Housing prices have a direct impact on both the national economy and people's livelihoods. An in-depth exploration of the spatial distribution and influencing factors of housing prices is crucial for promoting the healthy development of the real estate industry. This study utilizes housing price data and POI (Point of Interest) big data, employing GIS spatial analysis methods to examine the distribution characteristics and spatial correlations of housing prices in Longgang District, Shenzhen. Furthermore, it explores the mechanisms by which various factors contribute to housing price heterogeneity. The findings are as follows: (a) Longgang District exhibits a polycentric spatial structure, with the housing prices peaks located in the western urban core and the eastern Longgang Central City; (b) Housing prices display significant spatial agglomeration, with high-value clusters located in Longcheng, Bantian, Buji, Jihua, and Nanwan Subdistricts, while low-value clusters are found at the junctions of Pingdi, Baolong, Longcheng, and Longgang Subdistricts; (c) The relative influence of factors on housing prices within the district ranks as follows: economic factors > transportation factors > neighborhood environment factors.

Xiaomin Lao, Xiaohui Zhuo, Bo Xiong, Hui Yin. Study on Spatial Distribution Characteristics and Influencing Factors of Housing Prices in Longgang District, Shenzhen. Academic Journal of Environment & Earth Science (2025), Vol. 7, Issue 5: 61-69. https://doi.org/10.25236/AJEE.2025.070508.

[1] He Y, Guo Y, Han G, et al. Influence of Real Estate Regulation Policies on Housing Transaction Price: Taking 29 Cities in China as Examples[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2022, 58(02): 317-325.

[2] Chen Y, Yang H. Rethinking Real Estate Regulation and Long-Term Mechanism Construction: Examining Policies Since 2016[J]. Social Policy Research, 2025(03): 73-85+134-135.

[3] Duca J V, Muellbauer J, Murphy A. What drives house price cycles? International experience and policy issues[J]. Journal of Economic Literature, 2021, 59(3): 773-864.

[4] Yii K J, Tan C T, Ho W K, et al. Land availability and housing price in China: Empirical evidence from nonlinear autoregressive distributed lag (NARDL)[J]. Land use policy, 2022, 113: 105888.

[5] Xiao Y, Chen X, Li Q, et al. Exploring determinants of housing prices in Beijing: An enhanced hedonic regression with open access POI data[J]. ISPRS International Journal of Geo-Information, 2017, 6(11): 358.

[6] Yu X, Zhan D. An Analysis on the Spatial Pattern and Influencing Factors of House Price Index in 70 Large and Medium-Sized Cities in China[J]. Journal of Central China Normal University(Humanities and Social Sciences), 2022, 61(01): 40-51.

[7] Zhang W, Li H, Duan X. The Impacts of Rail Transit on Property Values——The Case of No.1 Line in Beijing[J]. Economic Geography, 2012, 32(02): 46-51+65.

[8] Tan R, He Q, Zhou K, et al. Administrative hierarchy, housing market inequality, and multilevel determinants: A cross-level analysis of housing prices in China[J]. Journal of Housing and the Built Environment, 2019, 34(3): 845-868.

[9] Peng J, Pan J, Chen Y, et al. A Study on the Spatial Effect of Public Service Facilities on Surrounding Housing Prices——Based on a case study of Wuhan comprehensive hospitals[J]. Price: Theory & Practice, 2022(07): 152-155+204.

[10] Li Y, Gu D, Liu Q, et al. POI-Based Impact of Water Landscape on Housing Price in the Central Area of Chongqing[J]. Modern Urban Research, 2022(09): 43-47+60.

[11] Tang H, Liu Y, Wu Z. Analysis of Spatial Heterogeneity of influencing factors of housing price based on poi data: a case study of Changsha[J]. Urban Problems, 2021(02): 95-103.

[12] Han Y, Yin S, Li Z. Spatial Differentiation and Influence Factors Analysis of County Region Housing Prices in the Yangtze River Delta[J]. Human Geography, 2018, 33(06): 87-95.

[13] Yuan S, Zhu C, Yang L. Spatial analysis of various land price and its influencing factors in Yangtze river delta agglomeration[J]. Resources and Environment in the Yangtze Basin, 2017, 26(10): 1538-1546.

[14] Shen T, Yu H, Zhou L, et al. On Hedonic Price of Second-Hand Houses in Beijing Based on Multi-Scale Geographically Weighted Regression: Scale Law of Spatial Heterogeneity[J]. Economic Geography, 2020, 40(03): 75-83.

[15] Xu X. China GDP spatial distribution of 1 kilometer grid dataset[DS]. Resource and Environmental Science Data Platform, 2017.

[16] Burrough P A, McDonnell R A, Lloyd C D. Principles of geographical information systems[M]. Oxford university press, 2015.

[17] Getis A. Spatial autocorrelation[M]//Fischer M M, Getis A. Handbook of Applied Spatial Analysis: Software Tools, Methods and Applications. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010: 255-278. 

[18] Mitchell A. The ESRI guide to GIS analysis: Geographic patterns & relationships: Vol. 1[M]. ESRI, Inc., 1999.

[19] Peng S. A Study On The Spatial Distribution And Dynamic Characteristics Of Commercial Housing Price In Nanjing Based On Poi Data[J]. Architecture & Culture, 2017(03): 64-68.

[20] Zhu L, Zong G, Chen L. Analysis of House Price Spillover Effect Based on ESDA Method and Spatial Econometric Model[J]. Journal of Industrial Technology and Economy, 2017, 36(03): 98-106.