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  • 1. The current issue and full text archive of this journal is available at The viabilityDetermining the viability of rental of rental priceprice to benchmark Islamic home financing products 69 Evidence from Malaysia Rosylin Mohd Yusof, Salina H. Kassim, M. Shabri A. Majid and Zarinah HamidDepartment of Economics, Kulliyyah of Economics and Management Sciences, International Islamic University Malaysia, Kuala Lumpur, MalaysiaAbstractPurpose – The purpose of this paper is to analyze the possibility of relying on the rental rate to priceIslamic home financing product.Design/methodology/approach – By comparing two models consisting of either rental rate orlending rate (LR) and selected macroeconomic variables that could influence property value, the studyfocuses on the Malaysian data covering the period from 1990 to 2006. The study adopts severaleconometric time-series analysis, such as the autoregressive distributed lag estimates, bi-variateGranger 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 thanthe LR to price Islamic home financing product. In particular, the rental rate is found to be resilient toshort-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 theIslamic home financing product. Results of this study contribute towards finding an alternativebenchmark for the Islamic home financing product which is currently using the conventional interestrate as its benchmark.Originality/value – To the best of the authors’ knowledge, the current study is the first of its kindwhich provides empirical evidence for the possibility of relying on the rental rate to price Islamic homefinancing product.Keywords Benchmarking, Rental value, Loans, Islam, Property finance, MalaysiaPaper type Research paper1. OverviewThe Islamic banking and finance industry has witnessed a rapid and spectacularexpansion since its inception in the last three decades. The number of Islamic financialinstitutions worldwide has increased from one in 1975 to over 300 today, operating inmore than 75 countries (El-Qorchi, 2005). These financial institutions are mainlyconcentrated in the Middle East and Southeast Asia, with increasing presence in Europeand the USA. The total assets of the banking and non-banking financial services beingoffered based on shari’ah principles throughout the world are estimated to be in the Benchmarking: An Internationalrange of US$800 billion to more than US$1 trillion which is almost fivefold of its Journal Vol. 18 No. 1, 2011magnitude five years ago. Islamic finance is said to be among the fastest growing pp. 69-85financial 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 finance18,1 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 viabilityagainst the rate of profits of Islamic home financing. The study hopes to shed some light of rental priceon the possibility of equilibrium rental values as an alternative benchmark against theconventional interest rates (such as LIBOR, KLIBOR, or EURIBOR). Despite the wideliterature collection on estimating, predicting, and identifying the significant variablesof property sector, studies on the relationship between the housing prices and rents andthe financial sector are still inadequate considering the vast development in the financial 71sector. In fact, studies on the link between dynamic pricing of property sector andIslamic financial system are meager. The rest of the study is organized as follows. The next section reviews the literatureon the relevant factors influencing the property rental values, the methods undertaken toempirically determine the property rental values, and the Islamic home financing asoffered by the Islamic banks in Malaysia. The following section discusses themethodology undertaken in this study including the empirical models and data issues.Subsequently, the results are presented and finally, the last section concludes.2. Literature reviewIn 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 mentionedin the existing literature. Subsequently, it highlights the major empirical methodsundertaken to determine the rental values. The reviews on these aspects of rental valueswould provide the basis for the variables included and method employed in this study.2.1 Factors determining property rental valuesVoluminous studies have been conducted to model, predict, and forecast rental values ofvarious types of property in both the developed and developing economies. Studies ondetermination of property rental rate establish the attributes most relevant ininfluencing property prices and incorporate these attributes in the pricing equation.Indeed, quantifying property rental is definitely a challenging task due to the complexnature of the housing market. Several factors have potential effects on the value of the property, leading to thedetermination of its RPs. Most studies group these possible factors into major categoriesof attributes that later can be quantified. Subsequently, these variables are employed inthe property valuation and being determined their significance in influencing theproperty prices. For instance, Linz and Behrmann (2004) provide three characteristics ofthe factors determining house prices, namely physical, locational, and generally pricevariables characteristics. Day (2003) categorizes the various attributes of housing intostructural, accessibility, neighborhood, and environmental characteristics. Meanwhile,Can (1990) highlights the importance