more modern and highly standardized. During the booming housing market, liquidity may get overpriced causing prices of apartments to overshoot. That is, apartment as a housing type is a desirable attribute, which may get overpriced during the booming market. Given that Gangnam submarket is the region affected the most by housing market speculation, we expect that collaterals in Gangnam submarket experience greater loan losses during the market downturn. Gangnam is a modern district with wide boulevards and excellent public schools. As discussed in Park, Bahng and Park it is possible that speculative activity is caused in part by desirable

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Table 6 Correlation coefficients of key variables. Variables

LGD

CLTV

Floor area

Auction duration

No. of failed auctions

No. of bidders

3-year treasury rate

DGDP

Unemployment rate

Panel A. Senior mortgages LGD 1.00 CLTV 0.68 Floor area 0.07 Auction duration 0.11 No. of failed auctions 0.25 No. of bidders 0.01 3-year treasury rate 0.02 DGDP 0.05 Unemployment rate 0.03

1.00 0.01 0.04 0.06 0.06 0.03 0.02 0.03

1.00 0.03 0.18 0.08 0.02 0.04 0.02

1.00 0.25 0.06 0.11 0.10 0.15

1.00 0.14 0.08 0.09 0.09

1.00 0.00 0.03 0.03

1.00 0.40 0.57

1.00 0.43

1.00

Panel B. Subordinated claims LGD 1.00 CLTV 0.69 Floor area 0.19 Auction duration 0.03 No. of failed auctions 0.30 No. of bidders 0.03 3-year treasury rate 0.06 DGDP 0.02 Unemployment rate 0.08

1.00 0.06 0.02 0.09 0.04 0.00 0.01 0.01

1.00 0.07 0.21 0.12 0.02 0.02 0.00

1.00 0.20 0.10 0.10 0.11 0.12

1.00 0.17 0.10 0.07 0.09

1.00 0.02 0.03 0.03

1.00 0.44 0.58

1.00 0.46

1.00

For senior mortgages LGD is the LGD of senior mortgages; CLTV is the CLTV of senior mortgages. For subordinated claims LGD is the LGD of subordinated claimants; CLTV is the CLTV of senior mortgages.

attributes of the Gangnam submarket. Therefore, Gangnam as a desirable community may get overpriced during the booming market leading to larger losses during the market downturn. We expect that larger housing units experience greater losses than smaller housing units during the market downturn. A larger housing unit, which would cost more, provides an investor a greater leverage effect during the booming housing market. Larger housing units, which are seen as a desirable attribute, may get overpriced during the booming market. As for foreclosure auction process variables we consider auction duration, the number of auctions held prior to sale and the number of bidders. Auction duration is expected to have a positive effect on LGD since the more time it takes to auction the property the greater becomes the loan loss to secured lenders. The coefficient of the number of auctions held is expected to be positive since the more auctions it takes to sell the property, the greater becomes the loan loss. The number of bidders is expected to reduce LGD since the buyer competition rises with the number of bidders at the auction. We use the same LGD regression models for subordinated claims as for senior mortgages. Subordinated claims consist of second mortgage loans obtained by the homeowner as well as other liens on the property. It is possible that there are households which borrow using the house as collateral for business and/or consumption. Since Korea is a full recourse loan regime, the homeowner is personally liable for his or her business loans as well as any other personal loans in Korea. Therefore, these claims become part of subordinated claims when the borrower’s house is foreclosed and auctioned upon default. The loss of financial institutions holding subordinated mortgage loans is reflected in the loss experienced by the entire class of subordinated claimants. Therefore, the loss experienced by the entire class of subordinated claimants provides useful information on the loss of financial institutions holding subordinated mortgage loans. As for the apartment subsamples, we expand the set of collateral characteristics since there is more standardized information on apartments than on detached houses. We add attributes including large apartment complex dummy, proximity to subway dummy, redevelopment option dummy and largest construction firm dummy and view. On one hand, desirable attributes may push up the sale price at the auction reducing the LGD. On the other hand, they may get overpriced during the boom increasing the LGD in the downturn.

Detached houses are often used in part or as a whole as rental housing for low income households in Korea. For example, in a two-story detached house the owner’s family lives on the second floor while the rooms on the first floor are remodeled as studios and rented out to one-person households or two-person households. In this case renters typically pay a combination of a one-time deposit and monthly rents.12 If the deposit is less than the limit set in the Housing Lease Protection Act and the occupancy date of tenants as shown in the official registry precedes the auction date, the deposit comes under the small rent deposit protection of the Act. It makes qualifying deposits become the most senior claim against the property. Therefore, when detached houses are foreclosed, homeowners have an incentive to add bogus tenants, who would qualify for small rental deposit protection, which is the most senior claim.13 Next, we investigate the effect of moral hazard on LGD. Specifically, we look for fraud cases involving bogus tenants in detached houses being auctioned. For the detached houses subsample we use a number of measures for fraud cases. We use Bogus Tenant 1, which is one if there are three or more tenants; zero, otherwise. We use Bogus Tenant 2, which is one if new tenants are added 30 days before the auction starts; zero, otherwise. We use Bogus Tenant 3, which is one if new tenants are added 60 days before the auction starts; zero, otherwise. Macro variables are rates of change in GDP, interest rates and unemployment rates as well as rates of change in house prices. We also consider their lag variables. We also examine the use of the year fixed effects as a proxy of all macro effects. Due to lack of data as well as multicolinearity between house price changes and CLTV, we do not use house price changes in the LGD regression model explicitly. Table 6 shows the correlation coefficients of regression variables. LGD shows positive correlation with CLTV, property floor area, duration of auction, number of auctions held, treasury rates, mortgage rates and change in GDP. CLTV has the highest

12 The combination of a large initial deposit and monthly rents mixes monthly rents with the traditional Chonsei, which is a large up-front deposit provided in lieu of monthly rents. 13 Note, however, that not all detached houses are used exclusively for rents. Correspondingly, there is some difference between investment properties in the U.S. and detached houses in Korea.

