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2021 , Vol. 38 >Issue 6: 782 - 790

DOI: https://doi.org/10.7523/j.issn.2095-6134.2021.06.008

Innovation Article

Research progress and review on spatial differentiation of urban housing price: a bibliometric analysis using CiteSpace

  • FU Runde ,
  • YANG Degang ,
  • JIN Chuanfen ,
  • CAI Tianyi ,
  • WU Shengqin ,
  • WAN Tian
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  • 1. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China;
    2. University of Chinese Academy of Sciences, Beijing 100049, China

Received date: 2019-12-04

  Revised date: 2020-01-21

  Online published: 2021-11-16

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Abstract

Spatial differentiation of urban housing price is an important issue in current urban development researches, also an important topic of urban geography and social economics. Based on the Citespace econometric analysis visualization tool and the papers related to the spatial differentiation of urban housing price published in Web of Science Core Collection databases, this paper analyzed the time characteristics of literature publication, subject distribution, collaboration network, co-citation network, literature research progress and characteristics. The research suggests that:1) The number of publications on the spatial differentiation of urban housing price study has been increasing since 1970s, and presented a significant two-stage development process. Mainly, studies focus on subjects of economics, environmental ecology, geography and urban management. 2)The United States and China are the countries with the largest number of publications. Among institutes, Chinese scientific research institutions are more closely linked. The largest research institution is Zhejiang University. The papers of Sherwin Rosen, Luc Anselin, et al provided an important knowledge source for existing researches. 3) Factors that affect the spatial differentiation of urban housing price can be divided into internal and external factors. And, the influence degree and mechanism of various factors are different. 4) Hedonic Pricing model is the most classic research method. With the development of spatial metrology and GIS, the combination of multiple methods becomes the general analysis framework of spatial differentiation of urban housing price studies. The development of network information technology has given birth to new research tools and directions. Through the analysis of the spatial differentiation of urban housing price research status, this paper proposes that future research should pay more attention to the general affect mechanism of regional and the combination effect of influence factors. The model should be constructed with both interpretability and efficiency when applying multiple sources of Spatio-temporal big data.

Key words： urban housing price; spatial differentiation; scientometric; bibliometrics

Cite this article

FU Runde , YANG Degang , JIN Chuanfen , CAI Tianyi , WU Shengqin , WAN Tian . Research progress and review on spatial differentiation of urban housing price: a bibliometric analysis using CiteSpace[J]. Journal of University of Chinese Academy of Sciences, 2021 , 38(6) : 782 -790 . DOI: 10.7523/j.issn.2095-6134.2021.06.008

