tématické články Measuring the Value of Urban Forest using the Hedonic Price Approach regionální studia

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1 Measurng the Value of Urban Forest usng the Hedonc Prce Approach Odhad hodnoty městských lesů metodou hedoncké ceny Jan Melchar 1 Charles Unversty Envronment Center Ondřej Vojáček Department of Envronmental Economcs, Unversty of Economcs, Prague Pavel Reger Department of Law, Unversty of Economcs, Prague Karel Jedlčka 2 Department of Mathematcs, Unversty of West Bohema, Plsner ABSTRACT12 A hedonc model to the Prague real estate market was appled n order to estmate the effects of the proxmty to urban forests and parks on prces of the real estate. Therefore, a large database of 1,701 observatons was constructed. Ths database contans structural aspects, accessblty characterstcs and envronmental varables on proxmty and sze of the nearest urban forest. Several regresson models have been developed and ther robustness has been tested n terms of the sgnfcance of parameters and the sze of varablty explaned. Results showed the sze of the flat s the most relevant varable explanng the prce of the real estate. A sgnfcant nverse relatonshp between the prce of the real estate and ts dstance from a metro staton and an urban forest has also been found. KEYWORDS hedonc prce, regresson analyss, geographcal nformaton system, property market, urban forests KLÍČOVÁ SLOVA hedoncká cena; regresní analýza; geografcký nformační systém; trh s bydlením, městské les Introducton Urban green open spaces have been developed as an ntegrated concept n urban and regonal plannng durng the last few decades n Europe. The hstory of these urban areas was especally related to the development of publc parks n the 19 th century as a reacton to urbanzaton and the poor lvng condtons n the ctes. Today, the concept of green areas has the potental of becomng a general and legtmate part of urban plannng, or so called green plannng. The urban green open spaces, urban forests, publc parks, park systems, green belts and other urban open spaces are valuable for ther socal, spatal and techncal mportance. 1 Ths research has been supported by the R&D Project 402/08/1659 Hedonc Wage Model: Estmaton of Hedonc Wage Dfferentals n the Czech Republc funded by the Czech Scence Foundaton. The support s gratefully acknowledged. 2 The work of author on ths artcle was supported by the Research Plan MSM The urban green areas are also mportant elements contrbutng to the well-beng of urban resdents. Indeed, urban forests and green spaces provde many benefts to the cty resdents, ncludng opportuntes for recreaton, relef from urban stresses and congeston, as well as aesthetc benefts to the resdents of the surroundng buldngs. In addton, green areas, especally urban forests, provde ecologcal benefts, for nstance, by contrbutng to the ambent ar and the water qualty and by offerng anmal habtats 3. Gven the development of the ctes, there s a growng pressure on the urban green areas n order to satsfy the ncreasng need for housng, commercal and ndustral spaces. Those often competng demands need to be traded-off by urban planners. Therefore, effectve land-use regulaton polces should ntegrate the value of the benefts of green open spaces 4. Snce several of those benefts are of publc nature, the economc lterature proposes varous valuaton methods n order to assess ther value. The most wdespread approach used n the lterature s based on the hedonc prce method (HPM) 5. The HPM dsentangles market nformaton on prce of the real estate n order to obtan the mplct prce of each characterstc of the housng bundle, ncludng open space characterstcs. The HPM s revealed preference method that nfers the open space value by estmatng the sales prce or value of a property as a functon of measures of proxmty to open space and other property and neghborhood characterstcs. The paper apples the HPM to the real estate market of Prague n the Czech Republc. The central hypothess n our paper s that urban green open spaces, represented by urban forests and parks, have a postve mpact on prces of the real estate. Therefore, several types of regresson models have been tested to nfer the sgnfcant determnants of the Prague real estate market, ncludng green structures. The dfferental premum on property value was estmated,.e. mplct value, 3 Accordng to economc termnology we speak about external benefts. 4 Here, an economc perspectve offers an opportunty to gude decson makers. The tradtonal economc approach, so called Cost-Beneft Analyss, nvolves tryng to balance the opportunty costs of resources wth the socal welfare benefts of alternatve projects. 5 On the other hand, stated preference methods such as contngent valuaton, choce experment, or choce analyss use specally desgned surveys that enable elct drectly ndvduals preferences for varous types of open space amentes. 13

