Ghebreegziabiher Debrezion Eric Pels Piet Rietveld

Transcription

1 TI /3 Tnbergen Insttute Dscusson Paper The Impact of Ral Transport on Real Estate Prces: An Emprcal Analyss of the Dutch Housng Markets Ghebreegzabher Debrezon Erc Pels Pet Retveld Vrje Unverstet Amsterdam, and Tnbergen Insttute.

2 Tnbergen Insttute The Tnbergen Insttute s the nsttute for economc research of the Erasmus Unverstet Rotterdam, Unverstet van Amsterdam, and Vrje Unverstet Amsterdam. Tnbergen Insttute Amsterdam Roetersstraat WB Amsterdam The Netherlands Tel.: +31(0) Fax: +31(0) Tnbergen Insttute Rotterdam Burg. Oudlaan PA Rotterdam The Netherlands Tel.: +31(0) Fax: +31(0) Please send questons and/or remarks of nonscentfc nature to dressen@tnbergen.nl. Most TI dscusson papers can be downloaded at

3 The mpact of ral transport on real estate prces: an emprcal analyss of the Dutch housng market Ghebreegzabher Debrezon Erc Pels Pet Retveld Free Unversty, Department of Spatal Economcs De Boelelaan 1105, 1081 HV Amsterdam Tel: Fax: E-mal: March 2006 Abstract: A hedonc prcng model s estmated to analyse the mpact of ralways on house prces n terms of dstance to ralway staton, frequency of ralway servces and dstance to the ralway lne. Correctng for a wde range of other determnants of house prces we fnd that dwellngs very close to a staton are on average about 25% more expensve than dwellngs at a dstance of 15 klometres or more. A doublng of frequency leads to an ncrease of house values of about 2.5%, rangng from 3.5% for houses close to the staton to 1.3% for houses far away. Fnally we fnd a negatve effect of dstance to ralways, probably due to nose effects. Two ralway staton references were used n the analyss: the nearest and most frequently chosen staton n the post code area. Ths dstncton ndcates that ralway staton accessblty s a more complex concept than one mght thnk. It nvolves competton between ralway statons. Key words: property value, ralway staton, accessblty, hedonc prcng method.

4 1. INTRODUCTION Hedonc prcng methods explan the value of real estate n terms of the features of the property. Ths approach treats a certan property as a composte of characterstcs to whch value can be attached. The sum of the value of the ndvdual characterstcs makes up the value of the property as a whole. Studes on real estate prces generally categorse the value bearng features of propertes nto three types namely: physcal, accessbly and envronmental (Fujta 1989; Bowes and Ihlanfeldt 2001). Several studes have been conducted focusng on dfferent features of nterest. Accessblty as provded by dfferent modes of transportaton and ralways n partcular also receved attenton. In order to sngle out the effect of ralway statons on property values, t s suggested n the lterature that statons should be seen as nodes n a transport network and places n an area (Bertoln and Spt 1998). Based on ths framework, recent emprcal studes treat the node feature and the place feature of a staton separately. The former characterstc accounts for the accessblty effect, whch s generally postve. The latter feature accounts for externaltes of the staton and can have both postve and negatve effects. Bowes and Ihlanfeldt (2001) ponted at the retal employment and crme that statons attract n addton to the accessblty feature of a staton. By ncludng the three categores of property features mentoned above ths paper examnes the effect of ralway statons on Dutch house prces. There are three types of ral servce n the Netherlands: lght ral servces (trams), heavy ral servces (metro lnes) and commuter ral servces. The servces of the frst two are lmted wthn the man ctes. However, the thrd type serves the whole country. Ths paper assesses the effect of accessblty provded by these commuter ralway statons on the prces of these houses. As a man accessblty compettor to ralways, hghway accessblty s represented n our analyss by means of dstance to ponts of hghway entry and exts. The accessblty and nusance effects of a ralway staton are functons of dstance between the staton and the house under consderaton. As the dstance ncreases, the mpact of both features on the house prce declnes. The level of accessblty at a ralway staton s measured by the qualty of the ralway network: the number of destnatons that can be reached from the staton, the frequency of servces at the staton, and other departure staton related facltes. Statons wth hgher network qualty (.e. a larger number of destnatons and a hgher frequency of trans) have a hgher accessblty ndex, and are expected to have a relatvely hgh postve effect on the 2

