Uncovering spatial heterogeneity in real estate prices via combined hierarchical linear model and geographically weighted regression
文献类型：期刊论文
| 作者 | Hu, Yigong2; Lu, Binbin2; Ge, Yong1; Dong, Guanpeng3 |
| 刊名 | ENVIRONMENT AND PLANNING B-URBAN ANALYTICS AND CITY SCIENCE
 |
| 出版日期 | 2022-01-22 |
| 页码 | 26 |
| 关键词 | Hedonic price model hierarchical linear model geographically weighted regression spatial heterogeneity sample scale |
| ISSN号 | 2399-8083 |
| DOI | 10.1177/23998083211063885 |
| 通讯作者 | Lu, Binbin(binbinlu@whu.edu.cn) ; Ge, Yong(gey@lreis.ac.cn) |
| 英文摘要 | Spatial heterogeneity is important for exploring data relationships between real estate price and its influential factors. The geographically weighted regression (GWR) technique has been frequently adopted for this purpose. In this study, we collected a second-hand real estate house price data set of Wuhan, in which each property is located the same as the community it belongs to. Thus, this data set possesses a typical characteristic, that is, dozens or even hundreds of observations could be allocated to one pair of coordinates, but vary in their attributes. This specific feature might lead to serious problems with bandwidth optimisations and coefficient estimates for calibrating the GWR model. We then proposed an extension by combining the hierarchical linear model (HLM) and GWR, namely HLM-GWR to cope with these problems. Results show that the HLM-GWR performs much better than the conventional GWR and HLM technique in terms of bandwidth optimisation, coefficient estimates. With a controlled simulation test, we again validated the advantage of the HLM-GWR model in comparison to both the HLM and GWR in handling this specific scenario. Overall, HLM-GWR is workable and should be recommended in this case or other scenarios with observations of similar spatial distributions. |
| WOS关键词 | HOUSE PRICES ; DISTRIBUTIONS ; SINGLE |
| 资助项目 | National Natural Science Foundation of China[NSFC: 41725006] ; National Natural Science Foundation of China[42071368] ; National Natural Science Foundation of China[42001115] ; National Natural Science Foundation of China[U2033216] |
| WOS研究方向 | Environmental Sciences & Ecology ; Geography ; Public Administration ; Urban Studies |
| 语种 | 英语 |
| WOS记录号 | WOS:000751364700001 |
| 出版者 | SAGE PUBLICATIONS LTD |
| 资助机构 | National Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/170260]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Lu, Binbin; Ge, Yong |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China 2.Wuhan Univ, Sch Remote Sensing & Informat Engn, 129 Luoyu Rd, Wuhan 430079, Peoples R China 3.Henan Univ, Yellow River Civilizat & Sustainable Dev Res Ctr, Kaifeng, Peoples R China |
| 推荐引用方式 GB/T 7714 | Hu, Yigong,Lu, Binbin,Ge, Yong,et al. Uncovering spatial heterogeneity in real estate prices via combined hierarchical linear model and geographically weighted regression[J]. ENVIRONMENT AND PLANNING B-URBAN ANALYTICS AND CITY SCIENCE,2022:26. |
| APA | Hu, Yigong,Lu, Binbin,Ge, Yong,&Dong, Guanpeng.(2022).Uncovering spatial heterogeneity in real estate prices via combined hierarchical linear model and geographically weighted regression.ENVIRONMENT AND PLANNING B-URBAN ANALYTICS AND CITY SCIENCE,26. |
| MLA | Hu, Yigong,et al."Uncovering spatial heterogeneity in real estate prices via combined hierarchical linear model and geographically weighted regression".ENVIRONMENT AND PLANNING B-URBAN ANALYTICS AND CITY SCIENCE (2022):26. |
入库方式： OAI收割
来源：地理科学与资源研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。