# RECORD 461f8c8aa68c6a2b5157bc03f6d689991ee3ac64 # TITLE: Estimating the Effects of Light Rail Transit (LRT) System on the Property Values in the Klang Valley, Malaysia: A Hedonic House Price Approach # YEAR: 2013.0 VENUE: nan DOI: 10.11113/jt.v61.1620 # AUTHORS: M. Dziauddin; S. Alvanides; N. Powe # PAGES: 13 CANDIDATE TABLE PAGES (1-based): [8, 9] # ABSTRACT: This study investigates the increased value of land in the form of house prices as a result of improved accessibility owing to the construction of Light Rail Transit (LRT) systems. Kelana Jaya Line LRT system is chosen as the case study in this research. Hedonic house price modelling is employed to estimate the effects of the LRT system on the prices of the house that are located within the radius of two kilometres from the Kelana Jaya LRT stations. Selling prices, structural attributes, land use and socio-economic attributes were collected from the database of Department of Valuation and Services of Malaysia, selected maps and reports. Fifty-five factors that are likely to influence a house price were identified and used to estimate the overall effects of the LRT system on it. However, only significant variables were included in the final deliberation and these were identified by using correlation analysis and modified step-wise procedures. The outcome of this study shows a positive relationship between the existence of the LRT system and house prices. In other words, properties that are located within close proximity to the LRT station are valued more than properties that are located further away. ===== PAGE 1 (score 11, numeric-paren 0, table-words 0, var-words 11) ===== 61:1 (2013) 35–47 | www.jurnalteknologi.utm.my | eISSN 2180–3722 | ISSN 0127–9696 Full paper Jurnal Teknologi Estimating the Effects of Light Rail Transit (LRT) System on the Property Values in the Klang Valley, Malaysia: A Hedonic House Price Approach Mohd Faris Dziauddina*, Seraphim Alvanidesb, Neil Powec aDepartment of Geography and Environment, Faculty of Human Sciences, Universiti Pendidikan Sultan Idris, 35900 Tanjong Malim, Perak, Malaysia bFaculty of Humanities and Social Sciences, Newcastle University, Newcastle upon Tyne, NE1 7RU, United Kingdom cSchool of Architecture Planning and Landscape, Claremont Tower Newcastle University Newcastle upon Tyne NE1 7RU, United Kingdom *Corresponding author: fdziauddin@yahoo.com Article history Received :19 September 2012 Received in revised form :4 December 2012 Accepted :12 February 2013 Graphical abstract Abstract This study investigates the increased value of land in the form of house prices as a result of improved accessibility owing to the construction of Light Rail Transit (LRT) systems. Kelana Jaya Line LRT system is chosen as the case study in this research. Hedonic house price modelling is employed to estimate the effects of the LRT system on the prices of the house that are located within the radius of two kilometres from the Kelana Jaya LRT stations. Selling prices, structural attributes, land use and socio-economic attributes were collected from the database of Department of Valuation and Services of Malaysia, selected maps and reports. Fifty-five factors that are likely to influence a house price were identified and used to estimate the overall effects of the LRT system on it. However, only significant variables were included in the final deliberation and these were identified by using correlation analysis and modified step-wise procedures. The outcome of this study shows a positive relationship between the existence of the LRT system and house prices. In other words, properties that are located within close proximity to the LRT station are valued more than properties that are located further away. Keywords: Light rail transit systems (LRT); property values; hedonic price model; Klang Valley Abstrak Kajian ini menyiasat peningkatan nilai tanah dalam bentuk harga rumah hasil daripada peningkatan ketersampaian menerusi pembinaan sistem Transit Aliran Ringan (TAR). Sistem TAR laluan Kelana Jaya dipilih sebagai kajian kes dalam penyelidikan ini. Model harga rumah hedonik digunakan untuk menganggarkan kesan sistem TAR pada harga rumah yang terletak dalam lingkungan dua kilometer dari stesen TAR Kelana Jaya. Harga jualan, ciri-ciri fizikal rumah, lokasi dan sosio-ekonomi dikumpul daripada pangkalan data Jabatan Penilaian dan Perkhidmatan Malaysia, peta terpilih dan laporan. Lima puluh lima faktor yang mungkin mempengaruhi harga rumah telah dikenal pasti dan digunakan bagi menganggarkan kesan keseluruhan sistem TAR di atasnya. Walau bagaimanapun, hanya pemboleh ubah yang signifikan secara statistik telah dimasukkan dalam perbincangan akhir dan ini telah dikenal pasti dengan menggunakan analisis korelasi dan prosedur ‘step-wise’ yang telah diubah suai. Hasil kajian ini menunjukkan terdapat hubungan positif antara kewujudan sistem TAR dan harga rumah. Dalam erti kata lain, hartanah yang terletak dalam jarak yang hampir dengan stesen TAR mempunyai nilai yang lebih tinggi berbanding hartanah yang terletak jauh. Kata kunci: Sistem transit aliran ringan (TAR); nilai hartanah; model harga hedonik; Lembah Klang © 2012 Penerbit UTM Press. All rights reserved. 