5 house prces. Ralway statons at the same tme mpose localsed negatve envronmental effects on house prces due to nose nusance. An mportant dfference between the two effects s that the accessblty effects are concentrated around nodes (ralway statons) whereas the negatve nose effects take place everywhere along the ralway lne. In ths paper we determne the mpact of the three ralway features namely: ralway staton proxmty and ral servce levels and proxmty to ralway lne on prces of resdental propertes. The data for the analyss n ths paper ncludes the sales and prces of resdental propertes n the Netherlands. Due to the transportaton cost and tme savngs made possble, households are expected to be wllng to pay hgher prces to lvng close to the staton compared wth other locatons. Ths s because the commutng (tme) costs are relatvely low when one lves near a staton. Furthermore, lesure actvtes that nvolve ral transport are more accessble. Ths paper only covers the sales of resdental propertes. In a follow up paper we ntend to cover the effect of ralway staton on commercal property values. 2. LITERATURE REVIEW Most land value theores have ther root n the work of Von Thünen (Von Thünen 1830), who tred to explan varatons n farmland values. Accordng to Von Thünen, accessblty to the market place explans the value dfference of farmlands for agrcultural lands havng smlar fertlty. In subsequent studes, economsts lke Alonso and Muth refned ths lne of reasonng nto a bd-rent analyss (Alonso 1964; Mute 1969). The basc dea behnd the bd-rent model s that every agent s prepared to pay a certan amount of money, dependng on the locaton of the land. Ths leads to a rent gradent that declnes wth dstance from the central busness dstrct (CBD) for stes that yeld equal utlty. Thus far n the analyses, the domnant factor explanng the dfference between land (property) values was the accessblty as measured by the dstance to the Central Busness Dstrct (CBD) and the assocated transportaton costs. The physcal characterstcs of the land (fertlty n the case of Thünen) were assumed gven. Thus the basc theory on real estate prces can be put forward as follows: as a locaton becomes more attractve, due to certan characterstcs, demand ncreases and thus the bddng process 3

6 pushes prces up. In most cases CBDs are the centres of many actvtes. Therefore, proxmty to the CBD s consdered as an attractve qualty that ncreases property prces. However, nvestments n transport nfrastructure reduce ths demand frcton around the CBD to some degree (Fejarang 1994) by attractng households to settle around the statons. Propertes close to the nvestment area (ralway statons) enjoy benefts from transportaton tme and cost savng as a result of the nvestment. It may be expected that a prce curve wll have a negatve slope; when we move away from the staton, prces decrease. The ntroducton of the hedonc prcng methodology by Rosen (1974) led to an easer way of attrbutng value to the propertes features. Therefore, n subsequent studes we observe the ntegraton of physcal, accessblty and envronmental characterstcs of the property n models tryng to explan the dfferences n property values. Accessblty remans an mportant feature for urban propertes. However, earler attempts to account for t by usng transportaton cost have been narrow. Attempts have been made to ntroduce a broader concept of accessblty by ncludng all features that contrbute to the potental of opportuntes of a locaton for economc and socal nteractons (Hansen 1959; Martellato et al. 1998). Though a comprehensve defnton of the concept s avalable, the lack of data and approprate measurng technques usually mples that smple measures are used. Thus, n the lterature we see a focus on a lmted number of factors only, especally a CBD orented nteracton related to employment and shoppng. In most property value studes, the other trp purposes are mssng from the model. The man focus of ths paper s the analyss of the mpact of ralway accessblty on resdental house prces. However, as Voth (1993) ponted out, hghway accessblty s an mportant compettor to ral accessblty. The presence of other facltes that ncrease accessblty lke hghways, sewer servces and other facltes nfluence the mpact area n the same fashon. The benefts of these facltes and servces are also captalsed nto urban property values (Damm et al. 1980). Thus, to sngle out the effect of ralway accessblty, competng modes of accessblty need to be ncluded along wth t. The motvatons for the studes on the mpact of ralway accessblty are dverse. The larger part of the lterature on ralways focuses on t as a feasble soluton to the rsng congeston posed by automoble traffc and urban sprawl. Ralway nvestment s expected to support a more compact 4

