MORTGAGE DEFAULT, PROPERTY PRICE AND BANKS’ LENDING BEHAVIOUR IN HONG KONG SAR is a research presented in the 9th International Conference on Computational and Financial Econometrics. December 13th 2015.

1. 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
9th International Conference on Computational and
Financial Econometrics
13th December 2015

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Introduction
Stability 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.

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Aim of the study
Most 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.

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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).

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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.

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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).

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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.𝐼(1), 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
(𝐴𝑅𝐷𝐿) 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

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Model specifications
Bounds test for 𝐴𝑅𝐷𝐿 𝑝, 𝑞1, 𝑞2, 𝑞3 model is used to examine the long-run
relationships and dynamic interactions among mortgage delinquency,
property prices, and banks’ lending behaviour.
For a dependent variable𝑌𝑡and three independent variables 𝑋1𝑡 , 𝑋2𝑡 and
𝑋3𝑡, the estimation procedure of the 𝐴𝑅𝐷𝐿 model can be estimated as
follows:
Step 1: the 𝐴𝑅𝐷𝐿 model of the conditional vector error correction model
(VECM) is going to be formulated as using OLS for the variables in turn:
∆ 𝑌𝑡 = 𝛼1 + 𝛽1 𝑌𝑡−1 + 𝛽2 𝑋1𝑡−1 + 𝛽3 𝑋2𝑡−1 + 𝛽4 𝑋3𝑡−1 +
𝑖
𝑝
𝛾𝑖∆ 𝑌𝑡−𝑖 +
𝑗
𝑞1
𝛿𝑗∆ 𝑋1𝑡−𝑗 +
𝑙
𝑞2
𝜑𝑙∆𝑋2𝑡−𝑙 +
𝑚
𝑞3
𝜂 𝑚 ∆𝑋3𝑡−𝑚 + 𝜀𝑡
Step 2: Calculate the F-test for joint significance of the variables’ lags to
test:
Null hypothesis of no cointegration: 𝐻0: 𝛽1 = 𝛽2 = 𝛽3 = 𝛽4 = 0
Against the alternative of cointegration: 𝐻1: 𝛽1 ≠ 𝛽2 ≠ 𝛽3 ≠ 𝛽4 ≠ 0

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Model specifications
Normalized 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-statistic≥ upper 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 𝐴𝑅𝐷𝐿 model
𝑌𝑡 = 𝛼1 + 𝑖=1
𝑝
𝛾𝑖 𝑌𝑡−𝑖 + 𝑗=0
𝑞1
𝛿𝐽 𝑋1𝑡−𝑗 + 𝑙=0
𝑞2
𝜑𝑙 𝑋2𝑡−𝑙 + 𝑚=0
𝑞3
𝜂 𝑚 𝑋3𝑡−𝑚 + 𝑣 𝑦𝑡
Step 4: estimate the short run dynamics and ECM:
∆ 𝑌𝑡 = 𝛼1 +
𝑖
𝑝
𝛾𝑖∆ 𝑌𝑡−𝑖 +
𝑗
𝑞1
𝛿𝑗∆ 𝑋1𝑡−𝑗 +
𝑙
𝑞2
𝜑𝑙∆𝑋2𝑡−𝑙 +
𝑚
𝑞3
𝜂 𝑚∆𝑋3𝑡−𝑚 + ϑ 𝑒𝑐𝑚 𝑡−1 + 𝑣∆𝑦𝑡

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Data
Monthly time series data is going to be used of the Hong Kong spanning
over the period from June 1998 to June2009.
𝑫: Residential Mortgage Loans Delinquency Ratio: Data is totally supplied
by the HKMA published on the Monthly Residential Mortgage Survey.
𝑳: 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.
𝑳𝑻𝑽 : 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)

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Data
𝑯𝑷: 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.

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Component Eigenvalue Difference Proportion
Cumulative
Proportion
1 7.727125 7.556405 0.9659 0.9659
2 0.170720 0.115303 0.0213 0.9872
3 0.055417 0.029166 0.0069 0.9942
4 0.026251 0.015871 0.0033 0.9974
5 0.010380 0.005163 0.0013 0.9987
6 0.005216 0.001837 0.0007 0.9994
7 0.003379 0.001866 0.0004 0.9998
8 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 Prices
Private Domestic Rents Private Flatted Factories Prices
Private Flatted Factories Rents Private Office Price
Privte Office Rents Private Retail Price
Private Retail Rent
Data
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Mortgage Delinquency