References

[1] Wang J F, Zhang T L, Fu B J. A measure of spatial stratified heterogeneity[J]. Ecological Indicators, 2016, 67:250-256.
[2] 邹利林, 杨俊, 胡学东.中国城市住宅价格时空演变研究进展与展望[J]. 地理科学进展, 2013, 32(10):1479-1489.
[3] Burgess E W. The growth of the city:an introduction to a research project[M]. Chicago:University of Chicago Press, 2008.
[4] Hoyt H. The structure and growth of residential neighborhoods in American cities[R]. Washington:Federal Housing Administration, 1939.
[5] Alonso W. The historic and the structural theories of urban form:their implications for urban renewal[J]. Land Economics, 1964, 40(2):227-231.
[6] Muth R F. Cities and housing:the spatial pattern of urban residential land use[J]. Land Use, 1969(5):591-592.
[7] Cheshire P, Mills E S. Handbook of regional and urban economics:applied urban economics[M]. Amsterdam:North Holland, 1999:1673-1790.
[8] Lancaster K J. A new approach to consumer theory[J]. Journal of Political Economy, 1966, 74(2):132-157.
[9] Rosen S. Hedonic prices and implicit markets:product differentiation in pure competition[J]. Journal of Political Economy, 1974, 82(1):34-55.
[10] 世界银行. 1989年世界发展报告[M]. 北京:中国财政经济出版社, 1989.
[11] 蔡媛媛,郑琰琳,袁丰.城市住宅价格国内外研究动态与未来展望[J]. 地域研究与开发, 2018, 37(1):46-53.
[12] 梁秀娟.科学知识图谱研究综述[J].图书馆杂志, 2009, 28(6):58-62.
[13] 李杰, 陈超美. CiteSpace:科技文本挖掘及可视化[M]. 北京:首都经济贸易大学出版社, 2017.
[14] Fang Y, Yin J, Wu B H. Climate change and tourism:a scientometric analysis using CiteSpace[J]. Journal of Sustainable Tourism, 2018, 26(1):108-126.
[15] 曹小曙, 胡培婷, 刘丹.电动汽车充电站选址研究进展[J]. 地理科学进展, 2019, 38(1):139-152.
[16] 尹丽春. 科学学引文网络的结构研究[D]. 辽宁大连:大连理工大学, 2006.
[17] 谢伶, 王金伟, 吕杰华. 国际黑色旅游研究的知识图谱:基于CiteSpace的计量分析[J]. 资源科学, 2019, 41(3):454-466.
[18] Newman M E J. Fast algorithm for detecting community structure in networks[J]. Physical Review E, Statistial, Nonlinear, and Soft Matter Physics,2004,69:(6 pt2) 066133.
[19] 马费成, 张勤. 国内外知识管理研究热点:基于词频的统计分析[J]. 情报学报, 2006, 25(2):163-171.
[20] 施生旭, 童佩珊. 基于CiteSpace的城市群生态安全研究发展态势分析[J].生态学报, 2018, 38(22):8234-8246.
[21] Hyun D, Milcheva S. Spatial dependence in apartment transaction prices during boom and bust[J]. Regional Science and Urban Economics, 2018, 68:36-45.
[22] 郭慧秀,拓星星,贾菲,等.国内城市房价时空分异及其动力机制研究进展[J].地域研究与开发,2016, 35(2):58-64.
[23] Salvati L, Ciommi M T, Serra P, et al. Exploring the spatial structure of housing prices under economic expansion and stagnation:the role of socio-demographic factors in metropolitan Rome, Italy[J]. Land Use Policy, 2019, 81:143-152.
[24] Fernandez L, Mukherjee M, Scott T. The effect of conservation policy and varied open space on residential property values:a dynamic hedonic analysis[J]. Land Use Policy, 2018, 73:480-487.
[25] Hyun D, Milcheva S. Spatio-temporal effects of an urban development announcement and its cancellation on house prices:a quasi-natural experiment[J]. Journal of Housing Economics, 2019, 43:23-36.
[26] Helgers R, Buyst E. Spatial and temporal diffusion of housing prices in the presence of a linguistic border:evidence from Belgium[J]. Spatial Economic Analysis, 2016, 11(1):92-122.
[27] Kim D, Jin J. The effect of land use on housing price and rent:empirical evidence of job accessibility and mixed land use[J]. Sustainability, 2019, 11(3):938.
[28] Pangallo M, Nadal J P, Vignes A. Residential income segregation:a behavioral model of the housing market[J]. Journal of Economic Behavior & Organization, 2019, 159:15-35.
[29] Xiao Y, Orford S, Webster C J. Urban configuration, accessibility, and property prices:a case study of Cardiff, Wales[J]. Environment and Planning B:Planning and Design, 2016, 43(1):108-129.
[30] Seo K, Golub A, Kuby M. Combined impacts of highways and light rail transit on residential property values:a spatial hedonic price model for Phoenix, Arizona[J]. Journal of Transport Geography, 2014, 41:53-62.
[31] Zhang D, Jiao J. How does urban rail transit influence residential property values? Evidence from an emerging Chinese megacity[J]. Sustainability, 2019, 11(2):534.