2 derved from structural attrbutes and from the proxmty to some neghborhood and urban forest attrbutes. Relevant nformaton used n the hedonc regresson analyss came from a sample of 1,701 resdental dwellngs. Bascally, our paper s rather emprcal than theoretcal. Our ambton was to develop and to show to the urban planners how the HPM could be used to derve monetary values of the envronmental amentes provded by urban open spaces such as urban forests. Our study s novel n two aspects. Frst, t s the frst HPM applcaton on housng market n the Czech Republc. Second, t shows how effectvely Geographcal Informaton System could be used when valung urban forests amentes. Secton 2 brefly revews the lterature focusng on the green open space valuaton n an urban context. In Secton 3, bases of the theoretcal foundatons of the hedonc approach are presented. Secton 4 presents the study area and explans how the varables enterng nto the hedonc model were defned. The emprcal models used n the regresson are presented n Secton 5 and the results n Secton 6. Secton 7 concludes. Valung Open Space wth Hedonc Prce Models Applcaton of the hedonc technque to evaluate the envronmental amentes has a long hstory. Hundreds of artcles have been publshed usng the hedonc prce model appled to the estmaton of the benefts of envronmental amentes. The HPM applcatons on valuaton of envronmental characterstcs have been wdespread, rangng from ar qualty to nose exposure, landscape and urban amentes. Applcatons to parks and open space frst appeared wth a few artcles n the 1970s, focusng prmarly on parks, followed by several studes n the 1990s and early 2000s lookng at a wde varety of open spaces. The focus of these studes ranges from urban and suburban parklands to nature preserves, forests, wetlands, and agrcultural lands. In our paper, the man pont s to examne the mpact of the urban forest and the parks on the real estate s prces. The HPM lterature has found that, lke any other land uses, urban forests and parks uses can actually be a source of both postve and negatve nfluences. Although negatve nfluences related to green structures are generally neglgble, t s underlned by the lterature that n some crcumstances, a green structure can result n a loss of value. The earlest studes usng the real estate s prces to mplctly value open spaces were focused on parks. Ktchen and Hendon (1967) looked at the dstance to a neghborhood park n Lubbock, Texas, and performed smple correlatons between prce of the house and dstance. They have shown that there s a sgnfcant postve correlaton between the house prce or values and the dstance: houses farther from the park are more valuable. Ths analyss could support the hypothess about the negatve nfluence, but the conclusons of ths study were based only on smple correlatons that do not take nto account other factors affectng the house prces. Usng more robust regresson analyss, some other authors proved that heavly used publc parks may have a negatve mpact on adjacent houses and may even decrease ther prces (Tyrvänen, 1997). In ths specfc context, negatve effects are more mportant than postve effects. In terms of postve mpacts, our references show that people are wllng to pay more for a house located close to an urban open space than for a house that does not offer ths amenty. Besde a few exceptons (Luttk, 2000; Schroeder, 1982), studes fnd that homes adjacent to naturalstc parks and open spaces are typcally valued at about 8 to 20 percent more than comparable propertes (Crompton, 2001). Values show a lnear declne wth dstance from the edge of an open space, wth a postve prce effect declnng to near zero at about half a mle away (Hammer et al., 1974; Tyrvänen et Mettnen, 2000; More et al., 1988). Also, the study