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Analysis on the Spatial-Temporal Evolution and Driving Factors of Urban Land Price in Kunming City, Southwest of China

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Analysis on the Spatial-Temporal Evolution and Driving Factors of Urban Land Price in Kunming City, Southwest of China Design Engineering ISSN: 0011-9342 Issue: 11 | Pages: 484 - 504 Analysis on the Spatial-temporal Evolution and Driving Factors of Urban Land Price in Kunming City, Southwest of China Rui Jiang1, Chunxue Liu2,*, Xiaowei Liu3, Kangchuan Su4, Lei Lu2, Jinhua Zi5 1School of Economics, Yunnan University of Finance and Economics, Kunming, China 2School of Urban and Environment, Yunnan University of Finance and Economics, Kunming, China 3Yunnan Land Resources Planning and Design Research Institute, Kunming, China 4School of Geographical Sciences, Southwest University, Chongqing, China 5Kunming Land Development and Consolidation Center, Kunming, China *Corresponding Author: Chunxue Liu Abstract: With the acceleration of urbanization, the distribution of land prices in many cities around the world has undergone rapid changes. It is necessary to simulate the evolution of urban land prices and analyze the change in driving factors. Choosing Kunming City as a case study, this study highlights a dynamic analysis process of land price distribution and its driving factors between 2008 and 2018 using variogram, Kriging valuation and GeoDetector. The results show that the distribution of residential and commercial land prices in Kunming is single-center, and that the center of gravity of high-value areas has shifted to the west. The spatial regularity of land price has weakened. The driving factors of land price include: floor area ratio, distance to the central business district, and distance to the nearest: subway station, public school, hospital, and park. The impact of well-known general hospitals on land prices has increased significantly, while the impact of other factors has declined. Influenced by school enrollment policies and the transformation of the business district, the impact of public primary schools on land prices has remained high, while the impact of public secondary [484] Design Engineering ISSN: 0011-9342 Issue: 11 | Pages: 484 - 504 schools has weakened. Keywords: Residential land price, Commercial land price, Spatial-temporal evolution, Driving factors, Kunming City. I. INTRODUCTION The land market is an important factor market, which has the function of stabilizing society and the economy. Land price is not only a barometer reflecting the economic development of a region, but also an important lever for the government to regulate the land market. Land price has been widely linked to issues of government regulation [1], land use [2], economic growth [3], and real estate market [4]. With the development of the economy, urban land prices in many countries have experienced rapid growth and stable fluctuations. In order to grasp the level of urban land prices, governments and research institutes have monitored prices for commercial, residential, industrial and other types of land. However, there is insufficient research on the spatial-temporal evolution of urban land price and the change in driving factors, which makes it difficult for central and local governments to make urban land spatial planning and land supply policies more effective. In addition, industrial lands are basically located in industrial agglomerations, which are greatly influenced by policy factors. Spatial planning and management are relatively easy. However, the driving factors behind residential and commercial land prices are multiple and it is thus difficult to formulate policies. This paper presents research on a spatial-temporal distribution simulation and on the analysis of change in the driving factors of urban residential and commercial land prices. With the deepening of land price research, the analysis of spatial-temporal evolution [5] and driving factors have become “hot spots”. Box-counting algorithm [6], digital land price model [7], GIS spatial analysis [8], and other methods are applied to the study of land price evolution. In the analysis of driving factors, the research objective has involved single factor [9] and multi-factors [10]. Research methods have developed from a linear model [11] to some complex models, including a geographically weighted regression (GWR) model [12], a gravitation model [13] and a feature price model [14]. However, changes in the degree of influence of driving factors are often neglected. GeoDetector (founded in 2010 [15]) offers the advantage of having considerably fewer constraints on the hypothesis. This can effectively overcome the problem of traditional analysis methods in dealing with driving factors geospatially [16]. This study attempts to use GeoDetector to analyze the change in driving factors of land price. [485] Design Engineering ISSN: 0011-9342 Issue: 11 | Pages: 484 - 504 Although each of the aforementioned methods have in-depth applications, each method alone is insufficient. An integration of spatial-temporal evolution and driving factors analysis is