Geographic Information Dataset of Urban Housing Price Changes in the Yangtze River Delta Region (2008?C2018)

Ma, Y. Z.^1,2  Li, X. L.^1,2  Song, W. X.^1,3*

1. Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences Nanjing 210008, China;

2. University of Chinese Academy of Sciences, Beijing 101408, Beijing, China;

3. Nanjing Institute of Geography and Limnology, Key Laboratory of Watershed Geographic Sciences, Chinese Academy of Sciences, Nanjing 210008, Jiangsu, China

Abstract: To study the factors influencing urban housing price changes in the Yangtze River Delta region, the authors used the national real estate transaction information platforms, such as Fangtianxia (www.fang.com) and 365 Taofang.com (www.house365.com), to search for data relevant to this research objective. Through data collection and analysis, house prices in 327 districts or counties in 41 cities in the Yangtze River Delta from 2008 to 2018 were itemized and reorganized, namely as the Geographic Information Data Set of Urban Housing Price Changes in the Yangtze River Delta Region (2008?C2018). This data set includes: (1) city and county boundary data in the region; (2) house prices in 41 cities and 327 districts or counties in the region from 2008 to 2018. The data show that house price is the monetized expression of the abundance of resources such as urban economy, human, society, and administration. The regional house price differentiation is a comprehensive indicator of differences in the ability of urban dominating resources. It is difficult for housing prices in the districts or counties of the Yangtze River Delta to achieve "club convergence" in a short period of time, so the gap in housing prices between core cities (e.g., Shanghai, Nanjing, Hangzhou) and other cities may continue to expand. The data set is stored in .xls and .shp formats and consists of three data files with a total volume of 14 MB (compressed to 1 file, 4.37MB). The research results based on this data set have been published in Geography Research, 2018, volume 37, issue 1.

Keywords: data of counties and districts; data of cities; housing price; Yangtze River Delta Region

1 Introduction

Since the implementation of the urban housing system reform in 1998, urban housing prices in China have been increasing steadily. The problem of high housing prices has attracted broader attention. Especially so for the megacities, where the excessive growth rate causes the housing price risk to rise, and real estate bubbles become common.

House prices depend on the level of development of a region or country, including its economic, social, political dimensions and so on, which together affect the development and stability of a given region (or country). In the process of rapid social development and urban renewal, housing prices can vary greatly according to differences in the various resource allocation capabilities among cities, and these price gaps are currently widening. Usually, the index system of such influencing factors of housing price differentiation is based on supply?Cdemand theory ^[1?C2] and urban hedonic price theory ^[3?C4]. The first theory explores the influencing factors of urban housing price differentiation from the perspective of equilibrium prices of housing supply and the second the demand and location equilibrium between manufacturer and consumer ^[5?C6]. Currently, scholars at home and abroad have focused on the specific factors that stem from aspects of the economy, society, manpower, and administration and that affect such price differentiations. They mainly include urban location and administrative level, population structure, wealth level of residents, mileage of traffic, immigration population scale, direct foreign investment, urban center, public services within cities, and traffic conditions, among others. ^[8-13]

Housing prices in the Yangtze River Delta region are one of the highest in the country, and they are increasing rapidly. The regional housing price difference is significant, in terms of its high research value. As an example of an integrated area, the Yangtze River Delta region has the advantages of a developed economy: population concentration, strong public service ability, and convenient transportation. It also benefits from close inter-city links, frequent factor mobility, and fewer obstacles. Although the level of integration among cities in the Yangtze River Delta region is high, the type of cities are various. This data set thus explores the spatial pattern and housing price differences in the Yangtze River Delta region, which can provide a new perspective on housing price differentiation in this region, one that may contribute to adjustments in housing development policy.

2 Metadata of the Dataset

The name, author, geographical region, data time, time resolution, data set composition, data publishing and service platform, data sharing policy, and other relevant information of the dataset^[14] are shown in Table 1.

Table 1  Metadata summary of Geographic Information Data Set of Urban Housing Price Changes in Yangtze River Delta Region (2008?C2018)

(To be continued on the next page)

(Continued)

3 Data Sources and Research Areas

3.1 Data Sources

The vector data of administrative boundary came from the diva-gis website (www.diva-gis. org). Individual obsolete boundary data have been readjusted according to the latest administrative divisions, which are inquired in the websites of municipalities. Housing price data came from national real estate trading information platforms, such as Fangtianxia (www.fang.com) and 365 Taofang (www.house 365.com). The data in these websites were collected by professional information collectors, or provided by developers and intermediaries. After that, the collected data were reorganized, filtered, with duplicates and outlier data eliminated, so as to compile timely and comprehensive housing price data set. The China Index Academy also uses the data from Fangtianxia as original data (details can be seen on the official website: industry.fang.com), so we presumed the data have good reliability. It should be pointed out 10 revoked or important functional areas have been eliminated from the data (in Excel), leaving 317 districts and counties in the vector data.

3.2 Research Areas

This data set includes three provinces and one province-level municipality in the Yangtze River Delta: Shanghai, Jiangsu, Zhejiang, and Anhui, totaling 41 cities and 327 districts or counties. Because of the significant differences in economic power, population, and traffic conditions among the cities, especially between core cities such as Shanghai, Nanjing, Hangzhou and other cities, this may cause spatial heterogeneity in overall housing prices^[16]. There're also certain gaps in the development level among downtown areas, suburbs, and counties, so there may be obvious spatial differentiation of housing prices in a city as well.