[32] Tan R H, Zhou K H, Xu H Z. Effects of urban road centrality on property values:spatial hedonic analysis of the housing market in Wuhan, China[J]. Journal of Urban Planning and Development, 2019, 145(2):05019005.
[33] Dai X Z, Bai X, Xu M. The influence of Beijing rail transfer stations on surrounding housing prices[J]. Habitat International, 2016, 55:79-88.
[34] Yang L C, Zhou J P, Shyr O F, et al. Does bus accessibility affect property prices?[J]. Cities, 2019, 84:56-65.
[35] Wen H, Zhang Y, Zhang L. Do educational facilities affect housing price? An empirical study in Hangzhou, China[J]. Habitat International, 2014, 42:155-163.
[36] Mittal J, Byahut S. Scenic landscapes, visual accessibility and premium values in a single family housing market:a spatial hedonic approach[J]. Environment and Planning B:Urban Analytics and City Science, 2019, 46(1):66-83.
[37] Hui E C M, Liang C. Spatial spillover effect of urban landscape views on property price[J]. Applied Geography, 2016, 72:26-35.
[38] Chica-Olmo J, Cano-Guervos R, Tamaris-Turizo I. Determination of buffer zone for negative externalities:effect on housing prices[J]. The Geographical Journal, 2019, 185(2):222-236.
[39] Batóg J, Forys' I, Gaca R, et al. Investigating the impact of airport noise and land use restrictions on house prices:evidence from selected regional airports in Poland[J]. Sustainability, 2019, 11(2):412.
[40] Cano-Urbina J, Clapp C M, Willardsen K. The effects of the BP deepwater horizon oil spill on housing markets[J]. Journal of Housing Economics, 2019, 43:131-156.
[41] Beltrán A, Maddison D, Elliott R J R. Is flood risk capitalised into property values?[J]. Ecological Economics, 2018, 146:668-685.
[42] Pilla F, Gharbia S S, Lyons R. How do households perceive flood-risk? The impact of flooding on the cost of accommodation in Dublin, Ireland[J]. Science of the Total Environment, 2019, 650:144-154.
[43] Wang Y, Wang S J, Li G D, et al. Identifying the determinants of housing prices in China using spatial regression and the geographical detector technique[J]. Applied Geography, 2017, 79:26-36.
[44] Chen J H, Ong C F, Zheng L Z, et al. Forecasting spatial dynamics of the housing market using support vector machine[J]. International Journal of Strategic Property Management, 2017, 21(3):273-283.
[45] Morillo Balsera M C, Martínez-Cuevas S, Molina Sánchez I, et al. Artificial neural networks and geostatistical models for housing valuations in urban residential areas[J]. Geografisk Tidsskrift-Danish Journal of Geography, 2018, 118(2):184-193.
[46] Cao K, Diao M, Wu B. A big data-based geographically weighted regression model for public housing prices:a case study in Singapore[J]. Annals of the American Association of Geographers, 2019, 109(1):173-186.
[47] Li S W, Ye X Y, Lee J, et al. Spatiotemporal analysis of housing prices in China:a big data perspective[J]. Applied Spatial Analysis and Policy, 2017, 10(3):421-433.
[48] Yao Y, Zhang J B, Hong Y, et al. Mapping fine-scale urban housing prices by fusing remotely sensed imagery and social media data[J]. Transactions in GIS, 2018, 22(2):561-581.
[49] Can A. Specification and estimation of hedonic housing price models[J]. Regional Science and Urban Economics, 1992, 22(3):453-474.
[50] Dubin R A. Spatial autocorrelation:a primer[J]. Journal of Housing Economics, 1998, 7(4):304-327.
[51] Anselin L. Spatial econometrics:methods and models[M]. Dordrecht, Netherlands:Springer, Science Netherlands, 1988.
[52] Helbich M, Brunauer W, Vaz E, et al. Spatial heterogeneity in hedonic house price models:the case of Austria[J]. Urban Studies, 2014, 51(2):390-411.
[53] Li Z Q, Fotheringham A S, Li W W, et al. Fast geographically weighted regression (FastGWR):a scalable algorithm to investigate spatial process heterogeneity in millions of observations[J]. International Journal of Geographical Information Science, 2019, 33(1):155-175.
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