# RECORD 327115a5186cc436d1749ca2acaa98314f882fee # TITLE: Bus stop, property price and land value tax: A multilevel hedonic analysis with quantile calibration # YEAR: 2015.0 VENUE: nan DOI: 10.1016/j.landusepol.2014.07.017 # AUTHORS: Yiming Wang; D. Potoglou; S. Orford; Yi Gong # PAGES: 11 CANDIDATE TABLE PAGES (1-based): [7, 8, 9, 10] # ABSTRACT: Based on a multilevel and quantile hedonic analysis regarding the local public bus system and the prices of residential properties in Cardiff, Wales, we find strong evidence to support two research hypotheses: (a) the number of bus stops within walking distance (300–1500 m) to a property is positively associated with the property's observed sale price, and (b) properties of higher market prices, compared with their cheaper counterparts, tend to benefit more from spatial proximity to the bus stop locations. Given these statistical findings, we argue that, land value tax (LVT), albeit a classic political idea dating back to the early 20th century, does have contemporary relevance and, with modern geographic information technologies, can be rigorously analysed and empirically justified with a view to actual implementation. Levying LVT will not only generate additional fiscal revenues to help finance the development and maintenance of local public infrastructures, but will also ensure a more just distribution of the economic welfare yielded by public investment. ===== PAGE 1 (score 9, numeric-paren 0, table-words 0, var-words 9) ===== Land Use Policy 42 (2015) 381–391 Contents lists available at ScienceDirect Land Use Policy j o ur na l ho me page: www.elsevier.com/locate/landusepol Bus stop, property price and land value tax: A multilevel hedonic analysis with quantile calibration Yiming Wang a,∗, Dimitris Potoglou b,c, Scott Orford b, Yi Gong d a The Bartlett School of Construction and Project Management, The Bartlett Faculty of Built Environment, University College London, London WC1E 7HB, UK b School of Planning and Geography, Cardiff University, King Edward VII Avenue, Cardiff CF10 3WA, UK c RAND Europe, Westbrook Centre, Milton Road, Cambridge CB4 1YG, UK d Sustainable Places Research Institute, Cardiff University, 33 Park Place, Cardiff CF10 3BA, UK a r t i c l e i n f o Article history: Received 20 October 2013 Received in revised form 30 May 2014 Accepted 26 July 2014 Keywords: Land value tax Transport infrastructure Bus stop Property price a b s t r a c t Based on a multilevel and quantile hedonic analysis regarding the local public bus system and the prices of residential properties in Cardiff, Wales, we find strong evidence to support two research hypotheses: (a) the number of bus stops within walking distance (300–1500 m) to a property is positively associated with the property’s observed sale price, and (b) properties of higher market prices, compared with their cheaper counterparts, tend to benefit more from spatial proximity to the bus stop locations. Given these statistical findings, we argue that, land value tax (LVT), albeit a classic political idea dating back to the early 20th century, does have contemporary relevance and, with modern geographic information technologies, can be rigorously analysed and empirically justified with a view to actual implementation. Levying LVT will not only generate additional fiscal revenues to help finance the development and maintenance of local public infrastructures, but will also ensure a more just distribution of the economic welfare yielded by public investment. © 2014 Elsevier Ltd. All rights reserved. Introduction In recent years, the United Kingdom (UK) has witnessed a revival of public interests in land value tax (LVT).1 The original idea of LVT dates back to George (1879), an American political economist, who, partly inspired by Smith (1863), posits that the value of land, ultimately, comes from the adjacent infrastructures and amenities invested by the whole community. Increments in land value due to public investment, thus, ought to be re-captured through LVT. The earliest political attempts to legislate LVT took place in the late Edwardian Britain (Short, 1997, Chapter 2). LVT was officially pro- posed in the 1909 finance bill (also known as “People’s Budget”), when David Lloyd George served as Chancellor of the Exchequer as a member of the governing Liberal party. However, the then Conservatives-dominated House of Lords, though passing the gen- eral budget in 1910, managed to veto the LVT proposal. A similar story happened later with the 1931 Finance Act, which contained ∗Corresponding author. +44 02076791890. E-mail address: yiming.wang@ucl.ac.uk (Y. Wang). 