7 urban structure and therefore t serves the urban plannng purpose (Goldberg 1981). Apart from reasons of showng that ralway nvestments do result n compact urbansaton, most studes n the area were conducted to provde evdence for the mplementaton of value capture schemes for fnancng ral nvestments (Cervero and Susantono 1999). Ths was based on the asserton that the value of proxmty to accessblty ponts s captalsed on the value of propertes around these statons. In general, the emprcal studes conducted n ths area are dverse n methodology and focus. Although the functonal forms can dffer from study to study, the most common methodology encountered n the lterature s hedonc prcng. However, no consstent relatonshp between proxmty to ralway statons and property values s recorded. Furthermore, the magntudes of these effects can be mnor or major. One of the earlest studes, Dewees (1976) analysed the relatonshp between travel costs by ralway and resdental property values. Dewees found that a subway staton ncreases the ste rent perpendcular to the faclty wthn a one-thrd mle to the staton. Smlar fndngs confrmed that the dstance of a lot from the nearest staton has a statstcally sgnfcant effect on the property value of the land (Damm et al. 1980). Consstent wth these conclusons, Grass (1992) later found a drect relatonshp between the dstance of the newly opened metro and resdental property values. Some of the extensvely studed metro statons n the U.S., though rangng from small to modest mpact, show that propertes close to the staton have a hgher value than propertes farther away (Gulano 1986; Bajc 1983; Voth 1991). However, there are studes whch have also found nsgnfcant effects (Lee 1973; Gatzlaff and Smth 1993). On the other hand, contrary to the general assumpton, Dornbusch (1975) and Lands et al. (1995) traced a negatve effect of staton proxmty. Evdence from other studes ndcates lttle mpact n the absence of favourable factors (Gordon and Rchardson 1989; Gulano 1986). For a detaled documentaton of the fndngs, we refer to (Vessal 1996; Smth and Huang 1995; NEORal 2001;NICS 2002). In general, some studes ndcate a declne n the hstorcal mpact of ralway statons on property values. Ths was attrbuted to mprovements n accessblty, advances n telecommuncatons, computer networks, and other areas of technology that were sad to make companes footloose n ther locaton choces (Gatzlaff and Smth 1993). 5

8 The mpact of ralway statons on property values vares due to several factors. Frst, ralway statons dffer from each other n terms of the level of servce provded explaned terms of frequency of servce, network connectvty, servce coverage etc. The meta analyss n Debrezon et al. (2006) shows that dfferent types of ralway statons have dfferent levels of mpact on property value. Commuter ralways have a relatvely hgh mpact on property value (Debrezon et al. 2006; Cervero and Duncan 2001; NEORal 2001; Cervero 1984). Ralway statons also dffer n the level and qualty of facltes. Statons wth hgher level and qualty of facltes are expected to have greater mpact on the surroundng propertes. The presence and number of parkng lots s one of the many staton facltes that got attenton n the lterature. Bowes and Ihlanfeldt (2001) found that statons wth parkng facltes have a hgher postve mpact on property values. In addton, the mpact a ralway staton produces depends on ts proxmty to the CBD. Statons whch le close to the CBD produce a greater postve mpact on the property value (Bowes and Ihlanfeldt 2001). In another study, Gatzlaff and Smth (1993) clam that the varaton n the fndngs of the emprcal work s attrbuted to local factors n each cty. Second, ralway statons affect resdental and commercal propertes dfferently. Most studes have treated the effect of ralway statons on the dfferent property types separately. The range of the mpact area of ralway statons s larger for resdental propertes, whereas the mpact of a ralway staton on commercal propertes s lmted to mmedately adjacent areas. Generally, t has been shown that the mpact of ralway statons on commercal propertes s greater than the mpact on resdental propertes wthn short dstance of the statons (Cervero and Duncan 2001; Wensten and Clower 1999). Ths fndng s n lne wth the asserton that ralway statons - as focal, gatherng ponts - attract commercal actvtes, whch ncrease commercal property values. However, contrary to ths asserton, Lands et al. (1995) determned a negatve effect on commercal property values. Thrd, the mpact of ralway statons on property value s subject to demographc segmentaton of neghbourhoods. Income and socal (racal) dvsons are common. Proxmty to a ralway staton s of hgher value to low-ncome resdental neghbourhoods than to hgh-ncome resdental neghbourhoods (Nelson 1998; Bowes and Ihlanfeldt 2001). The reason s that lowncome resdents tend to rely on publc transt and thus attach hgher value to lvng close to the staton. Because of the fact that ths group of people mostly depend on slow modes (walkng and 6

