1# RECORD 670518142f78514470ad2ea85cc37f0ccb4483aa2# TITLE: Low-emission zones, modes of transport and house prices: evidence from Berlin’s commuter belt3# YEAR: 2022.0 VENUE: Transportation DOI: 10.1007/s11116-022-10295-84# AUTHORS: E. Aydin; Kathleen Kürschner Rauck5# PAGES: 49 CANDIDATE TABLE PAGES (1-based): [11, 19, 20, 37, 38, 39, 40, 41, 42]6# ABSTRACT: We study the impact of a tightening of a private driving restriction in Germany’s capital, the city of Berlin, on house prices in its affluent suburbs. Using geo-referenced data on train stations, motorway access points and offers of single-family houses for sale from Germany’s leading online property broker ImmobilienScout24 in a spatially staggered DiD framework, we find evidence for sizeable price growth premia for houses located in walking distance of train stations that lie within 30 min commuting duration to Berlin main station. Property located in immediate vicinity (5 min walking distance) of train stations within 30–40 min commuting duration, however, face penalties. Our findings are of relevance for the design of public infrastructure planning policies that seek to accommodate and facilitate changes in local demands for alternative and more environmentally sustainable modes of transport induced by private driving restrictions.789===== PAGE 1 (score 6, numeric-paren 0, table-words 0, var-words 6) =====1011Vol.:(0123456789)12Transportation (2023) 50:1847–189513https://doi.org/10.1007/s11116-022-10295-8141 315Low‑emission zones, modes of transport and house prices: 16evidence from Berlin’s commuter belt17Eren Aydin1 · Kathleen Kürschner Rauck2,3 18Published online: 15 June 2022 19© The Author(s) 202220Abstract21We study the impact of a tightening of a private driving restriction in Germany’s capi-22tal, the city of Berlin, on house prices in its affluent suburbs. Using geo-referenced data 23on train stations, motorway access points and offers of single-family houses for sale from 24Germany’s leading online property broker ImmobilienScout24 in a spatially staggered DiD 25framework, we find evidence for sizeable price growth premia for houses located in walk-26ing distance of train stations that lie within 30 min commuting duration to Berlin main 27station. Property located in immediate vicinity (5 min walking distance) of train stations 28within 30–40 min commuting duration, however, face penalties. Our findings are of rel-29evance for the design of public infrastructure planning policies that seek to accommodate 30and facilitate changes in local demands for alternative and more environmentally sustain-31able modes of transport induced by private driving restrictions.32Keywords Low-emission zone · House prices · Berlin (Germany)33 JEL Classification R4 · R11 · R31 · Q5234Introduction35Internationally, environmental policy to reduce pollution levels caused by vehicle traffic 36is implemented most forcefully by means of private driving restrictions. This holds true 37also for member states of the European Union (EU). However, EU member states are free 38to pursue policies of their own choice to cope with pollution limits upheld by the EU. 39This freedom of choice makes evaluations of the policies chosen by EU member states 40highly relevant for decision-makers and also potentially informative for the design of future 41policies.42 * Kathleen Kürschner Rauck 43 44kathleen.kuerschner@unisg.ch451 46Universität Hamburg (UHH), Hamburg, Germany472 48Swiss Institute of Banking and Finance, University of St.Gallen (HSG), Unterer Graben 21, 499000 St. Gallen, Switzerland503 51Swiss Finance Institute (SFI), Geneva, Switzerland525354===== PAGE 11 (score 20, numeric-paren 0, table-words 4, var-words 10) =====5556185757Transportation (2023) 50:1847–1895 581 3592017). The data cover, and identify, stations that serve regional traffic via trains and stations 60that serve local public transportation. We disregard information on bus and tram stations 61and manually checked the operation status of stations identified in the data as being closed. 62Our final data set covers only counties in Brandenburg that share a direct border with Ber-63lin, and identifies 227 train stations, which function as reference points from which we 64calculate the walking distance bands (described above) for individual property in our data 65with the STATA module osrmtime (Huber and Rust 2016) using the shortest walking dis-66tance routes between each pairwise combination of sampled properties and train stations.67For our spatial difference-in-differences analysis, we define treatment and control group 68based on these walking distances (i.e., a purely local attribute) of observed property to train 69stations. The policy studied, however, was implemented in Berlin. As our analysis focuses 70on potential effects in the commuter belt of Berlin, the LEZ in Berlin can be considered 71a non-local/distant policy treatment. In order to emphasize the importance of Berlin as a 72commuter destination to its surrounding regions we utilize special data on the total number 73of commuters to Berlin and other municipality-level characteristics, provided by the