1.0 INTRODUCTION Rail transit systems, namely heavy and light transit systems are a public good and have been seen as serving a number of purposes and producing a number of public benefits, particularly to those areas that have been served by high service quality rail transit systems. These public benefits can be categorised into two; direct and indirect benefits. The direct benefits of rail transit systems are defined in terms of improved regional mobility, consumer savings, vehicle cost savings, energy conservation, improved mobility for non-drivers and disadvantaged groups, congestion reduction, roadway cost savings, increased traffic safety, and pollution emission reductions (see, for example, Litman, 2003, 2004a, 2004b, 2007, 2012; Banister and Banister, 1995; Knowles, 1996; Pucher, 2004). Alongside these direct benefits, the provision of high service quality of public transportation such as rail transit systems has also potentially influenced local land use and increased local property values (indirect benefits), particularly ===== PAGE 8 (score 49, numeric-paren 0, table-words 12, var-words 18) ===== 42 Mohd Faris Dziauddin, Seraphim Alvanides & Neil Powe / Jurnal Teknologi (Sciences & Engineering) 61:1 (2013) 35–47 α10LnSECONDARYSCHi + α11LnPRIMARYSCHi + α12LnPARKi + α13LnRECREATIONi + α14LnHOSPITALi + α15LnINDUSTRYi + α16LnMALAYi + εi (4) where the actual-distance to a station is given by NETDIST: the network-distance from the property to an LRT station measured in metres. The results from the hedonic house price models are presented in a standard format. This standard format is shown in Table 4. Note that the results from straight- line-distance and network-distance were separately displayed. As can be seen in Table 4, the results from the hedonic house price models include five attributes which represent the predictor, coefficient, standard error, t-value and VIF. The predictor is the variable that has been used to measure the house prices and it has included focused, structural, locational and socio-economic variables. The second column provides coefficients which represent the prices of each predictor used in the model. The standard errors of the coefficients are presented in the third column. The fourth column gives us the results of a t-value of the predictor. The greater t-value of the predictors implies that the greater its function in determining a house price. The multicollinearity level of the predictors used in the final model is shown in the final column, namely the variance inflation factor (VIF). Meanwhile, the adjusted R2 for the number of variables in the model is shown below each model. Table 2 Definition of variables and data sources Table 3 Characteristics of dependent and independent variables Vector Predictor Mean Standard deviation Minimum Maximum Dependent variable House price transactions (P) Independent variables Focus variables (R) Structural variables (S) Locational variables (L) Socio-economic and ethnic variables (N) LOGSELLING LOGSTRDIST LOGNETDIST LOGTIMESAVINGS LOGFLRAREA LOGBEDS TYPTRRD TYPSEMID TYPDETCH TYPCONDO LOGCBD LOGPRIMARYSCH LOGSECONDARYSCH LOGCOMMERCIAL LOGPARK LOGHOSPITAL LOGRECREATION LOGINDUSTRY LOGMALAY 12.49 6.73 7.15 2.15 6.17 1.07 0.46 0.01 0.12 0.19 9.08 6.65 6.65 6.62 6.94 7.87 8.67 7.19 3.73 0.59 0.64 0.65 1.14 0.68 0.26 0.49 0.12 0.32 0.39 0.29 0.82 0.64 0.99 2.27 0.89 0.49 0.55 0.24 10.82 4.33 4.33 -4.07 4.79 0.00 0.00 0.00 0.00 0.00 7.91 1.45 2.73 2.27 0.52 4.25 7.09 4.29 3.46 14.43 7.64 8.08 3.38 8.54 1.79 1.00 1.00 1.00 1.00 9.43 7.85 7.93 7.94 9.04 9.04 9.44 7.94 4.49 Vector Predictor Operational definition Units Data source Dependent variable House price transactions (P) Independent variables Focus variables (R) Structural variables (S) Locational variables (L) Socio-economic variables (N) SELLING STRDIST NETDIST TIMESAVINGS FLRAREA BEDS TYPTRRD TYPSEMID TYPDETCH TYPCONDO CBD PRIMARYSCH SECONDARYSCH COMMERCIAL PARK HOSPITAL RECREATION INDUSTRY MALAY House price transactions Straight-line-distance Network-distance Travel time savings to CBD Floor area Number of