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Y.W. Park, D.W. Bang / Journal of Banking & Finance 39 (2014) 192–210 Table 7 Estimation of the LGD regression models for senior mortgages (2006–2009). Variables

Model 1

Model 2

Model 3

Model 4

Coefficient

t-Value

Coefficient

t-Value

Coefficient

t-Value

Coefficient

t-Value

15.30

4.64a

15.44

4.67a

14.85

4.68a

8.89

5.86a

Loan characteristics CLTV-80 CLTV 50–60% CLTV 60–70% CLTV 70–80% CLTV 80–100% CLTV 100–120% CLTV 120% over Subordinated-claims-in-place dummy

0.59

67.88a

0.59

67.91a

0.69 0.14 2.09 6.53 20.83 35.12 0.04

1.34 0.28 3.78a 14.58a 39.04a 53.76a 0.12

6.33 20.62 34.92 0.02

15.14a 40.57a 55.04a 0.06

0.80

2.58a

0.78

2.49b

Collateral characteristics Apartment dummy Gangnam dummy Floor area (m2)

0.13 0.57 0.76

0.26 1.72c 2.38b

0.01 0.56 0.81

0.03 1.69c 2.53b

0.96 0.70 1.24

2.04b 2.18b 4.07a

0.93 0.75 1.26

1.98b 2.34b 4.11a

Auction characteristics Duration of auction (month) No. of failed auctions No. of bidders

0.09 2.72 0.11

2.24b 16.99a 5.33a

0.09 2.71 0.11

2.27b 16.89a 5.28a

0.07 2.72 0.09

1.86c 17.66a 4.62a

0.09 2.77 0.09

2.36b 18.08a 4.48a

0.57 0.03 1.07 0.79

1.95c 1.19 2.98a 1.93c

0.56 0.03 1.05 0.81

1.92c 1.11 2.94a 1.97b

0.60 0.05 0.85 0.36

2.13b 1.97c 2.47b 0.91 0.11 0.87 0.27

0.26 2.04b 0.74

Constant

Macroeconomic variables 3-year treasury rate DHPIt-12 DGDP Unemployment rate Year dummy 2007 Year dummy 2008 Year dummy 2009 Adj. R2 N

0.68 2590

0.68

0.70

2590

0.70

2590

2590

LGD is the LGD of senior mortgages and CLTV is the CLTV of senior mortgages. a Significance at 1% level. b Significance at 5% level. c Significance at 10% level.

1.0

0.8

0.6

LGD

correlation coefficient with LGD followed by the number of auctions held, duration of auction, property floor area and interest rates. Not surprisingly, correlations amongst macro variables are very high favoring the use of year fixed dummies to represent macro effects. Table 7 shows the regression estimates of the LGD model for the senior mortgages. In Model 1, we use a series of step functions of CLTV to measure the incremental LGD associated with step increases in CLTV. The reference CLTV in Model 1 is CLTV less than 50%. We find that: the LGD for CLTV intervals between 50% and 70% is not different from the LGD of the reference CLTV; the LGD for CLTV between 70% and 80% is 2.09% higher than the reference case; the LGD for CLTV between 80% and 100% is 6.53% higher than the reference case; the LGD for CLTV between 100% and 120% is 20.83% higher than the reference case; and the LGD for CLTV over 120% is 35.12% higher than the reference case. Given that Model 1 shows that LGD rises from about CLTV 80%, we build Model 2 where the reference CLTV is CLTV less than 80%. We find that: the LGD for CLTV between 80% and 100% is 6.33% higher than the reference case; the LGD for CLTV between 100% and 120% is 20.62% higher than the reference case; and the LGD for CLTV over 120% is 34.92% higher than the reference case. Fig. 6 shows the scatter plot of LGD as a function of CLTV. We find that LGD is flat at about 0% for CLTV up to about 80%; it rises linearly thereafter. Therefore, to make the model more compact we build Model 3 where we use a linear function bounded below by 80% CLTV (CLTV-80). We find that the slope of CLTV-80 is 0.59 showing that 1% increase in CLTV leads to 0.59% increase in LGD. Since Models 1 and 2 use many dummy variables, which can cause severe multicolinearity problem, we use Model 3 for detailed

0.4

0.2

0.0 0

40

80

120

160

200

CLTV Fig. 6. Scatter plot of LGD as a function of CLTV.

study. As discussed in Table 3 we find that out of 2590 cases there are 1699 cases with both senior mortgages and subordinated claims while the rest (891 cases) have only senior mortgages. Since the LTV limit applies for the sum of all residential mortgages combined in Korea, the presence of subordinated claims implies that the amount of senior mortgages on the same property is relatively low and there is a larger equity buffer for senior mortgages.14 Since the presence of subordinated claims proxies for a larger equity buffer for senior mortgages, we expect that the presence of the

14 In contrast, Zhang et al. (2010) argue that in the US the presence of second liens typically leads to a higher LTV and, consistent with this argument, Zhang et al. find a positive effect of second liens on LGD.

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Table 8 Estimation of the LGD regression models for senior mortgages by subperiods. Variable

Model 1 (2006–2007)

Model 2 (2008–2009)

Model 3 (2008–2009)

Coefficient

t-Value

coefficient

t-Value

Coefficient

t-Value

Constant

7.45

3.66a

11.02

5.06a

10.18

4.54a

Loan characteristics CLTV-80 Subordinated-claims-in-place dummy

0.54 0.45

46.91a 1.08

0.63 1.07

49.30a 2.33b

0.63 1.07

49.25a 2.34b

1.32 0.25 0.63

2.19b 0.59 1.54

1.05 1.05 2.05

1.43 2.25b 4.63a

0.95

1.29

1.87 0.26

4.07a 2.54b

0.13 3.85 0.12

2.79a 17.58a 3.71a

0.07 1.95 0.06

1.22 9.10a 2.60a

0.07 1.94 0.06

1.22 9.10a 2.58a

Collateral characteristics Apartment dummy Gangnam dummy Floor area (m2) Gangnam dummy  Floor area (m2) Auction characteristics Duration of auction (month) No. of failed auctions No. of bidders Adjusted R-squared N

0.71 1240

0.71 1350

0.71 1350

LGD is the LGD of senior mortgages and CLTV is the CLTV of senior mortgages. 2006–2007 correspond to the booming market; 2008–2009 correspond to the market downturn. a Significance at 1% level. b Significance at 5% level. c Significance at 10% level.