1 For the examples of Labour’s Land Value Campaign (http://www.labourland. org/) and the Liberal Democratic Party’s Action for Land Taxation and Economic Reform (http://libdemsalter.org.uk/). a LVT initiative passed by the ruling Labour party but was rejected again by the Tory government in 1934 (Wenzer, 2000). One of the latest efforts to seek legislation of LVT was in 2012 by Carolyn Lucas, a Green party member of the UK parliament (The UK Parliament, 2012). Although LVT was never implemented in Britain, its traces can be observed in many other places around the world, such as in the cities of Pittsburgh and Harrisburg in the American State of Penn- sylvania and a number of countries such as Australia, Denmark, Estonia, Russia, and New Zealand (Andelson, 2000; Bourassa, 1990; Dye and England, 2010; Wyatt, 1994). While the actual policy practice varies among these international cases, LVT has been increasingly justified as a way to finance the construction and maintenance of public transport infrastructures. The basic rationale remains quite the same as per George original (1879), that publicly invested transport network can promote the values of nearby pri- vately owned land plots, given their improved accessibility. From a political economy perspective, this part of added land value, if substantiated, becomes a kind of positive externalities which can be offset or captured through LVT. Otherwise, general tax payers (who generate government revenues) are essentially subsidising landowners who “quietly” extract the values of public transport infrastructures. Making this free-ride problem even more press- ing is the undersupply and underfunding of public transport in the http://dx.doi.org/10.1016/j.landusepol.2014.07.017 0264-8377/© 2014 Elsevier Ltd. All rights reserved. ===== PAGE 7 (score 201, numeric-paren 156, table-words 4, var-words 5) ===== Y. Wang et al. / Land Use Policy 42 (2015) 381–391 387 Table 3 Results of ordinary least squares, log(p) as dependent variable. Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Within 300 m Within 400 m Within 500 m Within 750 m Within 1000 m Within 1500 m ˇ s.e. ˇ s.e. ˇ s.e. ˇ s.e. ˇ s.e. ˇ s.e. Constant 10.506*** 0.025 10.479*** 0.027 10.438*** 0.028 10.417*** 0.034 10.343*** 0.039 10.269*** 0.048 Freehold (r) 0.120*** 0.009 0.121*** 0.009 0.123*** 0.009 0.126*** 0.009 0.127*** 0.009 0.125*** 0.009 New-built (n) 0.228*** 0.013 0.230*** 0.013 0.232*** 0.013 0.230*** 0.013 0.237*** 0.013 0.217*** 0.013 Detached (d) 0.651*** 0.016 0.654*** 0.016 0.654*** 0.016 0.647*** 0.016 0.644*** 0.016 0.645*** 0.016 Semi-detached (s) 0.468*** 0.014 0.471*** 0.014 0.469*** 0.014 0.465*** 0.014 0.464*** 0.014 0.465*** 0.015 Terraced (t) 0.360*** 0.014 0.361*** 0.014 0.359*** 0.014 0.356*** 0.014 0.354*** 0.014 0.357*** 0.014 Floor area (f) 0.004*** 0.000 0.004*** 0.000 0.004*** 0.000 0.004*** 0.000 0.004*** 0.000 0.004*** 0.000 Distance to CBD (z) 0.035*** 0.004 0.038*** 0.004 0.042*** 0.004 0.046*** 0.005 0.055*** 0.005 0.066*** 0.007 Sale in 2001 (Y1) 0.112*** 0.009 0.110*** 0.009 0.111*** 0.009 0.112*** 0.009 0.113*** 0.009 0.110*** 0.009 Sale in 2002 (Y2) 0.301*** 0.009 0.297*** 0.009 0.297*** 0.009 0.297*** 0.009 0.299*** 0.009 0.295*** 0.009 Sale in 2003 (Y3) 0.526*** 0.009 0.522*** 0.009 0.524*** 0.009 0.525*** 0.009 0.522*** 0.009 0.520*** 0.009 Sale in 2004 (Y4) 0.694*** 0.009 0.693*** 0.009 0.693*** 0.009 0.694*** 0.009 0.693*** 0.009 0.691*** 0.009 Sale in 2005 (Y5) 0.781*** 0.010 0.778*** 0.010 0.776*** 0.009 0.778*** 0.009 0.777*** 0.009 0.776*** 0.010 Sale in 2006 (Y6) 0.791*** 0.009 0.789*** 0.009 0.789*** 0.009 0.791*** 0.009 0.791*** 0.009 0.789*** 0.009 Sale in 2007 (Y7) 0.842*** 0.009 0.840*** 0.009 0.840*** 0.009 0.841*** 0.009 0.840*** 0.009 0.839*** 0.009 Sale in 2008 (Y8) 0.858*** 0.023 0.857*** 0.023 0.861*** 0.022 0.860*** 