9 bcycle) to access the statons, ralway staton adjacent locatons are expected to consttute poor segments. On the other hand, the hgh populaton movement n the mmedate locaton gves rse to the development of retal actvtes whch eventually captalze on commercal propertes, but t may at the same tme attract crmnalty (Bowes and Ihlanfeldt 2001). Bowes and Ihlanfeldt outlned that a sgnfcant relaton was observed between statons and crme rates. However, no proxmty varable shows a sgnfcant effect on retal employment. In ths model, the mmedate neghbourhood s affected by the negatve mpact of the staton. Thus the most mmedate propertes (wthn a quarter of a mle of the staton) were found to have an 18.7% lower value. Propertes that are stuated between one and three mles from the staton, however, are more valuable than those further away. 3. DATA AND DESCRIPTIVES (A) HOUSE CHARACTERISTICS The data used n the analyss of ths paper covers sales transactons of the Dutch resdental housng market for a perod of seventeen years from 1985 to These transactons are recorded by the Dutch Brokers Assocaton (NVM). The data ncorporate nformaton related to prce of the dwellngs, characterstcs of the dwellngs and some envronmental features. To further enrch the data set, each of the houses sold s geo-coded separately to enable us to compute the dstances to the ralway statons and hghway entry/ext ponts. Some houses are geo-coded at the precse house address level and the rest are geo-coded at the sx dgt (e.g. 1234XX) post code level, whch s an area comprsng up to about 50 houses. Apart from the house characterstcs, a number of accessblty and neghbourhood features are used. The land use data were acqured from the central offce of statstcs for the Netherlands (the CBS). These data are avalable at the four-dgt postal code level. Moreover, populaton related data are avalable at ths level of aggregaton. Income levels of the populaton n the post code area, the densty and populaton composton n partcular the share of foregners n the area, are used n our analyss. 7

10 The accessblty data relate to two transport modes: ralway and hghway. The locatons of all ralway statons and hghway entry/ext ponts are dentfed. The dstance from the houses to these ponts was determned by GIS methods. The dstance to the nearest hghway entry/ ext ponts s expected to account for the car based accessblty. Ths paper uses two references for a ralway staton: the nearest ralway staton and the most frequently chosen ralway staton. The nearest staton s easly determned usng GIS methods. The dentfcaton of the most frequently chosen staton was based on the survey study of the Dutch Natonal Ralway Company (NS). It s gven at the 4-dgt post code area level. Tables: Table 1: Descrptve statstcs of house characterstcs Mnmum Maxmum Mean Std. Devaton Dependent varable Transacton prce n Euros ,558, ,187 95,678 Independent varables 1. House features Surface area n sq. meters 11 99, Buldng age n years Total number of rooms Number of bathrooms Dummy varables Monument Gas heater Open freplace Garage Garden accessblty features Dstance to nearest ralway staton (m) 3 25,498 3, Dstance to most frequently chosen ralway staton (m) 10 35,643 4, Frequency (trans/day at the most frequently chosen staton) Frequency (at the nearest staton) Dstance to hghway entry/ext (m) 0 39,541 3, envronmental Household ncome n Euros (4 dgt postcode level) Populaton composton (percentage of foregners)

11 In Table 1 above some descrptve statstcs on the three categores of factors affectng property values are gven. For the physcal features of the houses we use a large number of relevant tems. Examples are surface area of the house (that ncludes the bult up and non-bult up part of the property), age of the house, the number of rooms and number of bathrooms; all these varables are contnuous. The rest of the physcal characterstcs, such as monumental status of the dwellng, the avalablty of gas heater, the presence of open fre place, the presence of garden and garage are ndcated by dummy varables. The mean values for some of these features are gven n Table 1. The descrptve statstcs are based on 663,024 houses sold n the tme perod consdered. The features n the accessblty category nclude dstance to the ralway staton, the frequency of trans and the dstance to the nearest hghway entry/ext pont (both wth respect to the most frequently chosen staton for resdents n the post code area and the nearest staton to the house). The analyss also ncludes the perpendcular dstance to ralway lnes n an effort to capture the nose effect of ralways. 200 Mean prce (000 Euros) Year Fgure 1: Mean prce of houses by year The dstance to the most frequently chosen staton s on average about 1 klometre longer than the average dstance to the nearest ralway staton. The average frequency of trans at the most frequently chosen staton s more than 100 trans per day over the average frequency of trans at 9

12 the nearest ralway staton. Ths gves and ndcaton of the trade-off travellers make between proxmty of statons and the level of servce they offer. Fgure 1 shows the average transacton prce n each year. Ths ncrease can be attrbuted to combned effect of nflaton and real value ncrease. (B) RAILWAY STATION CHARACTERISTICS The data of partcular nterest n ths study concerns the ralway accessblty and assocated nose or congeston. Ralway accessblty can be explaned by two features: the proxmty feature and servce level features. The frst feature s more or less captured by the dstance measure whereas varous features can contrbute to the servce level. Examples nclude the number of trans leavng the staton per tme unt, and network connectvty as measured by the number of destnatons served by the staton. In addton, t may also nclude facltes that supplement ralway transport. For example the avalablty of parkng space, the park and rde status of the staton and the avalablty of bcycle safes can be mentoned. The overall Dutch ralway network s composed of about 360 statons. Our data allows us to use the most frequently chosen departure staton for households aggregated at the 4-dgt post code level. Table 2: Descrptve statstcs for the ralway staton characterstcs No. statons Mnmum Maxmum Mean Std. Devaton Ral servce Frequency of trans per day Destnatons reached wthout a transfer Destnaton reached wth one transfer Travel demand Total Passenger turnout per day ,700 5,600 13,770 Staton type Inter-cty statons Staton Facltes (dummy varables) Tran tax Bcycle stand Bcycle safe Bcycle rent Park and Rde Parkng Tax Car rent Luggage depost Internatonal connecton

