1. The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1463-5771.htm
The viability
Determining the viability of rental of rental price
price to benchmark Islamic home
financing products
69
Evidence from Malaysia
Rosylin Mohd Yusof, Salina H. Kassim,
M. Shabri A. Majid and Zarinah Hamid
Department of Economics, Kulliyyah of Economics and Management Sciences,
International Islamic University Malaysia, Kuala Lumpur, Malaysia
Abstract
Purpose – The purpose of this paper is to analyze the possibility of relying on the rental rate to price
Islamic home financing product.
Design/methodology/approach – By comparing two models consisting of either rental rate or
lending rate (LR) and selected macroeconomic variables that could influence property value, the study
focuses on the Malaysian data covering the period from 1990 to 2006. The study adopts several
econometric time-series analysis, such as the autoregressive distributed lag estimates, bi-variate
Granger causality, and multivariate causality based on the vector error-correction model.
Findings – The study finds consistent evidence that the rental price (RP) is a better alternative than
the LR to price Islamic home financing product. In particular, the rental rate is found to be resilient to
short-term economic volatility, while in the long run, it is truly reflective of the economic fundamentals.
Practical implications – This feature of the RP renders it as a fair pricing mechanism for the
Islamic home financing product. Results of this study contribute towards finding an alternative
benchmark for the Islamic home financing product which is currently using the conventional interest
rate as its benchmark.
Originality/value – To the best of the authors’ knowledge, the current study is the first of its kind
which provides empirical evidence for the possibility of relying on the rental rate to price Islamic home
financing product.
Keywords Benchmarking, Rental value, Loans, Islam, Property finance, Malaysia
Paper type Research paper
1. Overview
The Islamic banking and finance industry has witnessed a rapid and spectacular
expansion since its inception in the last three decades. The number of Islamic financial
institutions worldwide has increased from one in 1975 to over 300 today, operating in
more than 75 countries (El-Qorchi, 2005). These financial institutions are mainly
concentrated in the Middle East and Southeast Asia, with increasing presence in Europe
and the USA. The total assets of the banking and non-banking financial services being
offered based on shari’ah principles throughout the world are estimated to be in the Benchmarking: An International
range of US$800 billion to more than US$1 trillion which is almost fivefold of its Journal
Vol. 18 No. 1, 2011
magnitude five years ago. Islamic finance is said to be among the fastest growing pp. 69-85
financial segments in the world with an estimated annual growth rate of 20 percent q Emerald Group Publishing Limited
1463-5771
(Bank Negara Malaysia, 2008). DOI 10.1108/14635771111109823

2. BIJ Against the backdrop of a burgeoning growth of the Islamic banking and finance
18,1 industry, it is not surprising that voluminous studies have been conducted on various
aspects of the industry. Among the recurring issues are shari’ah-compliancy of Islamic
banking products, development of new Islamic banking products and services,
measurement of the Islamic banks’ performances, and evaluation of the social and
macroeconomic implications of developing the Islamic financial system. Of these issues,
70 the one that is given due attention is the Islamic home financing as offered by Islamic
banks throughout the world. In general, Islamic home financing constitutes around
60-90 percent of total financing offered by Islamic banks in some parts of the world.
The most commonly practiced mode of Islamic home financing in Malaysia is based
on the murabahah (cost-plus arrangement) combined with the bai-bithaman-ajil (BBA)
(payment of price is deferred to a future date) contracts. Although BBA-murabahah is a
very popular mode of financing offered by Islamic banks all over the world (particularly
in Malaysia), the implementation is argued to be similar to that of a conventional home
financing. According to Obaidullah (2005), it is merely a profit rate or mark up for the
interest rate, hence, leads to the convergence of both the Islamic and the conventional
home financings. The issue of shari’ah permissibility on the BBA-murabahah is still a
subject of debate among the Muslim scholars, with scholars in the Middle East
disapprove of the implementation of the BBA-murabahah concept in home financing,
whereas in Malaysia, Indonesia, and Brunei; its implementation is widely acceptable on
the premise that it helps to kick-start and develop the industry.
An alternative mode of Islamic home financing developed in the recent years is the
Musharakah Muthanaqisah partnership (MMP) which is based on a diminishing
partnership contract. MMP is deemed to be more shari’ah-compliant by many scholars
and is more commonly practiced in the Middle Eastern countries, Canada, the USA, and
Australia. According to Meera and Abdul Razak (2006), apart from being consensus
shari’ah-compliant, it can be implemented to avoid riba (interest) totally since the MMP
uses rental index or house price index. At the same time, the MMP is argued to be able to
reduce the cost of financing the property and the duration of the financing. Based on the
MMP, both the client and bank co-own the property with an initial pre-determined share
of ownership, such as 10 percent (client) and 90 percent (bank). The client will gradually
redeem the bank’s share throughout the financing period until the property is fully
owned by the client.
In determining the rental rates in MMP, home financing offered by some Islamic
banks all over the world (such as American Finance House LARIBA and Islamic Bank of
Britain) are still tied to the implied or indicative conventional interest rates. Although
benchmarking against the conventional interest rates is permissible, Muslim scholars
urge that Muslim scholars (economists, in particular) should seek an alternative which is
not dependent on the conventional interest rates. In this regards, this study hopes to
contribute to the literature in establishing some empirical link between the property
sector and the Islamic banking sector based on the Malaysian case during the period
1990-2006. In the quest of modeling an alternative benchmark for Islamic home
financing, the paper hopes to shed some light on the possibility of rental price (RP) as an
alternative to the conventional interest rates in benchmarking the Islamic home
financing products.
This study therefore seeks to model an alternative benchmarking for the Islamic
home financing based on the Malaysian case. It attempts to examine the determinants