0.022 0.861*** 0.022 0.861*** 0.022 Adamstown (W1) −0.512*** 0.014 −0.508*** 0.014 −0.502*** 0.014 −0.499*** 0.014 −0.488*** 0.014 −0.465*** 0.015 Splott (W2) −0.513*** 0.012 −0.507*** 0.012 −0.493*** 0.012 −0.486*** 0.013 −0.464*** 0.014 −0.430*** 0.017 Cathays (W3) −0.196*** 0.020 −0.194*** 0.020 −0.186*** 0.020 −0.182*** 0.020 −0.164*** 0.020 −0.152*** 0.021 Heath (W4) −0.142*** 0.019 −0.137*** 0.019 −0.139*** 0.019 −0.143*** 0.019 −0.146*** 0.019 −0.144*** 0.019 Llanishen (W5) −0.240*** 0.024 −0.250*** 0.024 −0.257*** 0.024 −0.262*** 0.025 −0.269*** 0.025 −0.276*** 0.025 Penylan (W6) −0.064*** 0.011 −0.064*** 0.011 −0.062*** 0.011 −0.051*** 0.011 −0.039*** 0.011 −0.018 0.013 Pentwyn (W7) −0.648*** 0.010 −0.647*** 0.010 −0.643*** 0.010 −0.644*** 0.010 −0.638*** 0.010 −0.638*** 0.010 Plasnewydd (W8) −0.235*** 0.014 −0.230*** 0.014 −0.225*** 0.014 −0.229*** 0.014 −0.229*** 0.014 −0.218*** 0.014 Pontprennau (W9) −0.500*** 0.020 −0.502*** 0.020 −0.502*** 0.020 −0.506*** 0.020 −0.508*** 0.020 −0.505*** 0.020 Number of bus stops (ı) 0.003*** 0.001 0.003*** 0.001 0.003*** 0.000 0.001*** 0.000 0.001*** 0.000 0.001*** 0.000 Sample size 9655 9650 9656 9653 9659 9669 R2 0.850 0.850 0.850 0.851 0.850 0.849 F-test (df) 2176 (25) 2179 (25) 2185 (25) 2195 (25) 2186 (25) 2167 (25) p-Value 0.000 0.000 0.000 0.000 0.000 0.000 *p < 0.1; **p < 0.05. *** p < 0.001. ===== PAGE 8 (score 33, numeric-paren 0, table-words 12, var-words 4) ===== 388 Y. Wang et al. / Land Use Policy 42 (2015) 381–391 Table 4 Testing autocorrelation and homoscedasticity in OLS results. Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Within 300 m Within 400 m Within 500 m Within 750 m Within 1000 m Within 1500 m Independence of ε Durbin–Watson 1.773 1.777 1.779 1.779 1.779 1.776 p-Value 0.000 0.000 0.000 0.000 0.000 0.000 Spatial autocorrelation Moran’s I 0.150 0.150 0.149 0.149 0.150 0.149 Expected −0.0001 −0.0001 −0.0001 −0.0001 −0.0001 −0.0001 Standard deviation 0.0004 0.0004 −0.0004 0.0004 0.0004 0.0004 p-Value 0.000 0.000 0.000 0.000 0.000 0.000 Homoscedasticity Breusch and Pagan (1979) test 2(df) 0.233 (1) 0.101 (1) 0.533(1) 0.887(1) 1.503(1) 0.612(1) p-Value 0.6293 0.7512 0.4651 0.3461 0.2202 0.4341 (NaPTAN-v2.2) dataset (Department for Transport, 2007). These are also shown in Fig. 2. The number of bus stops within specified radii of each property (i.e., 300 m, 400 m, 500 m, 750 m, 1000 m, and 1500 m) was calculated using ArcGIS. Table 1 presents a summary of the variables included in the sample of properties in the dataset. Table 2 also shows that this is broadly a cross-sectional rather than panel dataset, as more than 75% of the properties have only been sold once during the 9-year period. Model results4 OLS results Table 3 presents the results of six ordinary least square (OLS) regressions based on the conventional hedonic approach pre- scribed in “A Hedonic Approach” section. The dependent variable in every regression is the natural logarithm of price (log(p)) and all of the six models attempt to capture the effect of Cardiff Bus on land value via ı, which measures the number of bus stops within different walkable radii (i.e., 300 m, 400 m, 500 m, 750 m, 1000 m, and 1500 m) around each property contained in the dataset. Fol- lowing Belsley et al. (1980) a variety of regression diagnostic tests were performed on the OLS models in order to check whether any of the regression assumptions had been violated. These included identifying outliers that may have a disproportionate influence on the models, and removing the corresponding observations if neces- sary; checking for serious multi-collinearity in the models; testing the normality of the error term; and checking for heteroscedasticity and autocorrelation in the error term. The latter tests are discussed in more detail in a later section. Model 1, for example, includes the explanatory variable with regard to the number of bus stops within 300 m around each prop- erty. The estimated parameter corresponding to this variable shows that the effect of Cardiff Bus on property price or, more accu- rately, on land value as reflected in property price, is positive and statistically significant at the 1% significance level. In other words, the more bus stops