1# RECORD 844f3013b415b6de89430c28be2c24c13f35196d2# TITLE: Using GIS and Hedonic in the modelling of spatial variation of housing price in Xiamen city3# YEAR: 2013.0 VENUE: nan DOI: 10.14246/irspsd.1.4_294# AUTHORS: Yuan Li; Lang-Jie He; W. Xu; Hui Wang; Zi-Hao He5# PAGES: 14 CANDIDATE TABLE PAGES (1-based): [9]6# ABSTRACT: Valuing the built environment with the support of GIS and housing price model is an interesting topic because it connects the spatial thinking with economic concerns. In this paper, we chose the ordinary commercial housing in Xiamen City as our research targets, and the technology of GIS and Hedonic price model were used. Xiamen city is a typical coastal city, and from the research result, we found that the distribution of higher housing price was kept consistent with the coastal areas of Xiamen city. Besides, the average housing price of Xiamen inner-island (the old urban island area) was far higher than the outer-island. To reveal the degree of different spatial elements affecting housing price, we chose 21 variables (three types: location characteristic, structure characteristic and neighbourhood characteristic) and measured them in GIS platform. The Hedonic model showed that the main elements that influencing the price of commodity housings in Xiamen included Property Management Fee, Distance to Commercial Center, Distance to Hospital, Distance to Primary School, Near Village or Not, Near Green space or Not, Near kindergarten or Not. At the end of the paper, we provided three suggestions to balance the spatial variation of housing price in Xiamen City.789===== PAGE 1 (score 9, numeric-paren 0, table-words 0, var-words 9) =====101129 12International review for spatial planning and sustainable development, Vol.1 No.4 (2013), 29-42 13ISSN: 2187-3666 (online) 14DOI: http://dx.doi.org/10.14246/irspsd.1.4_29 15Copyright@SPSD Press from 2010, SPSD Press, Kanazawa 16Using GIS and Hedonic in the modelling of spatial 17variation of housing price in Xiamen city 18Yuan Li¹*, Lang He¹, Wangtu Xu¹, Hui Wang¹, Zizhang He2 191 School of Architecture and Civil Engineering, Xiamen University 202 Xiamen Urban Planning & Design Institute 21* Corresponding Author, Email: Yako79@gmail.com22Received 16 December 2012; Accepted 5 July 201323Key words: 24GIS, Housing Price, Spatial Variation, Hedonic, Xiamen City 25Abstract: 26Valuing the built environment with the support of GIS and housing price 27model is an interesting topic because it connects the spatial thinking with 28economic concerns. In this paper, we chose the ordinary commercial housing 29in Xiamen City as our research targets, and the technology of GIS and 30Hedonic price model were used. Xiamen city is a typical coastal city, and from 31the research result, we found that the distribution of higher housing price was 32kept consistent with the coastal areas of Xiamen city. Besides, the average 33housing price of Xiamen inner-island (the old urban island area) was far higher 34than the outer-island. To reveal the degree of different spatial elements 35affecting housing price, we chose 21 variables (three types: location 36characteristic, structure characteristic and neighbourhood characteristic) and 37measured them in GIS platform. The Hedonic model showed that the main 38elements that influencing the price of commodity housings in Xiamen included 39Property Management Fee, Distance to Commercial Center, Distance to 40Hospital, Distance to Primary School, Near Village or Not, Near Green space 41or Not, Near kindergarten or Not. At the end of the paper, we provided three 42suggestions to balance the spatial variation of housing price in Xiamen City. 431.44BACKGROUND45Housing, which is one of the most basic human needs, is a hot economic 46issue and social issues related to people's livelihood. Many factors influence 47the housing price and many researchers try to reveal the distribution rules on 48spatial variation in housing price. Some people (Xu, 1997; Jiang and Zhu, 492005; Meng, Zhang et al., 2005; Mei and Li, 2008) used GIS spatial 50interpolation method to get spatial distribution map and contour map of 51housing price. Some people (Luo, He et al., 2002) took Lanzhou as a case 52study and constructed the SD-GIS space-time simulation model to study the 53space-time distribution changes of residential price. Some (Montero and 54Larraz, 2009) considered that space in the real estate market is important and 55official averages do not take into account the spatial correlation of housing 56price. They proposed the Kriging mean method to estimate mean housing 57price. Some (Fik, Ling et al., 2003) hold the view that absolute location must 58be viewed as interacting with other determinants of housing value. They 59presented an interactive variables approach and tested its ability to explain 60price variation in an urban residential housing market. Some (Zhang, Meng 61et al., 2004; Armstrong and Rodríguez, 2006) established the Hedonic price 62method, studied the relationships between different traffic convenient degree 63@Licensee SPSD Press 64This open access article is published under a 65Creative Commons [Attribution-66NonCommercial-NoDerivatives 4.0 67International] license. 68https://creativecommons.org/licenses/by-nc-nd/4.0/ 697071===== PAGE 9 (score 27, numeric-paren 0, table-words 9, var-words 5) =====7273Li, et al 743775 76 7710%. Combined with variance inflation factor (VIF) inspection, we weeded 78out the multicollinearity factors. Finally, there were 7 variables selected in 79the model. After running the regression analysis, the results were as shown 80in the Table 3. 