1# RECORD 327115a5186cc436d1749ca2acaa98314f882fee2# TITLE: Bus stop, property price and land value tax: A multilevel hedonic analysis with quantile calibration3# YEAR: 2015.0 VENUE: nan DOI: 10.1016/j.landusepol.2014.07.0174# AUTHORS: Yiming Wang; D. Potoglou; S. Orford; Yi Gong5# PAGES: 11 CANDIDATE TABLE PAGES (1-based): [7, 8, 9, 10]6# 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.789===== PAGE 1 (score 9, numeric-paren 0, table-words 0, var-words 9) =====1011Land12 Use13 Policy14 4215 (2015)16 381–39117Contents18 lists19 available20 at21 ScienceDirect22Land23 Use24 Policy25j26 o27 ur28 na29 l ho30 me31 page:32 www.elsevier.com/locate/landusepol33Bus34 stop,35 property36 price37 and38 land39 value40 tax:41 A42 multilevel43 hedonic44analysis45 with46 quantile47 calibration48Yiming49 Wang a,∗, Dimitris50 Potoglou b,c, Scott51 Orford b, Yi Gong d52a The53 Bartlett54 School55 of56 Construction57 and58 Project59 Management,60 The61 Bartlett62 Faculty63 of64 Built65 Environment,66 University67 College68 London,69 London70 WC1E71 7HB,72 UK73b School74 of75 Planning76 and77 Geography,78 Cardiff79 University,80 King81 Edward82 VII83 Avenue,84 Cardiff85 CF1086 3WA,87 UK88c RAND89 Europe,90 Westbrook91 Centre,92 Milton93 Road,94 Cambridge95 CB496 1YG,97 UK98d Sustainable99 Places100 Research101 Institute,102 Cardiff103 University,104 33105 Park106 Place,107 Cardiff108 CF10109 3BA,110 UK111a112 r113 t i c l e 114i n115 f o116Article117 history:118Received119 20120 October121 2013122Received123 in124 revised125 form126 30127 May128 2014129Accepted130 26131 July132 2014133Keywords:134Land135 value136 tax137Transport138 infrastructure139Bus140 stop141Property142 price143a144 b145 s t r146 a c t147Based148 on a multilevel149 and quantile150 hedonic analysis151 regarding152 the local public153 bus system and the prices154of155 residential156 properties157 in Cardiff, Wales,158 we find strong159 evidence160 to support161 two162 research163 hypotheses:164(a)165 the number166 of bus stops within walking167 distance168 (300–1500 m)169 to a property170 is positively associated171with172 the property’s173 observed174 sale175 price, and (b) properties176 of higher market177 prices, compared178 with their179cheaper180 counterparts,181 tend to benefit182 more183 from spatial184 proximity185 to the bus stop locations.186 Given these187statistical188 findings,189 we argue that, land value tax (LVT),190 albeit191 a classic political192 idea dating193 back to the early19420th195 century,196 does have contemporary197 relevance198 and, with modern199 geographic200 information201 technologies,202can203 be rigorously204 analysed205 and empirically206 justified207 with a view208 to actual implementation.209 Levying LVT210will211 not only generate additional212 fiscal revenues213 to help finance214 the development215 and maintenance216 of217local218 public219 infrastructures,220 but will also ensure221 a more just distribution222 of the economic223 welfare224 yielded225by226 public investment.227© 2014 Elsevier228 Ltd. All rights229 reserved.230Introduction231In232 recent233 years,234 the235 United236 Kingdom237 (UK)238 has239 witnessed240 a241 revival242of243 public244 interests245 in246 land247 value248 tax249 (LVT).1 The250 original251 idea252 of253LVT254 dates255 back256 to257 George258 (1879), an259 American260 political261 economist,262who,263 partly264 inspired265 by266 Smith267 (1863), posits268 that269 the270 value271 of272 land,273ultimately,274 comes275 from276 the277 adjacent278 infrastructures279 and280 amenities281invested282 by283 the284 whole285 community.286 Increments287 in288 land289 value290 due291to292 public293 investment,294 thus,295 ought296 to297 be298 re-captured299 through300 LVT.301The302 earliest303 political304 attempts305 to306 legislate307 LVT308 took309 place310 in311 the312 late313Edwardian314 Britain315 (Short,316 1997, Chapter317 2).318 LVT319 was320 officially321 pro-322posed323 in324 the325 1909326 finance327 bill328 (also329 known330 as331 “People’s332 Budget”),333when334 David335 Lloyd336 George337 served338 as339 Chancellor340 of341 the342 Exchequer343as344 a345 member346 of347 the348 governing349 Liberal350 party.351 However,352 the353 then354Conservatives-dominated355 House356 of357 Lords,358 though359 passing360 the361 gen-362eral363 budget364 in365 1910,366 managed367 to368 veto369 the370 LVT371 proposal.372 A373 similar374story375 happened376 later377 with378 the379 1931380 Finance381 Act,382 which383 contained384∗Corresponding385 author.386 +44387 02076791890.388E-mail389 address:390 yiming.wang@ucl.ac.uk391 (Y.392 Wang).3931 For394 the395 examples396 of397 Labour’s398 Land399 Value400 Campaign401 (http://www.labourland.402org/) and403 the404 Liberal405 Democratic406 Party’s407 Action408 for409 Land410 Taxation411 and412 Economic413Reform414 (http://libdemsalter.org.uk/).415a416 LVT417 initiative418 passed419 by420 the421 ruling422 Labour423 party424 but425 was426 rejected427again428 by429 the430 Tory431 government432 in433 1934434 (Wenzer,435 2000).436 One437 of438 the439latest440 efforts441 to442 seek443 legislation444 of445 LVT446 was447 in448 2012449 by450 Carolyn451 Lucas,452a453 Green454 party455 member456 of457 the458 UK459 parliament460 (The461 UK462 Parliament,4632012).464Although465 LVT466 was467 never468 implemented469 in470 Britain,471 its472 traces473 can474be475 observed476 in477 many478 other479 places480 around481 the482 world,483 such484 as485 in486 the487cities488 of489 Pittsburgh490 and491 Harrisburg492 in493 the494 American495 State496 of497 Penn-498sylvania499 and500 a501 number502 of503 countries504 such505 as506 Australia,507 Denmark,508Estonia,509 Russia,510 and511 New512 Zealand513 (Andelson,514 2000;515 Bourassa,516 1990;517Dye518 and519 England,520 2010;521 Wyatt,522 1994).523 While524 the525 actual526 policy527practice528 varies529 among530 these531 international532 cases,533 LVT534 has535 been536increasingly537 justified538 as539 a540 way541 to542 finance543 the544 construction545 and546maintenance547 of548 public549 transport550 infrastructures.551 The552 basic553 rationale554remains555 quite556 the557 same558 as559 per560 George561 original562 (1879), that563 publicly564invested565 transport566 network567 can568 promote569 the570 values571 of572 nearby573 pri-574vately575 owned576 land577 plots,578 given579 their580 improved581 accessibility.582 From583a584 political585 economy586 perspective,587 this588 part589 of590 added591 land592 value,593 if594substantiated,595 becomes596 a597 kind598 of599 positive600 externalities601 which602 can603be604 offset605 or606 captured607 through608 LVT.609 Otherwise,610 general611 tax612 payers613(who614 generate615 government616 revenues)617 are618 essentially619 subsidising620landowners621 who622 “quietly”623 extract624 the625 values626 of627 public628 transport629infrastructures.630 Making631 this632 free-ride633 problem634 even635 more636 press-637ing638 is639 the640 undersupply641 and642 underfunding643 of644 public645 transport646 in647 the648http://dx.doi.org/10.1016/j.landusepol.2014.07.0176490264-8377/©650 2014651 Elsevier652 Ltd.653 All654 rights655 reserved.656657658===== PAGE 7 (score 201, numeric-paren 156, table-words 4, var-words 5) =====659660Y.661 Wang662 et663 al.664 / Land665 Use666 Policy667 42668 (2015)669 381–391670 671387672Table673 3674Results675 of676 ordinary677 least678 squares,679 log(p)680 as681 dependent682 variable.683Model684 1685 686Model687 2688 689Model690 3691 692Model693 4694 695Model696 5697 698Model699 6700Within701 300702 m703 704Within705 400706 m707 708Within709 500710 m711 712Within713 750714 m715 716Within717 1000718 m719 720Within721 1500722 m723ˇ724 725s.e.726 727ˇ728 729s.e.730 731ˇ732 733s.e.734 735ˇ736 737s.e.738 739ˇ740 741s.e.742 743ˇ744 745s.e.746Constant747 74810.506***7490.025750 75110.479***7520.027753 75410.438***7550.028756 75710.417***7580.034759 76010.343***7610.039762 76310.269***7640.048765Freehold766 (r)767 7680.120***7690.009770 7710.121***7720.009773 7740.123***7750.009776 7770.126***7780.009779 7800.127***7810.009782 7830.125***7840.009785New-built786 (n)787 7880.228***7890.013790 7910.230***7920.013793 7940.232***7950.013796 7970.230***7980.013799 8000.237***8010.013802 8030.217***8040.013805Detached806 (d)807 8080.651***8090.016810 8110.654***8120.016813 8140.654***8150.016816 8170.647***8180.016819 8200.644***8210.016822 8230.645***8240.016825Semi-detached826 (s)827 8280.468***8290.014830 8310.471***8320.014833 8340.469***8350.014836 8370.465***8380.014839 8400.464***8410.014842 8430.465***8440.015845Terraced846 (t)847 8480.360***8490.014850 8510.361***8520.014853 8540.359***8550.014856 8570.356***8580.014859 8600.354***8610.014862 8630.357***8640.014865Floor866 area867 (f)868 8690.004***8700.000871 8720.004***8730.000874 8750.004***8760.000877 8780.004***8790.000880 8810.004***8820.000883 8840.004***8850.000886Distance887 to888 CBD889 (z)890 8910.035***8920.004893 8940.038***8950.004896 8970.042***8980.004899 9000.046***9010.005902 9030.055***9040.005905 9060.066***9070.007908Sale909 in910 2001911 (Y1)912 9130.112***9140.009915 9160.110***9170.009918 9190.111***9200.009921 9220.112***9230.009924 9250.113***9260.009927 9280.110***9290.009930Sale931 in932 2002933 (Y2)934 9350.301***9360.009937 9380.297***9390.009940 9410.297***9420.009943 9440.297***9450.009946 9470.299***9480.009949 9500.295***9510.009952Sale953 in954 2003955 (Y3)956 9570.526***9580.009959 9600.522***9610.009962 9630.524***9640.009965 9660.525***9670.009968 9690.522***9700.009971 9720.520***9730.009974Sale975 in976 2004977 (Y4)978 9790.694***9800.009981 9820.693***9830.009984 9850.693***9860.009987 9880.694***9890.009990 9910.693***9920.009993 9940.691***9950.009996Sale997 in998 2005999 (Y5)1000 10010.781***10020.0101003 10040.778***10050.0101006 10070.776***10080.0091009 10100.778***10110.0091012 10130.777***10140.0091015 10160.776***10170.0101018Sale1019 in1020 20061021 (Y6)1022 10230.791***10240.0091025 10260.789***10270.0091028 10290.789***10300.0091031 10320.791***10330.0091034 10350.791***10360.0091037 10380.789***10390.0091040Sale1041 in1042 20071043 (Y7)1044 10450.842***10460.0091047 10480.840***10490.0091050 10510.840***10520.0091053 10540.841***10550.0091056 10570.840***10580.0091059 10600.839***10610.0091062Sale1063 in1064 20081065 (Y8)1066 10670.858***10680.0231069 10700.857***10710.0231072 10730.861***10740.0221075 10760.860***10770.0221078 10790.861***10800.0221081 10820.861***10830.0221084Adamstown1085 (W1) 1086−0.512***10870.0141088 1089−0.508***10900.0141091 1092−0.502***10930.0141094 1095−0.499***10960.0141097 1098−0.488***10990.0141100 1101−0.465***11020.0151103Splott1104 (W2)1105 1106−0.513***11070.0121108 1109−0.507***11100.0121111 1112−0.493***11130.0121114 1115−0.486***11160.0131117 1118−0.464***11190.0141120 1121−0.430***11220.0171123Cathays1124 (W3)1125 1126−0.196***11270.0201128 1129−0.194***11300.0201131 1132−0.186***11330.0201134 1135−0.182***11360.0201137 1138−0.164***11390.0201140 1141−0.152***11420.0211143Heath1144 (W4)1145 1146−0.142***11470.0191148 1149−0.137***11500.0191151 1152−0.139***11530.0191154 1155−0.143***11560.0191157 1158−0.146***11590.0191160 1161−0.144***11620.0191163Llanishen1164 (W5)1165 1166−0.240***11670.0241168 1169−0.250***11700.0241171 1172−0.257***11730.0241174 1175−0.262***11760.0251177 1178−0.269***11790.0251180 1181−0.276***11820.0251183Penylan1184 (W6)1185 1186−0.064***11870.0111188 