1# RECORD 0893ce95f4ec80e891e9cf83dfb029d633e4a22d2# TITLE: HEDONIC REGRESSION ANALYSIS IN DETERMINING THE EFFECT OF GREEN ON HIGH RISE RESIDENTIAL3# YEAR: 2022.0 VENUE: PLANNING MALAYSIA DOI: 10.21837/pm.v20i21.11144# AUTHORS: Lizawati Abdullah; T. Mohd5# PAGES: 11 CANDIDATE TABLE PAGES (1-based): [8]6# ABSTRACT: In predicting house price, there are many influential variables, and each variable is identified as a price determinant. Theoretically, variables are divided into categories, namely locational and neighbourhood attribute, structural attribute, time attribute, and environment attribute. Green element is one of the attributes as describe under environment category. The attribute is important as other variables which significantly explained how people willing to pay intangible variable. The relationship between property price and attribute needs to be examined to understand the influence of the green element on property price. Thus, this research attempts to demonstrate the independent variables correlated to the house price, including the green variable, by using hedonic regression analysis. Hedonic regression analysis is a well-known approach in determining the relationship between two or more variables. Green element represents the green-rated obtained by the housing scheme as evidence that the building possesses sustainable characteristics. The cost of green is relatively high than a conventional building. A dataset of 934 house price transactions with 14 variables was analysed. From the analysis, it is concluded that green has a significant effect on the house price. The result was interpreted by the β coefficient of 0.065 explained in hedonic regression analysis. It signifies that green can add premium to the house price. House price results from multiple determinants represented by house attributes and the findings confirm that one of the environment attributes do give effect on the house price in Malaysia property market.789===== PAGE 1 (score 3, numeric-paren 0, table-words 1, var-words 1) =====1011 12 13 141 Senior Lecturer at Universiti Teknologi MARA Perak Branch. Email: lizaw327@uitm.edu.my 15PLANNING MALAYSIA: 16Journal of the Malaysian Institute of Planners 17VOLUME 20 ISSUE 2 (2022), Page 310 – 320 18HEDONIC REGRESSION ANALYSIS IN DETERMINING THE 19EFFECT OF GREEN ON HIGH RISE RESIDENTIAL 20 21Lizawati Abdullah1, Thuraiya Mohd2 22 231,2Department of Built Environment Studies and Technology, 24Faculty of Architecture, Planning and Surveying, 25UNIVERSITI TEKNOLOGI MARA, PERAK BRANCH, MALAYSIA 26 27 28Abstract 29 30In predicting house price, there are many influential variables, and each variable 31is identified as a price determinant. Theoretically, variables are divided into 32categories, namely locational and neighbourhood attribute, structural attribute, 33time attribute, and environment attribute. Green element is one of the attributes 34as describe under environment category. The attribute is important as other 35variables which significantly explained how people willing to pay intangible 36variable. The relationship between property price and attribute needs to be 37examined to understand the influence of the green element on property price. 38Thus, this research attempts to demonstrate the independent variables correlated 39to the house price, including the green variable, by using hedonic regression 40analysis. Hedonic regression analysis is a well-known approach in determining 41the relationship between two or more variables. Green element represents the 42green-rated obtained by the housing scheme as evidence that the building 43possesses sustainable characteristics. The cost of green is relatively high than a 44conventional building. A dataset of 934 house price transactions with 14 variables 45was analysed. From the analysis, it is concluded that green has a significant effect 46on the house price. The result was interpreted by the β coefficient of 0.065 47explained in hedonic regression analysis. It signifies that green can add premium 48to the house price. House price results from multiple determinants represented by 49house attributes and the findings confirm that one of the environment attributes 50do give effect on the house price in Malaysia property market. 