3. of retail property rents in order to come up with the rental values to be benchmarked The viability
against the rate of profits of Islamic home financing. The study hopes to shed some light of rental price
on the possibility of equilibrium rental values as an alternative benchmark against the
conventional interest rates (such as LIBOR, KLIBOR, or EURIBOR). Despite the wide
literature collection on estimating, predicting, and identifying the significant variables
of property sector, studies on the relationship between the housing prices and rents and
the financial sector are still inadequate considering the vast development in the financial 71
sector. In fact, studies on the link between dynamic pricing of property sector and
Islamic financial system are meager.
The rest of the study is organized as follows. The next section reviews the literature
on the relevant factors influencing the property rental values, the methods undertaken to
empirically determine the property rental values, and the Islamic home financing as
offered by the Islamic banks in Malaysia. The following section discusses the
methodology undertaken in this study including the empirical models and data issues.
Subsequently, the results are presented and finally, the last section concludes.
2. Literature review
In efforts to understand the relevance of RP as a pricing mechanism for home financing,
this section reviews the major factors determining property rental values as mentioned
in the existing literature. Subsequently, it highlights the major empirical methods
undertaken to determine the rental values. The reviews on these aspects of rental values
would provide the basis for the variables included and method employed in this study.
2.1 Factors determining property rental values
Voluminous studies have been conducted to model, predict, and forecast rental values of
various types of property in both the developed and developing economies. Studies on
determination of property rental rate establish the attributes most relevant in
influencing property prices and incorporate these attributes in the pricing equation.
Indeed, quantifying property rental is definitely a challenging task due to the complex
nature of the housing market.
Several factors have potential effects on the value of the property, leading to the
determination of its RPs. Most studies group these possible factors into major categories
of attributes that later can be quantified. Subsequently, these variables are employed in
the property valuation and being determined their significance in influencing the
property prices. For instance, Linz and Behrmann (2004) provide three characteristics of
the factors determining house prices, namely physical, locational, and generally price
variables characteristics. Day (2003) categorizes the various attributes of housing into
structural, accessibility, neighborhood, and environmental characteristics. Meanwhile,
Can (1990) highlights the importance of neighborhood characteristics in determining
RPs which include quality of schooling system, level of noise pollution, air quality,
proximity to parks, proximity to bodies of water, and quality of transportation system.
Other influential characteristics are physical characteristics, such as number of
bedrooms, number of bathrooms, floor area, and age of property; demographic
characteristics, such as median household incomes, crime rate, and cultural attractions;
policy-specific characteristics, such as rent regulations and rent subsidies; and
amenities/facilities characteristics, such as the availability of in-door pools,
gymnasiums, and covered parking.

4. BIJ In addition to the property-specific characteristics, there are also other
18,1 economic-related factors which influence the property prices, such as the transaction
or clustering effect. As shown by Palmon et al. (2004), factors, such as listing price,
closing price, number of days on the market, and number of available properties listed
during the transaction have significant impacts on the property prices. Some other
studies emphasize the importance of economic characteristics, such as wage levels,
72 business cycles or gross domestic product (GDP) levels, and interest rate environment in
affecting property prices during a particular time period. For example, Wong et al. (2003)
use econometric analysis to determine the impact of interest rate movements on house
prices from 1981 to 2001 in Hong Kong and find that house prices and interest rates are
negatively related in the pre-1997 period. However, in the post-1997 period, the negative
relationship seems to be non-existent.
Thorough analysis should ideally incorporate all the potentially relevant data that
reflect the degree of contribution of these factors in determining the rental rate of the
property. Similarly, any empirical models should be comprehensive enough to include
all the significant attributes in arriving at the property’s rental values. However, due to
the complexity of the housing market which is considered as multidimensional and
highly differentiated, most of the studies focus on selected major attributes determining
the house prices or rental rate for a particular location. For example, Marco (2007)
focuses on the location and demographic attributes in determining rental rate in the
New York City neighborhoods. Based on data collected from five community districts,
the study analyzes the relationship between selected indicators to represent the
demographic attributes and the median monthly rent as the dependent variable.
In particular, the physical characteristics are represented by the location of the property,
while the demographic factors are represented by the crime rate, median household
income, and percentage of rent-regulated housing. The study finds that the premiums
are charged based on the location of the property (rental rates are largely location
dependent) and all the identified demographic factors are significant in influencing the
median monthly rent. In a related study, Hui et al. (2007) analyze the importance of
physical characteristics (which include age, total floor area and occupancy rate), market
position and location as the possible factors determining the rental values of properties
in Hong Kong. Ibrahim et al. (2005) test the importance of physical characteristics
(floor area and floor level), distance from central business district and distance from
mass rapid transit station in determining the rental values in various sub-markets in
Singapore.
Other macroeconomic variables are also shown to be important in determining the
rental values of property. This includes economic output, interest rate, and vacancy rate
(Chow et al., 2002) and consumer expenditure, employment and economic output
(White et al., 2000). The study by Matysiak and Tsolacos (2003) analyses rental pricing
from a different dimension by examining the role of selected economic and financial
series which are used as leading indicators in explaining the monthly variation in
property rents in the UK. The leading indicators comprised of five financial variables
(treasury bill rate – TBR, yield of 20-year gilts, narrow-money supply, broad-money
supply and price on FTSE), three real economic variables (car registration, volume of
retail sales, and job vacancies), and two sentiment indicators (consumer confidence and
expectations in the property market development). The study finds that the economic
variables are influential factors in determining the rental values of property.

