Exploring Interaction Between Bank Lending and Housing Prices in Korea

Transcription

1 Exploring Interaction Between Bank Lending and Housing Prices in Korea A paper presented at the Housing Studies Association Conference 2012: How is the Housing System Coping? April University of York 20 th April 2012 Jun Ho Jeong Kangwon National University

2 1. Introduction Existing theoretical work A close correlation between bank lending and housing prices has been established. A collateral effect, wealth effect and balance sheet effect have been identified to explain a close linkage between bank borrowing and housing prices (e.g. Bernanke and Gertler, 1989; Goodhart and Hofmann, 2007) The credit condition could affect asset valuation reflecting the credit constraints on housing prices (e.g. Yamashita, 2007) This two-way causality may result in mutually reinforcing cycles in credit and housing markets.

3 1. Introduction Existing empirical work Empirical work is scare and its results are mixed - Collyns and Sehadji (2001): a contemporaneous causal linkage from credit growth to housing price changes in Hong Kong, Singapore and Korea and Thailand - Gerlach and Peng (2005): a short and long-term causality from property market to bank lending - Goodhart and Hofmann (2007); Oikarinen (2009) : a two way causality in 18 developed countries and Finland respectively. However, most of the studies have focused on a single equation setting except for Gerlach and Peng (2005), suffering from the endogeneity bias leading not to understanding the direction of causality between the two in a proper way.

4 1. Introduction Aims of this work Given the Korean contexts such as rapid financial liberalization and deregulation since the 1997 financial crisis, an extraordinarily unbalanced asset portfolio with a two-third of real estates in total household assets and a surge in housing prices in the 2000s, This work attempts to fill the gap by empirically exploring the pattern of causality between bank lending and housing prices based upon a long-term and short-term analysis. It also examines to what extent new policy measures such as the ceiling constraints of LTV and DTI have contributed to a reduction in housing prices and bank loans.

5 2. Data and Stylized Facts Housing Price, Bank Loan to Household, Interest Rate and GDP Real GDP Real Bank Loan to Household Real Apartment Price Real Interest Note: 1) Bank Household credit is the sum of CBs, SBs and NBFCs. 2) Normalized Housing prices declined during 1993 and 1998, but rose rapidly thereafter until late Household borrowing and output appear more stable than housing prices. Interest rates have continued to declined except for the 1998 financial crisis, but a little bit increase after 2005.

6 2. Data and Stylized Facts Annualized Growth Rates of Housing Price and Bank Credit Real Bank Loan to Household (%) Real Apartment Price (%) Since the 1998 financial crisis, price change in housing has been closely interrelated with credit growth in household.

7 2. Data and Stylized Facts A Share of Mortgage Loans in Total Household Borrowing 95.0 % of CB and SBs Loans in Total Loans to Household 90.0 % of Security against Housing in CBs and SBs Loans to Household The ratio of residential mortgage loans accounted for about 60% and has been stable for CBs and SBs loans to household.

8 2. Data and Stylized Facts Regulatory Change in Housing Finance (1) Housing Finance before the 1997 Financial Crisis Most of real estate development and investment related sectors were classified into the credit control sector. The top 10 chaebol was demanded to self-finance the purchase of real estates even for normal business activities as much as possible. Bank lending was banned for the construction and purchase of home with over 100m 2 per household. - Residential mortgage finance was very limited due to the large part of money channeling to the corporate sector Thus, real estate market was insulated from the formal financial market. - The housing finance of the formal financial sector accounted for only 38.4% in the total housing finance (Kim et al, 2002).

9 2. Data and Stylized Facts Regulatory Change in Housing Finance (2) Housing Finance after the 1997 Financial Crisis The designation of the real estate development and investment related sectors as the credit control sector were revised and the regulation of bank loans to household was lessened. - The amendment of the real estate trust law, the introduction of ABS, MBS and REIT etc. were carried out. The business strategy of financial corporations has moved from the corporate financing segment to the consumer one since the 1997 financial crisis. Thus, the constraints of bank loans to household on the supply side have been removed due to the financial deregulation since the 1997 financial crisis.