1189−0.064***11900.0111191 1192−0.062***11930.0111194 1195−0.051***11960.0111197 1198−0.039***11990.0111200 1201−0.0181202 12030.0131204Pentwyn1205 (W7)1206 1207−0.648***12080.0101209 1210−0.647***12110.0101212 1213−0.643***12140.0101215 1216−0.644***12170.0101218 1219−0.638***12200.0101221 1222−0.638***12230.0101224Plasnewydd1225 (W8)1226 1227−0.235***12280.0141229 1230−0.230***12310.0141232 1233−0.225***12340.0141235 1236−0.229***12370.0141238 1239−0.229***12400.0141241 1242−0.218***12430.0141244Pontprennau1245 (W9)1246 1247−0.500***12480.0201249 1250−0.502***12510.0201252 1253−0.502***12540.0201255 1256−0.506***12570.0201258 1259−0.508***12600.0201261 1262−0.505***12630.0201264Number1265 of1266 bus1267 stops1268 (ı)1269 12700.003***12710.0011272 12730.003***12740.0011275 12760.003***12770.0001278 12790.001***12800.0001281 12820.001***12830.0001284 12850.001***12860.0001287Sample1288 size1289 129096551291 129296501293 129496561295 129696531297 129896591299 130096691301R213020.8501303 13040.8501305 13060.8501307 13080.8511309 13100.8501311 13120.8491313F-test1314 (df)1315 131621761317 (25)1318 131921791320 (25)1321 132221851323 (25)1324 132521951326 (25)1327 132821861329 (25)1330 133121671332 (25)1333p-Value1334 13350.0001336 13370.0001338 13390.0001340 13410.0001342 13430.0001344 13450.0001346*p1347 <1348 0.1;1349 **p <1350 0.05.1351*** p1352 <1353 0.001.135413551356===== PAGE 8 (score 33, numeric-paren 0, table-words 12, var-words 4) =====135713583881359 1360Y.1361 Wang1362 et1363 al.1364 / Land1365 Use1366 Policy1367 421368 (2015)1369 381–3911370Table1371 41372Testing1373 autocorrelation1374 and1375 homoscedasticity1376 in1377 OLS1378 results.1379Model1380 11381 1382Model1383 21384 1385Model1386 31387 1388Model1389 41390 1391Model1392 51393 1394Model1395 61396Within1397 3001398 m1399 1400Within1401 4001402 m1403 1404Within1405 5001406 m1407 1408Within1409 7501410 m1411 1412Within1413 10001414 m1415 1416Within1417 15001418 m1419Independence1420 of1421 ε1422Durbin–Watson1423 14241.7731425 14261.7771427 14281.7791429 14301.7791431 14321.7791433 14341.7761435p-Value1436 14370.0001438 14390.0001440 14410.0001442 14430.0001444 14450.0001446 14470.0001448Spatial1449 autocorrelation1450Moran’s1451 I1452 14530.1501454 14550.1501456 14570.1491458 14590.1491460 14610.1501462 14630.1491464Expected1465 1466−0.00011467 1468−0.00011469 1470−0.00011471 1472−0.00011473 1474−0.00011475 1476−0.00011477Standard1478 deviation1479 14800.00041481 14820.00041483 1484−0.00041485 14860.00041487 14880.00041489 14900.00041491p-Value14920.00014930.00014940.00014950.00014960.0001497 14980.0001499Homoscedasticity1500 1501Breusch1502 and1503 Pagan1504 (1979)1505 test15062(df)1507 15080.2331509 (1)1510 15110.1011512 (1)1513 15140.533(1)1515 15160.887(1)1517 15181.503(1)1519 15200.612(1)1521p-Value1522 15230.62931524 15250.75121526 15270.46511528 15290.34611530 15310.22021532 15330.43411534(NaPTAN-v2.2)1535 dataset1536 (Department1537 for1538 Transport,1539 2007).1540 These1541are1542 also1543 shown1544 in1545 Fig.1546 2. The1547 number1548 of1549 bus1550 stops1551 within1552 specified1553radii1554 of1555 each1556 property1557 (i.e.,1558 3001559 m,1560 4001561 m,1562 5001563 m,1564 7501565 m,1566 10001567 m,1568 and156915001570 m)1571 was1572 calculated1573 using1574 ArcGIS.1575 Table1576 11577 presents1578 a1579 summary1580of1581 the1582 variables1583 included1584 in1585 the1586 sample1587 of1588 properties1589 in1590 the1591 dataset.1592Table1593 21594 also1595 shows1596 that1597 this1598 is1599 broadly1600 a1601 cross-sectional1602 rather1603 than1604panel1605 dataset,1606 as1607 more1608 than1609 75%1610 of1611 the1612 properties1613 have1614 only1615 been1616sold1617 once1618 during1619 the1620 9-year1621 period.1622Model1623 results41624OLS1625 results1626Table1627 31628 presents1629 the1630 results1631 of1632 six1633 ordinary1634 least1635 square1636 (OLS)1637regressions1638 based1639 on1640 the1641 conventional1642 hedonic1643 approach1644 pre-1645scribed1646 in1647 “A1648 Hedonic1649 Approach”1650 section.1651 The1652 dependent1653 variable1654in1655 every1656 regression1657 is1658 the1659 natural1660 logarithm1661 of1662 price1663 (log(p))1664 and1665 all1666of1667 the1668 six1669 models1670 attempt1671 to1672 capture1673 the1674 effect1675 of1676 Cardiff1677 Bus1678 on1679land1680 value1681 via1682 ı,1683 which1684 measures1685 the1686 number1687 of1688 bus1689 stops1690 within1691different1692 walkable1693 radii1694 (i.e.,1695 3001696 m,1697 4001698 m,1699 5001700 m,1701 7501702 m,1703 10001704 m,1705and1706 15001707 m)1708 around1709 each1710 property1711 contained1712 in1713 the1714 dataset.1715 Fol-1716lowing1717 Belsley1718 et1719 al.1720 (1980)1721 a1722 variety1723 of1724 regression1725 diagnostic1726 tests1727were1728 performed1729 on1730 the1731 OLS1732 models1733 in1734 order1735 to1736 check1737 whether1738 any1739of1740 the1741 regression1742 assumptions1743 had1744 been1745 violated.1746 These1747 included1748identifying1749 outliers1750 that1751 may1752 have1753 a1754 disproportionate1755 influence1756 on1757the1758 models,1759 and1760 removing1761 the1762 corresponding1763 observations1764 if1765 neces-1766sary;1767 checking1768 for1769 serious1770 multi-collinearity1771 in1772 the1773 models;1774 testing1775the1776 normality1777 of1778 the1779 error1780 term;1781 and1782 checking1783 for1784 heteroscedasticity1785and1786 autocorrelation1787 in1788 the1789 error1790 term.1791 The1792 latter1793 tests1794 are1795 discussed1796in1797 more1798 detail1799 in1800 a1801 later1802 section.1803Model1804 1,1805 for1806 example,1807 includes1808 the1809 explanatory1810 variable1811 with1812regard1813 to1814 the1815 number1816 of1817 bus1818 stops1819 