1# RECORD 290a4bf33ce230c96c2113bb929c287ce4514c652# TITLE: Borrowed sizes: A hedonic price approach to the value of netowrk structure in public transport systems3# YEAR: 2021.0 VENUE: nan DOI: 10.5198/jtlu.2021.16644# AUTHORS: Helena Bohman; Desirée Nilsson5# PAGES: 17 CANDIDATE TABLE PAGES (1-based): [11, 12]6# ABSTRACT: Property prices are known to be higher in places with high accessibility, such as in proximity to train stations and especially to commuter rail, than in places without this access. This study provides a better understanding of how regional accessibility, through the structure of railway networks, can influence local agglomeration economies by providing accessibility to large labor markets. Previous literature has shown a positive impact of proximity to railway stations on housing prices, and our study adds to the literature by analyzing the impact of network structure. We argue that public transport systems can support the benefits of city networks in line with Alonso’s concept of borrowed sizes (1973). Using network theory to measure accessibility provided by the network, we show that stations that provide accessibility to large labor markets across the region are perceived as more attractive by households. Cities in proximity to other cities are strengthened through their public transport links, which allow agglomeration benefits to be exploited by residents.789===== PAGE 1 (score 10, numeric-paren 0, table-words 0, var-words 10) =====10111 12Introduction13Infrastructure networks are important for providing accessibility to household and firms. Households 14need access to jobs, public services and leisure activities. Firms need access to labor, suppliers and cus-15tomers. The benefits of being close to other actors, often referred to as agglomeration economies, have 16been extensively studied in the field of urban and regional economics (Melo, Graham, & Noland, 172009). Investing in infrastructure such as roads and railways has therefore been a common way to 18increase accessibility and enhance economic activity. Railways offer high-capacity transportation but 19incur high fixed costs, and there is mixed evidence of the effects of such investments (Debrezion, Pels, 20& Rietveld, 2007; Higgins & Kanaroglou, 2016; Mohammad, Graham, Melo, & Anderson, 2013). 21Several explanations to these differences have been mentioned, such as different types of communica-22tions and different local context (Lieske, van den Nouwelant, Han, & Pettit, 2019). One reason for the 23differences is the extent to which investments contribute to achieving agglomeration economies (Chat-24Article history:25Received: September 6, 201926Received in revised form: April 277, 2020 28Accepted: September 28, 202029Available online: January 15, 30202131Copyright 2021 Helena Bohman & Désirée Nilsson32http://dx.doi.org/10.5198/jtlu.2021.166433ISSN: 1938-7849 | Licensed under the Creative Commons Attribution – Noncommercial License 4.0 34The Journal of Transport and Land Use is the official journal of the World Society for Transport and Land Use (WSTLUR) 35and is published and sponsored by the University of Minnesota Center for Transportation Studies. 36T J T L U http://jtlu.org37V. 14 N. 1 [2021] pp. 87–10338Helena Bohman39Malmö University and K2–The Swedish 40Knowledge Centre for Public Transport41helena.bohman@mau.se42Désirée Nilsson43Malmö University and K2–The Swedish 44Knowledge Centre for Public Transport45desiree.nilsson@mau.se46Borrowed sizes: A hedonic price approach to the value of network 47structure in public transport systems48Abstract: Property prices are known to be higher in places with high 49accessibility, such as in proximity to train stations and especially to 50commuter rail, than in places without this access. This study provides a 51better understanding of how regional accessibility, through the structure 52of railway networks, can influence local agglomeration economies by 53providing accessibility to large labor markets. Previous literature has 54shown a positive impact of proximity to railway stations on housing 55prices, and our study adds to the literature by analyzing the impact of 56network structure. We argue that public transport systems can support 57the benefits of city networks in line with Alonso’s concept of borrowed 58sizes (1973). Using network theory to measure accessibility provided 59by the network, we show that stations that provide accessibility to large 60labor markets across the region are perceived as more attractive by 61households. Cities in proximity to other cities are strengthened through 62their public transport links, which allow agglomeration benefits to be 63exploited by residents.64Keywords: Hedonic price model, network theory, centrality measures, 65labor market accessibility, commuter rail666768===== PAGE 11 (score 27, numeric-paren 0, table-words 4, var-words 13) =====69709771Borrowed sizes: A hedonic price approach to the value of network structure in public transport systems 72The descriptive statistics of the data in Table 3 provide some key insights into the region of Scania. 73About two percent of the single-family houses sold in 2016 had direct access to a beach, and the average 74house had a size of 117 square meters and was about 25 years old. As mentioned before, there are large 75differences in the geography of the region. There are also large differences in the average number of pas-76sengers per day travelling from the different stations in the railway network. 