done by Morancho (2003) ndcates that the prce of the real estate relates nversely wth the dstance that separates t from an urban green space. Ths result s n accordance wth Boltzer and Netusl (2000) who concluded that proxmty to an open-space can have a statstcally sgnfcant effect on home sellng prce. As well as wth Tyrvänen and Mettnen (2000) whose results demonstrated that a 1 km ncrease n the dstance from the nearest forested area leads to an average 5.9% decrease n the market prce of the dwellng. Besdes the accessblty to urban forests and parks, some emprcal evdence could be found that proved the sgnfcant nfluence of the surface of trees-covered areas on the prce of the real estate. Tyrvänen (1997) found that on average, an ncreased amount of forested areas n the housng area rased the apartment prces. Usng the lnear model, Boltzer and Netusl (2000) also found the sze of the open space has a postve and statstcally sgnfcant nfluence on the house prces. Results n Lutzenhser and Netusl (2001) confrmed the earler fndngs about the dfferental effects that natural areas and urban parks have on house prces. The authors found that houses near urban parks have lower prces, all else equal, whle those near natural areas have hgher prces. Natural area parks have the largest effect on house prces, and n general, the bgger the better that s, house prces ncrease wth the sze of the natural area. Although beng near an urban park s found to decrease prce, urban park acreage has a postve effect on prce: the larger the park, the hgher the average house prce, all else equal. Fnally, Baranzn and Schaerer (2007) also estmated the postve and sgnfcant coeffcents for both the accessblty and the surface of the trees-covered areas n the neghborhood of the dwellng whch mples that those characterstcs have a postve mpact on the rent. However, Morancho (2003) or Anderson and West (2003) found that the sze of parks and open space has no statstcally sgnfcant effect on the house prces. The studes revewed n ths secton cover a wde range of types of open space and a varety of ways of measurng them. Moreover, the studes are from dfferent geographc areas of the Unted States, as well as from other countres. All of these dfferences provde a rch source of nformaton on open space, but ther benefts vary from one study to another. In some cases, the margnal mplct prces are even negatve, meanng proxmty to some types of open space or ther sze 14

3 actually reduces the value of the house, and n other cases, they are not sgnfcantly dfferent from zero. Theoretcal Prncples of the Hedonc Prce Method The fundamental prncple of the hedonc prce method s that the utlty provded by heterogeneous goods s based upon the utlty yelded by ther varous characterstcs, rather than by the goods themselves (Lancaster, 1966). The HPM s based on the dea that propertes are not homogenous and can dffer wth respect to a varety of characterstcs. The method reles on the fact that house prces are affected by many factors such as the number of rooms, the sze of flat or garden, or the access to cty center. It s supposed that the prce of the real estate s determned by the partcular combnaton of characterstcs t dsplays. Any partcular property could be descrbed by the quanttes and the qualtes or the characterstcs of ts structure, locaton and envrons. To reveal the emprcal relatonshp between the prce of the real estate and ts characterstcs, the regresson analyss s used as the prmary statstcal tool. Property prces are regressed aganst sets of explanatory varables, these are typcally calculated usng structural attrbutes of the housng (such as number of bedrooms), neghborhood varables (qualty of local schools), accessblty varables (proxmty to ralway staton, urban parks) and envronmental varables (ambent nose level). The results of regresson analyss are used to derve a hedonc prce functon that ndcates how much the prce of the real estate wll change for a small change n each characterstc, holdng all other characterstcs constant. The hedonc prce functon can be used to determne how much must be pad more for a property wth an each extra unt of a partcular housng characterstc. Ths s known as the mplct prce of a property characterstc. Grlches (1971) and Rosen (1974) provded the theoretcal support for the development of the hedonc prce models. The