1# RECORD 3b4b454985f862f1a1d6165d8c81255275be3ddb2# TITLE: The effects of park and sea landscape on property value in a tourist city3# YEAR: 2022.0 VENUE: Frontiers in Environmental Science DOI: 10.3389/fenvs.2022.9670944# AUTHORS: Dequn Shi; Yuhui Zhang; Zeng-Xian Liang5# PAGES: 10 CANDIDATE TABLE PAGES (1-based): [6, 7]6# ABSTRACT: Theorists have traditionally considered the effects of single rather than multiple landscape attributes on property prices, and ignored the interaction between landscape-specific housing attributes. The aim of this paper is to analyze the hedonic attributes of property prices and compare the effects on property prices of two key landscape attributes. Seventeen hedonic attributes are used to build a hedonic-pricing model with five dimensions: structure, neighborhood and community, location, environment, and time. A sample of 688 valid houses within 1,000 m of Xiangshan Park in the city of Zhuhai, China is collected. The selected hedonic attributes are found to have significant effects on property prices, explaining around 91% of the observed variation in price. Proximity to the sea has a greater positive influence on property prices than proximity to parks. In addition, people prefer to buy properties with sea views than to live close to the sea.789===== PAGE 1 (score 12, numeric-paren 0, table-words 0, var-words 12) =====1011The effects of park and sea12landscape on property value in a13tourist city14Dequn Shi1, Yuhui Zhang2,3 and Zengxian Liang2,3*151Guangdong Vocational Institute of Public Administration, Guangzhou, China, 2Key Laboratory of the16Sustainable Development of Xinjiang’s Historical and Cultural Tourism, Xinjiang University, Urumqi,17China, 3School of Tourism Management, SunYat-sen University, Guangzhou, China18Theorists have traditionally considered the effects of single rather than multiple19landscape attributes on property prices, and ignored the interaction between20landscape-specific housing attributes. The aim of this paper is to analyze the21hedonic attributes of property prices and compare the effects on property22prices of two key landscape attributes. Seventeen hedonic attributes are used to23build a hedonic-pricing model with five dimensions: structure, neighborhood24and community, location, environment, and time. A sample of 688 valid houses25within 1,000 m of Xiangshan Park in the city of Zhuhai, China is collected. The26selected hedonic attributes are found to have significant effects on property27prices, explaining around 91% of the observed variation in price. Proximity to the28sea has a greater positive influence on property prices than proximity to parks. In29addition, people prefer to buy properties with sea views than to live close to30the sea.31KEYWORDS32landscape, property prices, hedonic analysis, urban tourism, Zhuhai city33Introduction34Attracting immigrants as well as temporary visitors has become an important goal of35many tourist-city authorities in the last few decades. The phenomenon of tourists’36residing for relatively long periods and even settling down fully in tourist cities has37expanded from Western countries to developing countries such as China (Liang et al.,382021). Tourists and residents cohabit in many tourist cities, which creates policy issues39(Rodriguez, 2001). The tourist city of Gold Coast in Australia’s “sun-belt” (Mullins, 1991)40attracts a large number of long-distance immigrants seeking a better climate and lifestyle41(Stimson and Minnery, 1998). The Costa del Sol in Spain, another famous tourist region,42receives a massive volume of foreign tourists and immigrants, especially retired migrants43(Rodriguez, 2001). The ancient town of Dali in southwestern China attracts both domestic44and international immigrants. This new population growth has stimulated a wave of45tourist-related real estate development in the last decade (Wu and Xu, 2010). Most new46immigrants purchase or rent houses close to areas of natural beauty such as beaches,47rivers, lakes and parks, or tourist attractions such as theme parks (Liang and Bao, 2015).48Landscape plays a critical role in the decision to rent or purchase a house in a tourist city49(Pitkanen, 2008).50OPEN ACCESS51EDITED BY52Rita Yi Man Li,53Hong Kong Shue Yan University, Hong54Kong