77Table 3. Descriptive statistics (variables in natural logs)78Variable79Mean80Median81Std. Dev.82Min83Max84PRICE8514.5088614.631870.778808918.41490SIZE914.764924.796930.38941.386956.65996AGE973.64983.892991.17210001015.756102DEED1030.96310411050.18910601071108BEACH1090.01811001110.13411201131114INC11512.41211612.4111170.14911811.85311913.082120POP1217.1171227.2091230.8451242.5651259.077126LOC_LABOR1272.6671282.331291.1911301.0871315.087132STATION_DIST1331.5471341.6091351.079136-3.6711373.47138PASSENGERS1397.5341407.2941411.4191424.2914310.682144CLOSENESS (GRAVITY)1457.8121467.9361471.3421485.14214910.702150DEGREE1514.2951524.4841530.5271542.8771554.936156BETWEENNESS (Öresund trains)1573.1831583.4661590.6591601.0991613.989162BETWEENNESS (Pågatåg trains)1630.02516401650.04416601670.178168 169 170 171Table 4 shows the results from the HPM. Columns (i) to (vi) show the results as station character-172istics are successively supplemented. Estimates using the base ordinary least squares model provided sig-173nificant results for robust Lagrange multiplier testing for both error and lag. However, the test statistics 174provided higher value for a spatial error model to be performed (Osland, 2010). A spatial error model 175(SEM) was applied to all regression models using generalized method of moments (GMM) estimations. 176The weight matrix was calculated using Euclidian distances, with a default bandwidth estimated using 177GeoDa Space software (Anselin, Syabri, & Kho, 2006). Standard errors were estimated based on Kele-178jian and Prucha (2010). Using SEM means that the estimated parameters will be efficient since lambda 179accounts for any misspecification as well as spatial autocorrelation in the models.180The ordinary variables used in HPM turn out as expected and are stable across the different model 181specifications used. Income and population in the local small area have a positive effect on price, as well 182as the number of employments in the municipality where people live. The number of jobs measure local 183agglomeration effects and show an elasticity of 0.05-0.06. The variable distance to station are as expected 184negative, with an elasticity of around -0.01. 185Station and network characteristics present interesting results. PASSENGERS show positive pa-186rameters over all specifications, although the parameters fall somewhat as more variables are added to 187the model. Adding CLOSENESS to the specification (column (ii)) improves the PseudoR2 to 0.479. 188The estimated coefficient represents a positive impact on sales prices of single-family houses. Thus, the 189significant result of this variable reflects that it measures the importance of accessibility to workplaces 190in other nodes of the network. In accordance with Osland and Thorsen (2008) our results show that 191192193===== PAGE 12 (score 107, numeric-paren 79, table-words 2, var-words 7) =====19419598196JOURNAL OF TRANSPORT AND LAND USE 14.1197accessibility to workplaces has a positive impact on housing prices. As the variable is a weighted mea-198sure of employments in the vicinity of destination stations, and distance is measured using the rail 199infrastructure, it also indicates the perceived utility of using public transport for work. This suggests 200that household value both the local agglomeration as well as the agglomerations provided through the 201network. Adding the variable DEGREE, which measures direct access to other stations, the estimated 202parameter also turns out significant and positive with an elasticity of around 0.09. Thus, the number of 203stations that you can reach without changing trains has a significant impact on the price of single-family 204houses. The relevance of not having to change trains in order to get to destinations in the network seem 205to enhance the importance of closeness to labor market in other nodes of the network. 206Table 4. Results from HPM regressions using spatial error model. Z values in parentheses, *** p<0.01, ** p<0.05, * p<0.1. 207N=18,273, all continuous variables in log form208(i)209(ii)210(iii)211(iv)212(v)213(vi)214CONSTANT2153.139 216(6.732)***2172.654 218(5.967)***2192.329 220(5.248)***2212.293 222(5.036)***2232.240 224(4.932)***2252.223 226(4.848)***227SIZE 2280.787 229(68.211)***2300.787 231(67.918)***2320.789 233(68.015)***2340.789 235(68.157)***2360.789 237(68.033)***2380.789 239(68.160)***240AGE 241-0.021 242(-5.367)***243-0.021 244(-5.353)***245-0.021 246(-5.484)***247-0.021 248(-5.467)***249-0.021250(-5.497)***251-0.021 252(-5.488)***253DEED2540.367 255(18.589)***2560.367 257(18.559)***2580.366 259(18.538)***2600.366 261(18.536)***2620.366 263(18.563)***2640.366 265(18.559)***266BEACH2670.413 268(13.019)***2690.413 270(13.015)***2710.415 272(12.998)***2730.415 274(12.986)***2750.415 276(13.000)***2770.415 278(12.988)***279INC 2800.532 281(15.052)***2820.512 283(14.107)***2840.525 285(14.443)***2860.523 287(14.466)***2880.528 289(14.482)***2900.526 291(4.534)***292POP 2930.043 294(5.114)***2950.041 296(4.783)***2970.042 298(4.932)***2990.042 300(4.938)***3010.043 302(4.955)***3030.042 304(4.973)***305LOC_LABOR 3060.062 307(5.739)***3080.055 309(5.187)***3100.060 311(5.687)***3120.061 313(5.686)***3140.061 315(5.758)***3160.062 317(5.732)***318STATION_DIST 319-0.016320(-2.431)**321-0.013 322(-2.118)**323-0.013 324(-2.124)**325-0.013 