subordinated claims has a negative influence on the LGD of the senior mortgages. We test this hypothesis using the subordinatedclaims-in-place dummy. The coefficient of the subordinated-claimsin-place dummy is negative and significant as expected. In Models 3 and 4 we find that the coefficient of the apartment dummy is positive and significant. This result is consistent with the volatility and liquidity hypothesis. We investigate whether Gangnam region, which is the submarket the most affected by housing market speculation, experiences a greater loss severity during the downturn. We find that the coefficient of Gangnam region dummy is 0.7 showing that the LGD of a property in Gangnam is 0.7% greater than elsewhere, all else being equal. This result is consistent with the hypothesis that housing market speculation leads to a higher LGD when the market corrects itself. We investigate whether larger housing units experience greater losses than smaller housing units. A larger housing unit, which would cost more, provides an investor a greater leverage effect during the booming housing market. Again we find the coefficient of floor area is positive and significant showing that collaterals with a larger floor area are associated with a larger loss. This result supports the view that larger housing units, the preferred vehicle for speculation during the booming housing market, suffer a greater LGD. We examine the effect of the three foreclosure auction process variables; auction time variable, the number of auctions held and the number of bidders. The coefficient of auction duration is positive and significant showing that the more time it takes to auction the collateral the greater becomes the loan loss. One month in auction leads to an added LGD of 0.7%, all else being equal. The coefficient of the number of auctions held is also positive and significant showing that the more auctions it takes to sell the collateral, the greater is the loan loss. Each incremental auction causes an incremental LGD of 2.72%, all else being equal. The number of bidders reduces LGD. The coefficient of the number of bidders is 0.09 indicating that each additional bidder reduces LGD by 0.09%. We consider three macro variables which are contemporaneous with LGD. While the coefficient of the 3-year treasury rate as well as unemployment level is not significant, the change in GDP is. This is consistent with the explanation that LGD is driven by the contemporaneous household income levels. We look for the combined

effect of the interest rate, the change in GDP and the unemployment rate using lagged variables. After considering the effect of macro variables carefully, we conclude that the sample period is too short to allow meaningful evaluation of the macro variables in the regression equation. We evaluate Model 4, where we replace all macro variables by year dummies. We find that the model performs as well as Model 3. This suggests that we can use year dummies to represent the macro effects without harming the model. In order not to distract from the main results of the paper, we will use year dummies in lieu of macro variables in some models. Now we investigate the effect of the housing market cycle on LGD. Accordingly, we estimate the LGD regression model by subperiods: one corresponding to booming market and the other corresponding to market downturn (2008–2009). Results are found in Table 8. The main features of the model, namely, the dominance of CLTV and foreclosure auction variables are similar to those of the full sample. We find that apartment dummy is positive and significant during the booming market (2006–207; Model 1), but not during the market downturn (2008–2009; Model 2). This is inconsistent with the liquidity hypothesis on LGD. An explanation for this result is that apartments may be the preferred vehicle of investment for investors in the foreclosure auction market since they are far more liquid than detached houses in Korea; this would cause apartments to have a lower LGD than detached houses mitigating the positive effect of liquidity on LGD during the down market. On the other hand, we find that Gangnam dummy is positive and significant only during the market downturn, but not during the booming market. This supports the explanation that housing units in Gangnam, which appreciated rapidly during the booming market, suffer a greater loss during the market downturn. Similarly, floor area is positive and significant during the market downturn, but not during the booming market. This result further supports the explanation that larger housing units, which are the preferred vehicle of speculation during the booming market, suffer a greater loss during the market downturn. Next, we expand on the effect of speculation during the booming market leading to a greater LGD during the down market by considering the combined effect of large property size and Gangnam submarket (Model 3). The interaction term between Gangnam

203

2590 N

2590

0.01 0.01 0.01 Adj. R2

2590 2590

0.30

2590

0.39

2590 2590

0.01

0.47 3.56a 0.53

0.01

3.87a

2590

4.16a

0.01

4.23a

0.01

3.69a

0.01

3.46a 0.25

52.29a 66.47 54.04a 66.53 57.02a 66.77 60.89a 66.26 66.25a 65.49 73.07a 64.71 78.46a 2.91a

LGD is the LGD of subordinated claimants. CLTV is the CLTV of senior claims. The rates of change in house prices (DHPI) have been calculated using KB House Price Index extending from 1quarter (Q1) through 8 quarters (Q8) for each loan in the sample. a Significance at 1% level. b Significance at 5% level. c Significance at 10% level.

2.91a 0.20 0.43

48.45a 66.22 63.99 0.69

Constant DHPI Q1 DHPI Q2 DHPI Q3 DHPI Q4 DHPI Q5 DHPI Q6 DHPI Q7 DHPI Q8

Model 8

Coefficient t-Value

Model 7

Coefficient t-Value

Model 6

Coefficient t-Value

Model 5

Coefficient t-Value

Model 4

Coefficient t-Value

Model3

Coefficient t-Value

Model 2

Coefficient t-Value

Model 1

Coefficient

Variable

Table 9 Estimation of CLTV regression models on lagged house price changes.

dummy and floor area is positive and significant. Specifically, the coefficient of floor area is 1.87 and the coefficient of the interaction term is 0.26 implying that for a Gangnam property LGD rises by 0.26% point for 1 m2 increase in floor size. We suspect that past house price changes influence CLTV, which in turn influences LGD. Consequently, we examine the effect of the change in the house price on CLTV, which is shown to be the primary driver of LGD. Results are found in Table 9. We consider eight models (Model 1 through Model 8) where we regress CLTV on the change in the house price index in the last quarter (DHPI Q1) through the change in the house price index in the last 8 quarters (DHPI Q8). We find the impact of the change in house price on CLTV is statistically significant up to quarter 8. That is, house price changes in the last eight quarters cumulatively affect the CLTV of defaulted borrowers, which in turn lead to an increase in LGD. This result is consistent with Zhang et al., who show that house price changes before the default show a long lagged effect on LGD. In Model 1 we find that one percentage point increase in the house price index in the last quarter (DHPI Q1) causes 0.69 percentage point drop in CLTV. This implies that 69% of the change in CLTV is caused by the change in house price. In Model 2 we find that one percent change in house price index in the last two quarters (DHPI Q2) causes 0.53% drop in CLTV. This is less than the effect of DHPI Q1 showing that the effect of the house price change two quarters ago is not as strong as that of the last quarter. We find a declining trend of the impact of the change in house price on CLTV in all subsequent quarters. One percent change in house price index in the last three quarters (DHPI Q3) causes 0.47% drop in CLTV. One percent change in house price index in the last four quarters (DHPI Q4) causes 0.43% drop in CLTV. One percent (DHPI Q5) causes 0.39% drop in CLTV. One percent change in house price index in the last six quarters (DHPI Q6) causes 0.30% drop in CLTV. The coefficients of changes in house price index over up to eight quarters are statistically significant indicating that house price changes within eight quarters influence CLTV; therefore LGD. Table 10 shows the regression estimates of the LGD model for the subordinated claims. Loss is higher for subordinated claims than senior claims in all CLTV levels as expected. In Model 1 we use CLTV less than 50% as the reference case and measure the LGD of the higher CLTV levels. The magnitude of the loss is comparable to that in Panel B of Table 5 for all CLTV levels. The coefficient of CLTV 50–60% is 11.10, which implies that the incremental LGD of CLTV 50–60% relative to the baseline LGD is 11.10%. This is consistent with the incremental LGD of 15.2% of CLTV 50–60% shown in Panel B of Table 5. Similarly, the coefficients of CLTV 60–70%, CLTV 70–80%, CLTV 80–100%, CLTV 100–120% and CLTV over 120% are 26.25, 37.19, 60.89, 73.74 and 76.76, respectively. Incremental LGDs implied by the coefficient estimates are 26.25%, 37.19%, 60.89%, 73.74% and 76.76%, respectively, which are close to the incremental LGDs reported in Panel B of Table 5, namely, 27.0%, 39.5%, 62.9%, 78.4%, and 80.6%, respectively. For the subordinated claims, the collateral characteristics have similar influence on LGD as for the senior mortgages. The coefficients of apartment dummy, Gangnam dummy and floor area tend to be positive and significant as they are for the senior mortgages shown in Table 7. We conclude that housing market cycle effects are also present in subordinated claims. The auction variables are highly influential on the magnitude of loss for subordinated claims as they are for senior mortgages. Of auction process variables, the number of failed auctions and the number of bidders are statistically significant. The coefficient of the number of failed auctions is 9.64 implying that every time auction fails the LGD increases by 9.64% for the subordinated claims. The coefficient of the number of bidders is 0.51 implying that