HPM relates the market prce of the housng commodty (house, flat or rental housng) wth the structural and accessblty characterstcs that defne t. It enables us to derve the monetary value of each characterstc that s calculated by observng the dfferences n the prces of the real estate sharng the same attrbutes. The basc hypothess s that the housng s formed by a heterogeneous set of characterstcs. Therefore, when buyng a house, the prce pad s consdered as the sum of the prces pad for each attrbute (called mplct prce). Ths could be expressed as follows: P 1, 2,..., f x x x n (1) where P s the market prce of the house and x 1, x 2,, x n are the housng characterstcs t embodes. The partal dervatves of the prce wth respect to the prevous varables, P/ x, provde nformaton on the margnal mplct prce for an addtonal unt of each characterstc. The set of structural and accessblty varables such as the sze of flat could not be the only determnants that explan the prce of the real estate. There are also envronmental aspects such as proxmty to a green area, nose level that could also explan the dfferences n ts market prce. Then, the prce functon specfed n equaton (1) can be formulated as follows: P f x, x,..., x, 1 2 n z (2) where P s agan the house market prce; x 1, x 2,, x n are structural and accessblty characterstcs and z s the envronmental varable wthout a market prce (the hedonc varable). The essence of the method conssts n fndng what porton of the prce s determned by the hedonc varable. Ths nformaton s obtaned agan by calculatng the partal dervatve of the prce wth respect to the varable z, P/ z, whch gves us the margnal mplct value for an addtonal unt of the envronmental asset and thus, allows us to obtan an estmate of ts monetary value. The theoretcal model specfed n equaton (2) has been used to obtan the mplct prce of the dstance to urban forests as the envronmental varable consdered n ths study. A more detaled exposton of the hedonc prce model could be found n Freeman (2003), Garrod and Wlls (2000) and Champ et al. (2003). Assocated wth the hedonc technque are several methodologcal ssues, some of them are more general and apply to any applcaton and some of them have specal sgnfcance to the study of open space. One ssue s the choce of functonal form. Parametrc models that have been used nclude the lnear, quadratc, log-log, loglnear, semlog, and the Box-Cox transformaton. In general, the theory underlyng the approach does not provde much gudance about whch of these functonal forms s the most approprate. Certan knds of Box-Cox transformatons are more flexble than other alternatves. However, many more coeffcents are estmated n such models. Cropper et al. (1988) show that when key explanatory varables are omtted, the smple lnear model s superor to the quadratc Box-Cox transformaton n generatng accurate margnal mplct prces. Ths s apparently due to the fact that the omtted varables lead to bas n more coeffcents n the more complcated verson of the model than they do n the smpler model. Omtted varables problems can be qute common n hedonc property value studes, snce t s dffcult to obtan all the house characterstcs that matter to consumers. Other ssues are whether the real estate market s n equlbrum or not, the problems wth measurement error and wth multcolnearty. Whle the measurement error relates to errors n the observed values of the dependent and explanatory varables beng used n the model, multcolnearty concerns the dffculty n the nterpretaton of hedonc estmates when the effects of several varables are closely lnked. An addtonal consderaton n some cases s whether the data can be treated as f they correspond to a sngle market, or whether they need to be dvded nto separate sub-markets. 15