within1820 3001821 m1822 around1823 each1824 prop-1825erty.1826 The1827 estimated1828 parameter1829 corresponding1830 to1831 this1832 variable1833 shows1834that1835 the1836 effect1837 of1838 Cardiff1839 Bus1840 on1841 property1842 price1843 or,1844 more1845 accu-1846rately,1847 on1848 land1849 value1850 as1851 reflected1852 in1853 property1854 price,1855 is1856 positive1857and1858 statistically1859 significant1860 at1861 the1862 1%1863 significance1864 level.1865 In1866 other1867words,1868 the1869 more1870 bus1871 stops1872 there1873 are1874 around1875 a1876 property,1877 the1878 higher1879the1880 value1881 of1882 land1883 beneath1884 that1885 property.1886 Similar1887 findings1888 are1889 also1890discernible1891 from1892 the1893 other1894 regressions1895 estimating1896 the1897 effect1898 of1899 num-1900ber1901 of1902 bus1903 stops1904 within1905 4001906 m,1907 500,1908 7501909 m,1910 10001911 m1912 and1913 15001914 m,1915respectively.1916By1917 back1918 transforming1919 the1920 magnitude1921 of1922 the1923 estimated1924 coeffi-1925cient1926 with1927 regard1928 to1929 the1930 number1931 of1932 bus1933 stops1934 within1935 3001936 m,1937 we1938can1939 calculate1940 the1941 marginal1942 increase1943 in1944 land1945 value1946 as1947 a1948 result1949 of1950 pla-1951cing1952 every1953 extra1954 bus1955 stop1956 around1957 a1958 property.1959 In1960 percentage,1961 this19624 OLS1963 results1964 are1965 estimated1966 in1967 IBM1968 SPSS1969 Statistics1970 20,1971 while1972 the1973 multi-level1974 analysis1975is1976 conducted1977 with1978 MLWin1979 v.3.201980 (Rasbash1981 et1982 al.,1983 2012).1984 The1985 quantile1986 regression1987 is1988exercised1989 using1990 an1991 R/SPSS1992 interface1993 based1994 on1995 the1996 original1997 “quantreg”1998 R1999 codes2000 by2001Koenker2002 (2007).2003added2004 land2005 value2006 equals2007 to2008 (e0.003 −2009 1)2010 ×2011 1002012 ≈2013 0.3%.2014 Examination2015 of2016the2017 coefficients2018 of2019 the2020 number2021 of2022 bus-stops2023 within2024 different2025 buffer2026zones2027 around2028 property2029 in2030 Table2031 32032 suggests2033 that2034 the2035 effect2036 of2037 bus2038stops2039 within2040 distance2041 is2042 relatively2043 stable2044 and2045 statistically2046 signifi-2047cant.2048 The2049 land2050 value2051 benefit2052 of2053 every2054 additional2055 bus2056 stop2057 within2058 a2059circular2060 catchment2061 area2062 larger2063 than2064 5002065 m2066 by2067 radius2068 is2069 about2070 0.1%2071of2072 the2073 corresponding2074 property2075 price.2076 The2077 figure2078 rises2079 to2080 0.3%2081 if2082 the2083size2084 of2085 catchment2086 shrinks2087 to2088 5002089 m2090 or2091 less2092 by2093 radius.2094 Overall,2095 all2096models2097 show2098 that2099 the2100 availability2101 of2102 public2103 transport2104 infrastructure2105can2106 significantly2107 promote2108 land2109 value2110 and2111 thereby2112 raise2113 property2114price.2115The2116 coefficient2117 of2118 z,2119 the2120 distance2121 to2122 Cardiff’s2123 central2124 business2125district2126 (CBD),2127 is2128 also2129 positive2130 and2131 statistically2132 significant.2133 Accord-2134ing2135 to2136 some2137 studies,2138 this2139 finding2140 is2141 counter-intuitive2142 and2143 against2144the2145 conventional2146 wisdom2147 about2148 urban2149 spatial2150 structure.2151 Per2152 clas-2153sic2154 urban2155 economic2156 theory2157 (e.g.,2158 Anas2159 et2160 al.,2161 1998),2162 property2163 prices2164should2165 decline2166 alongside2167 increase2168 in2169 the2170 properties’2171 distance2172 to2173 city2174centre.2175 However,2176 previous2177 studies2178 have2179 also2180 provided2181 several2182 expla-2183nations2184 for2185 the2186 positive2187 influence2188 of2189 distance2190 to2191 CBD2192 on2193 property2194price.2195 For2196 example,2197 the2198 assumption2199 of2200 a2201 declining2202 land2203 value2204 as2205distance2206 increases2207 from2208 CBD2209 is2210 based2211 on2212 the2213 adoption2214 of2215 a2216 mono-2217centric2218 location2219 choice2220 model2221 (Dubin2222 and2223 Sung,2224 1987),2225 while2226 Cardiff2227appears2228 to2229 be2230 a2231 rather2232 polycentric2233 city.2234Floor2235 area2236 is2237 also2238 a2239 positive2240 and2241 statistically2242 significant2243 factor2244 in2245terms2246 of2247 its2248 impact2249 on2250 property2251 price.2252 The2253 effect2254 of2255 floor2256 space2257 is2258consistent2259 across2260 all2261 six2262 models2263 presented2264 in2265 Table2266 3.2267 Following2268 a2269back2270 transformation2271 of2272 the2273 parameter2274 with2275 respect2276 to2277 floor2278 space,2279we2280 find2281 that2282 every2283 additional2284 square2285 metre2286 in2287 a2288 property2289 increases2290its2291 value2292 by2293 (e0.004 −2294 1)2295 ×2296 1002297 ≈2298 0.4%.2299We capture2300 mean2301 spatial2302 variation2303 in2304 property2305 price2306 by2307 using2308 a2309series2310 of2311 dummy-coded2312 variables2313 representing2314 the2315 wards2316 in2317 which2318each2319 property2320 is2321 located.2322 The2323 ward2324 named2325 Cyncoed2326 is2327 used2328 as2329 the2330reference2331 category2332 as2333 it2334 is2335 the2336 area2337 with2338 the2339 highest2340 average2341 prop-2342erty2343 price2344 in2345 the2346 data2347 set.2348 All2349 models2350 show2351 a2352 significant2353 difference2354 in2355property2356 price2357 from2358 Cyncoed,2359 although2360 the2361 last2362 regression2363 (model236462365 in2366 Table2367 3)2368 indicates2369 no2370 significant2371 difference2372 in2373 property2374 price2375between2376 Cyncoed2377 and2378 Penylan.2379All2380 other2381 coefficients2382 have2383 the2384 expected2385 signs2386 and2387 agree2388 with2389conventional2390 findings2391 in2392 