13 4. METHODOLOGY The hedonc prcng methodology s found to be effectve n snglng out the effect of one characterstc from a number of characterstcs composng a property (Rosen 1974). Ths paper uses ths approach to determne the effect of the three categores of house features n general and ralway accessblty n partcular. A sem logarthmc specfcaton s adapted. Thus, the dependent varable n our analyss s the natural logarthm of the transacton prce of resdental houses. A wde range of ndependent varables that are expected to explan the house prces are ncluded. These nclude the physcal characterstcs of the houses, envronmental amentes and the accessblty varables that correspond to the houses under study. Due to the fact that the data set covers a relatvely long perod, and house prces have ncreased contnuously durng the last decade temporal effects are also expected to play a role n explanng the varaton n the sales prce of houses. Thus, we nclude sales year dummes to capture the temporal effects. These account for the nflaton, real value changes and other temporal effects across the tme perod. To account for the spatal effect regonal dummes are ncluded at the muncpalty level. The man focus of the analyss here s the effect of ralway staton proxmty and servce qualty of the statons. We also nclude the effect of proxmty to hghway entry/ext ponts to account for competton by the car. MODEL SPECIFICATION Even though the data nclude a longer perod, we could not organse our data n a panel structure because there were not many repeated sales over the tme. Therefore, our data s organsed n a cross-sectonal pattern. The sem logarthmc hedonc specfcaton s wdely used n the property value lterature. Its use s motvated by the fact t gves robust estmates and t enables convenent coeffcent nterpretaton. The general structure of the model we adopt here s: ' ' ' = B0 + B1 X 1 + B2 X Bn X n ε (1) Ln(P ) + P s the prce house, 1 X X n are vectors of explanatory varables for the prce of house. The dependent varable s gven n the natural logarthmc form; thus the values of the coeffcents represent percentage change. The specfcatons used n the estmatons are gven by equatons 2 and 3. Dstances from the houses to ralway staton and lne and hghway entry /ext 11

14 ponts are dstngushed accordng to several dstance categores. The frst model ncludes the dstance and frequency effect (staton qualty) separately. The second model ncludes the nteracton between dstance and frequency. In both specfcatons proxmty to ralway staton and ralway lne are treated n pecewse fashon. Frequency of trans at the reference staton s gven n contnuous form. The models have the followng form: ln( tranprce ) = α + β' + β' + β' HC hw HouseChr + β' Dstcateghway Re gon Dregonal dc + β' Dstcategral + β + β' tme rallne Dtme + ε freq Drallne + β' ln(freqt ) Neghb Neghb (2) ln( tranprce ) = α + β' HC HouseChr + β' DC Freq + β' + β' hw Dstcateghway Re gon Dregonal + β' + β' tme Dstcategral rallne Drallne + β' Dtme + ε ln(freqt ) Neghb Neghb (3) Where tranprce represents the transacton prce of house ; HouseChr s a vector of house characterstcs for house, whch ncludes varables for type of house, surface area, total number of rooms, number of bathrooms, presence of garage and garden for the house, presence of gas heater and fre place, monument, age of the buldng; Dstcategr al s a vector of dummy varables representng the dstance category at whch house s located from a staton. To see the smoothness of the effect we use a 500 meters range categores except n the two nner crcle categores of the staton, whch are 250 meters each. Thus we have 31 categores of dstances up to 15,000 meter. Areas beyond ths lmt are taken as a reference group n the estmaton. FreqT s the frequency of trans at the staton to whch the dstance s computed and s gven n trans per day. In our analyss we make two staton consderatons: the nearest vs the most frequently chosen staton n the post-code area. s the Kronecker product to ndcate the cross producton of dstance classes and frequency of trans at the reference staton. Dstcateghway s a vector of dummes representng the dstance category at whch a hghway entry/ext pont s located from the house. In the same fashon as the ralway dstance categores, we also have 31 dstance categores for these varables. Drallne s a vector of two dummy varables representng at whch dstance category the house s locatng from the ralway lne. Ths s expected to account for the nose effect of trans. The ralway nose s expected to have localzed effect and thus we 12