4 Dscussons about these ssues and how to solve the problems ntroduced here could be agan found n Freeman (2003), Garrod and Wlls (2000) and Champ et al. (2003). Study Area and Data In order to apply the hedonc prce model, relevant nformaton about sales of dwellngs have been obtaned from the Prague s real estate market. Indvdual housng characterstcs ncludng sellng prce were obtaned from the real estate catalogue operatng by the Czech company realty.cz (see The data set contans nformaton on 1,701 apartments and flats sold to personal ownershp from 2005 to sprng See the geographcal poston of data set n Fgure 1 as t was detected by ArcExplorer. In ths study, the dstance from the dwellng to the cty center, the nearest tube staton (accessblty varables) and the nearest urban forest (envronmental varable) were measured by Geographcal Informaton System (GIS) and software ArcGIS respectvely. Fgure 2 depcts the geographcal poston of Prague s urban forests and parks consdered n the hedonc prce model. GIS was also an mportant tool for detectng wrong observatons (duplcty n observatons, wrong postons of dwellngs etc.). Altogether, 279 records have been detected as wrong, hereafter not used n the further analyss. All varables used n the regresson analyss, ncludng ther defntons and expected nfluence on the dependent varable, are summarzed n Table 1. Varable Prce s the sellng prce of flat expressed n CZK and represents the dependent varable. The data was collected from the reasonably stable perod of The data from ths tme perod has been combned by convertng the nomnal sellng prces to the 2005 prce level usng prce ndex for the real estate n Prague. The base year of the ndex s 2005, and the ndex s produced by the Czech Statstcal Offce. In the proposed regresson models the varable Prce s explaned by the followng explanatory varables: Sze the sze of flat (structural varable). Center dstance to the cty center represented by the Sant Wenceslas Statue (accessblty varable). Metro the proxmty to the nearest metro staton (accessblty varable). Forest dstance to the nearest urban forest n meters (envronmental varable). Altogether, 19 of the man urban forests and forest parks n Prague have been consdered. Surface surface of the nearest urban forest measured n hectares (envronmental varable). Bench number of benches n the nearest urban forest (envronmental varable). Tral length of trals n the nearest urban forest (km) (envronmental varable). The last four varables present hedonc varables. Ther ncluson n the hedonc prce equaton allows the estmaton of the nfluence of open green spaces and ther recreatonal aspects on the real estate market value. Accordng to emprcal evdence, the dstance to the nearest urban forest s supposed to be negatve,.e. flats and apartments farther from the urban forest are cheaper. The nfluence of the sze of urban forest or park could be both postve and negatve. The effect of recreatonal attrbutes such as the number of benches or the length of trals on the prce of the real estate was not prevously nvestgated n the lterature. The nfluence of these varables s ntutvely supposed to be postve. Table 2 gves mean, maxmum, mnmum and medan values for the dependent varable and the explanatory varables. The average prce of flat s 2.6 Ml. CZK and ranges from 300 thousands CZK to 25 Ml. CZK. Medan value s 1.9 Ml. CZK,.e. about 700 thousands lower than mean value. The sze of flat s n average 74 m 2. The average dstance from dwellng to the cty center s slghtly above 5 km, on the other hand the dstance to the nearest metro staton s n average 1.5 km, and the proxmty to the nearest urban forest s slghtly above 1.5 km. The surface of urban forests and parks s n average 119 hectares. These results ndcate, n general, Varable Descrpton Expected sgn 1 Prce Prce of the property n the CZK of the year 2005 DV 2 Structural varables Sze The area of the flat (m 2 ) + Accessblty varables Center Dstance to the cty center (m) - Metro Dstance to the nearest underground staton (m) - Envronmental and recreatonal varables Forest Dstance to the edge of the nearest urban forest (m) - Surface Surface of the nearest urban forest (ha) +/- Bench Number of benches n the nearest urban forest + Tral Length of trals n the nearest urban forest (km) + Note: 1 +ncreasng/ - decreasng effect on the purchase prce. / 2 dependent varable. Table 1: Structural, Accessblty and Envronmental Varables Enterng nto Hedonc model 16

5 Fgure 1: Geographcal Poston of the Data Set usng ArcExplorer Fgure 2: Geographcal Representaton of Prague s Urban Forests and Parks 17