the2393 hedonic2394 literature2395 (Edmonds,2396 1984).2397For2398 example,2399 a2400 property2401 under2402 a2403 freehold2404 tenure2405 is2406 likely2407 to2408 show2409 a2410higher2411 price2412 compared2413 with2414 a2415 leasehold2416 property.2417 Similarly,2418 a2419 newly2420built2421 property2422 tends2423 to2424 have2425 more2426 market2427 value2428 than2429 an2430 aged2431 prop-2432erty.2433 Detached2434 properties2435 exhibit2436 the2437 highest2438 prices,2439 followed2440 by2441semi-detached2442 properties2443 and2444 flats,2445 of2446 which2447 the2448 latter2449 is2450 defined2451as2452 the2453 reference2454 property2455 type2456 in2457 this2458 analysis.2459 Finally,2460 the2461 dummy2462variables2463 with2464 regard2465 to2466 price2467 differences2468 due2469 to2470 the2471 calendar2472 year2473when2474 a2475 property2476 was2477 sold2478 are2479 all2480 significantly2481 different2482 from2483 zero.2484The2485 year2486 of2487 20002488 is2489 used2490 as2491 the2492 reference2493 year,2494 while2495 the2496 remaining2497coefficients2498 indicate2499 a2500 constant2501 increase2502 in2503 property2504 price2505 over2506 time,2507partly2508 due2509 to2510 inflation2511 in2512 pound2513 sterling2514 and2515 partly2516 due2517 to2518 dynamics2519in2520 the2521 local2522 housing2523 market.252425252526===== PAGE 9 (score 108, numeric-paren 72, table-words 7, var-words 6) =====25272528Y.2529 Wang2530 et2531 al.2532 / Land2533 Use2534 Policy2535 422536 (2015)2537 381–3912538 25393892540Multilevel2541 results2542As2543 mentioned2544 earlier,2545 ordinary2546 least2547 square2548 (OLS)2549 regression2550may2551 involve2552 potential2553 issues2554 related2555 to2556 spatial2557 and2558 temporal2559 auto-2560correlations.2561 In2562 terms2563 of2564 spatial2565 autocorrelation,2566 the2567 prices2568 of2569properties2570 within2571 a2572 same2573 jurisdiction2574 (i.e.,2575 electoral2576 wards2577 in2578 our2579data)2580 are2581 likely2582 to2583 be2584 more2585 similar2586 than2587 those2588 outside.2589 In2590 terms2591 of2592temporal2593 autocorrelation,2594 properties2595 sold2596 within2597 the2598 same2599 year2600 may2601show2602 a2603 convergence2604 in2605 sale2606 price2607 vis-à-vis2608 those2609 transacted2610 within2611a2612 different2613 year.2614 To2615 account2616 for2617 both2618 the2619 geographic2620 as2621 well2622 as2623 tem-2624poral2625 autocorrelations,2626 our2627 OLS2628 models2629 would2630 have2631 to2632 be2633 expanded2634extensively2635 to2636 accommodate2637 (M2638 −2639 1)2640 ×2641 (X2642 −2643 1)2644 interaction2645 factors.2646 In2647contrast,2648 multilevel2649 modelling2650 analysis2651 appears2652 to2653 be2654 a2655 more2656 effi-2657cient2658 alternative2659 to2660 address2661 the2662 issue2663 of2664 autocorrelation2665 (Jones2666 and2667Bullen,2668 1994).2669Table2670 42671 identifies2672 a2673 problem2674 of2675 autocorrelation2676 underlying2677 all2678the2679 six2680 OLS2681 models.2682 The2683 Durbin–Watson2684 test2685 suggests2686 an2687 overall2688significant2689 serial2690 interdependence2691 of2692 the2693 error2694 terms2695 (ε)2696 in2697 every2698OLS2699 model.2700 Because2701 the2702 Durbin–Watson2703 result2704 is2705 more2706 sensitive2707to2708 temporal2709 autocorrelation,2710 we2711 further2712 exercise2713 a2714 Moran’s2715 I test2716(Anselin,2717 1988),2718 of2719 which2720 the2721 estimates2722 also2723 confirm2724 a2725 presence2726 of2727a2728 significant2729 albeit2730 weak2731 spatial2732 autocorrelation2733 (Moran’s2734 I ≈2735 0.15).2736However,2737 the2738 Breusch2739 and2740 Pagan2741 (1979)2742 test2743 for2744 the2745 whole2746 model2747suggests2748 that2749 heteroscedasticity2750 is2751 not2752 a2753 problem2754 in2755 the2756 error2757 terms2758and2759 they2760 have2761 constant2762 variance.2763 To2764 address2765 the2766 autocorrelation2767problem,2768 six2769 multilevel2770 regressions2771 (models2772 7–12)2773 are2774 further2775 con-2776ducted.2777 Since2778 over2779 75%2780 of2781 the2782 sampled2783 properties2784 are2785 sold2786 only2787 once2788between2789 20002790 and2791 2009,2792 we2793 estimate2794 the2795 multilevel2796 models2797 in2798 ref-2799erence2800 to2801 the2802 cross-sectional2803 hierarchy2804 shown2805 in2806 Fig.2807 1(a)2808 instead2809of2810 the2811 panel2812 structure2813 as2814 per2815 Fig.2816 1(b).2817Table2818 52819 illustrates2820 the2821 results2822 of2823 our2824 multilevel2825 modelling2826 anal-2827ysis.2828 Both2829 the2830 ward-by-ward2831 (22832ward)2833 and2834 year-to-year2835 variance2836(22837year) are2838 statistically2839 significant,2840 confirming2841 the2842 OLS2843 finding2844 that2845there2846 are2847 generalisable2848 differences2849 across2850 wards2851 and2852 years2853 in2854 terms2855of2856 property2857 price.2858 Around2859 19%2860 of2861 the2862 aggregate2863 variance2864 in2865 property2866price2867 (22868property) can2869 be2870 attributed2871 to2872 22873ward, compared2874 with2875 around287656%2877 to2878 22879year. Nevertheless,2880 ı,2881 which2882 measures2883 the2884 impact2885 of2886 bus2887stops2888 on2889 nearby2890 land2891 value,2892 remains2893 stable2894 and2895 is2896 thus2897 unbiased.2898This2899 essentially2900 confirms2901 the2902 robustness2903 of2904 the2905 OLS2906 estimates,2907 even2908in2909 face2910 of2911 the2912 identified2913 spatial2914 and2915 temporal2916 autocorrelations.2917Quantile2918 results2919Table2920 62921 reports2922 the2923 results2924 of2925 our2926 quantile2927 bivariate2928 regressions.2929As2930 noted2931 above,2932 we2933 conduct2934 this2935 quantile2936 analysis2937 mainly2938 as2939 a2940 post2941hoc2942 calibration2943 of2944 the2945 