1# RECORD 4d2cf36586abe5a8dcb4cd171a95a310955595202# TITLE: The influence of urban green and recreational areas on the price of housing in Zagreb3# YEAR: 2024.0 VENUE: Hungarian Geographical Bulletin DOI: 10.15201/hungeobull.73.3.34# AUTHORS: Mirela Turk Cerovečki; Zoran Stiperski5# PAGES: 20 CANDIDATE TABLE PAGES (1-based): [8, 10]6# ABSTRACT: Urban green and recreational spaces are important for residents and one of the foundations for the functioningof cities. They offer numerous benefits, including the influence on the price of real estate as an economic benefit.In this paper, the influence of green and recreational areas in Zagreb on the price of apartments in residential buildings is analysed using the hedonic pricing model. The data for the study was processed and analysed using a geographic information system. A local hedonic price model was developed. The results show that some of the observed green and recreational areas increase the price of housing due to their proximity, while others decrease it. Some areas have no influence on the price of housing. The influence of the observed areas on housing prices was heterogeneous. Spatial differences were also found in the influence of the observed areas on housing prices. The most attractive factors when choosing a place to live are Jarun and the parks in the city centre. The price of apartments that near the Jarun increases by 41 EUR/m2 if the apartment is 100 m closer to the Jarun. The price of the apartment increases by 91 EUR/m2 the closer the apartment is to the parks in the city centre (per 100 m). Apartments near Jarun and the parks in the city centre are not available for residents with lower socio-economic status. The forest areas, Maksimir Park and the banks of the Sava river are not favoured when choosing a place to live. Neighbourhoods near these areas are available to the lower socioeconomic status population. Bundek raises the price of one part of the apartments and lowers the price of the other. The results of this study can contribute to the study of green gentrification in Zagreb, but further research is needed on socio-economic indicators and other changes in the region.789===== PAGE 1 (score 15, numeric-paren 0, table-words 0, var-words 15) =====101124912Turk Cerovečki, M. and Stiperski, Z. Hungarian Geographical Bulletin 73 (2024) (3) 249–268.13DOI: 10.15201/hungeobull.73.3.314Hungarian Geographical Bulletin 73 2024 (3) 249–268.15Introduction16A city is a space that is characterized by a 17compact structure and whose elements are 18in constant interaction. One of the elements 19of the functional-spatial structure of the city 20are recreational areas. Recreational areas 21are open spaces with built infrastructure in22tended for recreational activities. Their basic 23characteristics are the predominant natural 24environment, which includes vegetation, i.e. 25greenery (forest, grass, flowers), and often 26also water areas (river, canal, lake) and a 27certain number of built objects (paths, roads, 28buildings, playgrounds, sports halls, sports 29facilities). These areas are used by users for 30recreational activities. The largest recrea31tional areas are generally located on the out32skirts of the city, as this is where the largest 33open spaces are available. Green areas in the 34city consist of parks, gardens, urban forests, 35promenades and other smaller areas that are 36covered with vegetation but have fewer or no 37buildings (they may include walking paths, 38benches and other rest areas). In addition to 39other functions, the green areas integrated 40into the urban structure also serve active and 41passive recreation.421 Postgraduate Studies in Geography. Department of Geography, Faculty of Science, University of Zagreb. 43Knezovec 148, Knezovec, 40000 Čakovec, Croatia. E-mail: mirela.turk@gmail.com442 Department of Geography; Faculty of Science, University of Zagreb, Marko Marulić square 19/II. 10 000 Zagreb; 45Croatia. E-mail: zstiper@geog.pmf.hr46The influence of urban green and recreational areas47on the price of housing in Zagreb48Mirela TURK CEROVEČKI 1 and Zoran STIPERSKI 249Abstract50Urban green and recreational spaces are important for residents and one of the foundations for the functioning 51of cities. They offer numerous benefits, including the influence on the price of real estate as an economic benefit. 