SAR, China55REVIEWED BY56David Brasington,57University of Cincinnati, United States58Mohd Faris Dziauddin,59Sultan Idris University of Education,60Malaysia61*CORRESPONDENCE62Zengxian Liang,63liangzx3@mail.sysu.edu.cn64SPECIALTY SECTION65This article was submitted to66Environmental Economics and67Management,68a section of the journal69Frontiers in Environmental Science70RECEIVED 12 June 202271ACCEPTED 12 September 202272PUBLISHED 27 September 202273CITATION74Shi D, Zhang Y and Liang Z (2022), The75effects of park and sea landscape on76property value in a tourist city.77Front. Environ. Sci. 10:967094.78doi: 10.3389/fenvs.2022.96709479COPYRIGHT80© 2022 Shi, Zhang and Liang. This is an81open-access article distributed under82the terms of the Creative Commons83Attribution License (CC BY). The use,84distribution or reproduction in other85forums is permitted, provided the86original author(s) and the copyright87owner(s) are credited and that the88original publication in this journal is89cited, in accordance with accepted90academic practice. No use, distribution91or reproduction is permitted which does92not comply with these terms.93Frontiers in Environmental Science94frontiersin.org950196TYPE Original Research97PUBLISHED 27 September 202298DOI 10.3389/fenvs.2022.96709499100101===== PAGE 6 (score 41, numeric-paren 0, table-words 10, var-words 14) =====102103independent variables concerning distance and area were104logarithmic; and Z-scores were used to non dimensionalize105the106ordinal107variables—property-management108fees109and110number of bus lines—and the dummy variables with actual111values. In addition, average values were calculated to replace112missing data. Table 2 shows the key descriptive statistics113obtained from the data processing.114Results115Effects of urban landscape on property116prices117The correlation coefficients obtained for the three key118landscape variables—sea view (SEAVIEW), distance from park119(DPAR) and distance from sea (DSEA)—and the dependent120variable,121property122sale price,123were124examined. The125three126landscape variables were found to be significantly correlated127with the dependent variable. Forced entry multiple linear128regressions were conducted using the software package SPSS12920.0. To prevent multicollinearity and determine the effect of130each landscape variable on property sale price, a separate131regression with controlled variables was carried out (see132Model 1, Table 3), followed by a regression for each landscape133variable (see Models 2–4, Table 3). Finally, a separate regression134with controlled variables and all of the landscape variables135(Model 5) was conducted, as shown in Table 3.136The137results138of139regressing140Model1411142indicated143that14412 controlled variables significantly influenced property145prices. LOT, FEE, LFAC, PUBS, MANA, DECORATION,146and POOL had positive and significant effects on property147price, while AGE, DEXE, and DCEM had a negative and148significant influence. These results were as expected, and in149line with the findings of most early studies. However, some150results were inconsistent with our predictions. TRSP and151PARKING had negative rather than positive effects on152property prices, while neither LIFT nor CENV had a153significant effect on property prices. Property prices are154generally155assumed156to157increase158with159the160number161of162accessible bus lines and the availability of parking spaces.163However, although public bus services offer convenient164transport165links,166they167also168increase169noise170and171traffic172congestion. As properties near transportation hubs are173subject to noise, air pollution and traffic congestion on a174daily basis, their sale price will be lower (Wu et al., 2008; Wen175et al., 2012). The field investigation conducted during this176study177revealed178that179car-parking180spaces181and182residential183properties in the Xiangshan Park area of Zhuhai are sold184separately; as a result, the cost of parking does not contribute185directly to property price. Xiangshan Park is located in the old186city center, where car-parking spaces are always insufficient,187despite the construction of new residential communities in188recent189years.190Most191new192parking193facilities194have195been196constructed