81Table 2. Comparison of three functions 82Function type 83R 84R2 85Adjusted R2 86Linear model 870.901 880.812 890.800 90Log-linear model 910.897 920.805 930.790 94Semi-logarithmic model 950.874 960.764 970.754 98Table 3. Regression coefficient analysis 99Variable 100Non-standardized 101coefficient 102Standardize103d coefficient 104t 105Sig. 106VIF 107B 108Std Error Beta 109Constant 1105274.659 1446.784 111 1123.646 113.000 114 115PropertyManagementFee (X21) 4880.947 445.076 116.613 11710.967 118.000 1191.270 120NextToCC (X2) 121-.602 122.153 123-.218 124-3.939 125.000 1261.247 127NextToHospital (X6) 1281.190 129.387 130.197 1313.071 132.003 1331.675 134NextToKPSchool (X3) 135-.271 136.127 137-.123 138-2.139 139.035 1401.354 141VillageEffect (X14) 142-2501.633 968.019 143-.139 144-2.584 145.011 1461.173 147GreenFieldEffect (X11) 1482974.278 1024.754 .163 1492.902 150.005 1511.289 152KGService (X10) 1532572.500 1004.815 .137 1542.560 155.012 1561.159 1575. 158RESULTS 159The regression coefficients for 7 characteristic variables of the linear 160model could be obtained by regression analysis. We calculated the housing 161Hedonic price model of Xiamen, as shown in Formula (7): 162 163Price=5274.659-0.602X2-0.271X3+1.19X6+2572.5X10+2974.278X11-2501.63 1643 X14+4880.947 X21 (7) 165 166According to the results of regression analysis, we could find the main 167elements that influence the price of commodity housings in Xiamen: 168PropertyManagementFee (X21), NextToCC (X2), NextToHospital (X6), 169NextToKPSchool (X3), VillageEffect (X14), GreenFieldEffect (X11), 170KGService (X10). 1711) Proximity of the commercial centers (X2) 172Generally speaking, commercial centers were the most active areas of a 173city as they provided various urban services and consumption choices for 174urban residents, which made it very convenient for people to live. 175Theoretically, the housing price in the surrounding area was relatively 176higher. It could be seen from the Hedonic price model that the variable 177coefficient of the distance to commercial centers was -0.602, suggesting that 178the distance from housing to business centers was inversely proportional to 179the housing price. With the distance between dwellings and business centers 180expanding, the residential price would fall. More specifically, every 1 181182183===== PAGE 10 (score 15, numeric-paren 0, table-words 2, var-words 11) =====18418538 186IRSPSD International, Vol.1 No.4 (2013), 29-42 187 188 189kilometre of the increase in distance would cause 602 RMB (Yuan) loss in 190sale price. 1912) Proximity of the hospitals (X6) 192With the improvement of living standards, people paid more attention to 193medical and health care than before. As an important kind of public service, 194medical care provided protection for the life and health of the residents. 195“Time is life”, the distances between dwellings and hospitals measured the 196residents’ medical convenience. However, the coefficient of the model 197doesn’t reflect the relationship that the houses price increases among with 198the distance increasing. The most reason is the models don’t differentiate the 199needs of health care service between different age groups. The young people 200are strong and seldom go to the hospital. They usually will never take 201hospital into account even reject live close to hospital. Meanwhile the 202elderly people are more often go to the hospital. Considering the access of 203the data, the Hedonic model didn’t specially research the age structure of 204families then ignored the elderly’s preference close to hospital. In the 205Hedonic price model, the variable coefficient of hospitalization was 1.19, 206and it meant that the sale price would increase 1,190 RMB as the distance to 207hospitals increase 1 kilometer. 2083) Proximity of the key primary schools (X3) 209Education was the foundation of the nation, which was related to the 210quality of people and long-term development of the country. As the 211high-quality education resources were limited and unevenly distributed, 212many parents in China rent or even bought houses near key schools in order 213to offer their children with better education. As can be seen from the above 214Hedonic price model, the key primary schools had a positive effect on 215housing price and the sale price would decrease 1,190 RMB per kilometer as 216the distance to provincial-demonstrated primary schools increased. 2174) Near village or not (X14) 218With the increasing development of Chinese economy, the process of 219urbanization was becoming faster and faster. Urban construction and urban 220area expanded to the outlying regions, which made “villages surrounded by 221cities” become the most common phenomenon. The area that had not been 222urbanized could be found in the urban area, and this was called “village in 223the city”. Cities were often close to rural settlements in the outreach process. 224The rural lands possessed features such as a high building density, a low 225greening rate, narrow and hindered streets, poor sanitation and so on because 226of land property restrictions and lack of reasonable and effective planning. 227Urban infrastructure and public service facility could not reach the whole 228city, so the development of urbanized area close to villages was kind of 229limited. As can be seen from the above Hedonic price model, villages had a 230negative effect on residential lands’ externality. The average price of the 231premises near the village was 2,501.633 RMB lower than the ones far away 232from the village. 2335) Near Greenland or not (X11) 234As the living standard improves and awareness of environmental 235protection enhances, residents were more willing to purchase houses with 236comfortable environment. Being the open space of a city, public green lands 237possessed functions like landscape, airing and leisure. They were provided 238with social, economic and ecological value, and made the comfortable living 239and working environment accessible to residents. It could be concluded from 240the Hedonic price model that public green lands had a positive effect on 241urban residential lands’ externality. The average price of the housings close 242