OLS2946 estimates,2947 in2948 case2949 price-based2950 hetero-2951geneities2952 lead2953 to2954 a2955 biased2956 coefficient2957 measure2958 of2959 ı.2960 We thus2961 use2962log(p)2963 as2964 the2965 dependent2966 variable2967 and2968 ı2969 as2970 the2971 only2972 independent2973variable2974 to2975 gauge2976 the2977 number2978 of2979 bus2980 stops2981 within2982 different2983 walkable2984distances,2985 from2986 3002987 m2988 to2989 15002990 m,2991 around2992 every2993 observed2994 property2995location.2996Generally2997 speaking,2998 Table2999 63000 indicates3001 relatively3002 weak3003 and/or3004insignificant3005 associations3006 between3007 the3008 number3009 of3010 bus3011 stops3012 and3013property3014 prices3015 at3016 the3017 10%3018 and3019 20%3020 quantiles,3021 where3022 the3023 observed3024property3025 prices3026 are3027 below3028 £74,0003029 in3030 the3031 dataset.3032 In3033 contrast,3034 the3035strongest3036 and3037 most3038 significant3039 correlations3040 can3041 be3042 found3043 for3044 proper-3045ties3046 at3047 the3048 80%3049 quantile,3050 priced3051 around3052 £195,000.3053 Those3054 even3055 more3056expensive3057 properties3058 at3059 the3060 90%3061 quantile3062 (i.e.,3063 £250.000),3064 however,3065seem3066 to3067 benefit3068 no3069 more3070 from3071 the3072 number3073 of3074 nearby3075 bus3076 stops3077 than3078those3079 at3080 the3081 80%3082 quantile,3083 even3084 though3085 they3086 tend3087 to3088 be3089 better3090 off3091compared3092 with3093 all3094 properties3095 below3096 the3097 80%3098 quantile,3099 especially3100when3101 we3102 increase3103 the3104 search3105 radius3106 to3107 more3108 than3109 7503110 m.3111While3112 confirming3113 the3114 overall3115 robustness3116 of3117 OLS3118 estimates3119 with3120regard3121 to3122 the3123 mean3124 of3125 ı,3126 the3127 results3128 of3129 our3130 quantile3131 analysis3132 do3133 sug-3134gest3135 ı3136 to3137 be3138 systematically3139 volatile3140 across3141 different3142 price3143 segments.3144Table3145 53146Results3147 of3148 multi-level3149 modelling,3150 log(p)3151 as3152 dependent3153 variable.3154Model3155 73156 3157Model3158 83159 3160Model3161 93162 3163Model3164 103165 3166Model3167 113168 3169Model3170 123171Within3172 3003173 m3174 3175Within3176 4003177 m3178 3179Within3180 5003181 m3182 3183Within3184 7503185 m3186 3187Within3188 10003189 m3190 3191Within3192 15003193 m3194ˇ03195s.e.3196 3197ˇ03198s.e.3199 3200ˇ03201s.e.3202 3203ˇ03204s.e.3205 3206ˇ03207s.e.3208 3209ˇ03210s.e.3211Constant3212 321310.735***32140.0703215 321610.710***32170.0713218 321910.671***32200.0713221 322210.660***32230.0733224 322510.590***32260.0753227 322810.520***32290.0793230Freehold3231 (r)3232 32330.116***32340.0093235 32360.117***32370.0093238 32390.119***32400.0093241 32420.122***32430.0093244 32450.123***32460.0093247 32480.121***32490.0093250New-built3251 (n)3252 32530.221***32540.0133255 32560.223***32570.0133258 32590.224***32600.0133261 32620.221***32630.0133264 32650.226***32660.0133267 32680.208***32690.0133270Detached3271 (d)3272 32730.659***32740.0163275 32760.662***32770.0163278 32790.663***32800.0163281 32820.655***32830.0163284 32850.653***32860.0163287 32880.654***32890.0163290Semi-detached3291 (s)3292 32930.476***32940.0143295 32960.480***32970.0143298 32990.478***33000.0143301 33020.473***33030.0143304 33050.472***33060.0143307 33080.474***33090.0143310Terraced3311 (t)3312 33130.369***33140.0143315 33160.371***33170.0143318 33190.370***33200.0143321 33220.366***33230.0143324 33250.364***33260.0143327 33280.367***33290.0143330Floor3331 area3332 (f)3333 33340.004***33350.0003336 33370.004***33380.0003339 33400.004***33410.0003342 33430.004***33440.0003345 33460.004***33470.0003348 33490.004***33500.0003351Distance3352 to3353 CBD3354 (z)33550.035***33560.0043357 33580.038***33590.0043360 33610.042***33620.0043363 33640.045***33650.0053366 33670.054***33680.0053369 33700.065***33710.0073372Number of3373 bus3374 stops3375 (ı)3376 33770.003***33780.0013379 33800.003***33810.0013382 33830.003***33840.0003385 33860.001***33870.0003388 33890.001***33900.0003391 33920.001***33930.000339423395ward33960.034**33970.0203398 33990.033**34000.0203401 34020.033**34030.0203404 34050.033**34060.0203407 34080.033**34090.0203410 34110.033**34120.020341323414year34150.099***34160.0163417 34180.099***34190.0163420 34210.098***34220.0163423 34240.099***34250.0163426 34270.098***34280.0163429 34300.099***34310.016343223433property34340.044***34350.0013436 34370.044***34380.0013439 34400.043***34410.0013442 34430.043***34440.0013445 34460.044***34470.0013448 34490.044***34500.0013451Contribution3452 of3453 23454ward to3455 23456property34570.1923458 34590.1883460 34610.1903462 34630.1893464 34650.1893466 34670.1883468Contribution3469 of3470 23471year to3472 23473property34740.5593475 34760.5633477 34780.5633479 34800.5663481 34820.5603483 34840.5633485Sample size3486 348796553488 348996503490 349196563492 349396533494 349596593496 349796693498*3499 p <3500 0.1.3501** p3502 <3503 0.05.3504*** p3505 <3506 0.001.350735083509===== PAGE 10 (score 74, numeric-paren 52, table-words 3, var-words 5) =====351035113903512 3513Y.3514 Wang3515 et3516 al.3517 / Land3518 Use3519 Policy3520 423521 (2015)3522 381–3913523Table3524 63525Results3526 of3527 quantile3528 bivariate3529 regressions,3530 log(p)3531 as3532 dependent3533 variable3534 and3535 ı3536 as3537 independent3538 variable.3539Quantile3540 3541ı3003542 m3543ı400 m3544ı5003545 m3546ı7503547 m3548ı10003549 m3550ı7503551 m3552ˇ3553 3554s.e.3555 3556ˇ3557 3558s.e.3559 3560ˇ3561 3562s.e.3563 3564ˇ3565 3566s.e.3567 3568ˇ3569 3570s.e.3571 3572ˇ3573 3574s.e.357510%3576 3577−0.0043578 35790.0033580 35810.0013582 35830.0023584 35850.007***35860.0023587 