State 74Office for Construction and Transport (Landesamt für Bauen und Verkehr—LBV) as time 75series data refined from data by the State Office for Statistics Berlin-Brandenburg (Amt für 76Statistik Berlin-Brandenburg), and the Federal Employment Agency (Bundesagentur für 77Arbeit - BA). Panel (a) of Fig. 2 illustrates the relevance of Berlin for surrounding regions 78by displaying the share of the population in the respective municipalities in Brandenburg 79commuting into Berlin in 2008. Counties in Brandenburg, that are adjacent to Berlin have an 80average share of 10.2% of the population (or close to 40% of commuters) commuting to Ber-81lin. In contrast on average only 1.2% of the population (or roughly only 8.5% of commuters) 82from the other counties in Brandenburg commute to Berlin. A systematic pattern between 83commuter inflow into Berlin from its suburbs becomes evident: regions/municipalities 84located closer to Berlin show higher numbers of workers commuting to Berlin. This holds 85true for previous and later years, as well. We therefore, restrict Berlin’s commuter belt in our 86estimations to counties in Brandenburg sharing a direct border with Berlin. Nevertheless, 87Table 1 Summary statistics by 88pre- and post-LEZ period (full 89estimation sample)90The table shows the mean of each variable for the entire estimation 91sample (129,521 observations) by pre-LEZ (stage 3) period January 922008–June 2009 (column (1)) and post-LEZ (stage 3) period Janu-93ary 2010–June 2011 (column (2)). Standard deviations are reported in 94square brackets95Pre-LEZ (stage 3)96Post-LEZ (stage 3)97(1)98(2)99House price [EUR]100198, 860101[117,977]102205, 442103[127,999]104log House price10512.067106[0.526]10712.081108[0.569]109Living space [m2]110134.830111[43.866]112136.829113[46.683]114Base area [m2]115871.641116[753.831]117914.932118[830.484]119# rooms1204.674121[1.267]1224.736123[1.314]124Age [years]12520.823126[31.286]12723.984128[33.448]129Under construction [%]1300.067131[0.250]1320.056133[0.230]134Detached house [%]1350.833136[0.373]1370.863138[0.344]139Observations14067,58814161,933142143144===== PAGE 12 (score 9, numeric-paren 0, table-words 1, var-words 7) =====1451461858147 148Transportation (2023) 50:1847–18951491 3150we also construct three commuting duration bands to analyze outcomes for different com-151muting distances, measured in driving duration, to Berlin main train station. The calculation 152of shortest driving durations is again done with the STATA module osrmtime (Huber and 153Rust 2016). Constructed commuting duration bands are divided into (i) 30 min or less, (ii) 154beyond 30–40 min, and (iii) beyond 40–60 min.13 Panel (b) of Fig. 2 displays the spatial dis-155tribution of train stations in Brandenburg and provides a visual reference for the constructed 156commuting bands. Out of the sampled 227 train stations, 33 are located in the closest com-157muting belt, 54 stations are located in 30–40 min driving duration from Berlin main station, 15859 train stations lie in the 40–60 min driving duration band, and the remaining 81 stations 159are located in 60 or more minutes driving duration to/from Berlin main station.160The third type of data we use contains information on the road network of Brandenburg, 161which we obtained from the Infrastructure for Spatial Information in the European Com-162munity (INSPIRE) and its corresponding web feature service (WFS) provided by the federal 163state of Brandenburg (Landesbetrieb Straßenwesen Brandenburg 2018). Metadata, such as 164geo-referenced motorway access points, are accessible via this interface. We used geographic 165Fig. 2 Population share of outbound commuters to Berlin (at municipality-level) and train stations in 166Brandenburg by commuting duration to Berlin main station. Notes Panel (a) displays the share of the popu-167lation in a respective municipality that is commuting to Berlin (darker shades of grey indicate higher shares 168of commuters). Panel (b) depicts exact (geo-referenced) locations of train stations for counties in Branden-169burg that share an administrative border with Berlin by commuting duration (marked by different symbols 170(see legend) and different shades of gray (darker shades indicate more distant locations)) to Berlin main 171station (marked by a cross). The LEZ inside the administrative borders of Berlin is also marked and high-172lighted by hatching17313 For our empirical analyses that explore stratified samples by commuting duration, we disregard distance 174bands in which train stations are located in more than a 60-minute drive to Berlin main station due to the 175small number of observations and their mismatch in terms of commuters to Berlin when compared to closer 176distance bands as illustrated in 2.177178179===== PAGE 19 (score 140, numeric-paren 111, table-words 2, var-words 3) =====1801811865182Transportation (2023) 50:1847–1895 1831 3184Table 2 Treatment effects—baseline results (semi-annually)185Dependent variable log House Price186Treatment effects187(1)188(2)189(3)190(4)191(5)192(6)193Treatment regions194 DISTi,STN 5 