t-Value

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Table 10 Estimation of LGD regression models for the subordinated claims (2006–2009). Variables

Model 1 Coefficient

Constant Loan characteristics CLTV-80 CLTV 50–60% CLTV 60–70% CLTV 70–80% CLTV 80–100% CLTV 100–120% CLTV 120% over Collateral characteristics Apartment dummy Gangnam dummy Floor area (m2) Auction characteristics Duration of auction (month) No. of failed auctions No. of bidders Adj. R2 N

Model 2 t-Value

Coefficient

Model 3 t-Value

25.75

3.42a

11.10 26.25 37.19 60.89 73.74 76.76

5.33a 12.94a 16.19a 31.40a 26.81a 21.28a

50.37 63.58 67.09

24.81a 21.45a 17.03a

1.02 3.68 7.75

0.45 2.52b 5.22a

6.68 2.29 9.25

0.26 9.64 0.51

1.47 13.39a 5.78a

0.26 10.17 0.53

0.60 1699

26.44

0.51 1699

3.18a

Coefficient

t-Value

31.67

3.35a

1.37

23.40a

2.72a 1.42 5.64a

13.40 0.05 10.27

4.84a 0.03 5.51a

1.36 12.77a 5.50a

0.21 10.62 0.53

0.96 11.74a 4.74a

0.36 1699

LGD is the LGD of subordinated claimants, CLTV is the CLTV of senior claims. a Significance at 1% level. b Significance at 5% level. c Significance at 10% level.

for every additional bidder the LGD decreases by 0.51% for the subordinated claims. Table 11 shows the explanatory power of the variables used. CLTV has the greatest explanatory power for the LGD. For senior mortgages CLTV accounts for 91.58% of errors explained. This is consistent with the explanation that the LGD is driven primarily by the equity in the collateral. The three auction variables account for 7.03% of errors explained. This supports the hypothesis that the foreclosure auction process is an important factor for LGD besides the equity in the collateral. The percentage of the error explained attributable to the three ‘‘desirable attributes’’ of housing units is 1.13%. The macro variables account for 0.25% of errors explained. The relative impacts of the explanatory variables on the LGD of the subordinated claims are by and large the same as for senior mortgages. Using the apartment subsample, we examine the effect of collateral characteristics on LGD since it is easier to collect detailed collateral information for apartments than for detached houses. Table 12 shows the LGD regression results for the apartment subsample. In Panel A we use the base model, which is Model 3 of Table 7. The coefficient of property floor area is positive and significant showing that the collateral with a larger floor area is shown to have a larger loss. The coefficient of proximity to subway dummy is not statistically significant. The coefficient of redevelopment dummy is not statistically significant. However, the coefficient of largest construction firm dummy, which is the largest four builder dummy, used as a measure of the reputation of the builder is negative and statistically significant. The coefficient of first floor dummy used as a measure of view is negative and statistically significant as well. Since the collateral characteristics influence the auction price, which in turn influences the LGD, we build a two-stage least squares model, where the auction price is a function of collateral characteristics and auction process variables and the LGD is a function of the fitted auction price, the Gangnam dummy, property floor size, auction variables and year dummies. For the LGD function we try to keep the same structure as the base model in

Table 11 Percentage of errors explained by various explanatory variables (2006–2009). Variables Panel A. Senior mortgages CLTV Subordinated-claims-in-place dummy Collateral characteristics (apartment dummy, Gangnam dummy and floor area) Auction characteristics (duration of auction, number of auctions held and number of bidders) Macroeconomics variables All variables N Panel B. Subordinated claims CLTV Collateral characteristics (apartment dummy, Gangnam dummy and floor area) Auction characteristics (duration of auction, number of auctions held and number of bidders) Macroeconomics variables All variables N

Percentage of errors explained 91.58 0.01 1.13 7.03 0.25 100.00 2590 80.74 7.40 11.42 0.44 100.00 1699

For senior mortgage loans LGD is the LGD of senior claimants; CLTV is the CLTV of senior claims. For subordinated claims LGD is the LGD of subordinated claimants; CLTV is the CLTV of senior claims.

Table 7 for comparison purposes. We find that the collateral characteristics and auction process variables influence the auction price as expected where adjusted R2 is 0.63. We also find that the fitted auction price has a negative influence on the LGD. The results of the two-stage least squares model show that collateral characteristics influence LGD via the auction price, which is not entirely clear in the two models shown in Panel A. Next, we examine the effect of bogus tenants. Landlords have an incentive to fill up the tenant registry with bogus renters to crowd out mortgage lenders taking advantage of the tenant protection law, which makes the tenants of record prior to the auction even more senior to secured lenders. In Table 13 we show the distribu-

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Y.W. Park, D.W. Bang / Journal of Banking & Finance 39 (2014) 192–210 Table 12 Estimation of the LGD regression models of senior claims for the apartment subgroup (2006–2009). Variable

Model 1 Coefficient

Panel A. Base model Constant Loan characteristics CLTV-80 Subordinated-claims-in-place dummy Collateral characteristics Gangnam Floor area Large apartment complex dummy Proximity to subway Redevelopment option dummy Largest construction firm dummy Floor level First floor dummy Auction characteristics Duration of auction (month) No. of failed auctions No. of bidders Adj. R2

