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題名 遙測指標在房價估算中的應用
Applications of remote sensing indices for property price estimation作者 葛思昊
Ge, Si-Hao貢獻者 林士淵
Lin, Shih-Yuan
葛思昊
Ge, Si-Hao關鍵詞 杭州市
特徵價格法
探索性空間數據分析
遙測指標
Hangzhou city
Hedonic price model
ESDA
Remote sensing日期 2021 上傳時間 2-Sep-2021 17:28:34 (UTC+8) 摘要 近年來，中國大陸的城市化發展迅速。隨著城市化的發展浪潮，房屋交易也變得更多。典型的例子是位於浙江省杭州市的房地產市場繁榮，因此被選為研究區域。貝殼找房是鏈家這一仲介旗下的房屋交易網站，該網站會發佈其平臺人員經手的成交案，我們通過貝殼找房收集了2019年1月至2020年9月的32,000多宗二手房屋交易記錄。這些屬性包括房價，房屋類型，房屋面積，方向，建築風格，電梯，裝修，建成年代和出售時間。基於收集的資料，旨在建立一個迴歸模型來估算房地產價格。為此，除了上面列出的屬性外，我們還收集了從遙測圖像中提取的NDVI、NDBI和LST等環境因素。通過高德地圖API獲取捷運站、中小學、公園廣場等POI點位。并且還加入距離舊CBD、距離新CBD以及距離西湖等變量。爲了驗證COVID-19以及遙測指數對於房價的影響，我們做了探索性空間數據分析。因爲受到COVID-19大流行的影響，杭州市2020年第一季度二手房屋成交極少，隨著疫情在杭州的結束，杭州市二手房市場在第二季度迎來大增長。2020年前三季度杭州市的NDVI較去年同期有明顯增長，而LST在房屋高成交區有略微的下降，不過杭州市整體的NDBI略有上升。最後將遙測資料獲得的指數被放入特徵價格模型中以執行迴歸計算，結果表明NDVI、NDBI和LST對於房價是顯著的。NDVI與房價正相關，而NDBI和LST則與房價負相關。
In recent years, the urbanization of mainland China has developed rapidly and the number of property transactions also increases largely. A typical example is a boom in the real estate market in Hangzhou, Zhejiang Province, so it was selected as the research area. "Shell Search" is a real estate trading website publishing the transaction cases handled by its platform. We collected about 32,000 second-handed houses transacted from January 2019 to September 2020 through Shell Search. In which the attributes, including housing price, housing type, housing area, orientation, architectural style, elevator, decoration, built year, sale time, etc., were recorded. Based on the collected data, we aimed to establish a regression model to estimate real estate prices.In addition to the attributes listed above, we also collected NDVI, NDBI, and LST extracted from remote sensing images to represent environmental factors. POI points such as MRT stations, primary and secondary schools, parks, and squares, were extracted through the Gaode Map API. We also added variables such as distance to the old CBD, the new CBD, and West Lake, in the regression model.Due to the impact of the COVID-19 pandemic, there were very few second-handed housing transactions in Hangzhou in the first quarter of 2020. With the end of the epidemic in Hangzhou, the second-handed housing market in Hangzhou ushered in significant growth in the second quarter. In the first three quarters of 2020, Hangzhou’s NDVI increased significantly compared to the same period last year, while LST declined slightly in areas with high housing transactions, but Hangzhou’s overall NDBI increased slightly.Finally, all the variables were put into the regression model. The results showed that NDVI, NDBI and LST were significant for housing prices. 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Estimating the influence of transport on house prices: evidence from Hong Kong. Journal of Property Valuation and Investment.Sobrino, J. A., Jiménez-Muñoz, J. C., Sòria, G., Romaguera, M., Guanter, L., Moreno, J., ... & Martínez, P. (2008). Land surface emissivity retrieval from different VNIR and TIR sensors. IEEE transactions on geoscience and remote sensing, 46(2), 316-327.William, M. S. (1991). Real Estate Appraisal. South-Western, New York.Wu, K.-y., et al. (2013). "Impacts of land use/land cover change and socioeconomic development on regional ecosystem services: The case of fast-growing Hangzhou metropolitan area, China." Cities 31: 276-284.Yamagata, Y., et al. (2016). "Value of urban views in a bay city: Hedonic analysis with the spatial multilevel additive regression (SMAR) model." Landscape and Urban Planning 151: 89-102.Yao, Y., et al. (2018). "Mapping fine‐scale urban housing prices by fusing remotely sensed imagery and social media data." 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(2019). 卷積神經網絡和隨機森林的城市房價微觀尺度製圖方法. 地球信息科學學報, v.21;No.138(02), 36-45.殷蕾茗 (2010). 杭州市住宅區位元分佈特徵研究, 浙江大學公共管理學院.富毅 (2006). 基於特徵價格的杭州市住宅價格空間分異研究, 浙江大學.王瑞玲. (2011). 我國商業銀行流動性風險管理研究, 山東大學.溫海珍, 張淩, & 彭魯鳳. (2010). 杭州市住宅價格空間分異:基於特徵價格的兩維度分析. 中國土地科學, 24(2), 51-56.溫海珍, 蔔曉慶, & 秦中伏. (2012). 城市湖景對住宅價格的空間影響——以杭州西湖為例. 經濟地理(11), 58-64.詹小麗(2018). 城市住宅價格的合理性分析——以杭州市為例, 浙江大學.Mitchell，Andy。ESRI GIS 分析手冊，第 2 卷。ESRI 出版社，2005。趙英時等（2012）.遙感應用分析原理與方法.科學出版社.三、網頁參考http://hzzjxh.com/news.php?id=222391http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson1-2/blackbody.htmlhttps://lbs.amap.com/api/webservice/downloadhttps://zhuanlan.zhihu.com/p/100993681 描述 碩士