15 compare the effect of nose on two nearby dstance aganst rest. Neghb s a vector of neghborhood characterstcs ncludng ncome, rato of foregners and rate of land use types. It s gven at the four-dgt post code level. Dregonal s a vector of dummy varables representng to whch muncpalty the house belongs. Dtme s a vector of tme dummy varables representng the year when the transacton took place. ε s the error term. The accessblty related varables ncluded are the dstances to the ralway statons, the frequency of trans at the statons and hghway entry/ ext ponts. The structural features consdered are the type of the houses, surface area of the houses, total number of rooms, number of bathrooms, presence of garage, garden, gas heater and fre place, monumental status of the houses, and age of the houses. Varables ncluded under the envronmental features are average household ncome, rato of foregners, and rate land use types at the post code level and regonal muncpalty dummy varables. The dstance to the ralway lne s another varable that can be consdered under the envronmental varables. Year dummes are also used to account for the temporal effect. All n all the total number of explanatory varables n the hedonc prcng models s 344. Of these 34 relate to house characterstcs, 28 to neghbourhood features, 16 to tme seres dummes and 203 to muncpalty dummes. The remanng 63 varables represent ralway and hghway accessblty. In the presentaton of estmatons below we focus on the mpact of the accessblty varables. The muncpalty dummes can be consdered to represent the many muncpalty specfc factors that may affect house values. Thus, the effects we fnd for the ralway staton proxmty have been corrected for muncpalty specfc mpacts. Generally, the prce of houses s expected to rse as one comes close to the ralway staton and/or hghway entry/ext ponts. At the same tme, the nfluence of a staton to the house prces s expected to ncrease wth the ncrease n the servce level provded by the staton as gven by frequency of trans and the number of destnatons drectly served by the staton. However, the latter two varables are hghly correlated, thus we prefer to nclude one of the two n our estmaton. We fnd the frequency varable more tellng snce t addresses schedulng and watng tme aspects, an mportant dmenson of generalzed costs. In addton, frequency s related to relablty snce delays are less dsturbng n the case of hgh frequency. 13

16 5. ESTIMATION RESULTS AND DISCUSSIONS Table 3 gves four estmaton results based on equatons 2 and 3. To save space we only report the coeffcents of the factors that relate to ralway aspects. The complete estmaton results are avalable upon request from the authors. The frst two estmatons correspond to the smple lnear effect of pecewse dstances and frequency of trans effect treated separately as gven by Equaton 2. The last two estmatons are based on the model gven by Equaton 3. The cross dstance-frequency estmaton gves the effect of frequency of trans on house prces for each of the dstance classes. The sem-log nature of the model makes the nterpretaton of the coeffcents easer. Each coeffcent for the dstance categores n the frst two estmatons shows the percentage effect on house prces of dstance to the staton compared to houses located beyond 15 klometres. Thus, we observe a dfference as bg as 32% n house prces for houses wthn 500m of the nearest staton and houses beyond 15 klometres from the statons. Ths dfference gets smaller n the case of the most frequently chosen staton effect (about 27%), where we encounter the peak house prce to be between 250 and 500 metres. The trend of the effect szes for ths specfcaton s gven n Fgure 2. Ths fgure shows rregularty n the dstance category of 7.5 to 8 klometers. Ths s due to the small number of observatons n ths category. Such rregulartes are nevtable when small dstance classes are used. The dfference between the dstance effect of the nearest and most frequently chosen staton s remarkable. The advantage of beng close to the staton s not so large n the case of the most frequently chosen staton compared wth the nearest staton. The reason s that the most frequently chosen staton apparently has extra qualtes that make t more attractve than the nearest staton. Hence, one may expect that dstance to the staton matters less n the prce effect on real estate. The mrror mage s that the qualty of the staton, as reflected among others by the frequency, has a larger effect. Ths explans why the frequency elastcty n Table 3 s so hgh for the most frequently chosen staton compared wth the nearest staton (0.09 versus 0.03). A doublng of frequency of trans at the most frequently chosen staton has 9% house prce ncrease n the post code area compared to 3% for the case of the nearest ralway staton (see the frst 2 columns of Table 3). Fnally, we fnd clear negatve effects of ralway nose on house values: houses located n the zone wthn 250 meters from a ralway lne are about 5% less expensve than houses located 500 meters or more. For the zone between meters ntermedate values are found. 14