5. 2.2 Methods undertaken to determine property rental values The viability
Methods to quantify and determine the property rental values can broadly be divided of rental price
into two approaches: the hedonic pricing model (HPM) and more recently, the
econometric analysis. The HPM analysis is a statistical technique which can be applied to
a series of property values, together with their associated characteristics to identify and
quantify the significance of the characteristics in determining the property’s value
(Dunse and Jones, 1998). It is a well-established technique which has been widely applied 73
for pricing of residential housing market and commonly used in the valuation of property
in the USA and the UK. Further extension of the HPM results in the automated valuation
model adopted by Ibrahim et al. (2005). In estimating the housing price for resale market
in Singapore for the period 1995-2000, the study finds that variables, such as floor area,
age, distance to central business district, and distance to the mass rapid transit are
significant determinants of the Housing Development Board resale flats’ price.
More recent approaches in determining property rental rates involve the applications
of sophisticated econometric analysis. This involves the adoption of various econometric
analyses ranging from the simple ordinary least square (OLS) analysis to the vector
error-correction model (VECM), variance decompositions analysis, and impulse
response functions. These analyses are intended to examine the dynamic causality
among the variables and to capture the relative strength of the causality among the
variables beyond the sample period. In other words, while the application of the OLS
analysis involves testing the explanatory power of the identified determinants on the
property rental rate, the other econometric analysis helps to provide further details on
the dynamic relationships among the variables. For example, Chaplin (1996) seeks to
demonstrate the importance and possible value of the procedure of modeling, predicting,
and forecasting commercial rents in office, industrial and retail markets in Great Britain
for the period 1986-1995. By employing the Pesaran et al. (1996) and VAR estimation, the
study concludes that the theory appears to be a better indicator of the “correct” model
structure than maximizing the historic fit. Meanwhile, White et al. (2000) and Ooi (2000)
employ time series technique and censored regression analysis to model property rents
in both Scotland and in the UK. In particular, White et al. (2000) compare and contrast the
distinctiveness and commonality of the three main sectors of the property market, and
Ooi (2000) links stock prices of the property sector to several macroeconomic variables.
For the emerging economies, studies conducted on the rental values of the property
markets mainly focused on the modeling, estimating, and predicting rental values for
the property sector. By employing regression analysis, Hui et al. (2007) explore the
relationship between market positioning and rents of retail facilities in Hong Kong for
the period 1997-2003. In identifying the macroeconomics determinants of private
housing in Singapore, Kim and Cuervo (1999) provide empirical evidence that housing
price in Singapore is cointegrated with real GDP, prime lending rate (LR), and private
housing starts.
2.3 Islamic home financing in Malaysia – as practiced by Bank Islam Malaysia Berhad
The Islamic financial system in Malaysia is enjoying a rapid and spectacular growth in
the last three decades. The first full-fledged Islamic bank in Malaysia – Bank
Islam Malaysia Berhad (BIMB) which was established since 1983, continues to poise
itself as a competitive and viable institution in the domestic financial infrastructure.
Against the backdrop of a conducive policy environment and a promising growth