bedrooms Terraced house Semi-detached house Detached house Condominium Proximity to CBD Proximity to primary schools Proximity to secondary schools Proximity to commercial areas Proximity to parks Proximity to hospitals Proximity to recreational areas Proximity to industrial areas Proportion of Malays Malaysia Ringgits (MYR) Metre Metre Minutes Square foot Number Dummy (0 or 1) Dummy (0 or 1) Dummy (0 or 1) Dummy (0 or 1) Metre Metre Metre Metre Metre Metre Metre Metre Proportion of Malays (for each Mukim) Department of Valuation and Property Services of Malaysia (DVPA) Calculated using GIS Calculated using GIS Calculated using GIS DVPA DVPA DVPA DVPA DVPA DVPA Calculated using GIS Calculated using GIS Calculated using GIS Calculated using GIS Calculated using GIS Calculated using GIS Calculated using GIS Calculated using GIS Malaysia Census 2000 ===== PAGE 9 (score 68, numeric-paren 0, table-words 15, var-words 23) ===== 43 Mohd Faris Dziauddin, Seraphim Alvanides & Neil Powe / Jurnal Teknologi (Sciences & Engineering) 61:1 (2013) 35–47 Table 4 The traditional hedonic specification of Model 3 and Model 4 Vector Predictor Straight-line distance (Model 3) Network distance (Model 4) Coefficient Standard error t-value VIF Coefficient Standard error t-value VIF Focus variables Structural variables Locational variables Socio-economic and ethnic variables Constant STRDIST NETDIST TIMESAVINGS FLRAREA BEDS TYPTRRD TYPSEMID TYPDETCH TYPCONDO CBD PRIMARYSCH SECONDARYSCH COMMERCIAL PARK HOSPITAL RECREATION INDUSTRY MALAY 35667.67 -10.56 2019.25 310.63 31485.34 71160.14 139696.87 173145.42 184622.86 -17.96 26.25 -11.11 -10.13 -1.13 3.40 -4.81 28.22 3673.45 1.74 4.95 296.16 13.09 3922.05 9509.88 22626.95 17052.20 10165.74 1.52 3.94 4.66 3.28 0.31 1.29 1.70 3.72 613.52 18.85 -2.16 6.82 24.07 8.01 7.44 6.19 10.16 18.23 -11.75 6.75 -2.40 -3.17 -3.33 2.64 -2.84 7.74 5.99 1.85 3.27 6.23 1.87 4.33 1.40 6.02 3.02 3.18 1.93 1.41 2.41 1.24 3.27 5.87 1.70 7.61 43739.2 -6.61 2046.17 312.65 31594.29 72143.93 138713.09 170849.93 184950.79 -18.61 26.57 -11.11 -9.54 -1.13 3.58 -4.65 26.91 3520.07 1.76 3.03 296.15 13.09 3922.04 9509.88 22626.95 17052.20 10165.73 1.55 3.93 4.66 3.28 0.31 1.28 1.70 3.71 605.85 18.89 -2.31 6.87 23.99 8.06 7.61 6.15 10.02 18.25 -11.92 6.79 -2.36 -2.94 -3.36 2.73 -2.76 7.23 5.83 1.70 3.30 6.35 1.87 4.29 1.40 6.03 3.03 3.33 1.93 1.41 2.50 1.24 3.32 5.80 1.80 7.37 R2 (adj.) = 78.2 per cent R2 (adj.) = 78.2 per cent 5.0 RESULTS: HEDONIC PRICE MODELS Table 4 presents the summary of the parameter estimates of Models 3 and 4 – the basic model for straight-line-distance and network-distance in estimating the effect of the LRT system on house prices. To reduce the complication of the interpretation process, continuous independent variables are deviated around their means. In other words, the models are estimated with respect to the average property size of 651.35 square feet. The results of both models show that most of predictor variables that have been used to estimate the LRT-house prices relationship produce correct signs (positive or negative) as theoretically expected, except for primary school and significance level of 0.01 and 0.05. In terms of the R2 statistic, the model explains the variation of the house price within a two-kilometre radius from an LRT station in the Klang Valley reasonably well for both straight-line and network-distance model with 78.2 per cent. The result of hedonic model also shows that the parameter estimates of several independent variables are found to be slightly different between these two models. (1) Value of the LRT system; An examination of Table 4 shows that the parameter estimates of focus variables are found to be statistically significant with the correct sign. Evidently, it can be seen that the house price decreases as we move further away from the LRT station – for every metre away from an LRT station, the value of a residential property decreases by MYR10.56 (straight-line-distance model) and by MYR6.61 (network-distance model). This implies that a residential property located anywhere within 1,000 metres of an LRT station would generally be valued at an average rate between MYR10,560 (straight-line-distance model) and MYR6,610 (network-distance model) more than a residential property located outside of this distance. As for the magnitude of effect, the LRT system has significantly contributed at -2.16 (t-value of straight-line- distance) and -2.31 (t-value of network-distance) in determining the house price in the study area. It is notable that the t-value of the parameter estimate of straight-line- distance measures is slightly higher than that of