1# RECORD d8941587e0ea17530b8486a47a4da824ad437bf02# TITLE: Rail Accessibility and Residential Unit Prices: Evidence from Melbournes Cranbourne Line3# YEAR: 2025.0 VENUE: Advances in Economics, Management and Political Sciences DOI: 10.54254/2754-1169/2025.cau284924# AUTHORS: Guanyu Pan5# PAGES: 19 CANDIDATE TABLE PAGES (1-based): [9, 10, 11, 14, 15]6# ABSTRACT: The paper assesses the capitalization of residential unit values along Melbourne's Cranbourne Line route. Beyond station proximity, this paper jointly assesses transportation accessibility, everyday service availability, dwelling attributes, and neighborhood socio-demographics as determinants of price. To enhance cross-sectional comparability in a market characterized by varied lot sizes and layouts, outcomes are assessed based on per-square-meter prices instead of total selling valuesan approach that remains underutilized in Australian studies but is immediately beneficial for planners and investors. Utilizing a hedonic pricing framework, this paper estimates a global ordinary least squares (OLS) model and a geographically weighted regression (GWR) to differentiate corridor-wide connections from regionally variable effects. Findings demonstrate that an increase in the number of bedrooms and the availability of on-site parking correlates with elevated unit costs, whereas increased distance from both the nearest rail station and the central business district relates to diminished unit prices. The GWR exhibits significant geographic variation, with the distance-to-station gradient differing markedly among station catchments. The evidence emphasizes the necessity for households to balance housing costs with effective access to the rail network; for policymakers, it illustrates that public transport initiatives influence property values and the distribution of urban opportunities, thereby informing station-area design, last-mile services, and transit-oriented development up-zoning. This method provides a replicable framework for corridor analysis and establishes a foundation for subsequent causal evaluations of specific investment occurrences.789===== PAGE 1 (score 15, numeric-paren 0, table-words 0, var-words 15) =====1011Proceedings of ICEMGD 2025 Symposium: Resilient Business Strategies in Global Markets12DOI: 10.54254/2754-1169/2025.CAU2849213© 2026 The Authors. This is an open access article distributed under the terms of the Creative Commons Attribution License 4.014(https://creativecommons.org/licenses/by/4.0/).15616Rail Accessibility and Residential Unit Prices: Evidence from17Melbourne’s Cranbourne Line18Guanyu Pan19School of Economics and Management, Shanghai Zhongqiao Vocational and Technical University,20Shanghai, China21a1711135746@gmail.com22Abstract. The paper assesses the capitalization of residential unit values along Melbourne's23Cranbourne Line route. Beyond station proximity, this paper jointly assesses transportation24accessibility, everyday service availability, dwelling attributes, and neighborhood socio-25demographics as determinants of price. To enhance cross-sectional comparability in a26market characterized by varied lot sizes and layouts, outcomes are assessed based on per-27square-meter prices instead of total selling values—an approach that remains underutilized28in Australian studies but is immediately beneficial for planners and investors. Utilizing a29hedonic pricing framework, this paper estimates a global ordinary least squares (OLS)30model and a geographically weighted regression (GWR) to differentiate corridor-wide31connections from regionally variable effects. Findings demonstrate that an increase in the32number of bedrooms and the availability of on-site parking correlates with elevated unit33costs, whereas increased distance from both the nearest rail station and the central business34district relates to diminished unit prices. The GWR exhibits significant geographic variation,35with the distance-to-station gradient differing markedly among station catchments. The36evidence emphasizes the necessity for households to balance housing costs with effective37access to the rail network; for policymakers, it illustrates that public transport initiatives38influence property values and the distribution of urban opportunities, thereby informing39station-area design, last-mile services, and transit-oriented development up-zoning. This40method provides a replicable framework for corridor analysis and establishes a foundation41for subsequent causal evaluations of specific investment occurrences.42Keywords: Transport