Influential Factors Of Urban Land Price Research Based On The Panel Data Regression Analysis

Land is not only a basic social and material conditions of production, and is an important community asset, along with rapid urban growth and the establishment of the land market, urban land prices gradually being social concern. Factors affecting urban land is more extensive and constantly changing. In the same city for different purposes or different land uses position the land price will vary, the research on the urban land price has also more emphasis on this, but few studies on urban land price differences between different regions within a certain influence factors. In recent years, the regional economic integration process continues to accelerate, in this context research on certain areas of the cities land price differences and socio-economic factors’ effect on urban land prices is particularly necessary, you can provide a theoretical basis for the establishment of regional land market value. A correct understanding of the differences between the urban land price and its influencing factors are very important to land economics for study and research, the rational management and use of urban land.In this paper, taking Shandong province included in the national urban land price monitoring system statistics of nine key cities as the study area, by collecting and collating the study area from 2008 to 2013 urban land price monitoring data, statistical data related to the impact of urban land price factors, with panel data regression analysis, the establishment of a comprehensive urban land, commercial service land, residential land and industrial land price factors overall regression model, in order to reveal the effects of factors on the premium difference between cities. Panel data is unified time-series data and cross-sectional data, which may well reflect the heterogeneity between samples. Studying by means of panel data regression analysis can better reflect factors of urban land price differences, avoid multicollinearity and other issues, and the results are more comprehensive and accurate.The main contents and results were as follows:(1) Urban land price level and changes in the study areaPrice level in the study area each city was different, Qingdao, Jinan, Yantai comprehensive and each use land prices were high, Linyi, Jining, Zaozhuang and other citys’ commercial service, residential land prices were low, Tai’an, Linyi and other citys’ industrial land prices were low. From 2008 to 2013 six years, the average comprehensive, commercial service, residential and industrial land prices were up 27.66%, 25.43%, 31.78%, 6.63%.(2) Urban land price factors selectedSummarized and outlines the factors affecting urban land from four aspects of economic, demographic, social and policy, etc. These factors included GDP, public revenue, per capita disposable income of urban residents, total retail sales of social consumer goods, real estate development and investment, the total population, urban population density, external traffic conditions, the number of primary and secondary schools, the per capita area of urban roads, urban standard operating vehicle number, the number of hospital beds, urban green coverage area, industrial electricity, public expenditure, city level, and so fundamental factors.(3) Research on the factors of urban land prices based on panel data regression analysisComprehensive land prices overall regression analysis showed that the city’s comprehensive land among the main factors in GDP, city level, urban population density and so on, where the GDP affected the value of the largest comprehensive land every 1% increase in integrated price rose 0.77%; the city level, followed by a 1% increase, integrated price rose 0.48%; the impact of social factors on the city’s comprehensive land were of a certain complexity.Commercial service land prices overall regression analysis showed that among urban commercial land price factors mainly GDP, external traffic conditions, city level and so on, which GDP had the maximum intensity for each 1% increase in the commercial service land price rose 0.66%, external traffic conditions and urban level factors followed.Residential land prices overall regression analysis showed that among urban residential land price factors mainly GDP, urban population density, external traffic conditions and so on, including GDP for residential land the impact of the greatest efforts, each 1% increase in residential land prices rose 1.38 percent, the urban population density and public expenditure factors followed, social factors on the differences between urban residential land had a certain complexity.Industrial land prices overall regression analysis showed that the main factors were public revenue, external traffic conditions, total population and so on, including the impact of public revenue on urban industrial land price had the maximum intensity for each 1% increase in industrial land prices rose 1.40%; external traffic conditions and the total population factors followed by a 1% increase, industrial land price rose 0.64% and 0.33% respectively; social factors also had an impact on industrial land price.Based on the study found that different factors influence the intensity of land price is different, the same factors of influence on the intensity of the different uses of the land price is not the same. Overall, economic and policy factors have a greater impact on urban land price in the study area, but some factors affect the results of more complex such as public expenditure, for both high and low level of urban land prices could be positive effects, there may be a negative effect.