Mortgage Default, Property Price and Banks’ Lending Behaviour in Hong Kong SAR.

9th International Conference on Computational and Financial Econometrics

Mortgage Default, Property Price and Banks’

Lending Behaviour in Hong Kong SAR.

Fawaz Khaled

Brunel University London


13th December 2015


IntroductionStability in banking sector has been always linked to stability in real estate market and the interdependence between the two has been widely researched in policy-oriented studies such as IMF (2000) and BIS (2001).

Literature on the property market and banking industry of Hong Kong identifies that mortgage performance is strongly driven by property price movements (Gerlach et al., 2005), because decrease in house prices undermine banks’ lending capacity: i) drop in collaterals values; ii) higher amounts of provisions, iii) deteriorating loan portfolios by the increase in the non-performing loans and delinquency ratios.

Banks’ lending behaviour, on one hand, influence property prices fluctuations through lax access to credit for house purchase which drives the prices up. On the other hand, it is driven by changes in real estate market through the use of collaterals.


Aim of the studyMost of the studies on the coincidence between credit cycle, real estate cycle and credit default has been conducted using a single equation set-up concentrating on the impact of one of these cycles on the other rather than the interaction between them (simultaneity problems).

Theoretically, the interdependence between property prices cycle, credit cycle and credit default suggests high association between the three of them, however, what needs more investigation is the direction of causality between them and the plausible long-run dynamic and short-run relation between these three variables. The main purpose of this study is filling this gap by testing the magnitude of causality between bank’s lending, residential property prices and mortgage default in Hong Kong in a multivariate cointegration framework that account for the long-run and short-run relations between the three cycles.


Why Hong Kong? 1) Housing mortgage loans constitute the main bulk of banks loans

portfolios (37% in 2002).

2) Lending policy in the Hong Kong is largely driven by fluctuations in property prices, making the later the largest sources of risk.

3) The history of property prices shows high volatility with several episodes of swings over the past two decades. These changes were as vigorous as in other countries with higher frequency (Fan and Peng, 2003).

4) Hong Kong dollar is linked to the US dollar and interest rates are predetermined by its peer in USA. Hence, cannot be used to safeguard financial stability against swings in property prices - macroprudential tools (LTV).


During booms episode, increase in property values provide higher borrowing capacity “collateralized loans” (Collyns and Senhadji, 2002), this motivates banks to expand their lending to gain higher market share leading to increase in credit availability, Bernanke et al. (1996) call this mechanism “financial accelerator”.

During busts episodes, when the market turns, depreciations in house prices result in high number of negative equity undermining borrowers’ capacity and banks’ capital position and resulting in higher non-performing loans and delinquencies (Campbell and Cocco, 2011).

Literature review Among other explanations of banks’ credit risk exposure, literature partly embraces the credit cycle-led hypothesis, the house prices cycle-led hypothesis and finally macroeconomic fundamentals-led hypothesis.


To dampen the impact of house prices on mortgage default, Hong Kong banks in 1991 started using 70% mortgage loans caps. Later in 1995, the Government enacted 70% LTV ratio as a long-run regulatory policy which found to control the evolution of credit risk and reduce the sensitivity of banking stability to fluctuations in property price (Wong et al., 2011).

Literature review

Campbell and Cocco (2011) find that high loan-to-value ratios at mortgage origination raise the probability of negative home equity and consequently the default probability.

An influential paper by Hott (2011) studied the interactive relationship between banks’ lending and real estate prices. Banks willingness to finance house purchases customers’ creditworthiness house prices. The feedback effects, he found housing demand availability of credit ( mortgages).


Generally speaking, two methods of cointegration test are in use: Engle and Granger approach (1987) and the Johansen approach (1988, 1991).

A common criticism pointed to these approaches is the assumption that the variables included in the estimation are non-stationary i.e., which prevent using neither of them as far as the included variables integrated of different orders.

Pesaran et al. (2001) proposed Autoregressive Distributed Lags model () or Bounds testing in which no restrictive assumptions need to be imposed in terms of the variables’ order of integration.

Hence, it is going to be employed in this study to assess the dynamic long-run and short-run relationships between mortgage default, property prices, bank’ lending behaviour accounting for the effect of loan-to-value policy.