3.3 Data Development Technology Route

Technical route: To study the housing price differentiation patterns in the Yangtze River data, firstly, we compared the mean value of housing prices in the data with those of the whole co- untry in 2008?C2018 and plotted them, and then summarized the characteristics of each stageof growth. Then we conducted a three-stage analysis of the growth rate in housing prices to identify the agglomeration characteristics. Finally, we visualized the data, trying to ascertain the overall and local differentiation pattern of urban housing prices, and proposed the future directions for follow-up research.

4 Data Composition and Results

4.1 Dataset Composition

Geographic Information Data Set of Urban Housing Price Change in Yangtze River Delta Region (2008?C2018) includes two parts: 1. The administrative boundary of the Yangtze River Delta region, for which the file format is .shp; 2. The housing price data of the cities and counties in the Yangtze River Delta region, for which the file format is .xls.

4.2 Data Results

The overall growth trend for urban housing prices in the data set appears periodic (Fig. 2), which is similar to China??s national pattern. There're three stages from 2008 to 2018. The first stage lasted from 2008 to 2011, when housing prices rose rapidly; the second stage was from 2011 to 2015, when housing prices remained stable; the third was from 2015 to 2018, marked by housing prices re-entering a period of faster growth.

For these three stages, an analysis of housing price growth rates is shown in Fig. 3.This revealed that the high-value growth rate was concentrated in Zhejiang, especially near Wenzhou, in the first-stage; the second-stage high value was concentrated in the Anhui and northern Jiangsu; that for the third-stage was around core cities, such as Shanghai, Nanjing, Hangzhou and Hefei.

Based on the data of 327 districts or counties in 2008?C2018, housing prices in the Yangtze River Delta generally showed a steady upward trend. Notably, growth for 2012?C2015 was relatively stable, when house prices rose by an average of just 1.36%. By contrast, in 2008?C2011 and 2016?C2018, housing prices rose rapidly, with average increases of 18.49% and 16.21%, respectively. As Fig. 4 shows, high housing prices mainly occurred in the central urban areas of Shanghai, Nanjing and Hangzhou. The highest value was found in the Shanghai Jing'an District in 2018, where the house price exceeded 90,000 yuan/m², and the maximum increase and the fastest growth rate occurred respectively in the Shanghai Jing'an District and Hefei Binhu New District. In 2008, only 30 areas had housing prices that exceeding 10,000 yuan/m², which accounted for only 9% of the total. By 2018, however, 163 areas attained housing prices of more than 10,000 yuan/m², accounting for half of the total.In northern Jiangsu and Anhui (except for Hefei), the prices of most areas have increased, but they have not yet surpassed the 10,000 yuan/m²; this exemplifies the geographic difference between the north and south in housing prices from the overall pattern of housing pric es in the data. The difference in housing prices between the counties and districts in a given city of the Yangtze River Delta was also significant, and this gap continues to widen. In 2008, the greatest gap was in Shanghai, where it exceeded 22,000 yuan/m²; by 2018, this gap coming close to 80,000 yuan/m². Over this period, Chizhou, which had the smallest gap in 2008, also expanded from 904 yuan/m² to 3,466 yuan/m², reflecting the partial differentiation pattern of urban housing prices in the Yangtze River Delta.

Figure 3  Three-stage hotspot analysis of housing price growth rates in the Yangtze River Delta region

Figure 4  Distribution map of housing prices of districts or counties in the Yangtze River Delta Region

5 Discussion and Conclusion

Against the background of rapid housing price growth, the greater risks, and the significant differences, this data set aimed at providing new research materials and perspectives for the spatial differentiation pattern, mechanisms, and effects of housing prices in the Yangtze River Delta region. According to the data visualization results, house prices in the region have risen steadily over 2008?C2018, showing periodic characteristics and overall and local differences in the spatial distribution. This study only combined house price data with vector data, and then developed a simple spatial analysis and description. Hence, it didn't consider endogenous factors, such as economy, society, administration and so on, and external factors, such as housing policy, economic situation, and spillover effects^[17?C18], to analyze the data in depth. Therefore, this data set provides a readily accessible database for future in-depth research on housing price trends. But the urban endogenous and external factors that can jointly affect housing price differentiation need to be further collected and collated. The next step should focus on data on the impact mechanisms and spatial effects underpinning the differentiation in housing prices that we revealed here. This enabled us to better explore the reality and critical factors governing such differentiation in the region, to understand housing prices in various cities, to provide a reference for the government to implement differentiated housing development policies, and to motivate commitment towards contributing to the healthy and stable operation of the real estate market. At the same time, we could also explore the mutual feedback mechanism(s) between housing price differentiation and the integration process, to provide fresh ideas for high-quality integration from the perspective of housing prices.

Author Contributions

Ma, Y. Z. analyzed data; Li, X. L. collated the housing price data and boundary data; Song, W. X. was responsible for the overall design of the dataset and its development and also collected housing price data. Ma, Y. Z. and Li, X. L. wrote the data paper

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