1# RECORD 1b06d5abfe32d650517ed54e21f8d196f135c6662# TITLE: Impact of light rail line on residential property values – a case of Sydney, Australia3# YEAR: 2021.0 VENUE: International Journal of Housing Markets and Analysis DOI: 10.1108/ijhma-03-2021-00334# AUTHORS: R. Abidoye; Felice Fam; O. Oshodi; A. Oyetunji5# PAGES: 46 CANDIDATE TABLE PAGES (1-based): [24]6# ABSTRACT: 7Purpose8The construction of new transportation infrastructure tends to affect the adjoining properties, economy and environment. In particular, studies have investigated the change in the value of properties due to increased access to transportation facilities. The purpose of this paper is to examine the impact of the recently completed light rail on residential property values in Sydney, Australia.91011Design/methodology/approach12Sales data of residential properties was extracted from the CoreLogic’s RP database. The hedonic pricing model was used to assess the effect of proximity to the light rail stops. Two models were developed for the announcement and construction phases of the light rail project.131415Findings16It was found that during the announcement phase, properties located within the 400 m radius from the station were 3.3% more expensive than those within the 400–800 radius. At the construction stage, the properties within the 0–400 m radius from the stops sold at 3.1% more than those within the 400–800 m radius. This study concludes that a positive relationship exists between the values of residential property and proximity to light rail stations.171819Practical implications20These findings would be useful for policymakers to develop land value capture programs for infrastructure funding and to real estate professionals and investors for investment in future transit-oriented development.212223Originality/value24Previous studies that aimed at examining the impact of light rails on residential properties values around universities are limited. Hence, this study provides a broad perspective on the impact of light rail on residential properties values.25262728===== PAGE 1 (score 4, numeric-paren 0, table-words 0, var-words 4) =====2930ternational Journal of Housing Markets and Analysi31Impact of Light Rail Line on Residential Property Values – A 32Case of Sydney, Australia 33Journal:34International Journal of Housing Markets and Analysis35Manuscript ID36IJHMA-03-2021-0033.R137Manuscript Type:38Research Paper39Keywords:40Light rail, property value, university, Residential property, Sydney, 41Australia42 43International Journal of Housing Markets and Analysis444546===== PAGE 24 (score 33, numeric-paren 14, table-words 9, var-words 1) =====4748nternational Journal of Housing Markets and Analysis49Table 4: The outputs of the two models50 51 52 Table 5: Regression Results of T1 (Announcement Phase)53Unstandardized54Standardized55Collinearity Statistics56Variable57Coefficient58Std. Error59Coefficient60P-Value61Tolerance62VIF63(Constant)64299072.6156562114.426660.00067AGE68-2.73769609.503700.000710.996720.457732.18774AREA751665.92176342.579770.228780.000***790.317803.15481BDRM8269979.4078321476.228840.152850.001***860.32873.12588BTHR8973740.1799030429.466910.103920.016**930.389942.56895PRKG9654604.6339721856.738980.076990.013**1000.7551011.325102LOT_SZ103-0.53510416.841105-0.0011060.9751070.8731081.146109P_TYPE110598145.43711160749.2891120.4931130.000***1140.2791153.587116D40011764979.19711845392.8381190.0431200.1531210.7731221.294123D80012422742.75212567041.9321260.0101270.7351280.8241291.214130* Significant at p < 0.10, ** Significant at p < 0.05, *** Significant at p < 0.001131Announcement phase is December 2012 – February 2015.132Table 6: Regression Results of T2 (Construction Phase)133Unstandardized134Standardized135Collinearity Statistics136Variable137Coefficient138Std. Error139Coefficient140P-Value141Tolerance142VIF143(Constant)144538918.94014555750.6641460.000147AGE148-1489.234149607.064150-0.0761510.015**1520.3521532.842154AREA1552816.495156394.7761570.2381580.000***1590.3021603.313161BDRM16282793.05816319860.1771640.1501650.000***1660.2581673.87168BTHR169-15883.71617026759.403171-0.0171720.5531730.4051742.467175PRKG176103929.70217720881.1981780.1051790.000***1800.7561811.322182LOT_SZ183-19.36018420.237185-0.0191860.3391870.8991881.113189P_TYPE190981064.70019151472.0851920.6511930.000***1940.2881953.467196D400197154805.10719848003.9121990.0672000.001***2010.772021.298203D800204120083.53320565190.0652060.0362070.066*2080.8792091.138210* Significant at p < 0.10, ** Significant at p < 0.05, *** Significant at p < 0.001211Construction phase is March 2015 – March 2020.212Model213R214R Square215Adjusted R Square216Std. Error of the Estimate217T12180.8992190.8082200.801221$236,520.07222T22230.9312240.8672250.864226$266,473.39227Page 23 of 45228International Journal of Housing Markets and Analysis229123022313232423352346235723682379238102391124012241132421424315244162451724618247192482024921250222512325224253252542625527256282572925830259312603226133262342633526436265372663826739268402694127042271432724427345274462754727648277492785027951280522815328254283552845628557286582875928860289290291===== PAGE 25 (score 0, numeric-paren 0, table-words 0, var-words 0) =====292293nternational Journal of Housing Markets and Analysis294Figure 1: Factors Affecting Land/Property Values295Source: Mohammad et al. (2013) 296Figure 2: Map of Scope of Study 297Source: https://transportnsw.info/298Page 24 of 45299International Journal of Housing Markets and Analysis300130123023303430453056306730783089309103101131112312133131431415315163161731718318193192032021321223222332324324253252632627327283282932930330313313233233333343343533536336373373833839339403404134142342433434434445345463464734748348493495035051351523525335354354553555635657357583585935960360