Model 2 t-Statistic 6.50a

10.57

6.12a

0.61 0.65

62.22a 1.88c

0.61 0.70

63.18a 2.06b

0.12 2.18 0.32 0.24 0.49 1.17 0.01

0.33 5.58a 0.71 0.65 1.08 2.23b 0.49

0.26 1.89 0.25 0.26 0.48 1.10

0.73 4.99a 0.56 0.71 1.09 2.10b

1.20

2.47b

0.11 2.54 0.08

2.63a 14.63a 3.75a

0.10 2.48 0.07

2.37b 13.97a 3.49a

0.70 2275

Variable

Model 1

0.70 2275

LGD

Loan characteristics Subordinated-claims-in-place dummy Collateral characteristics Auction price Gangnam Floor area Large apartment complex dummy Proximity to subway Redevelopment option dummy Largest construction firm dummy Floor level Auction characteristics Duration of auction (month) No. of failed auctions No. of bidders Year dummy 2007 Year dummy 2008 Year dummy 2009 Adjusted R-squared

t-Statistic

11.53

N

Panel B. Two-stage least squares regression model Constant

Coefficient

Auction price

10.26

3.41a

6.88

12.12a

1.41 1.33 3.40

2.35b 2.17b 5.10a

0.22 2.23 0.03 0.41 1.83 2.37

2.97a 7.47a 0.79 0.52 2.22b 3.26a

13.94

118.89a

0.40 1.26 0.04 0.11 0.49 0.23 0.02

16.43a 48.93a 1.27 4.35a 16.66a 6.59a 8.48a

0.01 0.07 0.01

3.47a 5.65a 4.10a

0.13

0.63

For senior mortgages LGD is the LGD of senior mortgages and CLTV is the CLTV of senior mortgages; t-statistics are shown in parentheses. a Significance at 1% level. b Significance at 5% level. c Significance at 10% level.

tion of the number of tenants in the detached houses subsample.15 According to the registry of residents maintained by the local government, 100 detached houses (32.3%) have no registered tenants while 210 detached houses (67.7%) have one or more tenants showing that indeed many owners of detached houses rent all or part of their properties.16 Proportions of detached houses with one tenant, two tenants, three tenants and more than three tenants are 24.5%, 18.4%, 9.4% and 15.5% of the sample, respectively. We suspect that when the number of registered tenants is excessive, for example, greater than three, some of them are bogus tenants, who could raise loan losses to lenders. 15 We could not verify the number of tenants for five detached houses. Therefore, the sample size for the study of bogus tenants is reduced to 310. 16 Tenants become registered tenants by evidencing the lease contract at the local government office.

We build three dummy variables to measure the presence of bogus tenants (Table 14). Bogus tenant dummy 1 is 1 if the number of tenants is greater than 3. Bogus tenant dummy 2 is 1 if tenants are added within 30 days prior to the first day of the auction process. Bogus tenant dummy 3 is 1 if tenants are added within 60 days prior to the first day of the auction process. We find that the coefficient of bogus tenant dummy 1 is positive and significant suggesting that bogus tenants increase the loan loss to lenders. The coefficient of 1.57 implies that the incremental LGD due to bogus tenants is 1.57%. Therefore, we conclude that there is some evidence that the cost of moral hazards or gaming to mortgage lenders may be present in LGD. There is a concern that the LGD model we estimate using the 2006–2009 Gangnam-Gangbuk dataset of 2590 auctions as reported in Table 7 suffers from omitted variables bias since the model does not include a comprehensive set of loan characteristics

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Table 13 Distribution of the number of tenants in detached houses. Number of tenants in a detached house

Number of detached houses

Percent of sample (%)

0 1 2 3 >3

100 76 57 29 48

32.3 24.5 18.4 9.4 15.5

Total 100%

310

100.0

The number of tenants in a detached house is based on those tenants who are found in the official registry of residents maintained by the local government office.

at the origination except subordinated-claims-in-place dummy.17 Since we cannot control for these variables as Good Auction does not provide them and neither is it possible to trace these loans to the master records of the lenders, we address this problem by building a separate dataset. We have assembled a new data set consisting of 452 observations of senior mortgages with a comprehensive set of time-oforigination data as well as time-of-foreclosure auction data. We start with 142,847 residential mortgages, which KHFC purchased between 2004 and 2007. We identify foreclosed loans with collaterals in Seoul and Busan. We obtain loan characteristics at the origination from the KHFC master record and the rest of the information from Good Auction. The final sample consists of 452 observations of senior mortgages. Out of 452 observations of senior mortgages, 285 observations have subordinated claims behind senior mortgages while 167 observations have no subordinated claims. There are some differences between mortgages offered by commercial lenders and those purchased by KHFC. Since one of the mandates of KHFC is to promote long term amortizing fixed interest rate mortgages, KHFC purchases only long term amortizing fixed interest rate mortgages while commercial banks tend to originate variable interest rate non-amortizing mortgages.18 As a result, the borrowers of KHFC may have more cautious temperament than those of commercial lenders. Another mandate of KHFC is to provide residential mortgages to low income households. Therefore, there is an effort on the part of KHFC to provide mortgages to low income households to whom commercial banks would ordinarily not. As a result, there is a slightly larger proportion of households with low credit rating in the KHFC mortgage portfolio than that of commercial lenders.19 Finally, the LTV limit of mortgages KHFC purchases is set at 70%, which is higher than that of mortgages offered by commercial lenders. Because of a potentially more conservative temperament of the KHFC clients, the default level of the KHFC sample could be lower than that of commercial lenders. On the other hand, due to the inclusion of households with low credit rating as well as a higher LTV limit on KHFC mortgages, the default level of the KHFC sample could be higher than that of commercial lenders. However, we do not expect that these differences would introduce systematic differences in the estimated results of the LGD factor model. That is,

17 However, it appears that the omitted variables problem is not severe since the goodness of fit of the model is already high. 18 The market shares of KHFC mortgages in the Korean residential mortgage markets for 2004, 2005, 2006, 2007 and 2012 are 1.67%, 3.26%, 2.84%, 3.56% and 9.01%, respectively. Residential mortgages outstanding in the banking sectors for 2004, 2005, 2006, 2007 and 2012 are 169.5, 190.0, 217.0, 221.4 and 316.9 trillion wons, respectively. 19 The proportions of KHFC borrowers with credit ratings from 7 to 10 are 9.51%, 5.49%, 2.86% and 0%, respectively; 1 is the best credit rating and 10 is the worst credit rating in Korea.