min1950.0015196(0.0279)1970.0090198(0.0280)1990.0077200(0.0296)2010.0261202(0.0797)2030.0480204(0.0289)205−0.0735206(0.0578)207 DISTi,STN 5−10 min208−0.0064209(0.0162)210−0.0005211(0.0171)212−0.0060213(0.0168)214−0.0167215(0.0306)216−0.0036217(0.0233)218−0.0139219(0.0236)220 DISTi,STN 10−15 min221−0.0123222(0.0142)223−0.0071224(0.0143)225−0.0066226(0.0144)2270.0356228(0.0348)229−0.0069230(0.0208)231−0.0284232(0.0202)233 DISTi,STN 15−20 min234−0.0141235(0.0208)236−0.0106237(0.0217)238−0.0160239(0.0187)240−0.0055241(0.0493)242−0.0090243(0.0204)244−0.0269245(0.0232)246 DISTi,MOT 10 min247−0.0551248(0.0194)249∗∗∗250−0.0659251(0.0235)252∗∗∗253−0.0586254(0.0555)255−0.0561256(0.0392)257−0.0527258(0.0329)259Treatment effects—train station260 POSTQ1 10−Q2 10 × DISTi,STN 5 min261−0.0210262(0.0406)263−0.0239264(0.0410)265−0.0212266(0.0402)2670.0894268(0.1145)269−0.0797270(0.0330)271∗∗2720.0374273(0.0637)274 POSTQ3 10−Q4 10 × DISTi,STN 5 min275−0.0192276(0.0539)277−0.0213278(0.0541)279−0.0264280(0.0551)2810.0978282(0.1590)283−0.1312284(0.0546)285∗∗2860.0227287(0.0412)288 POSTQ1 11−Q2 11 × DISTi,STN 5 min289−0.0373290(0.0547)291−0.0394292(0.0554)293−0.0525294(0.0549)2950.0484296(0.1547)297−0.1268298(0.0438)299∗∗∗300−0.0072301(0.0711)302 POSTQ1 10−Q2 10 × DISTi,STN 5−10 min3030.0031304(0.0190)3050.0003306(0.0190)307−0.0002308(0.0190)3090.0101310(0.0398)311−0.0069312(0.0262)313−0.0091314(0.0337)315 POSTQ3 10−Q4 10 × DISTi,STN 5−10 min3160.0108317(0.0189)3180.0116319(0.0203)3200.0027321(0.0217)322−0.0455323(0.0456)324−0.0443325(0.0228)326∗3270.0424328(0.0389)329 POSTQ1 11−Q2 11 × DISTi,STN 5−10 min3300.0507331(0.0214)332∗∗3330.0498334(0.0224)335∗∗3360.0353337(0.0216)3380.0244339(0.0470)340−0.0342341(0.0282)3420.0915343(0.0387)344∗∗345 POSTQ1 10−Q2 10 × DISTi,STN 10−15 min3460.0060347(0.0187)3480.0030349(0.0181)3500.0035351(0.0167)3520.0071353(0.0464)3540.0082355(0.0229)3560.0149357(0.0262)358 POSTQ3 10−Q4 10 × DISTi,STN 10−15 min3590.0175360(0.0200)3610.0154362(0.0193)3630.0081364(0.0185)365−0.0039366(0.0461)367−0.0209368(0.0275)3690.0528370(0.0225)371∗∗372 POSTQ1 11−Q2 11 × DISTi,STN 10−15 min373−0.0061374(0.0201)375−0.0096376(0.0197)377−0.0172378(0.0193)379−0.0169380(0.0554)381−0.0549382(0.0300)383∗3840.0324385(0.0342)386 POSTQ1 10−Q2 10 × DISTi,STN 15−20 min387−0.0181388(0.0227)389−0.0180390(0.0234)391−0.0142392(0.0237)393−0.0019394(0.0575)3950.0123396(0.0289)397−0.0122398(0.0367)399 POSTQ3 10−Q4 10 × DISTi,STN 15−20 min4000.0069401(0.0232)4020.0055403(0.0230)404−0.0001405(0.0215)4060.0184407(0.0617)4080.0257409(0.0261)410−0.0082411(0.0282)412 POSTQ1 11−Q2 11 × DISTi,STN 15−20 min4130.0162414(0.0241)4150.0123416(0.0244)4170.0039418(0.0227)419−0.0093420(0.0478)4210.0043422(0.0218)4230.0320424(0.0360)425Treatment effects—motorway426 POSTQ1 10−Q2 10 × DISTi,MOT 10 min4270.0295428(0.0176)429∗4300.0437431(0.0213)432∗∗4330.0316434(0.0365)4350.0452436(0.0610)4370.0122438(0.0250)439 POSTQ3 10−Q4 10 × DISTi,MOT 10 min4400.0187441(0.0186)4420.0301443(0.0191)4440.0292445(0.0352)4460.0092447(0.0445)4480.0052449(0.0253)450451452===== PAGE 20 (score 23, numeric-paren 5, table-words 4, var-words 9) =====4534541866455 456Transportation (2023) 50:1847–18954571 3458The dependent variable is the log of the nominal house price posted. Neighborhood characteristics at 1 km grid level (from 2009) include the unemployment rate, the share 459of buildings that are predominantly in commercial use, and the share of the foreign-born population. Standard errors are clustered at the municipality level. ***, **, * denote 460statistical significance at the 1%, 5% and 10% level461Table 2 (continued)462Dependent variable log House Price463Treatment effects464(1)465(2)466(3)467(4)468(5)469(6)470 POSTQ1 11−Q2 11 × DISTi,MOT 10 min4710.0188472(0.0233)4730.0054474(0.0230)4750.0807476(0.0460)477∗478−0.0134479(0.0449)480−0.0512481(0.0130)482∗∗∗483 Property characteristics484Yes485Yes486Yes487Yes488Yes489Yes490 Neighborhood characteristics (1 km grid)491No492Yes493Yes494Yes495Yes496Yes497 Municipality fixed effects498Yes499Yes500Yes501Yes502Yes503Yes504 Time fixed effects (quarters)505Yes506Yes507Yes508Yes509Yes510Yes511 Sample restrictions512None513None514Walking distance to train station:515≤ 40 min516≤ 40 min517≤ 40 min518≤ 40 min519&520commuting distance521train STN–Berlin main STN:522≤ 60-40 min523≤ 40-30 min524≤ 30 min525 # fixed effects (municipality level)526240527240528140529465304253122532 Obs.533129,521534129,52153590,74553613,28353734,93053836,117539 Within R25400.45415410.45585420.47385430.44585440.51275450.5258546547548===== PAGE 21 (score 23, numeric-paren 0, table-words 6, var-words 11) =====5495501867551Transportation (2023) 50:1847–1895 5521 3553of motorway access points were characterized by offer price growth premia compared to 554dwellings beyond 10 min driving distance to motorways. Anticipation of a reduction in 555negative externalities provided by (or associated with) such sites, and likewise adjacent 556roads, such as (noise) pollution, elevated traffic, and road congestion, may sign responsible 557for this finding. However, it should already be noted that coefficient estimates for proximity 558to motorways do not prove robust