6. BIJ of the Islamic banking industry, BIMB continues to offer a wide spectrum of financial
18,1 services to meet the increasing demands of its customers. One of the products offered by
BIMB that has gained competitiveness with the conventional banks is home financing.
For BIMB, the growth of home financing has been encouraging. The percentage of
financing extended to the purpose of house purchases (residential) to total financing
ranges from around 14-37 percent for the period of 1994-2005. More importantly, there
74 seems to be a remarkable increase in this financing type, reaching a high of 67 percent in
the recent years (2006 and 2007).
The Islamic home financing offered by the BIMB is based on the concepts of BBA,
Istisna’ and variable rate BBA. Its most popular home financing product is known as the
Baiti Home Financing-I which is based on the BBA-murabahah contract, a method of
sale with a deferred payment plan. It is said to be able to reduce borrower’s risks against
interest fluctuations. It offers the amount of financing of up to 100 percent with a
maximum repayment period of 30 years. By combining the murabahah arrangement
with the BBA, the customer is allowed to pay installments for the financing. In Malaysia,
the dominance of BBA as a mode of financing compared to the equity-based financing
like mudharabah and musyarakah may have received wide support among banking
practitioners as well as shari’ah advisors (Rosly, 2005). However, besides the limited
applications, the operational issues of BBA are also subject of debates among Muslim
scholars as well as banking practitioners. Nevertheless, it is not within the scope of this
study to highlight the ongoing debates pertaining to the operational issues of BBA and
murabahah.
It is also important to note that, compared to the conventional home loan, a unique
feature of the Islamic home financing which is based on the BBA focuses on profit-margin.
Profit margin is not subjected to fluctuations in interest rates. The conventional home
financing, on the other hand, relies on the interest rates. Conventional home financing
rates usually comprise of a base LR and adjusted accordingly to different banks.
3. Methodology
3.1 Autoregressive distributed lag approach
The autoregressive distributed lag (ARDL) approach adopted in this study was
introduced by Pesaran et al. (1996). The ARDL approach has numerous advantages.
Unlike the most widely adopted methods for testing cointegration, such as the
residual-based Engle and Granger (1987), and the maximum likelihood-based Johansen
(1988 and 1991) and Johansen and Juselius (1990) tests, the ARDL approach can be
applied regardless of the stationary properties of the variables in the samples and allows
for inferences on long-run estimates which is not possible under the alternative
cointegration procedures. In other words, this procedure can be applied irrespective of
whether the series are I(0), I(1), or fractionally integrated (Pesaran and Pesaran, 1997;
Bahmani-Oskooee and Ng, 2002), thus avoiding the problems resulting from
non-stationary time series data (Laurenceson and Chai, 2003).
Another advantage of this approach is that the model takes sufficient numbers of lags
to capture the data-generating process in a general-to-specific modeling framework
(Laurenceson and Chai, 2003). The ARDL method estimates ( p þ 1) k number of
regressions in order to obtain optimal lag-length for each variable, where p is the
maximum lag to be used and k is the number of variables in the equation. The model can
be selected using the model selection criteria, such as the adjusted R 2, Akaike information

7. criteria (AIC) and Schwartz-Bayesian criteria (SBC). The SBC is known as the The viability
parsimonious model (selecting the smallest lag length), whereas the AIC and adjusted R 2 of rental price
are known for selecting the maximum relevant lag length. This study reports the models
based on these criteria. Finally, the ARDL approach provides robust results for a smaller
sample size of the cointegration analysis.
The ARDL models used in this study can be written as the following general models:
75
RP t ¼ a0 þ a1 GDP t þ a2 TBR þ a3 CPI t þ a4 REERt þ et ð1Þ
LRt ¼ a0 þ a1 GDP t þ a2 TBR þ a3 CPI t þ a4 REERt þ et ð2Þ
where RP and LR are the rental price and lending rate, respectively, while the
macroeconomic variables employed are real GDP, interest rate (three-month TBR), and
consumer price index (CPI). Considering the high degree of openness of the Malaysian
economy, the external sector could have significant impact on the domestic economy.
Thus, we also include the real effective exchange rate (REER) variable in the models.
A dynamic error-correction model (ECM) can be derived from the ARDL model
through a simple linear transformation (Banerjee et al., 1993). The ECM integrates the
short-run dynamics with the long-run equilibrium, without losing the long-run
information. The error-correction representation of the ARDL models can be written as
follows:
X
k1 X
k2 X
k3
D ln RP t ¼ a0 þ bj D ln RP t2j þ cj D ln GDP t2j þ dj D ln CPI t2j
j¼1 j¼0 j¼0
Xk4 X
k5
þ ej D ln TBRt2j þ f j DREERt2j þ n1 ln RP t21 þ n2 ln GDP t21 ð3Þ
j¼0 j¼0
þn3 ln CPI t21 þ n4 ln TBRt21 þ n5 REERt21 þ jt
X
k1 X
k2 X
k3
D ln LRt ¼ a0 þ bj D ln LRt2j þ cj D ln GDP t2j þ dj D ln CPI t2j
j¼1 j¼0 j¼0
X
k4 X
k5
þ ej D ln TBRt2j þ f j DREERt2j þ n1 ln LRt21 þ n2 ln GDP t21 ð4Þ
j¼0 j¼0
þn3 ln CPI t21 þ n4 ln TBRt21 þ n5 REERt21 þ jt
The terms with the summation signs in the above equation represent the error-correction
dynamics, while the second part (terms with ns where s ¼ 1, 2, . . . , 5) corresponds to the
long-run relationship.
In the ECM model, the null hypothesis (H0: n1 ¼ n2 ¼ n3 ¼ n4 ¼ n5 ¼ 0), which
indicates the non-existence of the long-run relationship, is tested against the existence of a
long-run relationship. The calculated F-statistics of the H0 of no cointegration is
compared with the critical value tabulated by Narayan (2004). If the computed F-statistic
falls above the upper-bound critical value, the H0 of no cointegration is rejected. However,
if the test statistic falls below a lower bound, the H0 cannot be rejected. Finally, if it falls
inside the critical value band, the result would be inconclusive. Once cointegration