Model selection


Model specificationsBounds test formodel is used to examine the long-run relationships and dynamic interactions among mortgage delinquency, property prices, and banks’ lending behaviour.For a dependent variableand three independent variables and , the estimation procedure of the model can be estimated as follows:

Step 1: the model of the conditional vector error correction model (VECM) is going to be formulated as using OLS for the variables in turn:

∆ 𝑌 𝑡=𝛼1+𝛽1𝑌 𝑡− 1+𝛽2 𝑋 1𝑡 −1+𝛽3 𝑋 2𝑡 −1+𝛽4 𝑋 3 𝑡 −1+∑𝑖

𝑝

𝛾 𝑖∆𝑌 𝑡−𝑖+∑𝑗

𝑞1

𝛿 𝑗∆ 𝑋 1 𝑡− 𝑗+∑𝑙

𝑞2

𝜑𝑙∆ 𝑋 2 𝑡−𝑙+∑𝑚

𝑞3

𝜂𝑚 ∆𝑋 3 𝑡 −𝑚+𝜀𝑡

Step 2: Calculate the F-test for joint significance of the variables’ lags to test: Null hypothesis of no cointegration: Against the alternative of cointegration:


Model specificationsNormalized F-statistics to be compared with the critical upper and lower bounds values reported by Pesaran et al., (2001) for the cointegration test. Criteria: 1) The existence of cointegration if F-statisticupper value 2) Inconclusive cointegration if lower value F-statistic upper value 3) No cointegration if F-statistic than the F-statistic lower value.

Step 3: estimate coefficients of the long-run dynamic of the model

Step 4: estimate the short run dynamics and ECM:


DataMonthly time series data is going to be used of the Hong Kong spanning over the period from June 1998 to June2009.

Residential Mortgage Loans Delinquency Ratio: Data is totally supplied by the HKMA published on the Monthly Residential Mortgage Survey.

: Bank Lending behaviour: data on gross loans made in Hong Kong obtained from HKMA has been used since evidence shows that banks in the Hong Kong make loans to institutions whose construction and property development their main activity.

: Current Loan-To-Value: used to capture the impact of macroprudential tools. LTV data accessed from HKMA, then derived by dividing loan-to-value at mortgage origination of a particular month by the same month reported value of the Hong Kong Midland Property Price 100 Index (Clapp et al., 2001)


Data: Propriety Price Index: Property price index constructed as summary measure to represent the development in real estate property prices using data different types of properties (residential, private offices, private retail, and flatted factories) each of which prices and rentals provided by Rating and Valuation Department (RVD) and by employing principal component.

Considerations:

History of price and rentals indices for all types of properties reveals that all of them evolved in similar manner, then PCA helps overcoming multicollinearity problems.

Any type of property ownership or rental involves engaging in mortgage agreements and all of them are exposed to delinquency.


Component Eigenvalue Difference Proportion Cumulative Proportion

1 7.727125 7.556405 0.9659 0.96592 0.170720 0.115303 0.0213 0.98723 0.055417 0.029166 0.0069 0.99424 0.026251 0.015871 0.0033 0.99745 0.010380 0.005163 0.0013 0.99876 0.005216 0.001837 0.0007 0.99947 0.003379 0.001866 0.0004 0.99988 0.001513 --- 0.0002 1.0000

Principal Components Analysis for property price

0

100

200

300

400

500

600

700

-4

-2

0

2

4

6

8

10

98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13 14

HP Private Domestic PricesPrivate Domestic Rents Private Flatted Factories PricesPrivate Flatted Factories Rents Private Office PricePrivte Office Rents Private Retail PricePrivate Retail Rent

Data11

/1/19

986/1

/1999

1/1/20

008/1

/2000

3/1/20

0110

/1/20

015/1

/2002

12/1/

2002

7/1/20

032/1

/2004

9/1/20

044/1

/2005

11/1/

2005

6/1/20

061/1

/2007

8/1/20

073/1

/2008

10/1/

2008

5/1/20

09

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Mortgage Delinquency


StatisticsMean 0.672 -1.525 2.117 9.204

Median 0.660 -1.643 1.904 9.181Std. Dev. 0.491 1.114 0.495 0.339

Skewness 0.034 0.435 1.050 0.232Kurtosis 1.281 2.578 3.209 2.897

Jarque-Bera 16.403 5.174 24.673 1.257Probability 0.000 0.075 0.000 0.533

Observation 133 133 133 133

Descriptive statistics of the selected variablesData

Although pre-testing the variables’ stationarity is not binding condition, it should be performed to avoid inclusion variables that are or higher.