in the proceeding of this paper, which is why we refrain 559from putting much weight on the interpretation of the respective coefficients.560The specifications we considered so far employed the full estimation sample of detached 561and semi-detached houses listed for sale in the federal state of Brandenburg’s counties 562that share a border with Berlin in the periods January 2008–June 2009 (pre-LEZ (stage 3) 563period) and January 2010–June 2011 (post-LEZ (stage 3) period). We will consider next 564four regionally restricted sub-samples. The first sample restriction aims to obtain a more 565homogenous sub-sample of houses in the treatment and control regions by increasing their 566relative proximity. Specifically, we restrict the estimation sample to houses located within 56740 min walking distance to train stations, which eliminates from the control group property 568in very remote locations (-38,776 observations). The corresponding estimation results are 569shown in column (3) of Table 2. Associated price growth premia for houses located in 5705–10 min walking distance of train stations turn out no longer statistically significant in 571the first half of 2011 (the coefficient estimate is decreased in size, by about 1.5%-points, 572whereas the standard error is almost unchanged).573We next subject the sub-sample of house listings within 40 min walking distance of STNs 574to further regional restrictions so as to obtain more plausible long- and short-distance daily-575commuter belts around the city of Berlin. We use stratified estimation samples for this pur-576pose that are based on three commuting duration bands: For the long-distance daily-com-577muter belt, we consider only houses located within 40 min walking distance of train stations, 578from which the commuting duration (by car) to Berlin main station ranges between 60–40 579min (see column (4)), respectively, between 40–30 min in the next closest belt (see column 580(5)), and up to 30 min for the short-distance daily-commuter belt (see column (6)).22 House 581listings in the 60–40 min commuting duration band show no statistically significant price 582growth premia or penalties attached to train station proximity. Moreover, houses that are 583located within 40 min walking distance of stations and within 10 min driving distance to 584motorway access points receive statistically significant price growth premia (at the 10% level) 585of on average 8.1%-points only in the third half-year of the post-LEZ (stage 3) period. The 586latter finding, may again be attributable to (the anticipation of) lower congestion and (noise) 587pollution near motorways after the LEZ tightening on 1st January 2010. Likewise, nearby 588access to an alternative mode of transport (car) to accessible rail-road travel (in 40 min walk-589ing distance) may have been valued particularly strongly in regions at farther distance from 590Berlin, in which residents tend to rely more on a single mode of transport (car) for conven-591tional activities, such as shopping and recreation. Houses in the adjacent commuting dura-592tion band of 40–30 min driving duration that are listed for sale in immediate vicinity of train 593stations (in 5 min walking distance) exhibit sizeable price growth penalties (of on average 5947.97–13.12%-points) after the implementation of the third stage LEZ. Similarly, a tendency 595for smaller, yet still sizeable (and in the second half of 2010 also statistically significant) price 596growth penalties is observable for property in the 5–10 min walking distance range. This 597tendency is also observable for listings in the adjacent 10–15 min walking distance band, for 59822 Appendix Table 4 provides summary statistics for characteristics of house listings in each of these com-599muting duration bands (by distance to train stations) for the pre- and for the post-LEZ (stage 3) period.600601602===== PAGE 37 (score 25, numeric-paren 0, table-words 2, var-words 5) =====6036041883605Transportation (2023) 50:1847–1895 6061 3607Table 3 Summary statistics by distance to train station, pre- and post-LEZ period (full estimation sample)608Columns (1) to (12) show the mean of each variable for property located within 5 min (columns (1) and (7)), beyond 5–10 min (columns (2) and (8)), beyond 10–15 min 609(columns (3) and (9)), beyond 15–20 min (columns (4) and (10)) and beyond 20–40 min (columns (5) and (11)) walking distance of train stations, respectively, for property 610located beyond the 40 min walking distance threshold (columns (6) and (12)). Averages are calculated for the pre-LEZ (stage 3) period January 2008-June 2009 (columns (1) 611to (6)) and the post-LEZ (stage 3) period January 2010–June 2011 (columns (7) to (12)). Standard deviations are reported in square brackets612Pre-LEZ613Post-LEZ614≤ 5 min615> 5–10 min616> 10–15 min > 15–20 min617> 20–40 min618> 40 min619≤ 5 min620> 5–10 min621> 10–15 min622> 15–20 min623> 20–40 min624 > 40 min625(1)626(2)627(3)628(4)629(5)630(6)631(7)632(8)633(9)634(10)635(11)636(12)637House price [EUR]638192, 872639[101,419]640182, 517641[103,361]642187, 596643[81,304]644195, 924645[98,635]646210, 