Analysis of the Temporal and Spatial Patterns of Residential Prices in Qingdao and Its Driving Factors
As an important indicator of the level of urban economic development and the quality of the residents’ lives, housing prices are affected by various factors, such as the spatial distribution of the housing market, the housing characteristics of neighborhoods, and the location conditions. This paper...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/15/2/195 |
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| author | Yanjun Wang Yin Feng Kun Han Zishu Zheng Peng Dai |
| author_facet | Yanjun Wang Yin Feng Kun Han Zishu Zheng Peng Dai |
| author_sort | Yanjun Wang |
| collection | DOAJ |
| description | As an important indicator of the level of urban economic development and the quality of the residents’ lives, housing prices are affected by various factors, such as the spatial distribution of the housing market, the housing characteristics of neighborhoods, and the location conditions. This paper summarizes the spatial distribution of housing prices in Qingdao using GIS, analyzing spatial distribution characteristics, and combines these with the Geographically Weighted Regression (GWR) model to explore the influence of various factors, such as community attributes, location, transportation, and peripheral facilities on residential prices. The results show that from 2003 to 2023, residential housing prices in Qingdao exhibited a significant, continuous upward trend, with rapid growth in the early period and more stable growth in the later period; the spatial structure of residential prices evolved from a “single core” in Shinan District to a “double core + fan” structure involving both Shinan and Laoshan Districts, eventually forming a “double core + fan + mosaic” spatial layout; the green environment, congestion, leisure facilities, service management, and other community factors not only reflect the economic strengths and lifestyles of the residents, but also serve as key drivers of residential price differentiation. |
| format | Article |
| id | doaj-art-f4179d5267d64f0984c52450812b42dc |
| institution | Kabale University |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-f4179d5267d64f0984c52450812b42dc2025-01-24T13:26:08ZengMDPI AGBuildings2075-53092025-01-0115219510.3390/buildings15020195Analysis of the Temporal and Spatial Patterns of Residential Prices in Qingdao and Its Driving FactorsYanjun Wang0Yin Feng1Kun Han2Zishu Zheng3Peng Dai4College of Civil Engineering and Architecture, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Civil Engineering and Architecture, Shandong University of Science and Technology, Qingdao 266590, ChinaQingdao Tengyuan Design Office Co., Qingdao 266100, ChinaCollege of Civil Engineering and Architecture, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Civil Engineering and Architecture, Shandong University of Science and Technology, Qingdao 266590, ChinaAs an important indicator of the level of urban economic development and the quality of the residents’ lives, housing prices are affected by various factors, such as the spatial distribution of the housing market, the housing characteristics of neighborhoods, and the location conditions. This paper summarizes the spatial distribution of housing prices in Qingdao using GIS, analyzing spatial distribution characteristics, and combines these with the Geographically Weighted Regression (GWR) model to explore the influence of various factors, such as community attributes, location, transportation, and peripheral facilities on residential prices. The results show that from 2003 to 2023, residential housing prices in Qingdao exhibited a significant, continuous upward trend, with rapid growth in the early period and more stable growth in the later period; the spatial structure of residential prices evolved from a “single core” in Shinan District to a “double core + fan” structure involving both Shinan and Laoshan Districts, eventually forming a “double core + fan + mosaic” spatial layout; the green environment, congestion, leisure facilities, service management, and other community factors not only reflect the economic strengths and lifestyles of the residents, but also serve as key drivers of residential price differentiation.https://www.mdpi.com/2075-5309/15/2/195residential pricesspatial structuredriving factorsgeographically weighted regression models |
| spellingShingle | Yanjun Wang Yin Feng Kun Han Zishu Zheng Peng Dai Analysis of the Temporal and Spatial Patterns of Residential Prices in Qingdao and Its Driving Factors Buildings residential prices spatial structure driving factors geographically weighted regression models |
| title | Analysis of the Temporal and Spatial Patterns of Residential Prices in Qingdao and Its Driving Factors |
| title_full | Analysis of the Temporal and Spatial Patterns of Residential Prices in Qingdao and Its Driving Factors |
| title_fullStr | Analysis of the Temporal and Spatial Patterns of Residential Prices in Qingdao and Its Driving Factors |
| title_full_unstemmed | Analysis of the Temporal and Spatial Patterns of Residential Prices in Qingdao and Its Driving Factors |
| title_short | Analysis of the Temporal and Spatial Patterns of Residential Prices in Qingdao and Its Driving Factors |
| title_sort | analysis of the temporal and spatial patterns of residential prices in qingdao and its driving factors |
| topic | residential prices spatial structure driving factors geographically weighted regression models |
| url | https://www.mdpi.com/2075-5309/15/2/195 |
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