in older communities with lower property197prices, where there is more public space and land value is198lower, making parking spaces cheaper. In short, more parking199is available near properties with lower prices. Overall, Model2001 performed well, with an adjusted R2 value of 0.916 (F =201534.331, p = 0.00).202The regression results for Models 2 to 4 indicate the effects203of individual landscape variables, namely DPAR, DSEA, and204SEAVIEW, on property sale price. Model 1 (a separate205regression with controlled variables only) was compared206with Models 1 to 4, and DPAR (Model 2), DSEA (Model2073), and SEAVIEW (Model 4) were found to increase the208original model’s explanatory power by 0.1%, 0.1%, and2090.2%,210respectively.211Model2122213performed214well,215with216an217adjusted R2 value of 0.917 (F = 505.367, p = 0.00). The218regression results indicate that for every 1% increase in219distance from Xiangshan Park, property prices rise by2200.056%. The finding that proximity to Xiangshan Park221negatively affects property prices was contrary to our222expectation. Model 3 also performed well, with an adjusted223R2 value of 0.917 (F = 508.761, p = 0.00). With the exception of224distance to Martyrs’ Cemetery and access to elevators, all of225the independent variables in Model 3 significantly influenced226the sale price of housing in the area under study. According to227the main regression results, for every 1% increase in DSEA,228property prices dropped by 0.055%. As expected, therefore,229proximity to the seashore had a significant and positive230influence on property prices. Model 4 also performed well,231with an adjusted R2 value of 0.918 (F = 514.323, p = 0.00). All232TABLE 2 Descriptive statistics of variables.233Variable234Mean235Std. Dev236Max237Min238LOT2394.522400.372415.482423.26243FEE24402451.002463.34247−2.48248TRSP24902501.002513.73252−0.59253LFAC25402551.002561.13257−2.32258AGE25902601.002612.52262−2.46263PUBS2640.362650.482661.002670268MANA26902701.002712.27272−2.53273DECORATION27402751.002762.06277−2.65278CENV27902801.002811.80282−1.66283DEXE2846.792850.882867.552874.25288DCEM2896.582900.672917.502925.03293PARKING2940.452950.502961.002970298POOL2990.113000.313011.003020303LIFT3040.723050.453061.003070308DPAR3096.453100.403117.003125.43313DSEA3146.443150.973167.763174.65318SEAVIEW3190.163200.373211.003220323SALES3244.823250.453266.063273.56328Frontiers in Environmental Science329frontiersin.org33006331Shi et al.33210.3389/fenvs.2022.967094333334335===== PAGE 7 (score 129, numeric-paren 85, table-words 8, var-words 8) =====336337the independent variables in Model 4 significantly affected338property prices. The main regression results indicated that a3391% increase in the likelihood of a sea view increased property340prices by 0.059%. As expected, therefore, the variable Seaview341was found to have a significant positive influence on property342prices.343Model 5, which included the controlled variables and all344the landscape variables, performed well, with an adjusted345R2 value of 0.918 (F = 484.039, p = 0.00). When all three346landscape347variables348were349considered,350only351DSEA352and353SEAVIEW were found to have a significant influence on354property prices; the effect of DPAR was not significant. For355every 1% increase in the likelihood of a sea view, property356prices rose by 0.072%. For every 1% increase in DSEA,357property prices fell by 0.040%. People are most likely to358buy properties near the sea or with sea views. In addition,359comparison of the results for Model 3 and Model 4, with the360same controlled variables, revealed that Model 4 (adjusted361R2 = 0.918, F = 514.323, p = 0.00) had greater explanatory362power than Model 3 (adjusted R2 = 0.917, F = 508.761, p =3630.00). Even in Model 5, the standardized regression coefficient364of SEAVIEW (β = 0.072) was greater than that of DSEA365(β = −0.040), which indicates that sea views have a greater366influence on property prices than proximity to the sea.367Together, these results confirm that people prefer to own368properties with sea views rather than to live close to the sea.369Property-price premium370In the logarithmic model, the regression coefficients of each371continuous variable corresponded to the price elasticity of372housing characteristics; that is, when other variables remained373constant, a 1% change in a given variable was assumed to result in374a 1% change in housing price. Due to the presence of375discontinuous variables, unstandardized regression coefficients376cannot be used under conditions of semi-elasticity, so anti-377logarithmic regression coefficients were obtained. The results378are presented in Table 4.379The results for the individual landscape variables are as380follows: 1) Proximity to Xiangshan Park had a negative effect381on property values: every 1% increase in distance from the park382led to a 0.056% increase in property prices. 