Infrastructure, Spatial Modelling, Geographically Weighted43Regression, Land Value Capture441. Introduction451.1. Research background46Urban rail investment reshapes effective accessibility and, in turn, the spatial distribution of47residential value across metropolitan space. In large, polycentric cities this capitalization is rarely48uniform because station catchments differ in built form, travel options, and neighborhood49composition. Melbourne offers a pertinent context: it operates Australia’s most extensive suburban505152===== PAGE 9 (score 16, numeric-paren 0, table-words 2, var-words 9) =====5354Proceedings of ICEMGD 2025 Symposium: Resilient Business Strategies in Global Markets55DOI: 10.54254/2754-1169/2025.CAU28492561457Summary. In combination, the descriptive evidence, the global estimates, and the local (GWR)58surfaces indicate that rail and CBD accessibility, local service densities, and core structural features59are systematically related to per-m² prices, with substantive spatial variation in the accessibility–60price gradient along the study corridor.614. Empirical results and spatial diagnostics624.1. Descriptive statistics analysis63Table 2. Results of descriptive statistics64Variable65Mean66Std. Dev.67Min68Max69price707399.33712788.97725.947326969.774numbed752.72761.19770781079numbath801.57810.8182083784numparking851.44860.8087088689age9038.989128.689219317494disbustop950.63960.54970.01986.1299distrain1001.471011.941020.0510323.97104discbd10520.9310610.891072.1410848.04109numbustp110199.82111122.671124113560114numhospital1157.951165.16117011839119numschool12056.1312127.351222123199124medianage12535.701264.751271912854129medianincome130803.70131328.92132691331743134labor1350.551360.111370.111380.89139Figure 3. Histogram of the price variable140(data source: original)141The analysis begins with descriptive statistics for all variables—minimum, maximum, mean, and142standard deviation—reported in Table 2, with a histogram of the unit price displayed in Figure 2.143The mean unit residential price is 7,399.33 AUD/m², accompanied by a sizable standard deviation of144145146===== PAGE 10 (score 21, numeric-paren 0, table-words 2, var-words 8) =====147148Proceedings of ICEMGD 2025 Symposium: Resilient Business Strategies in Global Markets149DOI: 10.54254/2754-1169/2025.CAU28492150151512,788.97, indicating substantial dispersion in transaction values. This variability is further reflected152in the wide range of observed prices, from 5.94 to 26,969.7 AUD/m². Such dispersion is consistent153with uneven spatial conditions across the corridor: properties in locations with stronger transport154accessibility typically transact at higher prices, whereas those in more peripheral or less accessible155areas command lower values. Overall, the magnitude of the standard deviation underscores marked156heterogeneity in the price data. In addition, the distributional pattern in Figure 1 suggests that the157price variable is broadly consistent with an approximately normal form.1584.2. Correlation analysis159Table 3. Results of the correlation coefficient matrix160price161numbed162numbath163numparking164age165disbustop166distrain167price1681169numbed1700.40171711172numbath1730.36091740.66291751176numparking1770.34061780.45161790.35661801181age182-0.1571183-0.1224184-0.3182185-0.07131861187disbustop188-0.2815189-0.0328190-0.0758191-0.01681920.20991931194distrain195-0.29171960.0199197-0.05811980.02871990.32152000.51772011202discbd203-0.5922040.0606205-0.1148206-0.00342070.10292080.33792090.2244210numbustp2110.61562120.00892130.10662140.042215-0.0984216-0.2595217-0.2965218numhospital2190.57052200.132210.21682220.2171223-0.1735224-0.3103225-0.2537226numschool2270.4807228-0.04712290.07442300.0554231-0.0905232-0.332233-0.3376234medianage2350.132360.12482370.02912380.10072390.12162400.09812410.0261242medianincome2430.59432440.0862450.21052460.1106247-0.1424248-0.2649249-0.3228250labor2510.4217252-0.08312530.1167254-0.0053255-0.2473256-0.2946257-0.3985258discbd259numbustp260numhospital261numschool262medianage263medianincome264labor265price266numbed267numbath268numparking269age270disbustop271distrain272discbd2731274numbustp275-0.71732761277numhospital278-0.65452790.63032801281numschool282-0.60152830.52382840.5572851286medianage287-0.0285288-0.0268289-0.10352900.01742911292medianincome293-0.57922940.57022950.54582960.5742970.00932981299labor300-0.6733010.60293020.51633030.4737304-0.26673050.5953061307308309===== PAGE 11 (score 32, numeric-paren 0, table-words 6, var-words 14) =====310311Proceedings of ICEMGD 2025 