17 However, the measure of frequency of trans effect dscussed above s crude snce t s not dstance dependent. The pont s that for dwellngs close to a staton a frequency ncrease s probably of more mportance than for dwellngs far away. The last two columns of Table 3 provde the estmaton of the cross dstance-frequency effect. Doublng the frequency of trans n the nearest staton results n as much as 3.5% prce ncrease for houses located up to 2 klometres compared to the effect on dwellngs located beyond 15 klometres. Doublng the frequency of the most frequently chosen staton on the other hand results n about 3.0% prce ncrease for the same dstance secton. The pattern n the elastctes of frequency for the dfferent dstance categores s depcted graphcally n Fgure 3. These estmatons demonstrate that the value of property may depend on the proxmty to more than one ralway staton. We wll not nvestgate ths ssue n more detal here, but ths s an ndcaton that ralway staton accessblty s a more complex concept than one mght thnk: t nvolves competton between ralway statons. Furthermore, the percentage effect of dfferent levels of frequency s gven n Table 4 below. The table shows -not surprsngly- that the effect of ralway proxmty s largest n the case of a staton wth a hgh level of servce. Note that such a dfferentated effect s not present n the specfcaton gven by Equaton 2. However, the frequency mpact s smaller than one mght expect. The prce curves are clearly steeper around statons wth hgher frequences. Further, we fnd that even for statons wth a small number of trans a substantal effect of ralway presence s found. Note that ths estmaton s based on a specfcaton where correctons were carred out for a large number of other varables. In partcular, a dummy has been added for each muncpalty so that t has been assured that the results found do not capture the effects of other varables such as populaton densty or other muncpalty specfc factors. 15

18 Table 3: Estmaton of Ralway staton effects on house values: pecewse dstance effect (N.B. Only ralway related parameters are presented) Varable Cross dstance-frequency of trans effect Nearest Staton Most frequently Nearest Staton Most frequently chosen staton chosen Staton Coeffcent S.E. Coeffcent S.E. Coeffcent S.E. Coeffcent S.E. (Constant) *** *** *** *** raldst *** *** *** *** raldst250_ *** *** *** *** raldst500_ *** *** *** *** raldst1000_ *** *** *** *** raldst1500_ *** *** *** *** raldst2000_ *** *** *** *** raldst2500_ *** *** *** *** raldst3000_ *** *** *** *** raldst3500_ *** *** *** *** raldst4000_ *** *** *** *** raldst4500_ *** *** *** *** raldst5000_ *** *** *** *** raldst5500_ *** *** *** *** raldst6000_ *** *** *** *** raldst6500_ *** *** *** *** raldst7000_ *** *** *** *** raldst7500_ *** *** *** *** raldst8000_ *** *** *** *** raldst8500_ *** *** *** *** raldst9000_ *** *** *** *** raldst9500_ *** *** *** *** raldst10000_ *** *** *** *** raldst10500_ *** *** *** raldst11000_ *** *** *** *** raldst11500_ *** *** *** *** raldst12000_ *** *** *** *** raldst12500_ *** *** *** *** raldst13000_ *** *** *** raldst13500_ *** *** *** *** raldst14000_ *** *** *** raldst14500_ *** *** *** Log (frequency) *** *** rallne *** *** *** rallne250_ *** *** *** R square N 542, , , ,873 Lnear regresson model coeffcents wth standard errors of the estmates n parentheses *** stands for a sgnfcance level of less than 1% ** stands for a sgnfcance level of less than 5% * stands for a sgnfcance level of less than 10% 16

19 17 Fgure 3: Cross-effect of ralway staton dstance and frequency of trans on house prces most frequently chosen staton Nearest staton Dstance categores raldst250 raldst250_500 raldst500_1000 raldst1000_1500 raldst1500_2000 raldst2000_2500 raldst2500_3000 raldst3000_3500 raldst3500_4000 raldst4000_4500 raldst4500_5000 raldst5000_5500 raldst5500_6000 raldst6000_6500 raldst6500_7000 raldst7000_7500 raldst7500_8000 raldst8000_8500 raldst8500_9000 raldst9000_9500 raldst9500_10000 raldst10000_10500 raldst10500_11000 raldst11000_11500 raldst11500_12000 raldst12000_12500 raldst12500_13000 raldst13000_13500 raldst13500_14000 raldst14000_14500 raldst14500_ Elastcty of tran frequency Fgure 2: Effect of ralway staton dstance on house prces Nearest staton Dstance categores most frequently chosen staton raldst250 raldst250_500 raldst500_1000 raldst1000_1500 raldst1500_2000 raldst2000_2500 raldst2500_3000 raldst3000_3500 raldst3500_4000 raldst4000_4500 raldst4500_5000 raldst5000_5500 raldst5500_6000 raldst6000_6500 raldst6500_7000 raldst7000_7500 raldst7500_8000 raldst8000_8500 raldst8500_9000 raldst9000_9500 raldst9500_10000 raldst10000_10500 raldst10500_11000 raldst11000_11500 raldst11500_12000 raldst12000_12500 raldst12500_13000 raldst13000_13500 raldst13500_14000 raldst14000_14500 raldst14500_ Percentage effect