sample differences may cause the default levels of the KHFC mortgages to deviate from the mortgages of commercial lenders, but their influence on the factors of LGD is expected to be limited. We show the descriptive statistics of the 2004–2007 KHFC foreclosure auction sample in Table 15. The mean and median LGDs of senior mortgages are 9.6% and 0.0%, respectively. The mean CLTV and the mean LTV are 93.3% and 61.3%, respectively. The mean and median DTIs are 36.0% and 31.2%, respectively. The mean and median loan ages (time elapsed between the loan origination and the auction) are 12.9 months and 12.0 months, respectively. The mean and median loan amounts are 155.0 million wons and 162.0 million wons, respectively. There are subordinated claims in place behind 63.05% of the foreclosed senior mortgages. The collateral characteristics are as follows. The proportion of apartments is 97.1% showing that KHFC underwrites mortgages primarily for apartments during the sample period. The proportion of loans with collaterals located in Gangnam is 3.32% while 75% of collaterals are located in Seoul and 25% of collaterals are located in Busan. The mean and median rates of change of house prices between the time of loan origination and the beginning of auction are 15.8% and 17.7%, respectively. The borrower characteristics are as follows. The credit rating is between 1 and 10 with 1 being the best credit rating and 10, the worst credit rating. Since the maximum and median credit ratings of the sample are 9 and 7, respectively, we infer that half of the defaulted borrowers have credit rating between 7 and 9, a poor credit bracket. The mean borrower age is 42.2; the mean borrower annual income is 22.7 million wons. Both the mean borrower age and the mean borrower annual income are comparable to those of the entire KHFC mortgage sample.20 The proportion of dual income households is only 3.1%. The low occurrence of dual income households reflects the fact that in a typical Korean household the husband works and the wife is a fulltime homemaker. The foreclosure auction variables of the 2004–2007 Seoul-Busan sample are comparable to those of the 2006–2009 GangnamGangbuk sample. The mean and median durations of an auction are 7.4 months and 7.0 months, respectively. The mean and median numbers of failed auctions are 1.6 and 1.0, respectively. The mean and median numbers of bidders in an auction are 5.8 and 4.0, respectively. We estimate the LGD model using the new data set (2004–2007 Seoul-Busan foreclosure auction data). We show the estimation results of LGD specifications with time-of-origination variables in Table 16. At the time of loan application, lenders have only timeof-origination information on loans, collaterals and borrowers. Consequently, in Model 1 we estimate an LGD specification that uses only time-of-origination information on loans, collaterals and borrowers. We use the following loan characteristics at the time of origination: original LTV, DTI (debt-to-income ratios), loan age, loan size; the following collateral characteristics at the time of origination: apartment dummy, Gangnam district dummy, floor area and rate of change in house price; and the following borrower characteristics at the time of origination: credit rating, borrower age, income and dual income dummy. Following Qi and Yang, we construct Loan Size 60, which is one if loan size is less than 60% of the area median house price and zero, otherwise; Loan Size 80, which is one if loan size is less than 80% of the area median house price and zero, otherwise; and Loan Size 110, which is one if loan size is greater than 110% of the area median house price and zero, otherwise. We find that the coefficient of Loan Size 60 is less than that of 20 The mean borrower age and the mean annual income of all KHFC mortgage sample, which consist of 142,847 mortgages purchased by KHFC between 2004 and 2007, are 39.4 and 27.9 million wons, respectively.

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Y.W. Park, D.W. Bang / Journal of Banking & Finance 39 (2014) 192–210 Table 14 Estimation of the LGD regression models of senior mortgages for the detached houses subgroup (2006–2009). Variable

Model 1 Coefficient

t-Statistic

Coefficient

t-Statistic

Coefficient

t-Statistic

Constant

5.56

2.32b

5.90

2.45b

5.91

2.46b

Loan characteristics CLTV-80 Subordinated-claims-in-place dummy

0.50 0.33

26.75a 0.47

0.50 0.52

26.57a 0.74

0.50 0.55

26.62a 0.79

Collateral characteristics Gangnam Floor area Collateral Age

0.38 0.20 0.01

0.23 0.45 2.33b

0.56 0.41 0.01

0.34 0.94 2.07b

0.75 0.41 0.01

0.45 0.94 2.04b

Auction characteristics Duration of auction (month) No. of failed auctions No. of bidders

0.07 3.55 0.08

0.99 10.85a 1.13

0.06 3.58 0.08

0.88 10.84a 1.12

0.06 3.59 0.07

0.85 10.88a 1.08

1.57

1.96c

Existing claims Bogus tenant dummy 1 Bogus tenant dummy 2 Bogus tenant dummy 3

Model 2

Model 3

3.21

0.95 2.98

Adj. R2

0.74

N

0.74

310

1.26

0.74

315

315

For senior mortgages LGD is the LGD of senior mortgages and CLTV is the CLTV of senior mortgages. For subordinated claims LGD is the LGD of subordinated claims, CLTV is the CLTV of senior mortgages. We build three dummy variables to measure the presence of bogus tenants. Bogus tenant dummy 1 is 1 if the number of tenants is greater than 3. Bogus tenant dummy 2 is 1 if tenants are added within 30 days prior to the first day of the auction process. Bogus tenant dummy 3 is 1 if tenants are added within 60 days prior to the first day of the auction process. a Significance at 1% level. b Significance at 5% level. c Significance at 10% level.

Table 15 Descriptive statistics of the 2004–2007 Seoul–Busan foreclosure auction sample. Variables

N

Dependent variable LGD

N with a given characteristic (proportion)

452

9.6

0.0

52.9

0.0

12.7

Loan characteristics CLTV LTV DTI Loan age Loan amount (million wons) Subordinated-claims-in-place dummy

452 452 452 435 452 452

93.3 61.3 36.0 12.9 155.0 0.6

92.3 60.0 31.2 12.0 162.0 1.0

182.7 70.0 99.3 35.0 300.0 1.0

8.9 5.9 0.0 4.0 25.0 0.0

22.3 9.4 22.7 5.8 63.0 0.5

Collateral characteristics Apartment dummy Gangnam district dummy Seoul district dummy Floor area (m2) Rate of change in house price

452 452 452 452 452

1.0 0.0 0.3 80.6 15.8

1.0 0.0 0.0 82.4 17.7

1.0 1.0 1.0 202.7 115.4

0.0 0.0 0.0 30.5 60.7

0.2 0.2 0.4 19.6 21.5

Borrower characteristics Credit rating Borrower age Borrower income (million wons) Two income dummy

