政大機構典藏-National Chengchi University Institutional Repository(NCCUR):Item 140.119/137030
English  |  正體中文  |  简体中文  |  Post-Print筆數 : 27 |  Items with full text/Total items : 113822/144841 (79%)
Visitors : 51826723      Online Users : 549
RC Version 6.0 © Powered By DSPACE, MIT. Enhanced by NTU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
    •  Adv. Search
    Home ‧ Login ‧ Upload ‧ Help ‧ About ‧ Administer Goto mobile version


    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/137030


    Title: 遙測指標在房價估算中的應用
    Applications of remote sensing indices for property price estimation
    Authors: 葛思昊
    Ge, Si-Hao
    Contributors: 林士淵
    Lin, Shih-Yuan
    葛思昊
    Ge, Si-Hao
    Keywords: 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
    Date: 2021
    Issue Date: 2021-09-02 17:28:34 (UTC+8)
    Abstract: 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.
    Reference: 一、外文參考文獻
    Anselin, L. (1995). "Local indicators of spatial association—LISA." Geographical analysis 27(2): 93-115.
    As-syakur, A., Adnyana, I., Arthana, I. W., & Nuarsa, I. W. (2012). Enhanced built-up and bareness index (EBBI) for mapping built-up and bare land in an urban area. Remote Sensing, 4(10), 2957-2970.
    Bannari, A., Morin, D., Bonn, F., & Huete, A. (1995). A review of vegetation indices. Remote sensing reviews, 13(1-2), 95-120.
    Cannaday, R. E., & Sunderman, M. A. (1986). Estimation of depreciation for single‐family appraisals. Real Estate Economics, 14(2), 255-273.
    Cristóbal, J., Jiménez‐Muñoz, J. C., Sobrino, J. A., Ninyerola, M., & Pons, X. (2009). Improvements in land surface temperature retrieval from the Landsat series thermal band using water vapor and air temperature. Journal of Geophysical Research: Atmospheres, 114(D8).
    Cyr, L. (1993). Apport des indices de végétation pour l`évaluation de la couverture du sol en vue d`une modélisation spatiale de l`érosion. Memorire de maitrise en teledetection, Dept. de geographie et teledetection, Univ. de Sherbrooke, 1-160.
    Ellis, C. D., et al. (2006). "Retail land use, neighborhood satisfaction and the urban forest: an investigation into the moderating and mediating effects of trees and shrubs." Landscape and Urban Planning 74(1): 70-78.
    Escobedo, F. J., et al. (2015). "Urban forest structure effects on property value." Ecosystem Services 12: 209-217.
    Forster, B. (1983). "Some urban measurements from Landsat data." Photogrammetric Engineering and Remote Sensing 49: 1693-1707.
    Franco, S. F. and J. L. Macdonald (2018). "Measurement and valuation of urban greenness: Remote sensing and hedonic applications to Lisbon, Portugal." Regional Science and Urban Economics 72: 156-180.
    Getis, A. and J. Ord (1992). "The Analysis of Spatial Association by Use of Distance Statistics." Geographical analysis 24(3).
    Hamilton, S. E. and A. Morgan (2010). "Integrating lidar, GIS and hedonic price modeling to measure amenity values in urban beach residential property markets." Computers, Environment and Urban Systems 34(2): 133-141.
    Holben, B., & Fraser, R. S. (1984). Red and near-infrared sensor response to off-nadiir viewing. International Journal of Remote Sensing, 5(1), 145-160.
    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.
    Moran, P. A. P. (1950). "Notes on Continuous Stochastic Phenomena". Biometrika. 37 (1): 17–23.
    Nivens, H. D., et al. (2002). "Using satellite imagery in predicting Kansas farmland values." Journal of Agricultural and Resource Economics: 464-480.
    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.
    Kawamura, M., Jayamanna, S., & Tsujiko, Y. (1997). Quantitative evaluation of urbanization in developing countries using satellite data. Doboku Gakkai Ronbunshu, 1997(580), 45-54.
    Rosen, S. (1974). Hedonic prices and implicit markets: product differentiation in pure competition. Journal of political economy, 82(1), 34-55.
    Rossetti, J. and P. Raschky (2013). Valuation Of Australia’s Green Infrastructure: Hedonic Pricing Model Using The Enhanced Vegetation Index 34.
    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.
    So, H. M., Tse, R. Y., & Ganesan, S. (1997). 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." 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.
    饒萍, 王建力, & 王勇. (2014). 基於多特徵決策樹的建設用地信息提取. 農業工程學報, 30(012), 233-240.
    田慶久, & 閔祥軍. (1998). 植被指數研究進展. 地球科學進展, 13(4), 327-333.
    肖歷一. (2016). 房地產批量評估技術的應用分析. 中國房地產（學術版）, 000(003), 31-36.
    徐涵秋, & 杜麗萍. (2010). 遙感建築用地信息的快速提取. 地球信息科學學報, 012(004), 574-579.
    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.
    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=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
    Description: 碩士
    國立政治大學
    地政學系
    107257034
    Source URI: http://thesis.lib.nccu.edu.tw/record/#G0107257034
    Data Type: thesis
    DOI: 10.6814/NCCU202101278
    Appears in Collections:[Department of Land Economics] Theses

    Files in This Item:

    File Description SizeFormat
    703401.pdf5992KbAdobe PDF20View/Open


    All items in 政大典藏 are protected by copyright, with all rights reserved.


    社群 sharing

    著作權政策宣告 Copyright Announcement
    1.本網站之數位內容為國立政治大學所收錄之機構典藏，無償提供學術研究與公眾教育等公益性使用，惟仍請適度，合理使用本網站之內容，以尊重著作權人之權益。商業上之利用，則請先取得著作權人之授權。
    The digital content of this website is part of National Chengchi University Institutional Repository. It provides free access to academic research and public education for non-commercial use. Please utilize it in a proper and reasonable manner and respect the rights of copyright owners. For commercial use, please obtain authorization from the copyright owner in advance.

    2.本網站之製作，已盡力防止侵害著作權人之權益，如仍發現本網站之數位內容有侵害著作權人權益情事者，請權利人通知本網站維護人員(nccur@nccu.edu.tw)，維護人員將立即採取移除該數位著作等補救措施。
    NCCU Institutional Repository is made to protect the interests of copyright owners. If you believe that any material on the website infringes copyright, please contact our staff(nccur@nccu.edu.tw). We will remove the work from the repository and investigate your claim.
    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library IR team Copyright ©   - Feedback