there are around a property, the higher the value of land beneath that property. Similar findings are also discernible from the other regressions estimating the effect of num- ber of bus stops within 400 m, 500, 750 m, 1000 m and 1500 m, respectively. By back transforming the magnitude of the estimated coeffi- cient with regard to the number of bus stops within 300 m, we can calculate the marginal increase in land value as a result of pla- cing every extra bus stop around a property. In percentage, this 4 OLS results are estimated in IBM SPSS Statistics 20, while the multi-level analysis is conducted with MLWin v.3.20 (Rasbash et al., 2012). The quantile regression is exercised using an R/SPSS interface based on the original “quantreg” R codes by Koenker (2007). added land value equals to (e0.003 − 1) × 100 ≈ 0.3%. Examination of the coefficients of the number of bus-stops within different buffer zones around property in Table 3 suggests that the effect of bus stops within distance is relatively stable and statistically signifi- cant. The land value benefit of every additional bus stop within a circular catchment area larger than 500 m by radius is about 0.1% of the corresponding property price. The figure rises to 0.3% if the size of catchment shrinks to 500 m or less by radius. Overall, all models show that the availability of public transport infrastructure can significantly promote land value and thereby raise property price. The coefficient of z, the distance to Cardiff’s central business district (CBD), is also positive and statistically significant. Accord- ing to some studies, this finding is counter-intuitive and against the conventional wisdom about urban spatial structure. Per clas- sic urban economic theory (e.g., Anas et al., 1998), property prices should decline alongside increase in the properties’ distance to city centre. However, previous studies have also provided several expla- nations for the positive influence of distance to CBD on property price. For example, the assumption of a declining land value as distance increases from CBD is based on the adoption of a mono- centric location choice model (Dubin and Sung, 1987), while Cardiff appears to be a rather polycentric city. Floor area is also a positive and statistically significant factor in terms of its impact on property price. The effect of floor space is consistent across all six models presented in Table 3. Following a back transformation of the parameter with respect to floor space, we find that every additional square metre in a property increases its value by (e0.004 − 1) × 100 ≈ 0.4%. We capture mean spatial variation in property price by using a series of dummy-coded variables representing the wards in which each property is located. The ward named Cyncoed is used as the reference category as it is the area with the highest average prop- erty price in the data set. All models show a significant difference in property price from Cyncoed, although the last regression (model 6 in Table 3) indicates no significant difference in property price between Cyncoed and Penylan. All other coefficients have the expected signs and agree with conventional findings in the hedonic literature (Edmonds, 1984). For example, a property under a freehold tenure is likely to show a higher price compared with a leasehold property. Similarly, a newly built property tends to have more market value than an aged prop- erty. Detached properties exhibit the highest prices, followed by semi-detached properties and flats, of which the latter is defined as the reference property type in this analysis. Finally, the dummy variables with regard to price differences due to the calendar year when a property was sold are all significantly different from zero. The year of 2000 is used as the reference year, while the remaining coefficients indicate a constant increase in property price over time, partly due to inflation in pound sterling and partly due to dynamics in the local housing market. ===== PAGE 9 (score 108, numeric-paren 72, table-words 7, var-words 6) ===== Y. Wang et al. / Land Use Policy 42 (2015) 381–391 389 Multilevel results As mentioned earlier, ordinary least square (OLS) regression may involve potential issues