2) Proximity to the383seashore had a positive influence on property prices: every 1%384increase in distance from the sea decreased property prices by3850.055%. 3) A sea view increased total property prices by 9.308%.386TABLE 3 Results of multiple linear regression analysis.387Variable388Model 1389Model 2390Model 3391Model 4392Model 5393LOT3940.976(65.585)**3950.967(64.224)**3960.953(59.367)**3970.961(63.918)**3980.949(61.395)**399FEE4000.061(6.817)**4010.049(5.086)**4020.056(6.316)**4030.049(5.336)**4040.049(5.035)**405TRSP406−0.047(−8.016)**407−0.044(−7.563)**408−0.048(−8.367)**409−0.045(−7.897)**410−0.047(−8.248)**411LFAC4120.051(7.211)**4130.056(7.744)**4140.057(7.876)**4150.058(8.103)**4160.061(8.657)**417AGE418−0.071(−8.911)**419−0.073(−9.270)**420−0.072(−9.168)**421−0.080(−9.890)**422−0.078(−9.726)**423PUBS4240.075(5.990)**4250.087(6.660)**4260.072(5.746)**4270.086(6.824)**4280.081(5.933)**429MANA4300.035(4.263)**4310.033(4.052)**4320.033(4.061)**4330.036(4.420)**4340.035(4.289)**435DECORATION4360.026(4.941)**4370.027(5.108)**4380.027(5.055)**4390.026(5.002)**4400.026(5.032)**441CENV442−0.008(−1.374)443−0.011(−1.924)444−0.016(−2.598)**445−0.021(−3.297)**446−0.024(−3.741)**447DEXE448−0.033(−3.145)**449−0.033(−3.140)**450−0.053(−4.474)**451−0.028(−2.639)**452−0.041(−3.237)**453DCEM454−0.069(−4.950)**455−0.086(−5.751)**456−0.015(−0.757)457−0.039(−2.503)*458PARKING459−0.060(−4.013)**460−0.058(−3.875)**461−0.051(−3.339)**462−0.068(−4.556)**463−0.059(−3.928)**464POOL4650.070(3.476)**4660.092(4.321)**4670.083(4.117)**4680.081(4.079)**4690.086(4.120)**470LIFT4710.025(1.424)4720.039(2.183)*4730.026(1.541)4740.034(1.966)*4750.033(1.821)476DPAR4770.056(3.027)**478−0.007(0.408)479DSEA480−0.055(−3.656)**481−0.040(−3.085)**482SEAVIEW4830.089(4.500)**4840.072(3.265)**485Conatant4861.065(6.446)**4870.833(4.599)**4881.297(7.386)**4890.869(5.153)**4901.063(4.634)**491F492534.331**493505.367**494508.761**495514.323**496484.039**497R24980.9174990.9195000.9195010.9205020.920503Adj. R25040.9165050.9175060.9175070.9185080.918509VIF5101.151–3.5935111.154–4.1805121.152–9.0645131.151–4.4725141.152–6.552515N516688517688518688519688520688521Note: **p ≤0.01, *p ≤0.05.522Frontiers in Environmental Science523frontiersin.org52407525Shi et al.52610.3389/fenvs.2022.967094527528529===== PAGE 8 (score 22, numeric-paren 0, table-words 5, var-words 11) =====5305314) Proximity to Huafa Exercise Park, one of the three open spaces532in the area under study, also had a significant effect on property533prices. Every 1% increase in distance to Huafa Exercise Park led534to a 0.033% drop in property prices. Of all of the landscape535variables, only distance to Xiangshan Park (DPAR) had a536significant negative effect on property prices. There are three537reasons for this finding. First, Xiangshan Park is located in the old538center of Zhuhai, and properties closer to the park are sold at539lower prices. Second, as the park is the area’s main open space, it540is noisy and compromises the privacy of surrounding properties.541Third, a large proportion of the properties sampled in this study542were located between Xiangshan Park and Qinglv Road, as in543Figure 1, so properties closer to the park were farther from the544sea. This also explains a key finding from Model 5 (Table 3):545when all of the landscape variables were considered in a single546regression, distance to Xiangshan Park (DPAR) had a positive547but insignificant effect on property price.548Using average housing prices and hedonic attributes as549benchmarks, the marginal prices of the hedonic property550attributes were estimated. As shown in the descriptive statistics in551Table 