necessary to accurately analyze the reasons for the evolution of land prices to promote the stable development of regional land prices. Kunming is a regional international central city based in southwest China, which radiates to South and Southeast Asia. In recent years, the economy of Kunming has developed rapidly and in 2019 became a new Chinese first-tier city. To a certain extent, the city represents the developmental trend of Southwest China and as such was selected as a research case in this study. Through the collection of data relating to urban land price between 2008 and 2018, and the investigation of the change processes and driving factors associated with urban land price, we aim to explore the following three issues using variogram integrated with Kriging assessment and GeoDetector: 1) What is the change in the spatial correlation and distribution of urban land price over the past ten years? 2) What are the main driving factors of urban land price changes? 3) How do the driving factors affect the change in urban land price? II. STUDY AREA AND DATA ANALYSIS 2.1 Study Area The study area covers an area of approximately 320 km2 in the central region within the four districts of Kunming (Wuhua, Panlong, Xishan, and Guandu District) in Fig 1. The monitoring range of residential and commercial land, i.e., the study area, has not changed. The northeast and northwest of Kunming are mountainous areas, and the southwest is Dianchi Lake. The monitoring points basically cover the central urban area of Kunming. Promoted by the national strategy, the economy has developed rapidly and the urbanization process has sped up in Kunming. From 2008 to 2018, the residential land price increased from 2776 yuan/m2 ($396/m2) to 7334 yuan/m2 ($1047/m2) and the commercial land price increased from 5993 yuan/m2 ($855/m2) to 16604 yuan/m2 ($2369/m2). This represents the speed of development of potential cities. These tremendous changes make Kunming a good case study for analyzing changes in the distribution and driving factors of urban land prices. [486] Design Engineering ISSN: 0011-9342 Issue: 11 | Pages: 484 - 504 Fig 1:The study area and monitoring points distribution. 2.2 Data Analysis Dynamic monitoring of land prices was carried out in 2008 in Kunming, and annual monitoring data of residential and commercial land prices was collected from 2008 to 2018. This study collected 11 years of data from 43 commercial land price and 44 residential land price monitoring points in Kunming. From the perspective of data changes, the accuracy of data is affected because the growth rate is calculated using pushback data instead of time monitoring data in 2008. There was a rapid rise in land prices between 2009 and 2011. The annual growth rate of the residential land price increased from -2.67% to 94.51%, and the commercial land price increased from 22.48% [487] Design Engineering ISSN: 0011-9342 Issue: 11 | Pages: 484 - 504 to 52.25%. Between 2012 and 2018, a period of stable fluctuation of land prices was observed, with the range in price fluctuations being less than 7% with an overall upward trend. Combined with relevant research literature, the land price driving factors include: land structure factors (land area and floor area ratio), location traffic factors (distance to the central business district (CBD), distance to the nearest subway station, distance to the nearest bus station, distance to the nearest main road), and neighborhood factors (distance to the nearest: commercial district, recreational water area, university, primary and secondary school, park and hospital). The land structure factor data is derived from the monitoring point recorded data, the location of traffic factors and the neighborhood factors are obtained through vector data established by ArcGIS. The location of traffic and neighborhood factors changes with the development of social economy, and we checked the relevant data for each year to ensure that all facilities were established and in a state of use. III. METHODS 3.1 Variogram Simulation The variogram is a geostatistical method. It is commonly used in geology [17,18], and the land market [19], and has been widely used in housing and land price research in recent years [20]. Under the condition of the second-order stationary hypothesis, the calculation of the variogram γ ℎ is defined by Eq. 1 [21]: 푁 ℎ 1 γ∗ ℎ = 푍 푥 − 푍 푥 + ℎ 2 (1) 2푁 ℎ =1 Where N ℎ is the logarithm of the data pair 푍 푥 , 푍 푥 + ℎ , and h is the distance of the land price monitoring point. Z 푥 = 1,2, … , 푛 is the observation of the stationary regionalization variable Z 푥 at the spatial position 푥 . The data pair 푍 푥 , 푍 푥 + ℎ = 1,2,…,푁ℎ is an observation on point pair 푥, 푥+ℎ separated by ℎ in a certain direction. The variogram is generally represented by a variation curve. The spherical model is the most widely used, and is represented by Eq. 2: [488] Design Engineering ISSN: 0011-9342 Issue: 11 | Pages: 484 - 504 0 ℎ = 0 3ℎ ℎ3 γ ℎ = 퐶0 + 퐶 − 3 0 < ℎ ≤ 푎 (2) 2푎 2푎 퐶0 + 퐶ℎ > 푎 Where 퐶0 is the nugget constant, 퐶0 + 퐶 is the base value, 퐶 is the arch height, and a is the range of influence of the land price.
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