452 452 452 452

6.5 42.2 22.7 0.1

7 42.0 16.2 0.0

9 66.0 127.0 1.0

1 22.0 0.0 0.0

2.1 9.2 17.5 0.2

Auction characteristics Duration of auction (month) No. of failed auctions No. of bidders

452 452 452

7.4 1.6 5.8

7.0 1.0 4.0

21.0 13.0 30.0

2.0 0.0 1.0

2.3 1.0 5.3

Macroeconomic variables 3-year treasury rate (%) DGDP (%) Unemployment (%)

452 452 452

4.9 2.5 4.5

4.9 2.5 4.6

5.4 3.6 5.3

3.8 2.3 3.6

0.2 0.3 0.4

285 (63.05%) 439 (97.12%) 15 (3.32%) 339 (75.00%)

14 (3.10%)

Mean

Median

Max

Min

SD

Note: We use LGD of senior mortgages. Credit rating is between 1 and 10 with 1 being the best credit rating and 10, the worst credit rating.

Loan Size 80, which in turn is less than that of Loan Size 110. This result suggests that LGD increases with loan size. In contrast, Calem and LaCour-Little find that LGD decreases with loan size up to a point, then increases with loan size as the loan becomes very large. On the other hand, Qi and Yang find

that normalized loan size has a negative effect on LGD. They attribute the negative effect of loan size on LGD to the relatively fixed costs of liquidation and the more conservative underwriting standards generally applied to larger size (i.e., higher cost) properties.

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Table 16 Estimation of the LGD regression models of senior mortgages for the 2004–2007 Seoul–Busan foreclosure auction sample. Variables

Model 1 Coefficient

Constant Loan characteristics CLTV-80 LTV DTI Loan age Loan amount Loan amount2 Loan size 60 Loan size 80 Loan size 110 Subordinated-claims-in-place dummy Collateral characteristics Apartment dummy Gangnam district dummy Floor area (m2) Rate of change in house price Borrower characteristics Credit rating Borrower age Borrower income Dual income dummy Auction characteristics Duration of auction (month) No. of failed auctions No. of bidders Macroeconomic variables 3-year treasury rate (%) DGDP (%) Unemployment (%)

a b c

Model 2 t-Statistic

Coefficient

Model 3 t-Statistic

Model 4

Model 5

Coefficient

t-Statistic

Coefficient

69.03

3.00a

15.84

0.72

2.63

0.12

49.87

2.34b

61.16

1.95b

0.06 0.01 0.04

0.98 0.26 0.43

0.03 0.01 0.12

0.50 0.30 1.35

0.32 0.01 0.01 0.09

11.23a 0.26 0.37 1.01

0.05 0.02 0.12 0.90 0.00

0.85 0.84 1.15 2.23b 0.08

0.09 0.02 0.10

0.97 0.62 0.76

5.05 4.57 5.26

2.07b 2.59a 3.58a

0.78 0.27 2.42 4.00

0.34 0.16 1.75c 3.47a

0.63 0.53 0.32 2.72

0.29 0.33 0.24 2.44b

4.04 13.83 12.41

1.15 4.23a 4.04a

2.04 10.29 1.52 0.27

0.64 3.44a 0.50 8.08a

0.29 8.86 1.10

0.10 3.13a 0.38

3.29 18.82 12.33

0.03 0.07 0.06 8.52

0.09 1.09 0.31 2.12b

0.11 0.00 0.01 4.21

0.44 0.04 0.04 1.15

0.08 0.01 0.01 3.35

0.33 0.20 0.04 0.97

0.16 2.43 0.04

0.60 3.91a 0.40

0.76 0.58 0.44

0.31 0.25 0.30

0.06 4.84 0.70

0.02 1.92c 0.41

0.35 1.36 0.33

0.14 0.58 0.21

t-Statistic

Coefficient

t-Statistic

5.93 4.77 6.62

1.74c 2.21b 3.76a

0.95 5.29a 4.35a

4.65 15.66 13.11

1.16 4.41a 3.03a

0.09 0.07 0.07 6.36

0.32 1.15 0.40 1.61

0.17 0.07 0.06 6.67

0.47 0.89 0.24 1.01

0.17 1.84 0.39

0.06 0.73 0.23

0.40 2.89 1.13

0.12 0.79 0.56

Adjusted R-squared

0.13

0.28

0.37

0.16

0.13

N

452

452

452

452

339

Significance at 1% level, Significance at 5% level, Significance at 10% level.

We expect that collaterals in Gangnam, which appreciated the most during the booming market, would show a lower LGD. Consistent with the bubble hypothesis Gangnam district dummy has negative effect on LGD. The sign of Gangnam dummy is negative for the 2004–2007 sample (the booming market) while it is positive for the 2008–2009 subsample (the market downturn; Model 2 of Table 8) consistent with the fact that house prices in Gangnam region rose sharply during the boom, then fell sharply during the downturn. The property floor area has a negative effect on LGD. This result is consistent with the leverage effect of buying larger and more expensive houses during the booming market. On the other hand, this is opposite to the result we obtain for the 2008–2009 Gangnam-Gangbuk sample in Model 2 of Table 8, where the size effect turns positive during the market correction as greater speculation on larger properties leads to a larger correction. We expect the dual income dummy to have a negative effect on LGD since dual income couples are likely to be more resilient to labor income shock; therefore, lenders may experience lower loan losses on mortgages offered to dual income households than single income households. We find that dual income dummy has a negative sign as expected, but its statistical significance is weak at best. It is possible that some of the dual income households are low income families where both spouses have to work to support the family. We find that some of the loan characteristics as well as some of the borrower characteristics at the time of origination we consider

are not significant. It is possible that we fail to detect the effect of many of the time-of-origination characteristics including the original LTV on LGD either because the sample size is too small or the data is too noisy or the power of the model is weak. While most US studies (Clauretie and Herzog, 1990; Lekkas et al., 1993; Qi and Yang, 2009) find a positive effect of the original LTV on LGD, we find that the original LTV is not significant in our study. A contributing reason for why the original LTV is not a significant factor of LGD for the Korean mortgages in our sample may be that the original LTV is uniformly low; the LTV that borrowers would have chosen given their financial constraints would typically be different from the uniform LTV level so that there is little predictive power in the original LTV. We find that loan age does not have a significant effect on loss severity. This result contrasts with results found in some of the US studies. For example, Lekkas et al. (1993) and Pennington-Cross (2003) and Qi and Yang (2009) report a negative effect of loan age on LGD and Calem and LaCour-Little (2004) report a positive effect of loan age on LGD. The difference may arise from the fact that the loan ages of the defaulted loans of the 2004–2007 sample tend to be low. In Model 2, we add subordinated-claims-in-place dummy and house price changes to Model 1. We find that subordinatedclaims-in-place dummy is negative and significant. As discussed in relation to Table 7, since the LTV limit applies to the sum of all residential mortgages combined, the presence of subordinated claims implies that the amount of the senior mortgages on the same property is relatively low. Therefore, the presence of subordi-