network- distance measures. (2) Value of structural attributes; The result of the estimation indicates that the most influential factor in determining the house price is floor area. For every square-foot increase in the floor area, the house price increases by an average of around MYR310.63 for straight-line-distance model. Similarly, an increase around MYR312.65 is deduced for network-distance model. The greater magnitude of the effect of floor area was expected since floor areas are always associated with the size of the property – this is consistent with the most of the hedonic house price literature. In the case of number of bedrooms, a potential buyer has to pay on average MYR31,000 for one additional bedroom of a property for both models. As for property- type attribute, its role is only to indicate the price for different types of housing in the study area. As to be expected, the price for a detached or semi-detached house would be higher than a terraced house. After examining the results for both models, the conclusion that can be made is; there are significant differences in price between different types of housing. In other words, implicit prices of detached (MYR170,000), semi-detached (MYR139,000), condominium (MYR184,000) and terraced houses (MYR71,00) for both models should all have reflected some value-added by the attributes that they possess. In the case a condominium property, the explanation that could be given is; since a condominium property has various facilities such as swimming pool, sauna, club house, sport facilities, landscape and security, it can be expected that a condominium property has a positive effect on house price. The same thing can be expected from a detached property, since it stands exclusively on its own and normally it comes with a bigger plot of land with lower density – these attributes will definitely contribute to the positive effect on house price. ===== PAGE 10 (score 15, numeric-paren 0, table-words 0, var-words 15) ===== 44 Mohd Faris Dziauddin, Seraphim Alvanides & Neil Powe / Jurnal Teknologi (Sciences & Engineering) 61:1 (2013) 35–47 (3) Value of locational attributes; The results of the estimation have also confirmed the importance of locational attributes in determining house prices. Distance from the CBD is significant with the correct sign of the estimated parameter for both models. The model suggests that a rent gradient for the Klang Valley of around MYR17.96 per metre for the straight-line-distance model and MYR18.61 per metre for the network-distance model. This can be interpreted as a distance decay relationship between land rent and the distance from the CBD. For every metre away from the CBD (Kuala Lumpur city centre), the property value decreases around MYR17.96 and MYR18.61 for straight- line and network-distance respectively. The other locational attributes that also show significant contribution to the house price is the proximity to commercial areas. The interpretation that can be made is that for every metre away from the commercial areas, house price decreases at the rate of MYR10.15 (straight-line-distance model) and MYR9.54 (network-distance model). The parameter estimates for proximity to a secondary school also shows statistical significance with the anticipated sign. The implicit price for proximity to secondary schools suggests that for every metre away from secondary schools, house price would generally decrease by about MYR11 for both models. With regards to the proximity to primary schools, the result is statistically significant, however, with unexpected signs. The model suggests that for every metre away from primary schools, there is a degree of increment in housing value. It clearly shows that on average, for every metre away from primary schools, the house price experiences an increase by about MYR26 for both straight- line and network-distance models. This supports the study on the impact of school on house prices carried out by Cheshire and Sheppard (2004). The logical reason for the decreasing house value by being located too close to the primary schools is due to negative externalities such as noise and traffic congestion that can be associated with the existence of primary schools. The other possible reason is that house price would normally respond positively (increase) when located in the neighbourhood where the school is regarded to perform well in major examinations (in practice most of the major examinations take place in secondary schools). In other words, the quality of schools assessed by their performance in major examinations is found to be more important than just being located near