866647[126,365]648195, 664649[135,248]650185, 732651[86,800]652198, 525653[121,562]654198, 850655[101,863]656206, 116657[127,857]658213, 350659[125,083]660203, 527661[143,502]662log house price66312.049664[0.516]66512.061666[0.463]66712.054668[0.434]66912.082670[0.461]67112.131672[0.504]67312.002674[0.622]67512.019676[0.508]67712.080678[0.478]67912.092680[0.482]68112.108682[0.515]68312.134684[0.534]68512.017686[0.676]687Living space [ m2]688134.701689[42.601]690133.028691[39.033]692132.370693[41.247]694133.852695[42.559]696134.505697[43.217]698137.452699[47.869]700145.314701[51.176]702133.685703[43.938]704137.445705[44.492]706137.225707[48.838]708136.280709[45.032]710137.507711[48.752]712Base area [ m2]713830.591714[627.239]715785.793716[573.406]717796.840718[644.182]719826.397720[639.402]721807.164722[633.304]7231, 030.935724[978.51]725989.217726[704.739]727822.375728[587.856]729850.682730[707.006]731870.161732[746.92]733863.439734[725.359]7351, 039.131736[1,064.899]737# rooms7384.608739[1.115]7404.619741[1.209]7424.611743[1.216]7444.575745[1.200]7464.674747[1.231]7484.774749[1.380]7505.022751[1.504]7524.584753[1.235]7544.778755[1.230]7564.731757[1.349]7584.686759[1.257]7604.805761[1.391]762Age [years]76324.053764[32.606]76520.011766[31.868]76718.257768[29.852]76918.634770[29.750]77118.352772[28.571]77325.623774[34.272]77539.014776[40.218]77724.739778[33.314]77922.873780[32.674]78121.387782[32.296]78321.645784[31.532]78526.633786[35.168]787Under construction [%]7880.074789[0.262]7900.093791[0.291]7920.065793[0.247]7940.071795[0.257]7960.071797[0.257]7980.052799[0.222]8000.033801[0.178]8020.058803[0.234]8040.061805[0.240]8060.058807[0.233]8080.062809[0.241]8100.048811[0.214]812Detached house [%]8130.841814[0.366]8150.879816[0.326]8170.840818[0.367]8190.868820[0.338]8210.825822[0.380]8230.804824[0.397]8250.956826[0.205]8270.899828[0.301]8290.853830[0.354]8310.888832[0.315]8330.858834[0.349]8350.842836[0.365]837Observations8381,6128397,7728409,0118418,73684220,74484319,7138441,2548456,6498467,7038477,95884819,30684919,063850851852===== PAGE 38 (score 33, numeric-paren 0, table-words 2, var-words 5) =====8538541884855 856Transportation (2023) 50:1847–18958571 3858Table 4 Summary statistics by distance to train station, pre- and post-LEZ period (stratified estimation samples by commuting duration)859Pre-LEZ860Post-LEZ861≤ 5 min862> 5–10 min863> 10–15 min864> 15–20 min865> 20–40 min866≤ 5 min867> 5–10 min868> 10–15 min869> 15–20 min870> 20–40 min871(1)872(2)873(3)874(4)875(5)876(6)877(7)878(8)879(9)880(10)881Commuting duration from train station to Berlin main station: ≤30 min882House price [EUR]883234, 127884[114,192]885229, 283886[107,136]887207, 569888[81,732]889216, 468890[110,260]891258, 908892[162,539]893224, 596894[87,717]895221, 599896[106,789]897225, 662898[120,987]899222, 497900[136,071]901249, 759902[142,872]903log house price90412.259905[0.454]90612.258907[0.399]90812.175909[0.370]91012.190911[0.422]91212.337913[0.474]91412.241915[0.412]91612.221917[0.413]91812.232919[0.414]92012.195921[0.463]92212.310923[0.466]924Living space [ m2]925154.175926[50.705]927139.200928[43.682]929130.966930[37.999]931135.288932[43.382]933140.970934[46.721]935154.815936[58.415]937135.280938[39.745]939139.996940[46.641]941137.917942[51.960]943139.077944[45.648]945Base area [ m2]946819.342947[484.478]948747.671949[598.671]950648.467951[298.375]952740.473953[377.803]954714.017955[434.424]956854.121957[617.857]958694.986959[313.665]960699.915961[352.042]962794.356963[752.613]964766.727965[571.673]966# rooms9674.761968[0.888]9694.809970[1.311]9714.591972[1.111]9734.608974[1.182]9754.814976[1.231]9775.044978[1.708]9794.647980[1.198]9814.835982[1.186]9834.683984[1.255]9854.754986[1.292]987Age [years]98830.581989[37.199]99021.214991[33.224]99214.856993[26.434]99415.156995[27.106]99616.850997[27.476]99827.618999[34.586]100024.4981001[32.038]100219.1521003[28.960]100416.3811005[28.020]100619.0261007[30.403]1008Under construction [%]10090.1101010[0.313]10110.0871012[0.282]10130.0881014[0.283]10150.0811016[0.272]10170.0811018[0.273]10190.0561020[0.229]10210.0521022[0.221]10230.0581024[0.234]10250.0651026[0.247]10270.0711028[0.257]1029Detached house [%]10300.9311031[0.253]10320.8481033[0.359]10340.7801035[0.414]10360.8771037[0.329]10380.7761039[0.417]10400.9801041[0.139]10420.8641043[0.343]10440.8341045[0.372]10460.8781047[0.327]10480.8111049[0.392]1050Observations105146510522,71910533,87810544,21110557,687105630610572,44610583,22310593,80110607,3811061Commuting duration from train station to Berlin main station: >30-40 min1062House price [EUR]1063191, 8201064[91,768]1065183, 1441066[90,779]1067180, 4261068[80,933]1069191, 1821070[84,292]1071190, 3101072[87,883]1073177, 6321074[64,170]1075197, 8421076[135,129]1077189, 1951078[78,196]1079213, 2311080[88,101]1081208, 6321082[97,943]1083log house price108412.0921085[0.356]108612.0441087[0.367]108812.0221089[0.400]109012.0801091[0.398]109212.0671093[0.423]109412.0261095[0.360]109612.0891097[0.419]109812.0711099[0.409]110012.1921101[0.398]110212.1521103[0.446]1104Living space [ m2]1105126.3911106[34.637]1107128.9521108[31.350]1109130.7451110[41.201]1111133.2231112[41.116]1113129.9491114[39.729]1115130.8921116[39.293]1117134.5741118[47.729]1119131.9701120[37.999]1121138.7981122[44.334]1123134.8991124[43.644]1125Base area [ m2]1126777.8671127[644.262]1128762.5771129[423.161]1130825.3461131[670.034]1132865.5881133[727.418]1134782.4371135[531.040]1136885.2151137[528.220]1138810.6111139[551.878]1140850.8771141[761.195]1142852.4981143[666.293]1144826.9791145[577.650]1146# rooms11474.5291148[1.140]11494.4791150[1.030]11514.5311152[1.197]11534.4851154[1.108]11554.5351156[1.226]11574.6961158[1.246]11594.5291160[1.164]11614.6161162[1.119]11634.8121164[1.353]11654.6191166[1.222]1167Age [years]116815.2811169[24.470]117014.0801171[26.583]117215.9121173[28.507]117417.3001175[28.075]117616.0741177[26.619]117833.1621179[36.980]118020.0811181[30.609]118219.8511183[30.872]118420.2861185[30.263]118619.0081187[28.647]118811891190===== PAGE 39 (score 29, numeric-paren 0, table-words 3, var-words 5) =====1191119218851193Transportation (2023) 50:1847–1895 11941 31195Table 4 (continued)1196Pre-LEZ1197Post-LEZ1198≤ 5 min1199> 5–10 min1200> 10–15 min1201> 15–20 min1202> 20–40 min1203≤ 5 min1204> 5–10 min1205> 10–15 min1206> 15–20 min1207> 20–40 min1208(1)1209(2)1210(3)1211(4)1212(5)1213(6)1214(7)1215(8)1216(9)1217(10)1218Under construction [%]12190.0791220[0.270]12210.1301222[0.336]12230.0621224[0.241]12250.0781226[0.268]12270.0731228[0.260]12290.0291230[0.168]12310.0661232[0.249]12330.0851234[0.279]12350.0621236[0.242]12370.0551238[0.227]1239Detached house [%]12400.7491241[0.434]12420.9141243[0.280]12440.8741245[0.332]12460.8831247[0.322]12480.8681249[0.338]12500.9251251[0.264]12520.9401253[0.237]12540.8481255[0.359]12560.9031257[0.296]12580.8891259[0.314]1260Observations126185712623,33712633,50312643,01312658,577126651712672,53612682,93212692,53912707,1191271Commuting duration from train station to Berlin main station: >40-60 min1272House price [EUR]1273172, 3041274[53,866]1275156, 5051276[103,828]1277169, 0031278[64,789]1279157, 8181280[63,918]1281180, 5161282[83,735]1283193, 2231284[103,924]1285168, 2341286[101,026]1287182, 5951288[74,739]1289172, 3151290[162,274]1291182, 8031292[123,307]1293log house price129411.9861295[0.423]129611.8281297[0.500]129811.9531299[0.440]130011.8601301[0.529]130211.9941303[0.515]130412.0441305[0.537]130611.9121307[0.474]130812.0311309[0.422]131011.9141311[0.540]131211.9841313[0.501]1314Living space [ m2]1315134.9291316[34.016]1317128.5161318[37.472]1319142.3241320[47.449]1321131.6421322[40.445]1323133.8781324[44.003]1325156.6691326[53.286]1327128.3581328[41.074]1329148.0341330[49.046]1331134.7381332[50.593]1333137.5491334[46.586]1335Base area [ m2]1336851.0711337[420.426]1338815.7061339[558.355]13401, 096.5111341[1,080.054]1342943.6311343[783.474]1344880.0611345[671.788]13461, 265.1011347[990.815]1348914.9261349[818.143]13501, 123.3121351[1,004.446]13521, 014.0191353[691.435]1354980.0941355[840.182]1356# rooms13574.7821358[1.160]13594.4961360[1.177]13614.9061362[1.417]13634.7381364[1.412]13654.6991366[1.159]13675.3901368[1.560]13694.6331370[1.431]13715.1291372[1.494]13734.8031374[1.601]13754.7171376[1.206]1377Age [years]137821.1551379[30.418]138026.7671381[34.268]138228.5531383[36.871]138427.7121385[35.734]138619.4381387[27.986]138849.0031389[42.303]139025.6141391[34.474]139229.0991393[35.386]139427.4131395[37.794]139624.4211397[32.327]1398Under construction [%]13990.0001400[0.000]14010.0281402[0.165]14030.0281404[0.165]14050.0471406[0.213]14070.0551408[0.229]14090.0141410[0.117]14110.0701412[0.256]14130.0191414[0.138]14150.0381416[0.192]14170.0381418[0.188]1419Detached house [%]14200.9731421[0.164]14220.8731423[0.333]14240.9301425[0.255]14260.8131427[0.390]14280.8181429[0.836]14300.9831431[0.131]14320.8891433[0.314]14340.8951435[0.307]14360.9031437[0.296]14380.8651439[0.342]1440Observations144111014421,38514431,073144490714453,167144628714471,18214481,02714491,14714502,9981451Columns (1) to (10) show the mean of each variable for property located within 5 min (columns (1) and (6)), 5–10 min (columns (2) and (7)), 10–15 min (columns (3) and 1452(8)), 15–20 min (columns (4) and (9)), and 20–40 min (columns (5) and (10)) walking distance of train stations. Averages are calculated for the pre-LEZ (stage 3) period Janu-1453ary 2008-June 2009 (columns (1) to (5)) and the post-LEZ (stage 3) period January 2010–June 2011 (columns (6) to (10)). Standard deviations are reported in square brackets145414551456===== PAGE 40 (score 140, numeric-paren 111, table-words 2, var-words 3) =====1457145818861459 1460Transportation (2023) 50:1847–189514611 31462Table 5 Treatment effects—baseline results with bootstrapped standard errors (semi-annually)1463Dependent variable log House Price1464Treatment effects1465(1)1466(2)1467(3)1468(4)1469(5)1470(6)1471Treatment regions1472 DISTi,STN 5 min14730.00151474(0.0283)14750.00901476(0.0285)14770.00771478(0.0307)14790.02611480(0.0884)14810.04801482(0.0306)1483−0.07351484(0.0576)1485 DISTi,STN 5−10 min1486−0.00641487(0.0174)1488−0.00051489(0.0185)1490−0.00601491(0.0182)1492−0.01671493(0.0371)1494−0.00361495(0.0246)1496−0.01391497(0.0227)1498 DISTi,STN 10−15 