8. BIJ is confirmed, the long-run relationship between the RP or LR and macroeconomic
18,1 variables using the selected ARDL models are estimated. The last step of ARDL is to
estimate the associated ARDL ECM. Finally, to ascertain the goodness of fit of the
selected ARDL model, the diagnostic and the stability tests are conducted. The structural
stability test is conducted by employing the cumulative sum of recursive residuals
(CUSUM) and the cumulative sum of squares of recursive residuals (CUSUMSQ).
76
3.2 Vector error-correction model
To examine the multivariate causality relationship among the variables, the study
employs the VECM framework. The VECM regresses the changes in both the dependent
and the independent variables on lagged deviations. The multivariate causality test
based on VECM can, therefore, be formulated as follows:
DZt ¼ d þ Gi DZt21 þ · · · þ Gk DZt2k þ PZt2k þ 1t ð5Þ
where Zt is an n £ 1 vector of variables and d is an n £ 1 vector of constant. In our case,
Zt ¼ (RP, GDP, TBR, CPI, REER). G is an n £ n matrix (coefficients of the short-run
dynamics), P ¼ ab0 where a is an n £ 1 column vector (the matrix of loadings)
represents the speed of short-run adjustment to disequilibrium and b0 is an 1 £ n
cointegrating row vector (the matrix of cointegrating vectors) indicates the matrix of
long-run coefficients such that Yt converge in their long-run equilibrium. Finally, 1t is an
n £ 1 vector of white noise error term and k is the order of autoregression.
A test statistic is calculated by taking the sum of the squared F-statistics of G and
t-statistics of P. The multivariate causality test is implemented by calculating the
F-statistics (Wald-test) based on the H0 that the set of coefficients (G) on the lagged
values of independent variables are not statistically different from zero. If the H0 is not
rejected, then it can be concluded that the independent variables do not cause the
dependent variable. On the other hand, if P is significant (that is different from zero)
based on the t-statistics, then both the independent and dependent variables have a
stable relationship in the long run.
From equation (5), two channels of causation may be observed. The first channel is
the standard Granger test, examining the joint significance of the coefficients of the
lagged independent variables. Whereas, the second channel of causation is the
adjustment of the dependent variable to the lagged deviations from the long-run
equilibrium path, represented by the error-correction term (ECT). If the ECT is found to
be significant, it substantiates the presence of cointegration as established in the system
earlier and at the same time, it tells us that the dependent variable adjusts towards its
long-run level. From these tests, we can reveal four patterns of causal interactions
among pairs of the variables, i.e.:
(1) a unidirectional causality from a variable, say x, to another variable, say y;
(2) a unidirectional causality from y to x;
(3) a bi-directional causality; and
(4) an independent causality between x and y.
3.3 Data
In this analysis, the estimations of RP and LR are linked to the macroeconomic
variables. The macroeconomic variables employed in the study are real GDP, TBR,

9. inflation rate (CPI), and REER. The data are extracted from various issues of the Ministry The viability
of Finance (1990-2006), Malaysia’s Property Market Reports, the International Financial
Statistics published by International Monetary Fund (1990-2006) and various
of rental price
publications of the Bank Negara Malaysia. The study covers the period from 1990 to
2006. Except for LR and TBR, all other variables are transformed into natural logarithms.
For RP, the study uses the rental rate for the single storey houses as the proxy for the
property rental rate due to the unavailability of the aggregate housing rental index for 77
Malaysia and the heterogeneity of the houses in Kuala Lumpur.
4. Results and discussions
4.1 Results of ARDL approach
In estimating the short- and long-run relationships between the RP/LR and
macroeconomic variables, we need to determine the lag length of the first-differenced
variables. Bahmani-Oskooee and Bohl (2000) have shown that the results of this first
step are usually sensitive to the lag length. To verify this, in line with Bahmani-Oskooee
and Ng (2002), we impose the maximum lag length of two, due to small sample size
employed in this study. We then computed the F-statistics for the joint significance of
lagged levels of variables as in equations (1) and (2). Based on the significant F-statistics
of the long-run estimates, we chose lag length ¼ 1 for the model containing RP as the
dependent variable and lag length ¼ 0 for the model containing LR. As evidenced in
Table I, the computed F-statistics for both models suggest that there are cointegrating
relationships among the selected variables at the selected lag length. These results
confirmed that the inclusion of the selected lags into our models is justified.
The next step involves estimating the models using the appropriate lag length based
on the AIC. As shown in Table II, the results for the model involving the RP show that RP
is significantly affected by TBR and GDP in the long run. This finding seems to be
consistent with the fundamental theory of demand that income and substitution effects
influence the RPs for the properties or houses. The TBR is regarded as an alternative
investment for buying a house. An increase in the TBR negatively affects the RP such
that when interest rate rises, the return to investment in interest-bearing instrument
increases, thus reducing the demand for houses. On contrary, the increase in income
(GDP) signifies the increase purchasing power and thus increases the demand for houses
and in turn, creating an upward pressure on the RP.
When LR is used as the dependent variable, the TBR and REER are significantly
affecting the LR. The significance of TBR in influencing LR suggests the importance of
interest rate in determining the level of LR which is consistent with the common practice
of the commercial banks in Malaysia. With regard to the importance of REER
Lag length Rental price Lending rate
0 3.6073 * 4.2146 *
1 4.7462 * * 1.6302
2 0.48559 1.2780
Notes: F-statistics falls above the *95 and * *99 percent upper bounds; the relevant critical value Table I.
bounds are taken from Narayan’s (2004) Appendices A1-A3 for case II: with a restricted intercept and F-statistics for testing
no trend; number of regressors ¼ 4. They are 4.280-5.840 at the 99 percent significance level, 3.058- the existence of
4.223 at the 95 percent significance level and 2.525-3.560 at the 90 percent significance level long-run equation