Symposium: Resilient Business Strategies in Global Markets312DOI: 10.54254/2754-1169/2025.CAU2849231316314Table 3 reports the pairwise correlation matrix across variables. For dwelling attributes, the315number of bedrooms and bathrooms exhibits a positive association with unit price; that is, properties316with more bedrooms or bathrooms tend to transact at higher values. By contrast, the distance-based317accessibility measures are negatively correlated with price: the coefficients for distance to the318nearest bus stop, rail station, and the CBD are −0.2815, −0.2917, and −0.592, respectively.319Interpreted inversely, better accessibility (shorter distance) is associated with higher prices.320Regarding local services, the correlations between price and the counts within 5 km of bus stops,321hospitals, and schools are 0.6156, 0.5705, and 0.4807, respectively, indicating that denser service322provision co-varies positively with unit prices.3234.3. Estimation and analysis of the hedonic price model324In this section, the outcome variable is the per-square-metre transaction price for 691 residential325properties located along the rail corridor. The specification includes 13 covariates covering four326domains: transportation accessibility, everyday service availability (living convenience), dwelling327characteristics, and neighborhood socio-demographics. An ordinary least squares (OLS) hedonic328model is employed to recover corridor-wide associations between these factors and unit prices. The329objective is to quantify both the direction and magnitude of each relationship, thereby clarifying330which attributes are most closely linked to price differentials. Interpretation emphasizes not only331statistical significance but also economic significance (effect sizes and practical relevance), ensuring332that the findings speak to both inferential validity and policy usefulness.333Table 4. Regression estimation results of the characteristic price model334price335Coef.336Std. Err.337t338P>t339numbed340993.201341102.5133429.693430.000344numbath345-33.984346154.712347-0.223480.826349numparking350458.980351120.6993523.83530.000354age355-2.0483563.591357-0.573580.569359disbustop360-229.313361238.745362-0.963630.337364distrain365-139.86836656.781367-2.463680.014369discbd370-85.06637115.166372-5.613730.000374numbustp3756.2903761.0543775.973780.000379numhospital38018.53538124.7543820.753830.454384numschool3858.2673864.2823871.933880.054389medianage39023.65739120.3703921.163930.246394medianincome3952.1333960.3783975.643980.000399labor400-2713.7694011362.894402-1.994030.047404_cons4053494.8174061437.0114072.434080.016409N410374411F(13, 360)41277.96413Prob > F4140.000415R-squared4160.738417Adj R-squared4180.728419420421===== PAGE 12 (score 36, numeric-paren 0, table-words 9, var-words 17) =====422423Proceedings of ICEMGD 2025 Symposium: Resilient Business Strategies in Global Markets424DOI: 10.54254/2754-1169/2025.CAU2849242517426Table 4. (continued)427Root MSE4281666.9429The statistical evaluation focuses on overall fit and the explanatory contribution of the regressors430as inferred from the least-squares estimates. The dependent variable is the unit transaction price of431residential property (AUD/m²). Estimation results are reported in Table 4.432The hedonic specification attains an R² of 0.748 and an adjusted R² of 0.728, indicating that the433model explains 72.8% of the cross-sectional variation in unit prices. The joint significance test434yields F(13, 360) = 77.96 with p = 0.00, confirming significance at the 1% level and supporting the435model’s strong explanatory validity. In addition, most covariates are significant at 5% or better,436suggesting that the chosen characteristics meaningfully account for price differences and that the437fitted equation provides an adequate representation of the data.438At the variable level, numbed, numparking, distrain, discbd, numbustp, numschool,439medianincome, and labor exhibit p-values < 0.10. Within this set, numbed, numparking, discbd,440numbustp, and medianincome are significant at 1%; distrain and labor at 5%; and numschool at44110%. Signs are economically coherent: more bedrooms and on-site parking are associated with442higher unit values, while greater distances to the nearest station and to the CBD are associated with443lower values; denser local services (bus stops and schools within 5 km) co-vary positively with444prices.445Turning to magnitudes, the estimated coefficient on numbed is 993.201 (statistically significant),446implying that, holding other factors