10 2. Data and Stylized Facts Regulatory Change in Housing Finance (2) Financial restrictions on bank Lending due to housing booms during the 2000s The introduction of the upper limit of LTV ratio from 60% to 40% and that of DTI to 40% were carried out respectively on September 2002 and August 2005, depending on the local conditions. The stability of housing financial market has been undertaken by the LTV and DTI regulations respectively leading to the reduction of the impact of housing price shocks and of income shocks. - DTI as an ex ante measure of financial consumer s protection constraining over-borrowing beyond its economic affordability, but LTV as that of reducing an ex post loss of financial corporations. Real estate related financial regulations have moved from LTV focused measures to DTI ones since

11 3. Empirical Results Methodology for long-run relationships A standard model for the long-run movements of bank lending is that: The co-integrating VAR model is as follows: δτ ε The above model can be reformulated in vector error correction form, and the below model will be estimated by the Johansen methodology

12 3. Empirical Results Long-run Analysis Augmented Dickey-Fuller unit root test results Variables Level 1st difference Real GDP (T) 5.401** (C) Real bank loan to household (T) 2.984* (C) Real apartment price (T) 4.279** (C) Permits authorized for residential building construction (units) 3.108* (C) Real interest rate (T) 5.161** (C) Employment rate (C) 4.647** (C) Notes: 1) T and C indicate whether the test regression includes a time trend and a constant (T), and only a constant (C). 2) * and ** denote significance at the 5% and 1% level respectively. All of the time series except for the authorized permits of residential building variable are integrated of order one.

13 3. Empirical Results Co-integration Analysis (1994:1-2009:2) Lags Johansen trace test r=0 r 1 r 2 r ** β and α vectors β (SE) α (SE) Real bank loan Real GDP (0.113) Real interest rate (0.007) Real housing price (0.071) LR test for weak exogeneity of Y and P Χ 2 (2)=3.121 p value=0.210 Diagnostics Autocorrelation Heteroskedasticity Normality ** Notes: 1) Autocorrelation is based on a Lagrange Multiplier test up to order 4, Heteroskedasticity on a White s test, and normality on a Jarque-Berra test. 2) All tests refer to the system as a whole. 3) * and ** denote significance at the 5% and 1% level respectively. There is a single long-run relationship between housing prices, bank loans, interest rates and GDP during the sample period. - The long-run income elasticity of credit lager than one could be due to a result of financial liberalization since the 1997 financial crisis.

14 3. Empirical Results Short-run Dynamic Relationships (1) Dynamic movement of credit growth ΔL t OLS (white) (t value) Hausman test (t value) ΔL t (6.860) (6.547) ΔY (2.368) (2.414) ΔP t (6.610) (3.060) ΔP t (2.543) (2.707) (ΔRI t 1 ΔRI t 2 ) ( 5.096) ( 5.172) CI t ( ) ( ) C (3.734) (3.541) res (1.127) Adj R Autocorrelation Heteroskedasticity Normality Note: Autocorrelation is based on a Breusch-Godfrey Serial Correlation LM test up to order 3, Heteroskedasticity on a White s test, and normality on a Jarque-Berra test.

15 3. Empirical Results Dynamic movement of credit growth A general-to-specific approach was used in deriving the parsimonious model beginning with a model with four lags of dependent variables. An error correction term is highly significant, indicating that excess bank loans decrease credit growth in the next period. A change in real interest rates was significant at lag 1 and lag 2 but have reverse signs, and then the model was re-estimated, showing a negative sign for credit growth. To tackle the potential endogeneity, a Hausman test was carried out, showing the consistent OLS estimates. Thus, movements in housing prices appear to have played a significant role in increasing credit growth.