6 Varable Mean Standard Devaton Mn Medan Max Prce 2,665,771 2,010, ,430 1,960,800 25,200,000 Sze Center 5,139 3, ,809 20,435 Metro 1,436 1, ,774 Forest 1,637 1, ,240 7,087 Surface Bench Tral Table 2: Descrptve statstcs (N=1,701) that people prefer to lve n the suburb parts of Prague, qute far from the cty center, but wth an easy accessblty to the tube staton and the green area. Emprcal Models In general, the hedonc prce theory underlyng the approach does not provde much gudance about whch of these functonal forms s the most approprate. Parametrc models that have been used nclude the lnear, quadratc, loglog, log-lnear, sem-log, and the Box-Cox transformaton. In ths study, three functonal forms have been tested: lnear, sem-log and double-log. If the lnkng prce relatonshp wth the characterstcs of housng s assumed to be lnear (both the dependent and explanatory varables enter the regresson n ther lnear form), the equaton (2) becomes: P b x b x... b 1 1 1, 2,..., T 2 2 n x n b z z (3) where x 1, x 2,, x n, z are varables descrbng the attrbutes of housng, parameters b 1, b 2,, b n, b z represent the margnal mplct prce of each attrbute and ε s the error term. The margnal mplct prce for an addtonal unt of the envronmental good z s b z. Under the lnear specfcaton, the wllngness to pay for an addtonal unt remans constant,.e. t does not depend on the startng level of z. Ths assumpton s a strong restrcton snce, as Rosen (1974) ponts out, there are many reasons to suppose the relatonshp between the prce and the envronmental varable to be non-lnear. Thus, logarthmc specfcatons are frequently formulated, as follows. In the case of sem-log specfcaton, the log of the dependent varable s regressed aganst lnear explanatory varables: ln P b x b x... b x b z (4) n n In equaton (4), both ts slope and elastcty change at each pont are the same sgn as b n. Double-log s the specfcaton form where both the dependent and explanatory varables enter the regresson n ther log form, as follows: z ln P b1 ln x1 b2 ln x 2... bn ln x n bz ln z (5) A logarthmc model allows us to measure the mpact that changes n explanatory varables cause n the dependent varable n relatve terms. Then, the parameter b n represents the value of elastcty of correspondng varable. Estmated Results The nvestgaton of correlaton coeffcents revealed no sgnfcant degree of correlaton between ndependent varables. The hghest correlatons were found between the coeffcents for the dstance to the cty center and the dstance to the metro staton (Pearson correlaton coeffcent = ) and between the dstance to the cty center and number of benches ( ). Ths ndcates slght multcolnearty. Besdes the hgh value of correlaton coeffcent, the reason could also be unreasonable coeffcent for the metro staton whch was postve n the frst regresson analyss where all varables were ncluded. Multcolnearty has also been nvestgated by estmatng the varance nflaton factor (VIF). The hghest value of VIF was derved for Center varable (about 2.7) 6. Center varable seemed to be redundant n the model nfluencng Metro and Bench varables that s why t has been excluded from farther regresson analyss. Table 3 presents the fnal results of the ordnary least squares (OLS) regressons for the three specfed models. All explanatory varables are statstcally sgnfcant even at the sgnfcance level n each specfed model (except Tral varable n lnear model). Varables Sze, Metro, Forest and Bench have the expected sgns. For varables Surface and Tral, the negatve nfluence was derved. A bgger sze of flat means a hgher prce of the real estate, whle the prce decreases wth the dstance from the nearest metro staton. In accordance wth the lterature evdence, the negatve sgn of Forest varable confrmed that people are wllng to pay more for flats located close to urban forests and parks than for flats that do not offer ths amenty. Surface of urban forests and parks has negatve mpact on the prce,.e. the larger sze of urban forest or park, the lower the prce. Ths result suggests that nearby resdents could suffer from 6 Typcally, value of 10 s used as the threshold at whch multcolnearty s consdered as a problem, but ths s smply a rule of thumb. 18