related to spatial and temporal auto- correlations. In terms of spatial autocorrelation, the prices of properties within a same jurisdiction (i.e., electoral wards in our data) are likely to be more similar than those outside. In terms of temporal autocorrelation, properties sold within the same year may show a convergence in sale price vis-à-vis those transacted within a different year. To account for both the geographic as well as tem- poral autocorrelations, our OLS models would have to be expanded extensively to accommodate (M − 1) × (X − 1) interaction factors. In contrast, multilevel modelling analysis appears to be a more effi- cient alternative to address the issue of autocorrelation (Jones and Bullen, 1994). Table 4 identifies a problem of autocorrelation underlying all the six OLS models. The Durbin–Watson test suggests an overall significant serial interdependence of the error terms (ε) in every OLS model. Because the Durbin–Watson result is more sensitive to temporal autocorrelation, we further exercise a Moran’s I test (Anselin, 1988), of which the estimates also confirm a presence of a significant albeit weak spatial autocorrelation (Moran’s I ≈ 0.15). However, the Breusch and Pagan (1979) test for the whole model suggests that heteroscedasticity is not a problem in the error terms and they have constant variance. To address the autocorrelation problem, six multilevel regressions (models 7–12) are further con- ducted. Since over 75% of the sampled properties are sold only once between 2000 and 2009, we estimate the multilevel models in ref- erence to the cross-sectional hierarchy shown in Fig. 1(a) instead of the panel structure as per Fig. 1(b). Table 5 illustrates the results of our multilevel modelling anal- ysis. Both the ward-by-ward (2 ward) and year-to-year variance (2 year) are statistically significant, confirming the OLS finding that there are generalisable differences across wards and years in terms of property price. Around 19% of the aggregate variance in property price (2 property) can be attributed to 2 ward, compared with around 56% to 2 year. Nevertheless, ı, which measures the impact of bus stops on nearby land value, remains stable and is thus unbiased. This essentially confirms the robustness of the OLS estimates, even in face of the identified spatial and temporal autocorrelations. Quantile results Table 6 reports the results of our quantile bivariate regressions. As noted above, we conduct this quantile analysis mainly as a post hoc calibration of the OLS estimates, in case price-based hetero- geneities lead to a biased coefficient measure of ı. We thus use log(p) as the dependent variable and ı as the only independent variable to gauge the number of bus stops within different walkable distances, from 300 m to 1500 m, around every observed property location. Generally speaking, Table 6 indicates relatively weak and/or insignificant associations between the number of bus stops and property prices at the 10% and 20% quantiles, where the observed property prices are below £74,000 in the dataset. In contrast, the strongest and most significant correlations can be found for proper- ties at the 80% quantile, priced around £195,000. Those even more expensive properties at the 90% quantile (i.e., £250.000), however, seem to benefit no more from the number of nearby bus stops than those at the 80% quantile, even though they tend to be better off compared with all properties below the 80% quantile, especially when we increase the search radius to more than 750 m. While confirming the overall robustness of OLS estimates with regard to the mean of ı, the results of our quantile analysis do sug- gest ı to be systematically volatile across different price segments. Table 5 Results of multi-level modelling, log(p) as dependent variable. Model 7 Model 8 Model 9 Model 10 Model 11 Model 12 Within 300 m Within 400 m Within 500 m Within 750 m Within 1000 m Within 1500 m ˇ0 s.e. ˇ0 s.e. ˇ0 s.e. ˇ0 s.e. ˇ0 s.e. ˇ0 s.e. Constant 10.735*** 0.070 10.710*** 0.071 10.671*** 0.071 10.660*** 0.073 10.590*** 0.075 10.520*** 0.079 Freehold (r) 0.116*** 0.009 0.117*** 0.009 0.119*** 0.009 0.122*** 0.009 0.123*** 0.009 0.121*** 0.009 New-built (n) 0.221*** 0.013 0.223*** 