10. BIJ in influencing the LR, this finding suggests that in Malaysia, being a small and open
economy, is also affected by the external factors. As suggested by the results, the LR is
18,1 sensitive to changes in the external economy, while the RP is more reflective of changes
in the domestic economy.
4.2 Results of bi-variate causality analysis
78 The bi-variate Granger causality analysis shows the short-run causality between two
variables in a system. As shown in Table III, the results indicate that there is no
significant nexus of causality involving RP in the short run. In contrast, for the LR, there
is a significant unidirectional causality from GDP to LR at the 10 percent level. For the
macroeconomic variables, significant causality runs from GDP to TBR and GDP to CPI
where the joint F-statistic is significant at the 5 percent level for both cases. The result
also shows a significant F-statistic at the 10 percent level from CPI to REER.
4.3 Results of multivariate causality analysis
The multivariate analysis allows us to examine the transmission channel of which the
RP and LR are affected by the macroeconomic variables. The short-run causalities are
depicted by the t-statistics for the individual variables, while the long-run causalities are
indicated by the F-statistics for the ECT.
As reported in Table IV, there is no significant short-run causality running from
macroeconomic variables to RP. However, for the long-run relationship, all the
macroeconomic variables are significant in causing the RP as shown by the significant
ECT. These results re-affirmed the earlier findings based on the bi-variate Granger
causality and long-run ARDL estimates.
For LR, similar to RP, there is no significant short-run causality from the macroeconomic
variables to LR (Table V). However, contrary to RP, all the macroeconomic variables are
not significant in affecting the LR in the long run as reflected by the insignificant ECT.
The insignificance of the ECT when LR is the dependent variable instead of when RP is,
suggests that all the macroeconomic variables adjust towards long-run equilibrium when
RP is the dependent variable. This implies that in the long run, the RP is more reflective of
the fundamental economic conditions compared to the LR. In view of this, it can be further
implied that the rental rate is a better pricing mechanism for the Islamic home financing
product rather than the LR.
We then proceed to examine the stability of the long-run coefficients together with
the short-run dynamics. Following Pesaran and Pesaran (1997), we apply the CUSUM
and CUSUMSQ tests proposed by Brown et al. (1975) on both models. As highlighted
Rental price t-statistics Lending rate t-statistics
C 2.0556 1.6400 295.1572 2 4.7528 * * *
REER 0.4394 0.2957 11.4407 4.6616 * *
TBR 20.4394 * * * 27.8680 1.7870 * * 3.6785
GDP 0.8820 * * 3.4303 2.4628 0.8262
CPI 21.5750 21.7667 3.0562 0.3863
2
Adj 2 R ¼ 0.9790; D 2 W ¼ 2.7485 Adj 2 R 2 ¼ 0.9344; D 2 W ¼ 2.1326
Table II.
Long-run ARDL Notes: Significance at: *10, * *5 and * * *1 percent levels; D-W denotes Durbin-Watson test for
model estimates autocorrelation

11. The viability
Null hypothesis F-statistic Probability
of rental price
TBR does not Granger Cause RP 1.31056 0.2682
RP does not Granger Cause TBR 1.04619 0.3207
CPI does not Granger Cause RP 0.10092 0.7546
RP does not Granger Cause CPI 0.01124 0.9168
GDP does not Granger Cause RP 0.02443 0.8776 79
RP does not Granger Cause GDP 0.04177 0.8405
REER does not Granger Cause RP 0.52895 0.4769
RP does not Granger Cause REER 0.68907 0.4180
TBR does not Granger Cause LR 0.01778 0.8955
LR does not Granger Cause TBR 2.15550 0.1603
CPI does not Granger Cause LR 0.21804 0.6465
LR does not Granger Cause CPI 2.45005 0.1359
GDP does not Granger Cause LR 4.44288 0.0502 *
LR does not Granger Cause GDP 0.07992 0.7808
REER does not Granger Cause LR 0.93279 0.3477
LR does not Granger Cause REER 0.15086 0.7025
CPI does not Granger Cause TBR 1.25117 0.2789
TBR does not Granger Cause CPI 0.35271 0.5604
GDP does not Granger Cause TBR 4.76818 0.0433 * *
TBR does not Granger Cause GDP 0.00379 0.9516
REER does not Granger Cause TBR 1.39987 0.2530
TBR does not Granger Cause REER 0.05886 0.8112
GDP does not Granger Cause CPI 7.89342 0.0121 * *
CPI does not Granger Cause GDP 1.51753 0.2348
REER does not Granger Cause CPI 0.30456 0.5882
CPI does not Granger Cause REER 3.55251 0.0767 *
REER does not Granger Cause GDP 0.12581 0.7272
GDP does not Granger Cause REER 0.90162 0.3557 Table III.
Results of pair-wise
Note: Significance at: *10 and * *5 percent levels, respectively granger causality tests
by Bahmani-Oskooee and Ng (2002), the CUSUM and CUSUMSQ tests employ the
cumulative sum of recursive residuals based on the first set of observations and is
updated recursively and plotted against the break points. If the plots of the CUSUM and
CUSUMSQ statistics are found to be within the critical bounds of 5 percent level,
the H0 that all coefficients in the ECMs as in equations (3) and (4) are stable cannot be
rejected. On the other hand, if the lines are found to be crossed, the H0 of coefficient
constancy can therefore be rejected at 5 percent significance level. Based on the
graphical representations for CUSUM and CUSUMSQ tests for both models, the results
indicate no evidence of any significant structural instability[1].
5. Conclusion and recommendations
In efforts to determine the suitability of the RP to replace the conventional LR to
benchmark Islamic home financing product, this study compares two models comprising
the RP and LR as the dependent variables and selected macroeconomic variables, namely
GDP, TBR, CPI, and REER as the independent variables, and analyses the short- and
long-run dynamics among these variables. Based on the long-run ARDL estimates,
the study shows that the RP is significantly affected by the interest rate and income level,
while the LR is significantly affected by the interest rate and exchange rate.