52In this paper, the influence of green and recreational areas in Zagreb on the price of apartments in residential 53buildings is analysed using the hedonic pricing model. The data for the study was processed and analysed 54using a geographic information system. A local hedonic price model was developed. The results show that 55some of the observed green and recreational areas increase the price of housing due to their proximity, while 56others decrease it. Some areas have no influence on the price of housing. The influence of the observed areas 57on housing prices was heterogeneous. Spatial differences were also found in the influence of the observed 58areas on housing prices. The most attractive factors when choosing a place to live are Jarun and the parks in 59the city centre. The price of apartments that near the Jarun increases by 41 EUR/m2 if the apartment is 100 m 60closer to the Jarun. The price of the apartment increases by 91 EUR/m2 the closer the apartment is to the parks 61in the city centre (per 100 m). Apartments near Jarun and the parks in the city centre are not available for 62residents with lower socio-economic status. The forest areas, Maksimir Park and the banks of the Sava river 63are not favoured when choosing a place to live. Neighbourhoods near these areas are available to the lower 64socioeconomic status population. Bundek raises the price of one part of the apartments and lowers the price 65of the other. The results of this study can contribute to the study of green gentrification in Zagreb, but further 66research is needed on socio-economic indicators and other changes in the region.67Keywords: urban green areas, recreational areas, hedonic model, geographically weighted regression, real 68estate69Received April 2024, accepted August 2024.707172===== PAGE 8 (score 36, numeric-paren 0, table-words 13, var-words 4) =====7374Turk Cerovečki, M. and Stiperski, Z. Hungarian Geographical Bulletin 73 (2024) (3) 249–268.7525676example when mutually correlated variables 77are of great interest for research. Since the 78environmental variables are of interest, none 79of the highly correlated variables were omit80ted. Due to the use of georeferenced data, 81only the results of the local hedonic model 82are presented in this paper.83The geographically weighted regression 84was performed with 120 neighbours. The 85Gaussian weighting method was used, ac86cording to which the weight decreases ex87ponentially the further the point is from the 88observed point.89Results90The spatial autocorrelation in the local 91hedonic model was tested with the Mo92ran I index, which is 0.185698 (p-value = 930.00000, z-score = 12.780390) and shows 94a statistically significant clustering of the 95analysis results. The coefficient of determi96nation is R2 = 0.4090 and the adjusted coef97ficient of determination is adj. R2 = 0.3616 98(Table 2). There are differences in the coefficient 99of determination between the apartments 100(Figure 2). The highest coefficients of deter101mination are observed west of Maksimir 102Park and in the western part of Novi Zagreb 103(Kajzerica). The lowest coefficient of determi104nation is observed in the wider surroundings 105of the city centre (Donji grad, Trnje).106The statistical significance of the model 107was tested at the 10 percent level (p-value < 1080.1, t-value > 1.660). Since the attractiveness 109of certain factors in space is determined by 110their proximity (distance) to the apartment, 111a geographically weighted regression was 112used to account for apartments that are up to 113Table 2. Local (GWR) model coefficients114Variables115Coefficients116min117mean118max119Intercept120-36,531.74121-1808.92312218,759.15123Structural variables (S)124area125floor126-7.01288127-7.27306128-3.59618512954.1917130-0.424051131221.9165132Neighbourhood variables (N)133dist_public134dist_kinder135dist_elem136num_fax137dist_shop138dist_ind139dist_health140dist_hosp141num_kult142num_sportobj143num_sportigr144-0.490508145-0.91591146-2.562863147-1404.26148-1,533588149-1.073735150-1.153004151-0.606382152-111.232153-114.2322154-277.84361551.278091560.558948157-0.0147741586.95362159-0,0843141600.2099521610.2500671620.1549381637.15274164-11.7921165-3.033381664.154671672.487651683.054714169206.1931700,8419751712.3495031722.7590741732.718514174176.7045175173.7824176558.92177Environmental variables (E)178dist_zona179dist_forest180dist_jarun181dist_bundek182dist_bank183dist_maksimir184dist_park185-5.15309186-3.542074187-2.05610188-14.39090189-14.45573190-2.89827191-4.370271920.0799551930.0758581940.9728641950.983093196-1.0351731970.507891198-0.6642191993.652682005.278662018.4363920212.3698220317.620282045.951432050.91697206N207R2208Adjusted R220923142100.40902110.3616212Source: Authors’ own elaboration.213214215===== PAGE 9 (score 20, numeric-paren 0, table-words 4, var-words 12) =====216217257218Turk Cerovečki, M. and Stiperski, Z. Hungarian Geographical Bulletin 73 (2024) (3) 249–268.2192000 m away from a green or recreational area 220(Table 3). The distance given is the upper limit 221for a walk (about 25 minutes). Some studies 222have shown that the influence of the observed 223environmental elements on the property price 224extends up to a maximum distance of 2000 m 225(Melichar, J. and Kaprová, K. 2013), in some 226cases also over a shorter distance (Tyrväinen, 227L. and Miettinen, A. 2000; Biao, Z. et al. 2012; 228Daamsa, M.N. et al. 2019).229Although the neighbourhood variables 230are not the direct object of interest of this re231search, they should be briefly commented on. 232According to their mean values of the coef233ficients, four variables increase the value of 234real estate with their attractiveness (when all 235other variables in the regression are constant) 236(see Table 2). These are the distance from the 237elementary school, the distance from the 238shopping center, the number of higher edu239cation institutions and the number of cultural 240institutions within 500m of the apartment. 241The average of the coefficients shows a high 242value for the variables number of higher edu243cation and cultural institutions. The proxim244ity of the shopping center is an attractive fac245tor despite the crowds, noise and large built-246up areas (buildings, parking lots, roads).247Other neighbourhood variables show 248unattractive factors for housing. Increasing 249the distance from health institutions (health 250center, hospital) increases the price of real 251estate, which is contrary to expectations. The 252same was expected for the variables distance 253from the nearest public transport station and 254the nearest kindergarten. The coefficients of 255the variables number of sports facilities and 256number of sports fields within 500 m of the 257property show the opposite of what was ex258pected. Increasing the number of such facili259ties reduces the price of real estate. Possible 260causes are crowding and noise.261The results of the local hedonic model (GWR) 262show that there are large ranges of variation in 263Fig. 2. Coefficient of determination R2 in the local hedonic model. 1 = SRC Bundek, 2 = RSC Jarun. Urban 264park-forests: 3 = Čulinečina, 4 = Šestinski dol, 5 = Susedgrad, 6 = Lisičina, 15 = Mirogoj, 16 = Remetinski ka265menjak, 17 = Granešina (Urban of Youth), 18 = Remete, 19 = Oporovec, 20 = Novoselčina, 21 = Miroševečina, 26622 = Dankovečina, 23 = Zamorski Breg, 24 = Dotršćina, 26 = Grmošćica. Park-forests: 7 = Zelengaj, 8 = Jelenovac, 2679 = Vrhovec, 10 = Tuškanac-Dubravkin put-Cmrok, 12 = Kraljevec, 13 = Pantovčak, 14 = Prekrižje. 