國立政治大學
地政學系
107257034資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107257034 資料類型 thesis dc.contributor.advisor 林士淵 zh_TW dc.contributor.advisor Lin, Shih-Yuan en_US dc.contributor.author (Authors) 葛思昊 zh_TW dc.contributor.author (Authors) Ge, Si-Hao en_US dc.creator (作者) 葛思昊 zh_TW dc.creator (作者) Ge, Si-Hao en_US dc.date (日期) 2021 en_US dc.date.accessioned 2-Sep-2021 17:28:34 (UTC+8) - dc.date.available 2-Sep-2021 17:28:34 (UTC+8) - dc.date.issued (上傳時間) 2-Sep-2021 17:28:34 (UTC+8) - dc.identifier (Other Identifiers) G0107257034 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/137030 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 地政學系 zh_TW dc.description (描述) 107257034 zh_TW dc.description.abstract (摘要) 近年來，中國大陸的城市化發展迅速。隨著城市化的發展浪潮，房屋交易也變得更多。典型的例子是位於浙江省杭州市的房地產市場繁榮，因此被選為研究區域。貝殼找房是鏈家這一仲介旗下的房屋交易網站，該網站會發佈其平臺人員經手的成交案，我們通過貝殼找房收集了2019年1月至2020年9月的32,000多宗二手房屋交易記錄。這些屬性包括房價，房屋類型，房屋面積，方向，建築風格，電梯，裝修，建成年代和出售時間。基於收集的資料，旨在建立一個迴歸模型來估算房地產價格。為此，除了上面列出的屬性外，我們還收集了從遙測圖像中提取的NDVI、NDBI和LST等環境因素。通過高德地圖API獲取捷運站、中小學、公園廣場等POI點位。并且還加入距離舊CBD、距離新CBD以及距離西湖等變量。爲了驗證COVID-19以及遙測指數對於房價的影響，我們做了探索性空間數據分析。因爲受到COVID-19大流行的影響，杭州市2020年第一季度二手房屋成交極少，隨著疫情在杭州的結束，杭州市二手房市場在第二季度迎來大增長。2020年前三季度杭州市的NDVI較去年同期有明顯增長，而LST在房屋高成交區有略微的下降，不過杭州市整體的NDBI略有上升。最後將遙測資料獲得的指數被放入特徵價格模型中以執行迴歸計算，結果表明NDVI、NDBI和LST對於房價是顯著的。NDVI與房價正相關，而NDBI和LST則與房價負相關。 zh_TW dc.description.abstract (摘要) In recent years, the urbanization of mainland China has developed rapidly and the number of property transactions also increases largely. A typical example is a boom in the real estate market in Hangzhou, Zhejiang Province, so it was selected as the research area. "Shell Search" is a real estate trading website publishing the transaction cases handled by its platform. We collected about 32,000 second-handed houses transacted from January 2019 to September 2020 through Shell Search. In which the attributes, including housing price, housing type, housing area, orientation, architectural style, elevator, decoration, built year, sale time, etc., were recorded. Based on the collected data, we aimed to establish a regression model to estimate real estate prices.In addition to the attributes listed above, we also collected NDVI, NDBI, and LST extracted from remote sensing images to represent environmental factors. POI points such as MRT stations, primary and secondary schools, parks, and squares, were extracted through the Gaode Map API. We also added variables such as distance to the old CBD, the new CBD, and West Lake, in the regression model.Due to the impact of the COVID-19 pandemic, there were very few second-handed housing transactions in Hangzhou in the first quarter of 2020. With the end of the epidemic in Hangzhou, the second-handed housing market in Hangzhou ushered in significant growth in the second quarter. In the first three quarters of 2020, Hangzhou’s NDVI increased significantly compared to the same period last year, while LST declined slightly in areas with high housing transactions, but Hangzhou’s overall NDBI increased slightly.Finally, all the variables were put into the regression model. The results showed that NDVI, NDBI and LST were significant for housing prices. NDVI was positively correlated with housing prices, while NDBI and LST were negatively correlated with housing prices. en_US dc.description.tableofcontents 誌謝 IV摘要 VIAbstract VII目錄 IX圖目錄 XI表目錄 XIV第一章 緒論 1第一節 研究背景與動機 1第二節 研究目的 3第二章 理論基礎與文獻回顧 4第一節 不動產大量估價 4第二節 特徵價格法 6第三節 探索性空間資料分析 7第四節 遙測指數在房價估算的應用 11第五節 遙測指數 14第三章 研究方法與流程 25第一節 研究區域 25第二節 研究資料與研究工具 30第三節 研究流程 41第四章 研究成果 48第一節 杭州房價敘述統計 48第二節 房價與成交量的local Morans‘I空間分佈 64第三節 房屋成交量與遙測指標的空間分佈 69第四節 遙測指數加入房價迴歸計算 83第五章 結論與建議 89第一節 結論 89第二節 建議 90參考文獻 91一、外文參考文獻 91二、中文參考文獻 95三、網頁參考 97 zh_TW dc.format.extent 6136687 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107257034 en_US dc.subject (關鍵詞) 杭州市 zh_TW dc.subject (關鍵詞) 特徵價格法 zh_TW dc.subject (關鍵詞) 探索性空間數據分析 zh_TW dc.subject (關鍵詞) 遙測指標 zh_TW dc.subject (關鍵詞) Hangzhou city en_US dc.subject (關鍵詞) Hedonic price model en_US dc.subject (關鍵詞) ESDA en_US dc.subject (關鍵詞) Remote sensing en_US dc.title (題名) 遙測指標在房價估算中的應用 zh_TW dc.title (題名) Applications of remote sensing indices for property price estimation en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 一、外文參考文獻Anselin, L. 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