constant, each additional bedroom is associated with an increase447of approximately AUD 993.201 per m². The coefficient on numparking is 458.980 (statistically448significant), indicating that one additional parking space corresponds to roughly AUD 458.98 per m²449higher unit price, ceteris paribus. Together, these results underscore the sizeable and positive450contributions of core structural attributes—bedrooms and parking—to residential unit values along451the corridor.452Regarding transportation access, the estimated coefficient on distance to the nearest station453(distrain) is −139.868 and statistically significant, indicating a clear inverse gradient: for each454additional kilometre from a station, the unit sale price falls by roughly AUD 139.87. This conforms455to the a priori expectation that rail proximity is capitalized into value. A similar pattern holds for456distance to the CBD (discbd), whose coefficient is −85.066 and significant; moving one kilometre457farther from the metropolitan core is associated with a decrease of about AUD 85 per square metre,458ceteris paribus.459Turning to local convenience, the coefficient on bus-stop density within 5 km (numstop) equals4606.290 and is significant, implying that each additional nearby bus stop is associated with an increase461of approximately AUD 6.29 per square metre. The school count within 5 km (numschool) also462enters positively and significantly at 8.267, suggesting that increments in proximate educational463facilities are reflected in higher unit prices (≈ AUD 8.27 per additional school), holding other factors464constant.465For neighbourhood conditions, median income (medianincome) carries a positive, significant466coefficient of 2.133. Interpreted in levels, a one dollar increase in local median income corresponds467to an estimated AUD 2.133 rise in unit price, all else equal. This aligns with the notion that stronger468socio-economic profiles support higher willingness to pay.469Within the OLS hedonic framework, these results jointly indicate that structural attributes470dominate on average: additional bedrooms and on-site parking exhibit the largest positive471associations with per-m² prices, followed by access variables (distance to station and to the CBD).472473474===== PAGE 14 (score 35, numeric-paren 0, table-words 6, var-words 16) =====475476Proceedings of ICEMGD 2025 Symposium: Resilient Business Strategies in Global Markets477DOI: 10.54254/2754-1169/2025.CAU2849247819479Table 5. (continued)480numbath4813.3704825.186483609.72484-6.251485numparking4861.2464875.401488609.724896.239490age4911.9584924.669493609.724941.682495disbustop4961.7104974.699498609.724992.995500distrain5016.9215023.880503609.72504-19.825505discbd5060.4745074.132508609.725098.381510numbustp5114.8515124.539513609.72514-12.884515numhospital5164.0685173.827518609.72519-7.602520numschool5211.3555223.669523609.725243.804525medianage5261.3905274.458528609.725294.438530medianincome5314.2095324.966533609.72534-10.572535labor5360.9965374.761538609.725396.844540During GWR specification, this research first screened covariates for spatial non-stationarity. To541do so, this research estimated a global baseline that augments the hedonic controls with each542dwelling’s longitude and latitude and then applied variability tests to the resulting global543coefficients. Table 5 reports the outcomes of these diagnostics. Under the selection metric, a positive544value of the “diff-Criterion” indicates no evidence of spatial variation in the corresponding545parameter, whereas non-positive values point to geographically varying effects. On this basis, the546following variables exhibit significant spatial variability: numbed (bedrooms), numbath547(bathrooms), distrain (distance to the nearest rail station), numbustp (bus-stop count within the548buffer), numhospital (hospital count), and medianincome (neighbourhood income). By contrast,549numparking (on-site parking spaces), age (dwelling age), disbustp (distance to the nearest bus stop),550discbd (distance to the CBD), numschool (school count), medianage (neighbourhood median age),551and labor (labour-force participation) show no detectable spatial variability according to the same552criterion. Consequently, the GWR is estimated using only the subset of covariates flagged as553spatially varying—numbed, numbath, distrain, numbustp, numhospital, and medianincome—with554the per-square-metre residential price as the dependent variable. This strategy ensures that local555modelling effort is concentrated where parameters genuinely