Y.W. Park, D.W. Bang / Journal of Banking & Finance 39 (2014) 192–210

nated claims proxies for a larger equity buffer for senior mortgages. Consequently, we expect that the presence of the subordinated claims has a negative influence on the LGD of the senior mortgages. As expected, the coefficient of the subordinated claims dummy is negative and significant. We find that house price changes have a negative effect on loss severity as expected. We infer that the cumulative effect of house price changes until foreclosure auction is highly significant and negative showing that a fall in the house price leads to a rise in CLTV, which causes an increase in LGD. This result is consistent with Clauretie and Herzog as well as Zhang et al. In Model 3, we add CLTV, which reflects house price changes, subordinated-claims-in-place dummy and auction variables to the time-of-origination information. We find that the main results of the paper based on the 2006–2009 Gangnam-Gangbuk foreclosure auction sample do not change; CLTV and auction variables are highly significant. In Model 4 we use time-of-origination variables only; however, we replace relative loan size dummies with loan amount and loan amount squared following Calem and LaCour-Little, who find that the coefficient of loan amount is negative and the coefficient of loan amount squared is positive. We find that the coefficient of loan amount is positive and significant consistent with the result in Model 1, which shows that LGD increases with loan size. In Model 5 we use time-of-origination variables only on the Seoul dataset. We do not find any noticeable difference in the LGD factor model between the Seoul market and the combined markets. While not shown in a table, we estimate the LGD model using the Busan subsample separately. We find that qualitative aspects are the same as those of the whole sample (2004–2007 Seoul–Busan sample). Finally, we estimate the LGD model using the Gangnam subsample and the Gangbuk subsample separately. While not shown in the table, we find that qualitative aspects are identical to those of the whole sample (2006–2009 Gangnam–Gangbuk sample). We conclude that the key findings of our studies hold in all submarkets of the samples used.

5. Conclusions We investigate the loss given defaults of residential mortgages in Korea, a low LTV regime. We investigate the level and the factors of the LGD of foreclosed Korean mortgages by seniority, housing type, housing market cycle and submarkets. LGDs are measured for a sample of 2590 residential mortgages between January 2006 and January 2009. We find that the mean LGD is in 5–10% range for senior mortgages and in 30–50% range for the subordinated claims. The recovery performance indicates that the loss rates experienced by large commercial banks, which hold the senior mortgages, are very low consistent with the fact that residential mortgages in Korea are originated with low LTV. Subordinated claimants suffer substantial losses suggesting that the credit risk of subordinated claims is much larger than that of senior mortgages and that losses are likely to concentrate on savings banks and other smaller financial institutions which tend to carry substantial exposure to second claims. We find that the CLTV of defaulted loans rises sharply before the loan default. LGD rises sharply after CLTV rises above 80% suggesting that auctioned units sell at about 20% discount relative to appraisal value. From the study of LGD as a function of CLTV, we estimate that 80% CLTV is roughly the threshold CLTV, beyond which loss to the senior lender can occur. Consistent with works reported elsewhere, we find that CLTV matters the most for the loan loss severity estimation of residential mortgages. Furthermore, we find that foreclosure auction process is a critical determinant of the LGD, which has not been

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documented carefully elsewhere. While CLTV is the single most important determinant of LGD accounting for 80% of LGD, foreclosure auction variables account for almost 10% of variation in LGD. The auction-specific factors of importance are duration of auction, number of failed bids and number of bidders. The auction duration as well as the number of auctions held has a positive effect on loan losses while the number of bidders has a negative effect on loan losses. Consistent with the leverage hypothesis, we find that larger units show a larger LGD than smaller units during the market downturn, but we find the opposite effect of large property size on LGD during the booming market. Consistent with the bubble hypothesis, we document that housing units located in the Gangnam submarket show a larger LGD than those located elsewhere during the market downturn, but a lower LGD during the booming market. In addition, we find that the coefficient of the interaction term between Gangnam and floor area is positive and significant. This result is consistent with the notion of the speculative purchases of large housing units in Gangnam near the peak of the housing market around 2006. In summary, in a low LTV loan regime, the loss severity is minor for senior claimants and the loss is moderate even for subordinated claimants. Clearly, CLTV matters the most for the loan loss severity estimation of residential mortgage loans. However, auction process is a critical component of LGD. We estimate that the threshold CLTV, beyond which loss to lenders occurs, is about 80%. We have investigated how housing market cycles influence the LGD of secured lenders. We find evidence that during the housing market downturn collateral characteristics that are overvalued during the boom increase loss severity. Furthermore, we find that most collateral characteristics are priced in foreclosure auction markets influencing the LGD indirectly. We also find some evidence that secured lenders face an additional loss due to the gaming of the foreclosure auction process. Additionally, we document the negative (positive) effect of past price increases (decreases) on LGD. Furthermore, we show that past house price changes influence CLTV, which in turn influences LGD. Finally, we evaluate the effect of time-of-origination information about loans, collaterals and borrowers on LGD by constructing a separate data set, which consists of 452 observations of foreclosed senior mortgages purchased by Korea Housing Finance Corporation between 2004 and 2007. We find that of time-oforigination variables loan size, Gangnam district dummy and property size are significant; of post-origination variables CLTV, subordinated-claims-in-place dummy, the number of failed auctions and the cumulative house price change are significant. We find that some of the loan characteristics as well as the borrower characteristics at the time of origination we consider are not significant. It is possible that we fail to detect the effect of some of the time-of-origination characteristics on LGD either because of small sample size, noisy data or weak power of the models used. However, a contributing reason for why the original LTV is not a significant factor of LGD for the Korean mortgages in our sample may be that the original LTV is kept low uniformly for all borrowers so that there is little predictive power in the original LTV. Finally, the fact that the effect of property size on LGD is negative for the 2004–2007 sample (booming market) while it is positive for the 2008–2009 sample (market downturn) suggests a link between housing market cycles and LGD. Acknowledgments We would like to thank the editor and the referee, who helped us to improve our paper immeasurably. The first author benefited from the research work he did on the default behavior of KHFC (Korea Housing Finance Association) mortgages on behalf of KHFC.

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