20 Table 4: The relatve prce dfference of dwellngs at sample dstances compared wth dwellngs located beyond 15 klometres. (Based on cross dstance frequency specfcaton) m m m Dstance Frequency (trans/day) Nearest staton Mostly chosen staton Nearest staton Mostly chosen staton Nearest staton Mostly chosen staton % 16.8% 12.9% 12.9% 7.4% 7.0% % 19.8% 15.2% 15.2% 8.7% 8.3% % 22.8% 17.5% 17.5% 10.1% 9.5% % 25.8% 19.8% 19.8% 11.4% 10.8% % 28.7% 22.1% 22.1% 12.7% 12.0% When one wants to acheve an ncrease n real estate values along a ralway lne, there are several strateges. One strategy would be to ncrease the frequency of servce on exstng statons, and Table 4 shows the rather modest effects. Another strategy would be to create an extra staton. If two statons are located at dstances of say 10 klometres and a new staton s bult n between the two, the dstance to the nearest staton decreases up to a maxmum of 5 km. As ndcated by Table 4, the latter strategy would lead to an ncrease n house value of at most 6.7% (19.6%-12.9%) of the dwellngs located n the mmedate vcnty of the staton. Wth the present model t s not possble to nvestgate the consequences of adverse effects on travel tmes due to the extra stop. Note that, when we compare the effects of creatng an extra staton or a frequency ncrease, the frst manly affects property values n one locaton, whereas the latter would be benefcal for all statons where the tran would stop. 6. SUMMARY AND CONCLUSION Ths paper analyses the effect of ralway staton accessblty on the house prces. A cross sectonal hedonc prce model s estmated based on Dutch resdental house transacton n the years from 1985 to The model accounts for physcal, envronmental, temporal and accessblty features of the resdental houses. For each of these features a wde range of 18

21 varables s ncluded. The man focus of ths paper s, however, to analyse the effect of accessblty provded by ralway transport on property values. Most studes n ths area only consder the proxmty of propertes to ralway statons. However, ths approach s lmted because accessblty of ralway statons s more than proxmty to ralway statons. In other words, ralway statons are not chosen as departure ponts for reasons of proxmty alone. Thus, we need a better approach to address ralway accessblty n the analyss. Ralway accessblty s a functon of the dstance and the servce levels at the relevant departure ralway statons. The choce for a departure ralway staton s also affected by the levels of ral servce, network connectvty, servce coverage and facltes. Thus, t s possble for the resdental property value to react to an mportant ralway staton located farther away than a less mportant one located nearby. In ths respect most prevous studes had shortcomngs n that they neglect the choce process for a departure staton n ther property value effect analyss by stckng to the nearest ralway staton. Ths paper adds to the lterature n ths area n two respects. Frst, we make a dstncton between the nearest ralway staton to the property and the most frequently chosen staton n the post code area to whch the property under consderaton belongs. Second, a broader approach for addressng accessblty s appled by takng nto account the frequency of servces. The effects of proxmty and servce levels on property values are analysed. In addton we pay attenton to the dstance to ralway lnes to reflect potental nose and other dsturbance effects. Correctng for a wde range of other determnants of house prces we fnd that dwellngs very close to a staton are on average about 25% more expensve than dwellngs at a dstance of 15 klometres or more. Ths percentage ranges between 19% for low frequency statons and 33% for hgh frequency statons (see Table 4). A doublng of frequency leads to an ncrease of house values of about 2.5%, rangng from 3.5 for houses close to the staton to 1.3% for houses far away. Fnally we fnd a negatve effect of dstance to ralways, probably due to nose effects: wthn the zone up to 250 meters around a ralway lne prces are about 5% lower compared wth locatons further away than 500 meters. As a result of the two dstance effects, the prce gradent starts to ncrease as one moves away from a staton, followed by a gradual decrease after a dstance of about 250 meters. 19

22 Our estmatons reveal that the dstncton between nearest ralway staton and most frequently chosen ralway staton s mportant. In many cases the traveller does not choose the closest staton. Ths s an ndcaton that ralway staton accessblty s a more complex concept than one mght thnk, as t nvolves competton between ralway statons, a subject we ntend to address n a forthcomng paper. ACKNOWLEDGEMENT We would lke to take the opportunty to thank dfferent partes that contrbuted to our work by provdng data and nsghts. René van der Kruk provded us wth useful data he used for hs wetland study. The Dutch natonal Ralway Company (NS) was helpful at every stage by provdng data and nsghtful deas. Specal thanks go to Mark van Hagen and hs colleagues at NS. People at the GEODAN were also helpful by geo-codng the houses to make them ready for accessblty analyss and many others. 20

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