35880.004***35890.0013590 35910.002***35920.0003593 35940.001***35950.000359620%3597 35980.0043599 36000.0033601 36020.003*36030.0023604 36050.005***36060.0013607 36080.003***36090.0003610 36110.002***36120.0003613 36140.001***36150.000361630%3617 36180.011***36190.0023620 36210.007***36220.0023623 36240.007***36250.0013626 36270.003***36280.0013629 36300.002***36310.0003632 36330.001***36340.000363540%36360.013***36370.00236380.009***36390.0013640 36410.009***36420.0013643 36440.004***36450.0013646 36470.002***36480.0003649 36500.001***36510.000365250%3653 36540.013***36550.0013656 36570.013***36580.0013659 36600.009***36610.0013662 36630.004***36640.0003665 36660.003***36670.0003668 36690.001***36700.000367160%3672 36730.010***36740.0023675 36760.010***36770.0023678 36790.009***36800.0013681 36820.004***36830.0003684 36850.002***36860.0003687 36880.001***36890.000369070%3691 36920.013***36930.0023694 36950.013***36960.0023697 36980.008***36990.0013700 37010.003***37020.0003703 37040.002***37050.0003706 37070.001***37080.000370980%3710 37110.020***37120.0023713 37140.020***37150.0023716 37170.010***37180.0013719 37200.005***37210.0013722 37230.003***37240.0003725 37260.002***37270.000372890%3729 37300.011***37310.0023732 37330.011***37340.0023735 37360.008***37370.0013738 37390.005***37400.0013741 37420.003***37430.0003744 37450.002***37460.0003747* p3748 <3749 0.1.3750**3751 p3752 < 0.05.3753*** p3754 <3755 0.001.3756Specifically,3757 spatial3758 proximity3759 to3760 Cardiff3761 Bus3762 stops3763 tends3764 to3765 ben-3766efit3767 properties3768 at3769 the3770 high3771 end3772 of3773 the3774 local3775 real3776 estate3777 market.3778 The3779strongest3780 positive3781 externalities3782 can3783 be3784 observed3785 in3786 properties3787 above3788the3789 80%3790 price3791 quantile,3792 with3793 every3794 additional3795 bus3796 stop3797 within3798 a379915003800 m3801 buffer3802 zone,3803 for3804 example,3805 contributing3806 to3807 an3808 increase3809 by3810about3811 0.22%3812 in3813 property3814 sale3815 price3816 through3817 land3818 value3819 betterment.3820The3821 corresponding3822 figure3823 for3824 the3825 cheaper3826 properties3827 is3828 halved3829 to3830around3831 0.11%.3832Policy3833 implications3834The3835 results3836 of3837 our3838 regression3839 analysis3840 based3841 on3842 empirical3843 data3844from3845 Cardiff3846 have3847 some3848 important3849 policy3850 implications.3851 First3852 and3853foremost,3854 the3855 outcomes3856 of3857 our3858 ordinary3859 least3860 square3861 regression3862 and3863multilevel3864 modelling3865 analysis3866 provide3867 convincing3868 evidence3869 to3870 sup-3871port3872 a3873 classic3874 Georgist3875 hypothesis3876 in3877 the3878 case3879 of3880 Cardiff3881 Bus,3882 that3883 a3884significant3885 part3886 of3887 the3888 added3889 local3890 property3891 values3892 does3893 come3894 from3895the3896 land3897 beneath3898 them,3899 or3900 more3901 specifically,3902 from3903 the3904 land3905 plots’3906geographic3907 adjacency3908 to3909 the3910 bus3911 stop3912 locations.3913 This3914 finding3915 jus-3916tifies3917 a3918 potential3919 policy3920 intervention3921 in3922 terms3923 of3924 financing3925 Cardiff3926Bus3927 through3928 a3929 land3930 value3931 tax3932 mechanism,3933 given3934 the3935 substantiated3936positive3937 externalities3938 yielded3939 by3940 Cardiff3941 Bus.3942Second,3943 the3944 results3945 of3946 our3947 quantile3948 calibration3949 entail3950 some3951 even3952more3953 intriguing3954 practical3955 insights.3956 We find3957 that3958 the3959 positive3960 exter-3961nalities3962 of3963 Cardiff3964 Bus3965 are3966 distributed3967 unevenly3968 among3969 properties3970at3971 different3972 price3973 levels3974 in3975 the3976 local3977 real3978 estate3979 market.3980 Generally3981speaking,3982 according3983 to3984 Table3985 6, properties3986 at3987 the3988 high3989 end3990 (at3991 the3992 80%3993and3994 90%3995 quantiles)3996 of3997 the3998 market3999 tend4000 to4001 benefit4002 more4003 than4004 those4005 at4006the4007 low4008 end4009 (at4010 the4011 10%4012 and4013 20%4014 quantiles).4015 This4016 implicates4017 a4018 neces-4019sity4020 to4021 exercise4022 progressive4023 land4024 value4025 taxation,4026 whereby4027 properties4028at4029 different4030 price4031 levels4032 need4033 to4034 be4035 taxed4036 at4037 different4038 rates,4039 so4040 that4041the4042 uneven4043 distribution4044 of4045 positive4046 externalities4047 can4048 be4049 accordingly4050captured.4051 Our4052 estimation,4053 based4054 on4055 the4056 number4057 of4058 bus4059 stops4060 within4061a4062 15004063 m4064 radius4065 of4066 every4067 individual4068 property4069 included4070 in4071 our4072 sample4073data,4074 suggests4075 a4076 tentative4077 land4078 value4079 betterment4080 tax4081 rate4082 of4083 0.11%4084per4085 new4086 bus4087 stop4088 for4089 the4090 first4091 tier4092 of4093 properties4094 which4095 are4096 priced4097below4098 £195,0004099 and4100 0.22%4101 for4102 the4103 second4104 tier4105 of4106 properties4107 which4108are4109 of4110 higher4111 market4112 values.4113We notice4114 a4115 somewhat4116 similar4117 progressive4118 taxation4119 mechanism4120already4121 incorporated4122 in4123 Cardiff’s4124 existing4125 council4126 tax4127 system,4128 which4129divides4130 local4131 residential4132 properties4133 into4134 nine4135 different4136 bands4137 by4138their4139 estimated4140 values4141 and4142 applies4143 incremental4144 tax4145 rates.4146 There4147 is4148thus4149 a4150 possibility4151 of4152 introducing4153 an4154 embryonic4155 land4156 value4157 tax4158 ele-4159ment4160 into4161 the4162 current4163 council4164 tax4165 scheme,4166 depending4167 on4168 how4169 we4170translate4171 the4172 quantile4173 