12. 80
BIJ
18,1
Table IV.
rental price
causality tests for
Results of multivariate
Independent variables
Dependent Probabilities
variables DRP DCPI F-statistics DGDP F-statistics DREER F-statistics DTBR F-statistics ECTt2 1 for t-statistics Diagnostic tests
2
DRP – 0.1416 0.7123 0.2202 0.6461 0.8158 0.3817 0.1428 0.7112 2 0.6230 * * 2 2.1435 R -adj. ¼ 0.0370; DW ¼ 2.3232
DGDP 0.4046 0.5350 0.6459 0.4350 2 0.3932 0.5407 0.4110 0.5318 8.1306 0.6366 R 2-adj. ¼ 0.1588; DW ¼ 1.9354
DREER 0.0023 0.9626 0.6299 0.4406 0.1594 0.6957 2 0.0004 0.9835 2 0.4309 2 0.1564 R 2-adj. ¼ 0.0091; DW ¼ 1.8309
DTBR 0.4982 0.4919 4.5848 0.0503 * * 6.7318 * * 0.0212 0.0480 0.8298 2 9.4179 1.5931 R 2-adj. ¼ 0.3362; DW ¼ 2.2467
Notes: Significance at: * * *1, * *5 and *10 percent levels; ECTt2 1 is derived by normalizing the cointegrating vectors on the dependent variables, producing residual r; by
imposing restriction on the coefficients of each variable and conducting Wald test, we obtain F-statistics for each coefficient in all equations; DW denotes Durbin-Watson test
for autocorrelation

13. Independent variables
Dependent Probabilities
variables DLR F-statistics DCPI F-statistics DGDP F-statistics DREER F-statistics DTBR F-statistics ECTt2 1 for t-statistics Diagnostic test
2
DLR – 0.0002 0.9901 2.5145 0.1351 0.3897 0.5425 0.3294 0.5751 20.1192 20.3284 R -adj. ¼ 0.0444; DW ¼ 2.0583
DCPI 2.8568 0.1131 2 0.5354 0.4764 3.0348 * 0.1034 2.7209 0.1213 20.1122 21.6156 R 2-adj. ¼ 0.4216; DW ¼ 2.4182
DGDP 0.3834 0.5457 0.7062 0.4148 – 0.3638 0.5560 0.4220 0.5264 20.0912 20.6573 R 2-adj. ¼ 0.1542; DW ¼ 1.9854
DREER 0.0008 0.9782 2.2792 0.1534 0.1210 0.7331 – 0.0112 0.9171 0.0018 0.0350 R 2-adj. ¼ -0.1035; DW ¼ 1.7094
DTBR 0.4173 0.5288 4.8643 * * 0.0446 5.6582 * * 0.0322 0.0500 0.8263 – 0.1330 0.2301 R 2-adj. ¼ 0.2603; DW ¼ 2.2744
Notes: Significance at: * * *1, * *5 and *10 percent levels; ECTt2 1 is derived by normalizing the cointegrating vectors on the dependent variables, producing residual r; by imposing
restriction on the coefficients of each variable and conducting Wald test, we obtain F-statistics for each coefficient in all equations; DW denotes Durbin-Watson test for autocorrelation
Results of multivariate
The viability
Table V.
of rental price
lending rate
causality tests for
81