51 52 53Keywords: House price, green, house variables, hedonic regression analysis 54 55 565758===== PAGE 8 (score 20, numeric-paren 0, table-words 6, var-words 4) =====5960PLANNING MALAYSIA 61Journal of the Malaysia Institute of Planners (2022) 62 63 64317 65© 2022 by MIP 66Table 5: The model 67Independent 68variables 69Unstandardized Coefficients 70t 71Sig. 72B 73Std. Error 74(Constant) 759.084 76.105 7786.147 780.000 79GEORGETOWN 80.067 81.029 822.322 83.020 84OTHER TOWN 85.097 86.022 874.346 88.000 892020 90-.131 91.024 92-5.523 93.000 942019 95-.077 96.023 97-3.347 98.001 992018 100-.051 101.027 102-1.847 103.065 1042017 105-.058 106.026 107-2.226 108.026 109TENURE 110.022 111.029 112.745 113.456 114NUMBER OF ROOMS 115-.003 116.014 117-.197 118.844 119HILLVIEW 120-.056 121.026 122-2.131 123.033 124SEAVIEW 125.155 126.023 1276.651 128.000 129FACILITIES 130-.167 131.020 132-8.374 133.000 134SECURITY 135.176 136.039 1374.529 138.000 139OCCUPANCY 140-.077 141.018 142-4.304 143.000 144DEVELOPER 145.180 146.017 14710.535 148.000 149GREEN -GBI 150.065 151.033 1521.987 153.047 154LOGSIZE 155-.080 156.020 157-3.979 158.000 159LOGLEVEL 160.016 161.010 1621.564 163.118 164LOGAGE 165.007 166.019 167.393 168.694 169a. Dependent Variable: LOGPRICE PSM 170Sources: Author’s research (2021) 171 172Table 5 shows the ∝ and 𝛽 coefficient value. Green-GBI variable 173indicates +0.065 coefficients. Other variables such as developer and security 174indicate high coefficient value of 0.180 and 0.176 respectively. All the transform 175variables that were included in the model together with the log form variable, i.e 176floor size, floor level and age of building. A linear combination form to explain 177𝛽 coefficient is as follows. 178 179LN (Price per square metre) = 1809.084 +0.067(GEORGETOWN) +0.097(OTHERTOWN) -0.131(Y_2020) 181-0.077(Y_2019) -0.051(Y_2018) -0.058(Y_2017) +0.22(TENURE) 182-0.003(ROOMS) -0.056(HILLVIEW) +0.155(SEAVIEW) 183-0.167(FACILITIES) +0.176(SECURITY) -0.077(OCCUPANCY) 184+0.180(DEVELOPER) +0.065(GREEN-GBI) -0.080(LOGSIZE) 185+0.016(LOGLEVEL) +0.007(LOGAGE) 186187188===== PAGE 9 (score 3, numeric-paren 0, table-words 1, var-words 1) =====189190Lizawati Abdullah & Thuraiya Mohd 191Hedonic Regression Analysis in Determining the Effect of Green on House Price 192 193© 2022 by MIP 194318 195From this regression analysis, the selection of the GBI certificate 196variable suggests the positive sign of the variable. This shows evidence of support 197by other literature (Evangelista et al., 2019)(Li Zhang et al., 2017)(Jayantha & 198Man, 2013)(Fesselmeyer, 2018)(Limao Zhang et al., 2018) in which the 199discussion reported that there are differences in prices if the property is green or 200GBI certified increased. The beta coefficient is 0.065 and this associates with the 201p value. Therefore, the model obviously shows there is a statistically significant 202change in value if the property is located at Timur Laut District if it is certified 203green. Out of the 18 control variables entered in the model, 13 variables were 204statistically significant. Further, it can be defining trough t values of the 205independent variables. 206 207CONCLUSION 208This paper analyses the green certificate effect on condominium price in Timur 209Laut District in Penang Island. The analysis uses database which comprises of a 210combination of several data including information from condominium 211transaction sale data. The data was compiled and extracted by NAPIC, and it is 212to be used for the purpose of valuation and research related to property. NAPIC 213is an organisation responsible to promote and provide information on property 214market in Malaysia. 215From the analysis done, it is found that green certification relatively 216gives an impact on property price. The percentage of premium if compared to 217non-certified building is about 6.5 per cent. The result is supported by a valuer 218who also estimated about 5-10 of price increment if condominium in Penang is 219green (Abdullah et al., 2018). Hence, the effect might vary according to the 220locality. Property market as well as green building should be explored differently 221in their local context. Even though most of the time green building is associated 222with high cost of construction, but the value of green is not only about cost. It 223represents the market, the demand, and the economics of the property. Value can 224be influenced by other factors. With more incentive, knowledge and promotion 225given by the government, green development can become the upcoming trend 226and demand. 227Specifically, the contributions of these findings can be explained in 228several contexts. First, this research adds “green” into the residential hedonic 229pricing literature especially from the perspective of local context. It is estimated 230that green can become a common consideration in the hedonic research paradigm. 231Next, this study encourages additional research on the effects of green 232development on housing sector. The results of this research indicated a positive 233and statistically significant premium for transactions with green as a housing 234feature. The findings from this study are parallel with most of green effect studies 235done previously. The biggest challenge here is to educate public on the awareness 236of sustainability and green development. Empirical studies have shown 237