7 the nose, traffc, and other dsamentes usually assocated wth busy parks. The same nterpretaton could be outlned related to the negatve sgn of Tral varable. The hgher densty of trals n urban forests and parks and the more vstors, the larger dsamenty nearby resdents wll have. On the other hand, the statstcal sgnfcance was approved for the number of benches wth the postve nfluence on the prce. The more benches n park or forest could result n hgher recreatonal experence. Varable Lnear model Sem-log model 1 Sze 4,3617*** *** Metro *** ** Forest ** *** Surface -1,335.11*** *** Bench 1,630.71*** *** Tral -7, *** Double-log model 1 ln(sze).9753*** ln(metro) *** ln(forest) *** ln(surface) *** ln(bench).1175*** ln(tral) ** constant -422,551.6*** 298.8*** 10.8*** R-squared Adj R-squared AIC 5, Note: 1 dependent varable as Ln of prce; *p<.05; **p<.01; ***p<.001 Table 3: Hedonc prce models (N=1701) The varablty of the prces of the real estate measured by R 2 adjusted s accounted dfferently n the estmated models. Lnear model explans 60% of the varaton n the prces of the real estate, sem-log accounts 68%. However, double-log model accounts for 69% of the prce varance, thus provdes the best ft from all these models. The goodness of ft of the estmated statstcal model was ranked accordng to ther Akake s nformaton crteron (AIC). The preferred model s agan double-log, because s the one wth the lowest AIC value. Usng the estmated coeffcents presented n Table 3, the relatonshp between the dstance to the nearest urban forest and the prce of the real estate wth a gven set of characterstcs can be approxmated by a hedonc prce functon. The hedonc prce functon derved for each model s presented n Fgure 3. The hedonc prce functons show the decrease n the prce of the real estate as the dstance from urban forest ncreases whle holdng the other varables constant. Fgure 3: Hedonc Prce Functon for Each Specfed Model However, the prce reducton s qute moderate. Ths ndcates low elastcty,.e. the percentage change n the prce of flat assocated wth a 1% change n the proxmty to an urban forest. The elastcty s -5.68% for lnear model, for double-log model and for sem-log model. Nevertheless, consderng the hedonc prce functon derved from double-log model, t s evdent that the prce of flat falls down sgnfcantly up to 500 meters from dwellng, then decreases slghtly. Accordng to the obtaned coeffcents n double-log model and havng the varables at ther mean values the mplct prces for each explanatory varable were calculated that n our regresson models sgnfcantly determnes the prce of the real estate n Prague: Each addtonal square meter n the flat sze ncreases the prce by 1,588 CZK, An ncrease n the dstance from the metro staton about 1 km decreases the prce by 58 thousands CZK, and An ncrease n the dstance from urban forest about 100 m declne the prce by 4.3 thousands CZK. Ths means that a 1 km ncrease n the dstance from the nearest forested area leads to an average 1.61% decrease n the market prce of the dwellng n Prague. Conclusons Accordng to the lterature revew on hedonc models used to assess the nfluences of urban green open spaces on resdental property values, the central hypothess about the postve mpact of green was confrmed. Our results yeld two mportant nsghts for urban plannng mplcaton. Frst, the key hypothess about the postve mpact of urban forests and parks was confrmed, thus the proxmty prncple of urban green areas was approved for the cty of Prague. However, the sze of urban forests and parks has negatve nfluence on the prce of the real estate whch could be accounted to nose and traffc assocated wth busy parks. Second, the structural varables such as the sze of flat contrbute to the prce of the real estate more substantally that the proxmty and the green open space attrbutes. The sze of 19

8 flat contrbutes ten to thrty tmes more than other determnants that were analyzed. As precedng hedonc prcng studes have shown, the effects of urban envronmental amentes such as urban forest on the prce of the real estate are measurable and sgnfcant. Usng a hedonc prce model and ordnary least squares regresson, a hedonc prce functon flats has been estmated n whch the sale prce s related to the proxmty and the sze of urban forests n the cty of Prague. Together wth a set of conventonal explanatory varables such as the sze of flat, the proxmty to the nearest metro staton, the dstance and the sze of the nearest urban forest or forested park, ncludng number of benches and length of trals as envronmental and recreatonal varables were ncluded n the rght-hand sde of the regresson. Three types of regresson models have been tested. Compared to lnear and sem-log, double-log model accounts for 69% of the prce varance, thus provdes the best ft from all these models. Regresson analyss revealed