0.013 0.224*** 0.013 0.221*** 0.013 0.226*** 0.013 0.208*** 0.013 Detached (d) 0.659*** 0.016 0.662*** 0.016 0.663*** 0.016 0.655*** 0.016 0.653*** 0.016 0.654*** 0.016 Semi-detached (s) 0.476*** 0.014 0.480*** 0.014 0.478*** 0.014 0.473*** 0.014 0.472*** 0.014 0.474*** 0.014 Terraced (t) 0.369*** 0.014 0.371*** 0.014 0.370*** 0.014 0.366*** 0.014 0.364*** 0.014 0.367*** 0.014 Floor area (f) 0.004*** 0.000 0.004*** 0.000 0.004*** 0.000 0.004*** 0.000 0.004*** 0.000 0.004*** 0.000 Distance to CBD (z) 0.035*** 0.004 0.038*** 0.004 0.042*** 0.004 0.045*** 0.005 0.054*** 0.005 0.065*** 0.007 Number of bus stops (ı) 0.003*** 0.001 0.003*** 0.001 0.003*** 0.000 0.001*** 0.000 0.001*** 0.000 0.001*** 0.000 2 ward 0.034** 0.020 0.033** 0.020 0.033** 0.020 0.033** 0.020 0.033** 0.020 0.033** 0.020 2 year 0.099*** 0.016 0.099*** 0.016 0.098*** 0.016 0.099*** 0.016 0.098*** 0.016 0.099*** 0.016 2 property 0.044*** 0.001 0.044*** 0.001 0.043*** 0.001 0.043*** 0.001 0.044*** 0.001 0.044*** 0.001 Contribution of 2 ward to 2 property 0.192 0.188 0.190 0.189 0.189 0.188 Contribution of 2 year to 2 property 0.559 0.563 0.563 0.566 0.560 0.563 Sample size 9655 9650 9656 9653 9659 9669 * p < 0.1. ** p < 0.05. *** p < 0.001. ===== PAGE 10 (score 74, numeric-paren 52, table-words 3, var-words 5) ===== 390 Y. Wang et al. / Land Use Policy 42 (2015) 381–391 Table 6 Results of quantile bivariate regressions, log(p) as dependent variable and ı as independent variable. Quantile ı300 m ı400 m ı500 m ı750 m ı1000 m ı750 m ˇ s.e. ˇ s.e. ˇ s.e. ˇ s.e. ˇ s.e. ˇ s.e. 10% −0.004 0.003 0.001 0.002 0.007*** 0.002 0.004*** 0.001 0.002*** 0.000 0.001*** 0.000 20% 0.004 0.003 0.003* 0.002 0.005*** 0.001 0.003*** 0.000 0.002*** 0.000 0.001*** 0.000 30% 0.011*** 0.002 0.007*** 0.002 0.007*** 0.001 0.003*** 0.001 0.002*** 0.000 0.001*** 0.000 40% 0.013*** 0.002 0.009*** 0.001 0.009*** 0.001 0.004*** 0.001 0.002*** 0.000 0.001*** 0.000 50% 0.013*** 0.001 0.013*** 0.001 0.009*** 0.001 0.004*** 0.000 0.003*** 0.000 0.001*** 0.000 60% 0.010*** 0.002 0.010*** 0.002 0.009*** 0.001 0.004*** 0.000 0.002*** 0.000 0.001*** 0.000 70% 0.013*** 0.002 0.013*** 0.002 0.008*** 0.001 0.003*** 0.000 0.002*** 0.000 0.001*** 0.000 80% 0.020*** 0.002 0.020*** 0.002 0.010*** 0.001 0.005*** 0.001 0.003*** 0.000 0.002*** 0.000 90% 0.011*** 0.002 0.011*** 0.002 0.008*** 0.001 0.005*** 0.001 0.003*** 0.000 0.002*** 0.000 * p < 0.1. ** p < 0.05. *** p < 0.001. Specifically, spatial proximity to Cardiff Bus stops tends to ben- efit properties at the high end of the local real estate market. The strongest positive externalities can be observed in properties above the 80% price quantile, with every additional bus stop within a 1500 m buffer zone, for example, contributing to an increase by about 0.22% in property sale price through land value betterment. The corresponding figure for the cheaper properties is halved to around 0.11%. Policy implications The results of our regression analysis based on empirical data from Cardiff have some important policy implications. First and foremost, the outcomes of our ordinary least square regression and multilevel modelling analysis provide convincing evidence to sup- port a classic Georgist hypothesis in the case of Cardiff Bus, that a significant part of the added local property values does come from the land beneath them, or more specifically, from the land plots’ geographic adjacency to the bus stop locations. This finding jus- tifies a potential policy intervention in terms of financing Cardiff Bus through a land value tax mechanism, given the substantiated positive externalities yielded by Cardiff Bus. Second, the results of our quantile calibration entail some even more intriguing practical insights. We find that the positive exter- nalities of Cardiff Bus are distributed unevenly among properties at different price levels in the local real estate market. Generally speaking, according to Table 6, properties at the high end (at the 80% and 90% quantiles) of the market tend to benefit more than those at the low end (at the 10% and 20% quantiles). This implicates a neces- sity to exercise progressive land value taxation, whereby properties at different price levels need to be taxed at different