14. BIJ The significance of the interest rate (TBR) in affecting the RP is consistent with the
18,1 fundamental theory of demand that income and substitution effects influence the RPs for
the properties or houses. The TBR is regarded as an alternative investment for buying a
house. An increase in the TBR negatively affects the RP such that when interest rate
rises, the return to investment in interest-bearing instrument increases, thus reducing
the demand to rent and own houses. These findings lend support to fundamental
82 economic theory which states that income and substitutes affect the demand for houses
and in turn affect the rental values. Meanwhile, the significant relationship between
TBR and LR is obvious, since by convention, all types of interest rates tend to move in
tandem overtime. The results also showed that RP is significantly affected by GDP,
while the LR is significantly affected by the exchange rate, suggesting that the RP is
more reflective of the real economic activity in the long run compared to the LR.
Next, the short-run bi-variate causality analysis suggests a significant causality
running from GDP to the LR, while there is no significant causality running from any
of the macroeconomic variables to RP. The significant short-run causality suggests
that the LR is rather volatile in the short run, while the RP is relatively stable. Moving
on to the long-run multivariate causality based on the VECM, the findings suggest
that all the macroeconomic variables are significant in causing the RP as reflected by the
significant ECT, while none of the macroeconomic variables are significant in causing
the LR in the long run. This finding suggests that the macroeconomic variables adjust
towards a long-run equilibrium when RP is the dependent variable, implying that, the
RP is a relevant indicator to predict the real economy in the long run.
The short-run stability of the RP and its long-run sensitivity to changes in the
macroeconomic conditions compared to the LR suggest that the RP could be an ideal and
relevant indicator to price the Islamic home financing product. The results of the study
has shown the merits of using the RP to benchmark the Islamic home financing product
due to its short-run stability and close relation to the macroeconomic conditions in the
long run.
The negative relationship between the RP and interest rate (TBR) in the long run has
major implications. Our results suggest that when the cost of borrowing is high, demand
for houses is low as reflected by the low RP. In the context of this study, it is obvious that
rental rate that we are proposing as a benchmark for Islamic home financing product is
truly depending on cost of borrowing which is interest rate. A major deduction from this
finding is that as long as the economic system depends on interest rate to determine the
cost of borrowing, the demand for housing will continue to depend on the interest rate.
On another note, with regard to benchmarking Islamic home financing product, our
results suggest that the RP is able to substitute the conventional interest rate which is the
current practice of benchmarking the Islamic home financing. Second, from an investor
perspective, the RP reflects the return by investing in properties, while TBR is the return
from investing in interest-earning instruments, such as bonds. In this case, TBR is
regarded as an alternative investment from buying a house.
The practical implementation of the findings, however, seems to be costly. If the
Islamic banks were to adopt the RP to benchmark their home financing products, there is
a need to have a special valuation department at the bank level to accommodate the
request for specific segment of home financing. While benchmarking on the conventional
interest rate can be conveniently applicable to all types of houses, the application of
RP to determine the price for the home financing can be cumbersome. The bank needs

15. to do a survey of the applicable RP for properties of the same characteristics to come up The viability
with the average RP for a particular property. In view of the additional costs to be of rental price
incurred, banks might hesitate to adopt this approach.
At the same time, a wide-scale implementation of adopting the RP to benchmark the
Islamic home financing product also need the support from the demand side or the
consumer side. In this regard, consumer education plays a critical role in ensuring that
the new Islamic home financing product will be well received by the customers. 83
The authority, in particular the central bank and the commercial banks need to introduce
the concept and explain thoroughly to the consumer the merits that this system is
providing. Consumer education is also important as part of Islamic banks’ risk
management. There is a possibility that the customers opt for cheaper home financing
products which are based on the conventional LR when the interest rate environment is
low. As being shown by the study, while the RP is sensitive to the macroeconomic
conditions in the long run, in the short run, it is rather stable. The time lag needed for the
RP to reflect changes in the macroeconomy could be a weak point of this system and
should be well communicated to the customers.
Despite this, it is important to caveat that thus far, we have assumed that the data for
the dependent and independent variables are measured without errors. Thus, in the
regression of rental rate on macroeconomic variables, we assume that the data are
accurate and they are not guess estimates, averaged or rounded off in any manner. When
there are errors in the dependent variable (i.e. rental values and LRs), the estimates are
unbiased as well as consistent but they are less efficient. On the other hand, if there are
errors in the independent variables (i.e. macroeconomic variables), the estimates
are biased as well as inconsistent. This study employs only the average rental values
which incorporate the heterogeneity of several types of houses in Kuala Lumpur. More
comprehensive analysis to better estimate the rental values by conducting survey to
gather for primary data in some selected geographical areas in the country is
recommended. This would therefore enhance the rigor of rental values being used as an
alternative benchmarking. Owing to the very complex nature of the housing market, the
study might provide bias and inconclusive results.
The results of this study have opened a wide variety of possible extension and areas
that warrant further research. First, a multidimensional framework which adopts the
hedonic model that includes the physical, locational, and economic attributes of the
property. This model is a well-established technique which has been widely applied for
pricing of residential housing market and commonly used in the valuation of property in
the USA and the UK. Second, for a more rigorous analysis on the rental values, we propose
the use of rental index for Malaysia to be incorporated. Unlike more developed economies,
Malaysia currently relies on rental values rather than rental index to reflect the
performance of the property sector. The construction of rental index which incorporates
the heterogeneity of the houses and better estimates the performance of the property
sector is very much needed in this quest of modeling an alternative benchmarking.
Perhaps, more deliberations and concerted efforts among the practitioners in the property
sector, bankers as well as academicians would bring more favourable results.
Note
1. To conserve space, we do not include the graphical plots in this paper. The plots are
available upon requests from the authors.

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regeneration locations”, Journal of Property Investments and Finance, Vol. 23 No. 1,
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Corresponding author
Salina H. Kassim can be contacted at: ksalina@iiu.edu.my
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