differ across space, while variables that556behave globally are retained in their pooled (non-local) form.5574.4.3. Regression result558Table 6. Coefficient estimation results for GWR regression model559Variable560Estimate561Standard Error562t(Est/SE)563Intercept5641569.7989565276.88735665.6695567numbed568874.488156980.289857010.8916571numbath57282.3344573121.28785740.6788575distrain576-12.17145778.2071578-1.4830579numbustp5809.30655810.928358210.0254583numhospital58456.84175852.990458619.0082587medianincome5881.66845890.33395904.9972591592593===== PAGE 15 (score 35, numeric-paren 0, table-words 9, var-words 13) =====594595Proceedings of ICEMGD 2025 Symposium: Resilient Business Strategies in Global Markets596DOI: 10.54254/2754-1169/2025.CAU2849259720598Table 6 reports the GWR estimates for the subset of covariates identified as spatially varying.599Except for numbath, whose local effect is not statistically distinguishable from zero, all other600coefficients are significant. The local estimate for numbed is 874.4881, indicating a strong positive601association between additional bedrooms and unit price, consistent with the global OLS benchmark.602The coefficient on distrains equals −12.1714, confirming that greater separation from the nearest rail603station is linked to lower per-m² values. Among convenience and neighborhood factors, the bus-stop604count within 5 km (numbustp) carries a positive and significant coefficient of 9.3065, the hospital605count (numhospital) enters at 56.8417, and median income (medianincome) at 1.6684; each point to606higher unit prices where service density and socio-economic status are stronger. Overall, the607direction and significance of the GWR coefficients closely mirror the OLS results, reinforcing the608robustness of the baseline relationships while allowing for geographically varying intensities across609the corridor.610Table 7. Descriptive statistics for regression coefficients of GWR model611Variable612Mean613Median614STD615Min616Max617Range618Intercept6191918.27956202058.26286211814.3240622-2777.01776235764.67956248541.6972625numbed626677.0088627612.9522628440.3635629-181.71336301698.84056311880.5538632numbath633-119.8000634-239.7589635426.9448636-1522.6875637742.80146382265.4889639distrain640-38.8792641-7.9860642174.6705643-718.9209644420.38946451139.3103646numbustp6479.02366489.14216495.0306650-0.690065122.315165223.0051653numhospital65469.983765561.560965641.2920657-4.1845658189.7227659193.9072660medianincome6612.41296622.12276631.7585664-0.88216658.23296669.1151667The GWR estimation yields location-specific parameters for each explanatory variable, enabling668us to observe how effects differ across individual observations. To synthesize these local estimates,669this research compute descriptive statistics for each coefficient; the summary is reported in Table 7,670which lists the mean, median, standard deviation, minimum, maximum, and the dispersion range671(max–min). The results show substantial variation both across variables and within any given672variable, confirming spatial non-stationarity in the determinants of unit prices. In other words, the673same attribute does not exert a uniform influence throughout the corridor; its magnitude and, in674some cases, direction depend on spatial context. Consistent with this heterogeneity, the GWR675specification delivers superior goodness-of-fit relative to the global linear model, indicating higher676explanatory accuracy and more reliable local inference.677Interpreting the medians of the local coefficients is informative because they summarize the678central tendency of the effects across more than half of the sample locations. In line with prior679evidence, intrinsic dwelling characteristics remain the most potent contributors to price formation.680Focusing on the spatially modelled blocks—transport accessibility, amenities, and community681characteristics—Table 7 indicates that, within amenities, the hospital count (numhospital) exhibits682the largest median absolute effect, followed by the bus-stop count (numbustp). The median683coefficient on distance to the nearest rail station (distrain) ranks third and carries a negative sign,684implying that greater separation from a station is associated with lower unit prices. Median685neighbourhood income (medianincome) ranks fourth and is positive, consistent with higher local686purchasing power supporting higher values. Taken together, the signs and relative magnitudes687conform to expectations: accessibility penalties are negative, while richer service bundles