rates, so that the uneven distribution of positive externalities can be accordingly captured. Our estimation, based on the number of bus stops within a 1500 m radius of every individual property included in our sample data, suggests a tentative land value betterment tax rate of 0.11% per new bus stop for the first tier of properties which are priced below £195,000 and 0.22% for the second tier of properties which are of higher market values. We notice a somewhat similar progressive taxation mechanism already incorporated in Cardiff’s existing council tax system, which divides local residential properties into nine different bands by their estimated values and applies incremental tax rates. There is thus a possibility of introducing an embryonic land value tax ele- ment into the current council tax scheme, depending on how we translate the quantile hedonic approach featured in this study into the council taxation system used in practice and also on whether a large enough dataset covering the entire Cardiff Council jurisdiction will become available in the near future. Third, our overall empirical analysis defies a conventional wisdom that public infrastructure always serves public interest. Arguably, the case of Cardiff Bus illustrates a free-ride scenario, wherein the most wealthy property owners, intentionally or unintentionally, end up extracting the values of publicly funded transport infrastructures. A similar finding has also been made recently by Banister and Thurstain-Goodwin (2011, pp. 216–221), who suggest land value betterment created by the rail networks to be even more extensive. In this sense, levying land value tax is not only to capture economic externalities, but also to ensure socioeconomic justice and fairness, as per the Edwardian tradition. Conclusion and future research In this paper, we explore the potential viability of levying land value tax within a local UK context. We focus on the empirical and technical aspects by asking how to assess the spatialised relations between the local public transport infrastructures and the land val- ues of nearby residential properties. We answer this question by studying the case of Cardiff Bus. Based on our multilevel and quan- tile hedonic analysis, we find strong evidence with regard to the positive externalities of Cardiff Bus network towards the market values of nearby residential properties. Given the uneven external- ity distribution between properties at different price levels, we call for a progressive land value tax scheme. Because of the political context of our study, we consider action research a key direction of our future research. We would like to take more opportunities to use our research to educate and inform the general public about why land value tax (LVT) is an economically as well as socially desirable idea. Potential opposi- tions to LVT are less likely from an efficiency perspective, since most classic research has already confirmed that purely taxing on incomes from land would not distort resource allocation (Mills, 1981; Tideman, 1982; Wildasin, 1982). More recent research also shows that it is actually easier than thought to implement neutral or non-distortional land taxation (Arnott, 2005). Even with errors in land valuation, the collection of LVT has been found to result in no more distortions than the conventional property taxation scheme (Chapman et al., 2009). Resistances to LVT are thus more likely about the distributional effects of land taxation, or put simply, regarding how to split the bills that fund public investments. Con- troversial as this question is, we hope our current study and future research on land value tax will help the voters make an informed decision. Acknowledgements The research reported in this paper is funded by the British Academy/Leverlulme Small Research Grant initiative (ref: SG122122). All errors lurking in the paper are the authors’. ===== PAGE 11 (score 12, numeric-paren 0, table-words 0, var-words 12) ===== Y. Wang et al. / Land Use Policy 42 (2015) 381–391 391 References Al-Mosaind, M.A., Dueker, K.J., Strathman, J.G., 1993. Light-rail Transit Stations and Property Values: A Hedonic Price Approach. 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