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Statistics 𝑫 𝒕 𝑯𝑷 𝒕 𝑳𝑻𝑽𝒕 𝑳 𝒕
Mean 0.672 -1.525 2.117 9.204
Median 0.660 -1.643 1.904 9.181
Std. Dev. 0.491 1.114 0.495 0.339
Skewness 0.034 0.435 1.050 0.232
Kurtosis 1.281 2.578 3.209 2.897
Jarque-Bera 16.403 5.174 24.673 1.257
Probability 0.000 0.075 0.000 0.533
Observation 133 133 133 133
Descriptive statistics of the selected variables
Data
Although pre-testing the variables’ stationarity is not binding condition, it
should be performed to avoid inclusion variables that are 𝐼(2)or higher.
Variables 𝑫 𝒕 𝑯𝑷 𝒕 𝑳𝑻𝑽𝒕 𝑳 𝒕
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

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Equations
SIC
Lag
F-statistic
5%
𝐼(0)
5%
𝐼(𝟏)
10%
𝐼(0)
10%
𝐼(0)
Outcomes
5% 10%
𝑭 𝑫(𝑫|𝑳𝑻𝑽, 𝑯𝑷, 𝑳) 4 12.5792*** 4.45 5.58 3.80 4.85 Cointegration Cointegration
𝑭 𝑳(𝑳|𝑳𝑻𝑽, 𝑫, 𝑯𝑷) 1 11.9045*** 3.30 4.44 2.77 3.80 Cointegration Cointegration
𝑭 𝑯𝑷(𝑯𝑷|𝑳𝑻𝑽, 𝑫, 𝑳) 3 1.7164 3.30 4.44 2.77 3.80
No
Cointegration
No
Cointegration
𝑭 𝑳𝑻𝑽(𝑳𝑻𝑽|𝑯𝑷, 𝑫, 𝑳) 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
𝐷𝑡 has been considered below.

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𝑨𝑹𝑫𝑳 (𝟒, 𝟎, 𝟎, 𝟎) Selected based on Schwarz Bayesian Criterion.
Dependent variable is 𝑫 𝒕
Regressors Coefficient Standard Error T-ratio Probability
𝑯𝑷 𝒕 -0.27520*** 0.048676 -5.6537 0.000
𝑳 𝒕 0.31927*** 0.086428 3.6941 0.000
𝑳𝑻𝑽 𝒕 0.49913*** 0.071860 6.9458 0.000
𝑫𝑽 𝒕 0.16017*** 0.058633 2.7318 0.007
𝑪 3.9443*** 0.88450 4.4594 0.000
𝑻𝒓𝒆𝒏𝒅 -0.014585*** 0.0010080 -14.4692 0.000
Estimated Long-Run Coefficients using the ARDLApproach
Results and Discussion

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𝑨𝑹𝑫𝑳 (𝟒, 𝟎, 𝟎, 𝟎) Selected based on Schwarz Bayesian Criterion.
Dependent variable is ∆𝑫 𝒕
Regressors Coefficient Standard Error T-Ratio Probability
∆ 𝑫−𝟏 0.11612 0.077881 1.4910 0.139
∆ 𝑫−𝟐 -0.018715 0.076737 -0.24389 0.808
∆ 𝑫−𝟑 0.21730*** 0.070114 3.0992 0.002
∆ 𝑯𝑷 -0.026376*** 0.0045149 -5.8420 0.000
∆ 𝑳 0.030600*** 0.0068312 4.4795 0.000
∆ 𝑳𝑻𝑽 0.047839*** 0.0099041 4.8302 0.000
∆ 𝑫𝑽 0.015352** 0.0064700 2.3728 0.019
∆𝑻𝒓𝒆𝒏𝒅 -0.0013979*** 0.1950E-3 -7.1673 0.000
𝒆𝒄𝒎−𝟏 -0.095844*** 0.014570 -6.5783 0.000
Error Correction Representation for the Selected ARDL Model
Results and Discussion
The 𝒆𝒄𝒎 𝒕−𝟏 confirm long-run and implies that any disequilibrium due to
previous shocks is corrected and converges back to the long-run equilibrium.
𝑒𝑐𝑚

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𝑅2 = 0.99866 Adjusted 𝑅2 = 0.99856
Serial Correlation: 𝑥2
(12) = 20.44 [0.059] F(12,107) = 1.68 [0.081]
Functional Form: 𝑥2
(1) = 0.076 [0.782] F(1,118) = 0.069 [0.792]
Normality 𝑥2(2) = 9.69 [0.008] Not applicable
Heteroscedasticity 𝑥2(1) = 5.65 [0.017] F(1,127) = 5.82 [0.017]
Diagnostic Tests of ARDL VECM Model of mortgage delinquency
Results 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

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Conclusions
The 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 ( ARDL ) 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 𝑯𝑷 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)).

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 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

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attention
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