min1499−0.01231500(0.0147)1501−0.00711502(0.0148)1503−0.00661504(0.0140)15050.03561506(0.0353)1507−0.00691508(0.0213)1509−0.02841510(0.0209)1511 DISTi,STN 15−20 min1512−0.01411513(0.0207)1514−0.01061515(0.0217)1516−0.01601517(0.0192)1518−0.00551519(0.0513)1520−0.00901521(0.0207)1522−0.02691523(0.0222)1524 DISTi,MOT 10 min1525−0.05511526(0.0199)1527∗∗∗1528−0.06591529(0.0232)1530∗∗∗1531−0.05861532(0.0620)1533−0.05611534(0.0410)1535−0.05271536(0.0404)1537Treatment effects – train station1538 POSTQ1 10−Q2 10 × DISTi,STN 5 min1539−0.02101540(0.0418)1541−0.02391542(0.0426)1543−0.02121544(0.0414)15450.08941546(0.1305)1547−0.07971548(0.0396)1549∗∗15500.03741551(0.0643)1552 POSTQ3 10−Q4 10 × DISTi,STN 5 min1553−0.01921554(0.0551)1555−0.02131556(0.0551)1557−0.02641558(0.0566)15590.09781560(0.1822)1561−0.13121562(0.0566)1563∗∗15640.02271565(0.0461)1566 POSTQ1 11−Q2 11 × DISTi,STN 5 min1567−0.03731568(0.0543)1569−0.03941570(0.0546)1571−0.05251572(0.0581)15730.04841574(0.1841)1575−0.12681576(0.0445)1577∗∗∗1578−0.00721579(0.0706)1580 POSTQ1 10−Q2 10 × DISTi,STN 5−10 min15810.00311582(0.0202)15830.00031584(0.0203)1585−0.00021586(0.0198)15870.01011588(0.0447)1589−0.00691590(0.0273)1591−0.00911592(0.0319)1593 POSTQ3 10−Q4 10 × DISTi,STN 5−10 min15940.01081595(0.0213)15960.01161597(0.0231)15980.00271599(0.0219)1600−0.04551601(0.0505)1602−0.04431603(0.0244)1604∗16050.04241606(0.0370)1607 POSTQ1 11−Q2 11 × DISTi,STN 5−10 min16080.05071609(0.0229)1610∗∗16110.04981612(0.0241)1613∗∗16140.03531615(0.0230)16160.02441617(0.0529)1618−0.03421619(0.0297)16200.09151621(0.0402)1622∗∗1623 POSTQ1 10−Q2 10 × DISTi,STN 10−15 min16240.00601625(0.0192)16260.00301627(0.0186)16280.00351629(0.0171)16300.00711631(0.0466)16320.00821633(0.0236)16340.01491635(0.0284)1636 POSTQ3 10−Q4 10 × DISTi,STN 10−15 min16370.01751638(0.0204)16390.01541640(0.0198)16410.00811642(0.0187)1643−0.00391644(0.0486)1645−0.02091646(0.0278)16470.05281648(0.0242)1649∗∗1650 POSTQ1 11−Q2 11 × DISTi,STN 10−15 min1651−0.00611652(0.0200)1653−0.00961654(0.0199)1655−0.01721656(0.0186)1657−0.01691658(0.0583)1659−0.05491660(0.0310)1661∗16620.03241663(0.0360)1664 POSTQ1 10−Q2 10 × DISTi,STN 15−20 min1665−0.01811666(0.0227)1667−0.01801668(0.0232)1669−0.01421670(0.0244)1671−0.00191672(0.0609)16730.01231674(0.0303)1675−0.01221676(0.0343)1677 POSTQ3 10−Q4 10 × DISTi,STN 15−20 min16780.00691679(0.0244)16800.00551681(0.0241)1682−0.00011683(0.0213)16840.01841685(0.0639)16860.02571687(0.0278)1688−0.00821689(0.0283)1690 POSTQ1 11−Q2 11 × DISTi,STN 15−20 min16910.01621692(0.0248)16930.01231694(0.0253)16950.00391696(0.0222)1697−0.00931698(0.0517)16990.00431700(0.0228)17010.03201702(0.0353)1703Treatment effects – motorway1704 POSTQ1 10−Q2 10 × DISTi,MOT 10 min17050.02951706(0.0197)17070.04371708(0.0219)1709∗∗17100.03161711(0.0415)17120.04521713(0.0703)17140.01221715(0.0284)1716 POSTQ3 10−Q4 10 × DISTi,MOT 10 min17170.01871718(0.0199)17190.03011720(0.0212)17210.02921722(0.0393)17230.00921724(0.0487)17250.00521726(0.0364)172717281729===== PAGE 41 (score 23, numeric-paren 5, table-words 4, var-words 9) =====1730173118871732Transportation (2023) 50:1847–1895 17331 31734Table 5 (continued)1735Dependent variable log House Price1736Treatment effects1737(1)1738(2)1739(3)1740(4)1741(5)1742(6)1743 POSTQ1 11−Q2 11 × DISTi,MOT 10 min17440.01881745(0.0256)17460.00541747(0.0248)17480.08071749(0.0525)1750−0.01341751(0.0460)1752−0.05121753(0.0423)1754 Property characteristics1755Yes1756Yes1757Yes1758Yes1759Yes1760Yes1761 Neighborhood characteristics (1 km grid)1762No1763Yes1764Yes1765Yes1766Yes1767Yes1768 Municipality fixed effects1769Yes1770Yes1771Yes1772Yes1773Yes1774Yes1775 Time fixed effects (quarters)1776Yes1777Yes1778Yes1779Yes1780Yes1781Yes1782 Sample restrictions1783None1784None1785Walking distance to train station1786≤ 40 min1787≤ 40 min1788≤ 40 min1789≤ 40 min1790&1791commuting duration1792train STN–Berlin main STN:1793≤ 60-40 min1794≤ 40-30 min1795≤ 30 min1796 # fixed effects (municipality level)1797240179824017991401800461801421802221803 Obs.1804129,5211805129,521180690,745180713,283180834,930180936,1171810 Within R218110.454118120.455818130.473818140.445818150.512718160.52581817The dependent variable is the log of the nominal house price posted. Neighborhood characteristics at 1 km grid level (from 2009) include the unemployment rate, the share 1818of buildings that are predominantly in commercial use, and the share of the foreign-born population. Standard errors are bootstrapped with 500 replications. ***, **, * denote 1819statistical significance at the 1%, 5% and 10% level182018211822===== PAGE 42 (score 152, numeric-paren 122, table-words 2, var-words 2) =====1823182418881825 1826Transportation (2023) 50:1847–189518271 31828Table 6 Evolution of treatment effects—stratified estimation samples by commuting duration with boot-1829strapped standard errors (semi-annually)1830Dependent variable log House Price1831Treatment effects1832(1)1833(2)1834(3)1835(4)1836Treatment regions1837 DISTi,STN 5 min1838−0.08441839(0.0583)1840−0.16931841(0.1056)18420.04741843(0.0286)1844∗18450.04951846(0.0594)1847 DISTi,STN 5−10 min1848−0.02801849(0.0253)1850−0.13541851(0.0416)1852∗∗∗1853−0.00701854(0.0249)1855−0.07421856(0.0416)1857∗1858 