hedonic4174 approach4175 featured4176 in4177 this4178 study4179 into4180the4181 council4182 taxation4183 system4184 used4185 in4186 practice4187 and4188 also4189 on4190 whether4191 a4192large4193 enough4194 dataset4195 covering4196 the4197 entire4198 Cardiff4199 Council4200 jurisdiction4201will4202 become4203 available4204 in4205 the4206 near4207 future.4208Third,4209 our4210 overall4211 empirical4212 analysis4213 defies4214 a4215 conventional4216wisdom4217 that4218 public4219 infrastructure4220 always4221 serves4222 public4223 interest.4224Arguably,4225 the4226 case4227 of4228 Cardiff4229 Bus4230 illustrates4231 a4232 free-ride4233 scenario,4234wherein4235 the4236 most4237 wealthy4238 property4239 owners,4240 intentionally4241 or4242unintentionally,4243 end4244 up4245 extracting4246 the4247 values4248 of4249 publicly4250 funded4251transport4252 infrastructures.4253 A4254 similar4255 finding4256 has4257 also4258 been4259 made4260recently4261 by4262 Banister4263 and4264 Thurstain-Goodwin4265 (2011,4266 pp.4267 216–221),4268who4269 suggest4270 land4271 value4272 betterment4273 created4274 by4275 the4276 rail4277 networks4278to4279 be4280 even4281 more4282 extensive.4283 In4284 this4285 sense,4286 levying4287 land4288 value4289 tax4290is4291 not4292 only4293 to4294 capture4295 economic4296 externalities,4297 but4298 also4299 to4300 ensure4301socioeconomic4302 justice4303 and4304 fairness,4305 as4306 per4307 the4308 Edwardian4309 tradition.4310Conclusion4311 and4312 future4313 research4314In4315 this4316 paper,4317 we4318 explore4319 the4320 potential4321 viability4322 of4323 levying4324 land4325value4326 tax4327 within4328 a4329 local4330 UK4331 context.4332 We focus4333 on4334 the4335 empirical4336 and4337technical4338 aspects4339 by4340 asking4341 how4342 to4343 assess4344 the4345 spatialised4346 relations4347between4348 the4349 local4350 public4351 transport4352 infrastructures4353 and4354 the4355 land4356 val-4357ues4358 of4359 nearby4360 residential4361 properties.4362 We answer4363 this4364 question4365 by4366studying4367 the4368 case4369 of4370 Cardiff4371 Bus.4372 Based4373 on4374 our4375 multilevel4376 and4377 quan-4378tile4379 hedonic4380 analysis,4381 we4382 find4383 strong4384 evidence4385 with4386 regard4387 to4388 the4389positive4390 externalities4391 of4392 Cardiff4393 Bus4394 network4395 towards4396 the4397 market4398values4399 of4400 nearby4401 residential4402 properties.4403 Given4404 the4405 uneven4406 external-4407ity4408 distribution4409 between4410 properties4411 at4412 different4413 price4414 levels,4415 we4416 call4417for4418 a4419 progressive4420 land4421 value4422 tax4423 scheme.4424Because4425 of4426 the4427 political4428 context4429 of4430 our4431 study,4432 we4433 consider4434 action4435research4436 a4437 key4438 direction4439 of4440 our4441 future4442 research.4443 We would4444 like4445to4446 take4447 more4448 opportunities4449 to4450 use4451 our4452 research4453 to4454 educate4455 and4456inform4457 the4458 general4459 public4460 about4461 why4462 land4463 value4464 tax4465 (LVT)4466 is4467 an4468economically4469 as4470 well4471 as4472 socially4473 desirable4474 idea.4475 Potential4476 opposi-4477tions4478 to4479 LVT4480 are4481 less4482 likely4483 from4484 an4485 efficiency4486 perspective,4487 since4488most4489 classic4490 research4491 has4492 already4493 confirmed4494 that4495 purely4496 taxing4497 on4498incomes4499 from4500 land4501 would4502 not4503 distort4504 resource4505 allocation4506 (Mills,45071981;4508 Tideman,4509 1982;4510 Wildasin,4511 1982).4512 More4513 recent4514 research4515 also4516shows4517 that4518 it4519 is4520 actually4521 easier4522 than4523 thought4524 to4525 implement4526 neutral4527or4528 non-distortional4529 land4530 taxation4531 (Arnott,4532 2005).4533 Even4534 with4535 errors4536in4537 land4538 valuation,4539 the4540 collection4541 of4542 LVT4543 has4544 been4545 found4546 to4547 result4548in4549 no4550 more4551 distortions4552 than4553 the4554 conventional4555 property4556 taxation4557scheme4558 (Chapman4559 et4560 al.,4561 2009).4562 Resistances4563 to4564 LVT4565 are4566 thus4567 more4568likely4569 about4570 the4571 distributional4572 effects4573 of4574 land4575 taxation,4576 or4577 put4578 simply,4579regarding4580 how4581 to4582 split4583 the4584 bills4585 that4586 fund4587 public4588 investments.4589 Con-4590troversial4591 as4592 this4593 question4594 is,4595 we4596 hope4597 our4598 current4599 study4600 and4601 future4602research4603 on4604 land4605 value4606 tax4607 will4608 help4609 the4610 voters4611 make4612 an4613 informed4614decision.4615Acknowledgements4616The4617 4618research4619 4620reported4621 4622in4623 4624this4625 4626paper4627 4628is4629 4630funded4631 4632by4633 4634the4635British4636 Academy/Leverlulme4637 Small4638 Research4639 Grant4640 initiative4641 (ref:4642SG122122).4643 All4644 errors4645 lurking4646 in4647 the4648 paper4649 are4650 the4651 authors’.465246534654===== PAGE 11 (score 12, numeric-paren 0, table-words 0, var-words 12) =====46554656Y.4657 Wang4658 et4659 al.4660 / Land4661 Use4662 Policy4663 424664 (2015)4665 381–3914666 46673914668References4669Al-Mosaind,4670 M.A.,4671 Dueker,4672 K.J.,4673 Strathman,4674 J.G.,4675 1993.4676 Light-rail4677 Transit4678 Stations4679 and4680Property4681 Values:4682 A4683 Hedonic4684 Price4685 Approach.4686 Center4687 for4688 Urban4689 Studies,4690 School4691 of4692Urban4693 and4694 Public4695 Affairs,4696 Portland4697 State4698 University,4699 Portland,4700 OR,4701 USA.4702Anas,4703 A.,4704 Arnott,4705 R.,4706 Small,4707 K.,4708 1998.4709 Urban4710 spatial4711 structure.4712 J.4713 Econ.4714 Lit.4715 36,47161426–1464.4717Andelson,4718 R.V.,4719 2000.4720 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