of neighborhood characteristics in determiningRPs 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 ofbedrooms, number of bathrooms, floor area, and age of property; demographiccharacteristics, such as median household incomes, crime rate, and cultural attractions;policy-specific characteristics, such as rent regulations and rent subsidies; andamenities/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 other18,1 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 viabilityMethods to quantify and determine the property rental values can broadly be divided of rental priceinto two approaches: the hedonic pricing model (HPM) and more recently, theeconometric analysis. The HPM analysis is a statistical technique which can be applied toa series of property values, together with their associated characteristics to identify andquantify 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 73for pricing of residential housing market and commonly used in the valuation of propertyin the USA and the UK. Further extension of the HPM results in the automated valuationmodel adopted by Ibrahim et al. (2005). In estimating the housing price for resale marketin 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 aresignificant determinants of the Housing Development Board resale flats’ price. More recent approaches in determining property rental rates involve the applicationsof sophisticated econometric analysis. This involves the adoption of various econometricanalyses ranging from the simple ordinary least square (OLS) analysis to the vectorerror-correction model (VECM), variance decompositions analysis, and impulseresponse functions. These analyses are intended to examine the dynamic causalityamong the variables and to capture the relative strength of the causality among thevariables beyond the sample period. In other words, while the application of the OLSanalysis involves testing the explanatory power of the identified determinants on theproperty rental rate, the other econometric analysis helps to provide further details onthe dynamic relationships among the variables. For example, Chaplin (1996) seeks todemonstrate the importance and possible value of the procedure of modeling, predicting,and forecasting commercial rents in office, industrial and retail markets in Great Britainfor the period 1986-1995. By employing the Pesaran et al. (1996) and VAR estimation, thestudy concludes that the theory appears to be a better indicator of the “correct” modelstructure than maximizing the historic fit. Meanwhile, White et al. (2000) and Ooi (2000)employ time series technique and censored regression analysis to model property rentsin both Scotland and in the UK. In particular, White et al. (2000) compare and contrast thedistinctiveness and commonality of the three main sectors of the property market, andOoi (2000) links stock prices of the property sector to several macroeconomic variables. For the emerging economies, studies conducted on the rental values of the propertymarkets mainly focused on the modeling, estimating, and predicting rental values forthe property sector. By employing regression analysis, Hui et al. (2007) explore therelationship between market positioning and rents of retail facilities in Hong Kong forthe period 1997-2003. In identifying the macroeconomics determinants of privatehousing in Singapore, Kim and Cuervo (1999) provide empirical evidence that housingprice in Singapore is cointegrated with real GDP, prime lending rate (LR), and privatehousing starts.2.3 Islamic home financing in Malaysia – as practiced by Bank Islam Malaysia BerhadThe Islamic financial system in Malaysia is enjoying a rapid and spectacular growth inthe last three decades. The first full-fledged Islamic bank in Malaysia – BankIslam Malaysia Berhad (BIMB) which was established since 1983, continues to poiseitself 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 financial18,1 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, there74 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 viabilityparsimonious model (selecting the smallest lag length), whereas the AIC and adjusted R 2 of rental priceare known for selecting the maximum relevant lag length. This study reports the modelsbased on these criteria. Finally, the ARDL approach provides robust results for a smallersample 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 ð2Þwhere RP and LR are the rental price and lending rate, respectively, while themacroeconomic variables employed are real GDP, interest rate (three-month TBR), andconsumer price index (CPI). Considering the high degree of openness of the Malaysianeconomy, 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 modelthrough a simple linear transformation (Banerjee et al., 1993). The ECM integrates theshort-run dynamics with the long-run equilibrium, without losing the long-runinformation. The error-correction representation of the ARDL models can be written asfollows: 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 jtThe terms with the summation signs in the above equation represent the error-correctiondynamics, while the second part (terms with ns where s ¼ 1, 2, . . . , 5) corresponds to thelong-run relationship. In the ECM model, the null hypothesis (H0: n1 ¼ n2 ¼ n3 ¼ n4 ¼ n5 ¼ 0), whichindicates the non-existence of the long-run relationship, is tested against the existence of along-run relationship. The calculated F-statistics of the H0 of no cointegration iscompared with the critical value tabulated by Narayan (2004). If the computed F-statisticfalls 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 fallsinside 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 macroeconomic18,1 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 viabilityof Finance (1990-2006), Malaysia’s Property Market Reports, the International FinancialStatistics published by International Monetary Fund (1990-2006) and various of rental pricepublications of the Bank Negara Malaysia. The study covers the period from 1990 to2006. 