16 3. Empirical Results Short-run Dynamic Relationships (2) Dynamic movements of housing price changes ΔP t 2SLS (t value) OLS (white) (t value) ΔP t ΔY ΔL ΔL t ΔRI t ΔEmp t ΔUnit t Regime dummy C Adj R Autocorrelation Heteroskedasticity Normality *** *** Notes: 1) Autocorrelation is based on a Breusch-Godfrey Serial Correlation LM test up to order 3, Heteroskedasticity on a White s test, and normality on a Jarque-Berra test. 2) *** denotes significance at the 1% level.

17 3. Empirical Results Dynamic movement of housing price changes The parsimonious model was derived from fitting a model of four lags of the dependent variables, with three variables being added and the potential endogeneity bias being tackled. The interest rate variable is significant as expected with a negative sign. Unlike a conventional expectation, the housing permit unit and employment rate variables show opposite signs. - With regard to the first, the housing market during the 2000s seems to have worked as an asset market unlike general commodity markets. - In the case of the employment rate variable, it could be due to either the speculative wait and see strategy resulting from an excessive rise in housing prices in a short term, or an employment rise in a large part from non-regular jobs leading to a reduction in housing investment.

18 3. Empirical Results Regulatory Change and Credit Growth Regulatory Change and Credit Growth (1) ΔL t Model 1 (t value) Model 2 (t value) ΔL t (6.905) (5.803) ΔY (2.437) (2.612) ΔP t (6.887) (5.716) ΔP t (2.682) (4.633) (ΔRI t 1 ΔRI t 2 ) ( 5.050) ( 5.577) CI t ( 8.819) ( ) C (3.890) (5.207) Regime*CI( 1) ( 0.778) Regime*ΔPt ( 1.198) Regime*ΔPt ( 3.732) Adj R AR H N ***

19 3. Empirical Results Regulatory Change and Credit Growth (2) ΔL t Model 3 (t value) Model 4 (t value) Model 5 (t value) ΔL t (6.487) (6.233) (5.554) ΔY (2.513) (2.285) (2.556) ΔP t (6.180) (5.938) (5.612) ΔP t (3.912) (1.860) (4.503) (ΔRI t-1 -ΔRI t-2 ) ( 5.362) ( 4.910) ( 5.424) CI t ( ) ( 9.965) ( ) C (4.225) (3.753) (4.911) Policy1*ΔPt ( 0.887) ( 1.114) Policy1*ΔPt ( 2.854) ( 3.634) Policy2*ΔPt ( 0.918) ( 0.999) Policy2*ΔPt ( 0.065) ( 2.166) Adj-R AR H N 7.826**

20 3. Empirical Results Policy intervention and its impact on bank lending Using a dummy to capture the regime shift around 2002, its impact on banking lending are explored. - According to the intensity of policy intervention into the real estate financial market, the Policy1 indicates the introductory period of LTV from 2002: 4 to 2005: 2 and the Policy2 denotes the introduction of DTI as well as LTV from 2005: 3 to 2009: 2. The dummy series is significant especially for 3 lag of housing price, meaning that bank lending decreases credit growth in the next three period. Due to this lagged relation between housing price and bank loan changes, the Policy2 indicating the period of more intensified policy intervention would be insignificant But, when including both the Policy1 and 2, both are significant for 3 lag of housing prices, leading to a reduction in housing price in the next three period.

21 4. Summary and Conclusion The contemporaneous interaction between credit growth and bank lending to household in Korea seems to arise from banking lending responding to housing prices, although the past value of lending could predict the future value of housing price vice versa in a significant way. The sensibility of credit to housing prices reduced since the introduction of financial constraints on banking loans such as LTV and DTI making banks tighten credit standards. The regulatory change in the early and mid-200s played a significant role in stabilizing financial and real estate markets. The boom and bust cycle of the housing market in the 2000 s could be due to a change in expectations about economic uncertainty leading to demand for property, given its highly inelastic supply, and then a large increase in price, but this hypothesis could not be plausible without financial deregulations resulting an increase in financial accessibility.