and688689690===== PAGE 16 (score 29, numeric-paren 0, table-words 3, var-words 23) =====691692Proceedings of ICEMGD 2025 Symposium: Resilient Business Strategies in Global Markets693DOI: 10.54254/2754-1169/2025.CAU2849269421695stronger socio-economic context are positively associated with residential unit prices, with the696intensity of these associations varying meaningfully across space.697The standard deviation of the local regression coefficients quantifies dispersion around each698variable’s mean effect and, by extension, the extent of spatial non-stationarity in how that variable699relates to unit prices. Larger dispersion indicates greater place-to-place variability in influence. As700reported in Table 7, the standard deviation for distance to the nearest station is 174.6705, signalling701substantial regional differences in the valuation of rail proximity; among all predictors, this variable702exhibits the most pronounced spatial variability, implying that the price sensitivity to access is far703from uniform along the corridor. By comparison, the standard deviation for the hospital count is70441.2920, indicating meaningful but more moderate heterogeneity that likely reflects differing local705preferences for healthcare access. In contrast, the income coefficient’s standard deviation of 1.7585706suggests relatively limited spatial variation in the income–price association.707Extrema of the local coefficients further delineate how effects vary across locations. For the708station-distance variable, the minimum local coefficient is −718.9209 and the maximum is709420.3894. Thus, in some catchments an additional kilometre from the nearest station is associated710with a decrease in unit price of up to 718.9209, whereas in other areas the same increase in distance711corresponds to an increase in unit price of up to 420.3894. These opposing tails underscore the712corridor’s strong spatial heterogeneity: depending on local conditions—such as noise exposure,713crowding, walkability, parking supply, or the availability of alternative modes—greater separation714from a station can either penalize or, in select contexts, be associated with higher residential values.7155. Discussion716On a per‑square‑metre basis, residential values increase with core structural attributes—most717notably bedrooms and on‑site parking—and with the density of proximate services such as bus stops718and schools within the local catchment. By contrast, distance penalties are evident for both the719nearest rail station and the CBD, indicating that effective access to the trunk network and major720employment is priced in. For a cross‑section, the global model exhibits strong fit and concentrates721statistical significance on variables with direct behavioural channels (internal space, access/egress722cost and convenience, everyday services). However, the local modelling layer makes it equally clear723that these averages mask substantial geographic variation: the station‑distance gradient is materially724steeper in some catchments than others, and in a few locations the relationship weakens or even725reverses—an empirical signal that proximity benefits are being offset by local disamenities such as726noise, air‑quality burdens, or crowding [20]. That pattern is also consistent with well‑known issues727in hedonic prediction and segmentation, where attribute effects differ across submarkets and model728performance improves when such heterogeneity is acknowledged [21].729Positioning these results against prior research shows both consistencies and useful divergences.730Classic urban development perspectives underline that network investments reshape urban structure731and land bids over time . Environmental‑quality studies demonstrate that households capitalize732cleaner air and related amenities into housing prices, reinforcing why local externalities can733attenuate transport premia when left unmitigated [22]. Amenity‑specific evidence further indicates734that micro‑environmental benefits such as wider views and streetscape quality are reflected in735transaction values and that urban ecological assets can raise willingness to pay in dense districts736[23]. Broader policy frameworks around compact growth also mediate outcomes by aligning737transport supply with place‑based development while heterogeneous landscape change and738urbanization trajectories condition how accessibility interacts with built form [24]. Consistent with739the present findings, corridor and station‑area studies in transit‑oriented settings detect price premia740