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 theproperty rental rate due to the unavailability of the aggregate housing rental index for 77Malaysia and the heterogeneity of the houses in Kuala Lumpur.4. Results and discussions4.1 Results of ARDL approachIn estimating the short- and long-run relationships between the RP/LR andmacroeconomic variables, we need to determine the lag length of the first-differencedvariables. Bahmani-Oskooee and Bohl (2000) have shown that the results of this firststep are usually sensitive to the lag length. To verify this, in line with Bahmani-Oskooeeand Ng (2002), we impose the maximum lag length of two, due to small sample sizeemployed in this study. We then computed the F-statistics for the joint significance oflagged levels of variables as in equations (1) and (2). Based on the significant F-statisticsof the long-run estimates, we chose lag length ¼ 1 for the model containing RP as thedependent variable and lag length ¼ 0 for the model containing LR. As evidenced inTable I, the computed F-statistics for both models suggest that there are cointegratingrelationships among the selected variables at the selected lag length. These resultsconfirmed that the inclusion of the selected lags into our models is justified. The next step involves estimating the models using the appropriate lag length basedon the AIC. As shown in Table II, the results for the model involving the RP show that RPis significantly affected by TBR and GDP in the long run. This finding seems to beconsistent with the fundamental theory of demand that income and substitution effectsinfluence the RPs for the properties or houses. The TBR is regarded as an alternativeinvestment for buying a house. An increase in the TBR negatively affects the RP suchthat when interest rate rises, the return to investment in interest-bearing instrumentincreases, thus reducing the demand for houses. On contrary, the increase in income(GDP) signifies the increase purchasing power and thus increases the demand for housesand in turn, creating an upward pressure on the RP. When LR is used as the dependent variable, the TBR and REER are significantlyaffecting the LR. The significance of TBR in influencing LR suggests the importance ofinterest rate in determining the level of LR which is consistent with the common practiceof the commercial banks in Malaysia. With regard to the importance of REERLag length Rental price Lending rate0 3.6073 * 4.2146 *1 4.7462 * * 1.63022 0.48559 1.2780Notes: 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 testingno trend; number of regressors ¼ 4. They are 4.280-5.840 at the 99 percent significance level, 3.058- the existence of4.223 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 is18,1 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 analysis78 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 2.1326Table II.Long-run ARDL Notes: Significance at: *10, * *5 and * * *1 percent levels; D-W denotes Durbin-Watson test formodel estimates autocorrelation
  • 11. The viabilityNull hypothesis F-statistic Probability of rental priceTBR does not Granger Cause RP 1.31056 0.2682RP does not Granger Cause TBR 1.04619 0.3207CPI does not Granger Cause RP 0.10092 0.7546RP does not Granger Cause CPI 0.01124 0.9168GDP does not Granger Cause RP 0.02443 0.8776 79RP does not Granger Cause GDP 0.04177 0.8405REER does not Granger Cause RP 0.52895 0.4769RP does not Granger Cause REER 0.68907 0.4180TBR does not Granger Cause LR 0.01778 0.8955LR does not Granger Cause TBR 2.15550 0.1603CPI does not Granger Cause LR 0.21804 0.6465LR does not Granger Cause CPI 2.45005 0.1359GDP does not Granger Cause LR 4.44288 0.0502 *LR does not Granger Cause GDP 0.07992 0.7808REER does not Granger Cause LR 0.93279 0.3477LR does not Granger Cause REER 0.15086 0.7025CPI does not Granger Cause TBR 1.25117 0.2789TBR does not Granger Cause CPI 0.35271 0.5604GDP does not Granger Cause TBR 4.76818 0.0433 * *TBR does not Granger Cause GDP 0.00379 0.9516REER does not Granger Cause TBR 1.39987 0.2530TBR does not Granger Cause REER 0.05886 0.8112GDP does not Granger Cause CPI 7.89342 0.0121 * *CPI does not Granger Cause GDP 1.51753 0.2348REER does not Granger Cause CPI 0.30456 0.5882CPI does not Granger Cause REER 3.55251 0.0767 *REER does not Granger Cause GDP 0.12581 0.7272GDP does not Granger Cause REER 0.90162 0.3557 Table III. Results of pair-wiseNote: Significance at: *10 and * *5 percent levels, respectively granger causality testsby Bahmani-Oskooee and Ng (2002), the CUSUM and CUSUMSQ tests employ thecumulative sum of recursive residuals based on the first set of observations and isupdated recursively and plotted against the break points. If the plots of the CUSUM andCUSUMSQ 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 berejected. On the other hand, if the lines are found to be crossed, the H0 of coefficientconstancy can therefore be