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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. NDVI was positively correlated with housing prices, while NDBI and LST were negatively correlated with housing prices.
參考文獻 一、外文參考文獻
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Hindsley, P., et al. (2013). "Gulf views: toward a better understanding of viewshed scope in hedonic property models." The Journal of Real Estate Finance and Economics 47(3): 489-505.
Hindsley, P., Hamilton, S. E., & Morgan, O. A. (2013). Gulf views: toward a better understanding of viewshed scope in hedonic property models. The Journal of Real Estate Finance and Economics, 47(3), 489-505.
Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote sensing of environment, 83(1-2), 195-213.
Kaufman, Y. J., & Tanre, D. (1992). Atmospherically resistant vegetation index (ARVI) for EOS-MODIS. IEEE transactions on Geoscience and Remote Sensing, 30(2), 261-270.
Jensen, R., et al. (2004). "Using remote sensing and geographic information systems to study urban quality of life and urban forest amenities." Ecology and Society 9(5).
Jensen, R., Gatrell, J., Boulton, J., & Harper, B. (2004). Using remote sensing and geographic information systems to study urban quality of life and urban forest amenities. Ecology and Society, 9(5).
Jiao, L., Xu, G., Jin, J., Dong, T., Liu, J., Wu, Y., & Zhang, B. (2017). Remotely sensed urban environmental indices and their economic implications. Habitat International, 67, 22-32.
Jiménez‐Muñoz, J. C., & Sobrino, J. A. (2003). A generalized single‐channel method for retrieving land surface temperature from remote sensing data. Journal of geophysical research: atmospheres, 108(D22).
Lancaster, K. J. (1966). A new approach to consumer theory. Journal of political economy, 74(2), 132-157.
Li, W., et al. (2015). "A comparison of the economic benefits of urban green spaces estimated with NDVI and with high-resolution land cover data." Landscape and Urban Planning 133: 105-117.
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Panduro, T. E. and K. L. Veie (2013). "Classification and valuation of urban green spaces—A hedonic house price valuation." Landscape and Urban Planning 120: 119-128.
Pettorelli, N., Vik, J. O., Mysterud, A., Gaillard, J. M., Tucker, C. J., & Stenseth, N. C. (2005). Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends in ecology & evolution, 20(9), 503-510.
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Rosen, S. (1974). Hedonic prices and implicit markets: product differentiation in pure competition. Journal of political economy, 82(1), 34-55.
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Saphores, J.-D. and W. Li (2012). "Estimating the value of urban green areas: A hedonic pricing analysis of the single family housing market in Los Angeles, CA." Landscape and Urban Planning 104(3-4): 373-387.
Sengupta, S. and D. E. Osgood (2003). "The value of remoteness: a hedonic estimation of ranchette prices." Ecological Economics 44(1): 91-103.
Sirmans, S., Macpherson, D., & Zietz, E. (2005). The composition of hedonic pricing models. Journal of real estate literature, 13(1), 1-44.
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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." Transactions in GIS 22(2): 561-581.
Yoo, S., et al. (2012). "Variable selection for hedonic model using machine learning approaches: A case study in Onondaga County, NY." Landscape and Urban Planning 107(3): 293-306.
Yu, D. and C. Wu (2006). "Incorporating remote sensing information in modeling house values." Photogrammetric Engineering & Remote Sensing 72(2): 129-138.
Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International journal of remote sensing, 24(3), 583-594.
Zient, J., Zient, E. N., & Sirmanas, G. S. (2008). Determinants of house prices: A quantile regression aggregation bias. J. Real Estate Fin. Econ, 37, 317-333.

二、中文參考文獻
曹雲鋒, 王正興, & 鄧芳萍. (2010). 3種濾波算法對 NDVI 高質量數據保真性研究. 遙感技術與應用, 25(1), 118-125.
陳強. (2014). 高級計量經濟學及Stata應用(第二版). 高等教育出版社.
丁燦彧, 胡希軍, 吳德政, & 劉偉樂. (2018). 3種時序ndvi重構方法對比分析. 中南林業科技大學學報, 038(004), 10-15.
林素菁, & 林祖嘉. (2001). 台灣地區住宅供給彈性之估計. 住宅學報, 10(1), 17-27.
林祖嘉, & 馬毓駿. (2007). 特徵方程式大量估價法在台灣不動產市場之應用. 住宅學報, 16(2), 1-22.
劉文龍, 張雪華, & 黃昌洋. (2014). 杭州市住宅價格的影響因素研究. 工程經濟, 000(005), 54-59.
花敬群(2010).電腦大量估價模型於實務應用之探討.金融聯合徵信雙月刊(12),27-36.
金桂榮. (2012). 房地產稅徵管中的房地產批量評估問題研究. 中國註冊會計師, 000(011), 102-106.
李杭燕, 頡耀文, & 馬明國. (2009). 時序NDVI數據集重建方法評價與實例研究. 甘肅省遙感學會學術會議.
馬曉冬, 馬榮華, & 徐建剛. (2004). 基於esda-gis的城鎮群體空間結構. 地理學報(06), 1048-1057.
馬曉熠, & 裴韜. (2010). 基於探索性空間數據分析方法的北京市區域經濟差異. 地理科學進展(12), 1555-1561.
毛豐付, 羅剛飛, & 潘加順. (2014). 優質教育資源對杭州學區房價格影響研究. 城市與環境研究, 000(002), 53-64.
吳振華 (2014). 基於RS與GIS的城市生態景觀對房價的影響研究——以徐州市主城區為例, 中國礦業大學.
金逸蘭. (2017). 杭州市軌道交通對住房價格影響的時空效應分析,浙江大學.
郭鈮. (2003). 植被指數及其研究進展. 乾旱氣象, 21(004), 71-75.
錢銘傑. RVI與NDVI在植被信息提取中的應用比較. 第七屆ArcGIS暨ERDAS中國用戶大會.
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姚堯, 任書良, 王君毅, & 關慶鋒. (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.
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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.
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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=222391
http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson1-2/blackbody.html
https://lbs.amap.com/api/webservice/download
https://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-Yuanen_US
dc.contributor.author (Authors) 葛思昊zh_TW
dc.contributor.author (Authors) Ge, Si-Haoen_US
dc.creator (作者) 葛思昊zh_TW
dc.creator (作者) Ge, Si-Haoen_US
dc.date (日期) 2021en_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) G0107257034en_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) 107257034zh_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 VI
Abstract 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/#G0107257034en_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 cityen_US
dc.subject (關鍵詞) Hedonic price modelen_US
dc.subject (關鍵詞) ESDAen_US
dc.subject (關鍵詞) Remote sensingen_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 estimationen_US
dc.type (資料類型) thesisen_US
dc.relation.reference (參考文獻) 一、外文參考文獻
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三、網頁參考
http://hzzjxh.com/news.php?id=222391
http://gsp.humboldt.edu/OLM/Courses/GSP_216_Online/lesson1-2/blackbody.html
https://lbs.amap.com/api/webservice/download
https://zhuanlan.zhihu.com/p/100993681
zh_TW
dc.identifier.doi (DOI) 10.6814/NCCU202101278en_US