2, the average logarithmic price was 4.82. As shown in Table 4,552the marginal prices of the logarithmic variables were obtained from553the elastic coefficient, and the marginal prices of the other variables554were derived from the semi-elastic coefficient. As indicated in555Table 4, 1) each additional 100 m from Xiangshan Park556increased557property558prices559by56010,550561yuan562RMB;5632)564each565additional 100 m from the seashore decreased property prices by5667,640 yuan RMB; 3) a sea view increased total property prices by5671,27,280 yuan RMB, which indicates that the landscape variable568Seaview had a significant positive influence on property prices; and5694) each additional 100 m to Huafa Exercise Park decreased property570prices by 3,760 yuan RMB.571Conclusion and discussion572Tourist cities in China today, benefiting from several tourist573attractions, generally offer a livable environment and the facilities574required to attract not only temporary visitors, but also new-rich575immigrants from the northern provinces, Macao and Hong576Kong. However, prospective house buyers consider a series of577property-related factors, such as landscape variables, before578making a purchase decision. The aim of this study was to579determine the effects of landscape characteristics on the580market price of residential properties in a mature tourist city.581A larger set of landscape attributes than used in most previous582studies—several of which investigated only one attribute—was583examined. The HPM was used to analyze 688 properties sampled584from 22 residential areas within 1,000 m of Xiangshan Park,585offering valuable insights into the effects of multiple landscape586features on the property-price premium. More specifically, an587important theoretical question is answered: how much is a588tourist-city immigrant willing to spend on various landscape589attributes of a property? The major findings are as follows. First,590LOT, FEE, LFAC, PUBS, MANA, DECORATION, and POOL591had significant positive effects on property prices, while AGE,592DEXE, and DCEM had significant negative effects. Contrary to593expectation, TRSP and PARKING had a negative influence on594property prices, and neither LIFT nor CENV had a significant595effect on property prices. These controlled independent variables596explained 91.6% of the observed variation in property prices.597Second, people were found to prefer properties close to the sea,598especially having a sea view than those close to large parks.599However,600it601remains602unclear603whether604immigrants605purchase properties close to attractive landscape features606primarily to enjoy a good view or to use the available open607space for leisure activities such as exercise, hiking, riding, and608walking. In many early studies, the effects of landscape and609other facilities on property prices were found to vary610substantially according to the usage of these facilities. Some611early evidence was obtained that parks designed for passive612use have a greater positive influence on property prices than613parks designed for active use (More et al., 1982; Crompton,6142005). Crompton’s review of the literature on the effects of615landscape features on property prices revealed that properties616“abutting or fronting a passive park” are of particular value617(Crompton,6182001a).619A620passive621area622is623generally624an625undeveloped space or environmentally sensitive area in626which minimal human activity occurs. Passive areas do not627usually provide sports infrastructure or other opportunities to628engage in strenuous physical activity, although they may629contain open space. For a property buyer, a passive area is630a quiet landscape that provides a good view rather than an631opportunity for recreation. Therefore, properties with a good632TABLE 4 The price elasticity/semi-elasticity of landscape variables.633Variable634Regression coefficients635Elasticity coefficients (%)636Semi-elasticity637coefficients (%)638Marginal price639DPAR6400.0566410.05664210,550 yuan/100 m643DSEA644−0.055645−0.055646−7,640 yuan/100 m647SEAVIEW6480.0896499.3086501,27,280 yuan651DEXE652−0.033653−0.033654−3,760 yuan/100 m655Frontiers in Environmental Science656frontiersin.org65708658Shi et al.65910.3389/fenvs.2022.967094660