rejected at 5 percent significance level. Based on thegraphical representations for CUSUM and CUSUMSQ tests for both models, the resultsindicate no evidence of any significant structural instability[1].5. Conclusion and recommendationsIn efforts to determine the suitability of the RP to replace the conventional LR tobenchmark Islamic home financing product, this study compares two models comprisingthe RP and LR as the dependent variables and selected macroeconomic variables, namelyGDP, TBR, CPI, and REER as the independent variables, and analyses the short- andlong-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 variablesDependent Probabilitiesvariables DRP DCPI F-statistics DGDP F-statistics DREER F-statistics DTBR F-statistics ECTt2 1 for t-statistics Diagnostic tests 2DRP – 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 2.3232DGDP 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 1.9354DREER 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 1.8309DTBR 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 2.2467Notes: Significance at: * * *1, * *5 and *10 percent levels; ECTt2 1 is derived by normalizing the cointegrating vectors on the dependent variables, producing residual r; byimposing 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 testfor autocorrelation
  • 13. Independent variablesDependent Probabilitiesvariables DLR F-statistics DCPI F-statistics DGDP F-statistics DREER F-statistics DTBR F-statistics ECTt2 1 for t-statistics Diagnostic test 2DLR – 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 2.0583DCPI 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 2.4182DGDP 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 1.9854DREER 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 1.7094DTBR 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 2.2744Notes: 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 imposingrestriction 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 the18,1 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 fundamental82 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 viabilitywith the average RP for a particular property. In view of the additional costs to be of rental priceincurred, banks might hesitate to adopt this approach. At the same time, a wide-scale implementation of adopting the RP to benchmark theIslamic home financing product also need the support from the demand side or theconsumer side. In this regard, consumer education plays a critical role in ensuring thatthe new Islamic home financing product will be well received by the customers. 83The authority, in particular the central bank and the commercial banks need to introducethe concept and explain thoroughly to the consumer the merits that this system isproviding. Consumer education is also important as part of Islamic banks’ riskmanagement. There is a possibility that the customers opt for cheaper home financingproducts which are based on the conventional LR when the interest rate environment islow. As being shown by the study, while the RP is sensitive to the macroeconomicconditions in the long run, in the short run, it is rather stable. The time lag needed for theRP to reflect changes in the macroeconomy could be a weak point of this system andshould be well communicated to the customers. Despite this, it is important to caveat that thus far, we have assumed that the data forthe dependent and independent variables are measured without errors. Thus, in theregression of rental rate on macroeconomic variables, we assume that the data areaccurate and they are not guess estimates, averaged or rounded off in any manner. Whenthere are errors in the dependent variable (i.e. rental values and LRs), the estimates areunbiased as well as consistent but they are less efficient. On the other hand, if there areerrors in the independent variables (i.e. macroeconomic variables), the estimatesare biased as well as inconsistent. This study employs only the average rental valueswhich incorporate the heterogeneity of several types of houses in Kuala Lumpur. Morecomprehensive analysis to better estimate the rental values by conducting survey togather for primary data in some selected geographical areas in the country isrecommended. This would therefore enhance the rigor of rental values being used as analternative benchmarking. Owing to the very complex nature of the housing market, thestudy might provide bias and inconclusive results. The results of this study have opened a wide variety of possible extension and areasthat warrant further research. First, a multidimensional framework which adopts thehedonic model that includes the physical, locational, and economic attributes of theproperty. This model is a well-established technique which has been widely applied forpricing of residential housing market and commonly used in the valuation of property inthe USA and the UK. Second, for a more rigorous analysis on the rental values, we proposethe 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 theperformance of the property sector. The construction of rental index which incorporatesthe heterogeneity of the houses and better estimates the performance of the propertysector is very much needed in this quest of modeling an alternative benchmarking.Perhaps, more deliberations and concerted efforts among the practitioners in the propertysector, 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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