11 = Maksimir 268Park, 25 = Sava dam. Source: Compiled by the authors.269270271===== PAGE 10 (score 18, numeric-paren 0, table-words 5, var-words 6) =====272273Turk Cerovečki, M. and Stiperski, Z. Hungarian Geographical Bulletin 73 (2024) (3) 249–268.274258275Table 3. Results of the local hedonic model for apartments up to 2000 m away from a green or recreational area276Variables277Count278Percentage279Statistic for significant 280properties (coefficients)281Part of the apartment price* 282in EUR283Non sign284Sign**285Sign -286Sign +287Sign288Sign -289Sign +290min291max292mean293min294max295mean296dist_zona297dist_forest298dist_jarun299dist_bundek300dist_bank301dist_maksimir302dist_park303930304852305188306663074863081803096203101202311723312213313893143831519031648731760931814331921232050321332203234573245933255803261327393283532919033030331563324633353334573357336513374433851339203401003415634283430344943454934680347034844349923501003516352-5.153090353-3.542070354-0.621160355-1.512264356-0.4325293570.167898358-1.9453743593.6526803605.2786603610.2968803620.6317053631.2857633642.5801303650.9083573660.0830593670.337271368-0.406531369-0.2319063700.7058263710.623056372-0.907034373-515374-354375-62376-151377-4337817379195380365381528382303836338412938525838691387838837389-41390-233917139262393-91394*Part of the apartment price for statistically significant apartments by individual variable, the change was estimated for every 100 m of distance from a 395green or recreational area. The Sign (-) shows a decrease in price with increasing distance from the observed green or recreational area, and the Sign (+) 396shows the inverse relationship. The unit of measurement is EUR/m2. **Statistically significant (p < 0.1). Source: Authors’ own elaboration.397the coefficients of the individual environmental 398variables (see Table 2). These results should be 399viewed with caution as they include apartments 400for which a single environmental variable is not 401statistically significant. For this reason, we will 402not interpret them here. We will interpret the 403results for the apartments for which the envi404ronmental variables are statistically significant 405(p < 0.1, t > 1.660) and which are located at a dis406tance of up to 2000 m from the observed green 407or recreational area (see Table 3). 408The variable distance to the nearest green 409or recreational area (dist_zona) is statistically 410significant for 56 percent of the apartments. It 411has a negative sign for 51 percent of the apart412ments, which means that proximity to this area 413increases the price of the apartment. Analogous 414to the coefficients, the value range of the price 415share associated with this variable ranges 416from -515 to 365 EUR/m2, with an average of 4178 EUR/m2 (see Table 3). The geographical distri418bution of the coefficients (Figure 3) shows that 419the coefficients with a negative sign are concen420trated in the western and south-western part of 421the city, while the coefficients with a positive 422sign predominate in the rest of the city, with 423the exception of the area around the forests of 424Mirogoj, Remetinski kamenjak and Remete.425Proximity to an urban park-forest or park-for426est (dist_forest) has a statistically signifi427cant effect on 46 percent of apartments up to 4282000 m away. The coefficients of the variables 429have a positive sign for most apartments (80%). 430According to the coefficients, the share of hous431ing price attributable to proximity to the near432est forest ranges between -354 and 528 EUR/m2, 433i.e., the extreme values, and the mean value is 43437 EUR/m2 (see Table 3). The coefficients with a 435negative sign (the variable increases the price 436of the apartment with decreasing distance from 437the forest) are located near the seven park-for438ests in the area of the historic city centre (Park-439forests Zelengaj, Jelenovac, Vrhovec, Tuškanac-440Dubravkin put-Cmrok, Kraljevec, Pantovčak 441and Prekrižje) and two urban park-forests 442(Zamorski Breg, Šestinski dol). It can be seen 443that other forest areas lower the price of housing 444the closer they are to the forest (coefficients with 445a positive sign) (Figure 4).446447448===== PAGE 11 (score 3, numeric-paren 0, table-words 0, var-words 3) =====449450259451Turk Cerovečki, M. and Stiperski, Z. Hungarian Geographical Bulletin 73 (2024) (3) 249–268.452Fig. 3. Coefficients of the variable distance to the nearest green or recreational area. Numbers 1–25: see Fig. 2. 453Source: Compiled by the authors.454Fig. 4. Coefficients of the variable distance to the nearest urban park-forest or park-forest. Numbers 1–25: see 455Fig. 2. Source: Compiled by the authors.456