the sze of flat as the varable wth the greatest explanatory power. Other statstcally sgnfcant varables are the dstance to the nearest metro staton and to the nearest urban forest. As expected, the sze of flat and the number of benches n park affect the prce of flat postvely, on the other hand, the dstance to the metro staton and to the nearest urban forest decrease the prce of the real estate. Accordng to the estmates derved by double-log model, each 100 m further away from urban forest means a drop of 4.3 thousands CZK n the prce of the real estate. It means that a 1 km ncrease n the dstance from an urban forest leads to an average 1.61% decrease n the prce of the real estate. Compared to other studes that had assessed ths effect at about 6 to 20 %, the dstance effect n ths study can be consdered as modest. Although, the nfluence s moderate, the proxmty to an urban forest s a sgnfcant determnant nfluencng people s preferences where to lve. Resumé Použl jsme model hedoncké ceny na trhu s bydlením v Praze, abychom odhadl výš vlvu vzdálenost městských lesů a parků na cenu bydlení. Vytvořl jsme datový vzorek o 1701 pozorováních, který obsahuje stavební atrbuty bydlení, prvky dostupnost a envronmentální atrbuty týkající se vzdálenost do nejblžšího městského lesa a jeho rozlohu. Vytvořl jsme několk regresních modelů a testoval jejch spolehlvost z hledska významnost parametrů a velkost vysvětlované varablty. Výsledky ukázaly, že rozloha bytu je nejvíce významná proměnná, která vysvětluje cenu bydlení. Také jsme zjstl významný negatvní vztah mez cenou bydlení a vzdáleností ke stanc metra a městského lesa. References ANDERSON, S. T., WEST, S. E. (2006): Open space, resdental property values, and spatal context. Regonal Scence and Urban Economcs. 36, pp BARANZINI, A., SCHAERER, C. A. (2007): A sght for sore eyes: Assessng the value of vew and landscape use on the housng market. Paper presented at the Internatonal Conference on Regonal and Urban Modelng, EcoMod, Free Unversty of Brussels, 1-2 June BOLITZER, B., NETUSIL, N. R. (2000): The mpact of open spaces on property values n Portland, Oregon. Journal of Envronmental Management. 59(3), pp CHAMP, P. A., BOYLE, K. J., BROWN T. C. (Eds.) (2003): A Prmer on Nonmarket Valuaton. Kluwer Academc Press: Boston. ISBN: CROMPTON, J. L. (2001): Parks and Economc Development. Washngton D. C.: Amercan Plannng Assocaton. CROPPER, M. L., LELAND, B. D., McCONNELL, K. E. (1988): On the Choce of Functonal Form for Hedonc Prce Functons. Revew of Economcs and Statstcs. 70(4), pp FREEMAN, A. M. III. (2003) The Measurement of Envronmental and Resource Values: Theory and Methods. Resources for the Future: Washngton, DC. ISBN: GARROD, G., WILLIS, K. G. (2000): Economc Valuaton Of The Envronment. Edward Elgar: Cheltenham. ISBN: GRILICHES, Z. (1971): Prce Indexes and Qualty Change. Harvard Unversty Press. Cambrdge, MA. HAMMER, T. R., COUGHLIN, R. E., HORN, E. T. (1974): The Effect of a Large Park on Real Estate Value. Journal of the Amercan Insttute of Planners. 40, pp KITCHEN, J. W., HENDON, W. S. (1967): Land Values Adjacent to an Urban Neghborhood Park. Land Economcs. 43(3), pp LANCASTER, K. J. (1966): A new approach to consumer theory, Journal of Poltcal Economy. 74, pp LUTTIK, J. (2000): The Value of Trees, Water and Open Space as Reflected by House Prces n the Netherlands. Landscape and Urban Plannng. 48, pp LUTZENHISER, M., NETUSIL, N. R. (2001): The Effect of Open Spaces on a Home s Sale Prce. Contemporary Economc Polcy, 19(3), pp MORANCHO, A. B. (2003): A hedonc valuaton of urban green areas. Landscape and Urban Plannng, 66, pp MORE, T. A., STEVENS, T. H., ALLEN, P. G. (1988): Valuaton of Urban Parks, Landscape and Urban Plannng. 15, pp ROSEN, S. (1974): Hedonc prces and explct markets: producton dfferentaton n pure competton. Journal of Poltcal Economcs. 82, pp SCHROEDER, T. D. (1982): The Relatonshp of Local Park and Recreaton Servces to Resdental Property Values. Journal of Lesure Research. 14(3), pp TYRVÄINEN, L. (1997): The Amenty Value of the Urban Forest: An Applcaton of the Hedonc Prcng Method. Landscape and Urban Plannng. 37, pp TYRVÄINEN, L., MIETTINEN, A. (2000): Property prces and urban forest amentes. Journal of Envronmental Economcs and Management. 39, pp

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