DISTi,STN 10−15 min1859−0.03851860(0.0212)1861∗1862−0.07081863(0.0375)1864∗1865−0.00901866(0.0208)1867−0.12581868(0.0403)1869∗∗∗1870 DISTi,STN 15−20 min1871−0.02971872(0.0222)1873−0.09281874(0.0500)1875∗1876−0.01011877(0.0211)1878−0.00441879(0.0394)1880Treatment effects – train station1881 POSTQ1 10−Q2 10 × DISTi,STN 5 min18820.04311883(0.0646)18840.10311885(0.0906)1886−0.07901887(0.0385)1888∗∗1889−0.08051890(0.0482)1891∗1892 POSTQ3 10−Q4 10 × DISTi,STN 5 min18930.03301894(0.0450)18950.12581896(0.1019)1897−0.12581898(0.0546)1899∗∗1900−0.12841901(0.0804)1902 POSTQ1 11−Q2 11 × DISTi,STN 5 min1903−0.00771904(0.0704)19050.11991906(0.1550)1907−0.12921908(0.0442)1909∗∗∗1910−0.13071911(0.0975)1912 POSTQ3 11−Q4 11 × DISTi,STN 5 min19130.03191914(0.0670)19150.19341916(0.1624)1917−0.12951918(0.0452)1919∗∗∗1920−0.13191921(0.1137)1922 POSTQ1 12−Q2 12 × DISTi,STN 5 min19230.05051924(0.0753)19250.24371926(0.1819)1927−0.14811928(0.0599)1929∗∗1930−0.14981931(0.1333)1932 POSTQ3 12−Q4 12 × DISTi,STN 5 min1933−0.05101934(0.0792)19350.17671936(0.2254)1937−0.16121938(0.1081)1939−0.16331940(0.1477)1941 POSTQ1 10−Q2 10 × DISTi,STN 5−10 min1942−0.00631943(0.0313)19440.06931945(0.0361)1946∗1947−0.00671948(0.0276)19490.04001950(0.0438)1951 POSTQ3 10−Q4 10 × DISTi,STN 5−10 min19520.04531953(0.0372)19540.16271955(0.0444)1956∗∗∗1957−0.04181958(0.0244)1959∗19600.03111961(0.0525)1962 POSTQ1 11−Q2 11 × DISTi,STN 5−10 min19630.09161964(0.0393)1965∗∗19660.25241967(0.0709)1968∗∗∗1969−0.03261970(0.0305)19710.06901972(0.0597)1973 POSTQ3 11−Q4 11 × DISTi,STN 5−10 min19740.08071975(0.0425)1976∗19770.28501978(0.0765)1979∗∗∗1980−0.05571981(0.0399)19820.07101983(0.0655)1984 POSTQ1 12−Q2 12 × DISTi,STN 5−10 min19850.00201986(0.0686)19870.24841988(0.1074)1989∗∗19900.03191991(0.0422)19920.18751993(0.1006)1994∗1995 POSTQ3 12−Q4 12 × DISTi,STN 5−10 min1996−0.02091997(0.0540)19980.26721999(0.1046)2000∗∗20010.10512002(0.0488)2003∗∗20040.28852005(0.0999)2006∗∗∗2007 POSTQ1 10−Q2 10 × DISTi,STN 10−15 min20080.01552009(0.0280)20100.03982011(0.0233)2012∗20130.00652014(0.0238)20150.08822016(0.0290)2017∗∗∗2018 POSTQ3 10−Q4 10 × DISTi,STN 10−15 min20190.05502020(0.0236)2021∗∗20220.09012023(0.0373)2024∗∗2025−0.02362026(0.0284)20270.10492028(0.0386)2029∗∗∗2030 POSTQ1 11−Q2 11 × DISTi,STN 10−15 min20310.03162032(0.0357)20330.08012034(0.0498)2035−0.05772036(0.0306)2037∗20380.11722039(0.0522)2040∗∗2041 POSTQ3 11−Q4 11 × DISTi,STN 10−15 min20420.00572043(0.0468)20440.06542045(0.0619)2046−0.00582047(0.0326)20480.21622049(0.0528)2050∗∗∗2051 POSTQ1 12−Q2 12 × DISTi,STN 10−15 min20520.00442053(0.0594)20540.07702055(0.0780)2056−0.03292057(0.0435)20580.23522059(0.0765)2060∗∗∗2061 POSTQ3 12−Q4 12 × DISTi,STN 10−15 min20620.02402063(0.0485)20640.10882065(0.0822)2066−0.02242067(0.0401)20680.29212069(0.0893)2070∗∗∗2071 POSTQ1 10−Q2 10 × DISTi,STN 15−20 min2072−0.01042073(0.0341)20740.03512075(0.0380)20760.01552077(0.0303)20780.01102079(0.0437)2080 POSTQ3 10−Q4 10 × DISTi,STN 15−20 min2081−0.00892082(0.0278)20830.06012084(0.0402)20850.02742086(0.0278)20870.02112088(0.0501)2089 POSTQ1 11−Q2 11 × DISTi,STN 15−20 min20900.02952091(0.0346)20920.12432093(0.0463)2094∗∗∗20950.00312096(0.0227)2097−0.00462098(0.0583)2099 POSTQ3 11−Q4 11 × DISTi,STN 15−20 min21000.05442101(0.0331)2102∗21030.17302104(0.0655)2105∗∗∗2106−0.01452107(0.0335)2108−0.02462109(0.0731)2110 POSTQ1 12−Q2 12 × DISTi,STN 15−20 min21110.02042112(0.0400)21130.16502114(0.0789)2115∗∗2116−0.01092117(0.0315)2118−0.02232119(0.0871)2120 POSTQ3 12−Q4 12 × DISTi,STN 15−20 min21210.03402122(0.0438)21230.20312124(0.0734)2125∗∗∗21260.02132127(0.0281)21280.00802129(0.0950)2130 linear trend21310.01182132(0.0075)21330.02622134(0.0091)2135∗∗∗2136 linear trend × DISTi,STN 5 min2137−0.01672138(0.0142)2139−0.00012140(0.0095)2141 linear trend × DISTi,STN 5−10 min2142−0.02142143(0.0072)2144∗∗∗2145−0.01362146(0.0076)2147∗2148 linear trend × DISTi,STN 10−15 min2149−0.00622150(0.0070)2151−0.02332152(0.0061)2153∗∗∗2154 linear trend × DISTi,STN 15−20 min2155−0.01252156(0.0076)21570.00092158(0.0066)215921602161===== PAGE 43 (score 16, numeric-paren 0, table-words 4, var-words 8) =====2162216318892164Transportation (2023) 50:1847–1895 21651 32166Table 6 (continued)2167Dependent variable log House Price2168Treatment effects2169(1)2170(2)2171(3)2172(4)2173Sample restrictions2174walking distance to train station: ≤ 40 min2175&2176commuting duration train STN–Berlin main STN:2177≤ 30 min2178> 30-40 min2179 # fixed effects (municipality level)2180222181422182 Obs.218348,450218447,9492185 Within R221860.533721870.533621880.535521890.53592190The dependent variable is the log of the nominal house price posted. Specifications (1) through (4) include 2191municipality and time fixed effects as well as property- and neighborhood-specific characteristics at 1 km 2192grid level (from 2009), which include the unemployment rate, the share of buildings that are predominantly 2193in commercial use, and the share of the foreign-born population. Standard errors are bootstrapped with 500 2194replications. ***, **, * denote statistical significance at the 1%, 5% and 10% level2195