to an ordinary school. Thus, it is reasonable to expect house prices to increase for every metre away from primary schools after considering the explanation that has been given above. Proximity to parks is also found to be statistically significant in determining house prices with the expected signs. The parameter estimates for proximity to parks is MYR1.13 per metre (rent gradient) for both models. Similarly, proximity to recreational areas is also found to be significant in determining house prices. For the proximity to recreational areas, the parameter estimates indicate that for every metre away from recreational areas, the house price reduces at the rate of MYR4 for both models. In terms of proximity to health centres that is hospitals, the parameter estimates suggest that house price increases by about MYR3 for straight-line and network- distance models of every metre away from this amenity. Continuing the observation of the study, the price-distance function for proximity to industrial areas shows positive signs. The estimated parameter indicates that there is an increase in house price at the rate of MYR28.22 (straight- line-distance model) and MYR26.91 (network-distance model) for every metre away from industrial areas. The rationale behind this observation is that being located adjacent to industrial areas suggests that residential property is prone to suffer from traffic congestion and air and noise pollution. Additionally, the norm of industry workers to populate surrounding areas close to factories for example, result in social problems and hence, brings the perceived value of properties in the area down. Therefore, it is very understandable at least in the context of this study (where house price increases for every metre away from industrial areas) since people tend to avoid negative externalities caused by the industry, the associated community and its activities. (4) Neighbourhood variables; The only significant factor of socio-economic and ethnic attributes in determining house prices in this study is the percentage of Malay ethnic (Malay proportion). The model suggests that if a property is located in the areas with a higher percentage of Malay ethnics, it would result in an increase in housing price with an average of MYR3,600 (straight-line-distance model) and MYR3,500 (network-distance model). The contribution of the Malay ethnic towards higher house prices in these areas can be reasoned out by examining the history of these affected areas. Unlike today’s well spread- out city of Kuala Lumpur or CBD of the Klang Valley, over 40 years ago these areas were considered to be suburban areas for the Klang Valley. Those days, these areas were considered to be affordable among the ethnic Malays while the actual downtown of the city centre was dominated by high-income dwellers. However, due to the rapid urbanisation in the Klang Valley these areas that were once considered as suburban areas have inevitably become an integral part of the city of Kuala Lumpur that are well connected by the roads and highways with no distinction of differences. These newly integrated demographically Malay dominated areas have quickly emerged as attractive housing estates among middle and high income Malays. The house price difference can easily be explained through the gain of premium value for the mentioned areas. Consequently, observation can be made today that the more affluent second and third generation of the Malays would have the preference to purchase a house in these Malay dominated areas since they are willing to pay a higher price in order to be located in these areas with perceived advantages of becoming an integral part of the city and belonging to a community of the same race that they are familiar with. 6.0 CONCLUSIONS AND POLICY IMPLICATIONS The study in the Klang Valley was carried out in order to provide empirical evidence for the estimation of the negative rent gradient with respect to the trade-off between the improvement of accessibility to the CBD and house prices. Hedonic house price models was employed – the role of hedonic house price models is to provide empirical evidence of a negative land rent gradient for a unit of house from an LRT station. In other words, it has been assumed that house prices would decrease for every metre further away from an LRT station. The estimating of the effects of the LRT system on house prices using hedonic house price models in this study reveals a number of key findings. Firstly, the hedonic house price models estimate that houses located within two kilometres of an LRT station in the Klang Valley decrease in price as the distance