Variables

Level -4.552*** -2.515 -1.597 -3.776**

First difference -4.9157*** -5.173*** -9.347*** -11.665***

Phillips-Perron (PP) test statistics at level and first difference


Equations SIC Lag F-statistic

Outcomes

5% 10%

4 12.5792*** 4.45 5.58 3.80 4.85 Cointegration Cointegration

1 11.9045*** 3.30 4.44 2.77 3.80 Cointegration Cointegration

3 1.7164 3.30 4.44 2.77 3.80 No Cointegration

No Cointegration

1 2.6229 4.10 5.17 3.52 4.52 No Cointegration

No Cointegration

Results of the Bound tests at 5% and 10%

Results and Discussion

Since our main focus is to investigate causality among mortgage delinquency, property prices and banks’ lending, the model normalized for has been considered below.


Selected based on Schwarz Bayesian Criterion.Dependent variable is

Regressors Coefficient Standard Error T-ratio Probability-0.27520*** 0.048676 -5.6537 0.0000.31927*** 0.086428 3.6941 0.0000.49913*** 0.071860 6.9458 0.0000.16017*** 0.058633 2.7318 0.0073.9443*** 0.88450 4.4594 0.000

-0.014585*** 0.0010080 -14.4692 0.000

Estimated Long-Run Coefficients using the ARDL Approach

Results and Discussion


Selected based on Schwarz Bayesian Criterion.Dependent variable is

Regressors Coefficient Standard Error T-Ratio Probability0.11612 0.077881 1.4910 0.139

-0.018715 0.076737 -0.24389 0.8080.21730*** 0.070114 3.0992 0.002

-0.026376*** 0.0045149 -5.8420 0.0000.030600*** 0.0068312 4.4795 0.0000.047839*** 0.0099041 4.8302 0.0000.015352** 0.0064700 2.3728 0.019

-0.0013979*** 0.1950E-3 -7.1673 0.000-0.095844*** 0.014570 -6.5783 0.000

Error Correction Representation for the Selected ARDL ModelResults and Discussion

The confirm long-run and implies that any disequilibrium due to previous shocks is corrected and converges back to the long-run equilibrium.𝑒𝑐𝑚=𝐷− 0 .32∗ L+0.27∗𝐻𝑃− 0.50∗𝐿𝑇𝑉 +0.14∗𝑇𝑅𝐸𝑁𝐷− 3.94∗𝐶− 0.16∗𝐷𝑉


0.99866 Adjusted 0.99856

Serial Correlation: (12) = 20.44 [0.059] F(12,107) = 1.68 [0.081]

Functional Form: (1) = 0.076 [0.782] F(1,118) = 0.069 [0.792]

Normality (2) = 9.69 [0.008] Not applicable

Heteroscedasticity (1) = 5.65 [0.017] F(1,127) = 5.82 [0.017]

Diagnostic Tests of ARDL VECM Model of mortgage delinquencyResults and Discussion

Shrestha and Chowdhury (2005), Fosu and Magnus (2006), Rafindadi and Yusof (2013)

Coefficients stability CUSUM and CUSUMQ for ECM Plot of cumulative sum of recursive residuals Plot of cumulative sum of squares of recursive residuals


ConclusionsThe study investigates the long-run equilibrium relationship and short run dynamic between the mortgage default, property prices, bank’ lending behaviour and loan-to-value in Hong Kong by employing the Autoregressive-Distributed Lag () bounds test technique for cointegration on time series.

Overall, results reveal that mortgage delinquency is highly influenced by loan-to value caps, banks’ lending behaviour and the fluctuations in property prices there is evidence of cointegrating relationship among these variables.

Property prices is highly significant and negatively impact default through enhancing the borrowers’ ability to payback their debts affected by the appreciation in collaterals’ values “net wealth channel” , hence, contribute to the decline defaults probability. Our finding is consistent with Bernanke et al., (1999) and Kiyotaki and Moore (1997) Collyns and Senhadji, (2002)).


Banks’ lending is found to positively influence mortgage delinquency suggesting that an increase in banks’ lending exposes banks to higher probability of mortgage default. This outcome is consistent with expectations and empirical evidences (Gerlach and Peng, 2005). In case of Hong Kong, banks’ exposure to the real estate market is considerably huge given that residential mortgage comprised 24 % of the total issued loans for use in the Hong Kong, at the end of 2007.

Loan-to-value has showed the highest coefficient highlighting the essential importance of this tool in reducing the level of mortgage default in the Hong Kong.

Our finding shows that any disequilibrium in the long-run relationship is corrected and converges back to long-run equilibrium with a relatively good speed of adjustment.

Conclusions


Thanks for your attention

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Mortgage Default, Property Price and Banks’ Lending Behaviour in Hong Kong SAR.

Economy & Finance

Transcript of Mortgage Default, Property Price and Banks’ Lending Behaviour in Hong Kong SAR.