326(-2.076)**327-0.013 328(-2.121)**329-0.013 330(-2.082)**331PASSENGERS 3320.034 333(6.644)***3340.029 335(5.886)***3360.019 337(3.516)***3380.026 339(3.703)***3400.020 341(3.566)***3420.026 343(3.563)***344CLOSENESS (Gravity)3450.187 346(2.991)***3470.172 348(2.733)***3490.175 350(2.855)***3510.184 352(2.937)***3530.181 354(2.864)***355DEGREE3560.089 357(3.843)***3580.087 359(3.789)***3600.092 361(3.977)***3620.090 363(3.933)***364BETWEENNESS (Öresund 365train)366-0.303 367(-1.173)368-0.291 369(-0.988)370BETWEENNESS (Pågatåg 371train)372-0.742 373(-0.797)374-0.260 375(-0.239)376Lambda3770.935 378(95.694)***3790.928 380(89.860)***3810.921 382(83.961)***3830.921 384(83.558)***3850.920 386(83.278)***3870.921 388(83.099)***389AIC390-17706.67391-19279.98392-19626.16393-19751.39394-19702.72395-19758.01396Pseudo R23970.4493980.4793990.5094000.5104010.5124020.512403404405===== PAGE 13 (score 11, numeric-paren 0, table-words 1, var-words 9) =====40640799408Borrowed sizes: A hedonic price approach to the value of network structure in public transport systems 409The two variables representing BETWEENNESS capture transfer possibilities in two types of re-410gional public transport networks. The Öresund train network stops at fewer stations allowing for direct 411and faster mobility between nodes, while the Pågatåg network covers more stations in the region provid-412ing commuter trains to also smaller places. Both betweenness measures turn out insignificant. In the 413context of regional train networks, transfer possibilities in nodes does not seem to impact the perceived 414value of single-family houses.415This article addresses the importance of regional public transport system and access to agglomera-416tion economies in regional economic development. Most previous studies that consider the importance 417of accessibility using hedonic price model performs it in a more local context. The regional setting in 418this study incorporate more heterogeneity compared to local settings, which explain the lower fit of the 419model. AIC statistics are provided as a complement to the pseudo r2, and indicate that the variables 420added contribute to the model specification, and improve as the network variables are added. 4215 422Discussion423This study aims to provide a better understanding of regional development by focusing on how network 424structure and node characteristics of public transport network influence willingness to pay for housing. 425By introducing measures based on network theory, we can analyze not only local agglomerations, but 426also how the network can help exploit agglomerations accessible through the network. Thus, by using 427measures that capture network characteristics we can better understand how regional accessibility can 428influence local agglomeration economies.429Four characteristics have been explored by using three centrality measures and the size of stations 430in terms of passengers travelling. These variables are not highly correlated among themselves, which in-431dicates that they capture different aspects of the network. Among the control variables in the base speci-432fication, number of employments in home municipality is included. Including network characteristics 433provide measures of access to regional agglomerations in local markets. Although the explanatory power 434of the models are lower than most HPM estimates, the findings are robust across the specifications. 435Results show that the network variables improve the fit of the model and thus contribute to the variables 436used in the base model. In the words of Alonso (1973), local markets can benefit from borrowed ag-437glomerations accessible through the public transport network. 438The first network variable introduced was the closeness measure, applied as a gravity specification, 439and it captures accessibility to labor markets, as measured through the indirect effects offered by the re-440gional rail system. The estimate of this variable implies an elasticity of 0.18 - 0.20, all else equal. Using 441our definition of this measure, the stations with the lowest gravity measures provide a labor market of 442slightly less than 20,000 employed people. The station with the highest corresponding number provides 443accessibility to approximately 214,000 employments. As an example of how important this aspect is in 444economic terms, one can compare a house close to the station providing the least accessibility (low close-445ness score) to one demonstrating the highest accessibility (high closeness score). A house by the station 446providing the largest accessibility would then sell for nearly three times the price of an identical house 447by the station of the lowest accessibility. This effect is in addition to the effect of the local labor market 448which is already controlled for. In other words, although the exact figures should be interpreted with 449caution, the findings do suggest an important contribution of the network.450The second centrality measure (Degree) captures the possibility of reaching stations without chang-451ing trains. We interpret this as a trip quality indicator; measuring to what extent a station offers uninter-452rupted trips across the network. As expected, this feature has a significant impact on property values 453with an elasticity of around 0.09. The positive impact of direct connections is not surprising. Given that 454