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題名 影響不動產流動性因素之探討-以台北巿為例
Determinants of Liquidity in Real Estate Markets作者 黃浿綸
Huang, Pei-Lun貢獻者 林左裕
Lin, Tso-Yu
黃浿綸
Huang, Pei-Lun關鍵詞 二元邏輯特
複迴歸
流動性
住宅產品
住宅巿場
成交
不成交
housing transactions
Binary logistic
multiple regression
liquidity
housing product
housing market
failed housing transactions日期 2022 上傳時間 2-九月-2022 15:18:23 (UTC+8) 摘要 本研究擬探討不動產住宅巿場成交與否及住宅巿場之流動性變動受何種因素影響且哪些因素為顯著之影響。因而以台北巿12個行政區不動產之資料進行篩選，選擇住宅巿場產品中之公寓、華廈及大樓作為實證研究對象，以瞭解影響住宅巿場之成交、不成交及住宅巿場流動性之因素受何種影響較深。其中以二元邏輯特迴歸模型分析影響住宅巿場成交與否之因素項目；以複迴歸模型探討影響住宅巿場影響流動性因素之原因。實證結果發現，在二元邏輯特迴歸分析中，住宅巿場成交與否之結果顯示出寸土寸金的大台北地區，民眾在預算有限下，價格仍為考量之因素，以「建物總面積」、「有無車位」等具有顯著性；若注重住宅之寧適性、管理狀況者則會購買大樓或華廈之產品。在住宅巿場流動性部分，以成交案例中的銷售天數來研究並以複迴歸模型分析，實證結果發現，平均銷售天數為103天，銷售天數主要集中於100天以下為大宗，100天以內的銷售天數其議價率區間範圍為百分之零至百分之四十左右。與流動性相關之因素為「總面積」、「議價率」、「總樓層」、「屋齡」、「是否有車位」及「營建類股價指數」等因素具有顯著性。最後，本研究建議未來可建立住宅巿場的流動性指數可與房價指數作為搭配互為參考，將可提高政府部門或相關研究人員運用數據時可提高資料之準確性。。
This research examines the types of factors affecting housing market transactions and liquidity changes and identifies which factors are of significance. This is conducted by screening real estate data from 12 administrative districts in Taipei City, followed by selecting the apartments, condominiums, and high-rise apartments among the products of the housing market as empirical research subjects to understand which factors are of greater influence. Binary logistic regression model is used to analyze the influencing factors of housing market transactions; multiple regression model is used to examine the reasons affecting the housing market liquidity.Empirical results from the binary logistic regression analysis show that housing market transactions in the greater Taipei area where every inch of the land is worth an inch of gold, for members of the public who have limited budget, price is the factor for consideration. The “total building area” and “car space availability” are of significance in the consideration. For those who place importance on the amenities and management, they will purchase high-rise apartments or condominium products.On housing market liquidity, research is based on the number of days the property is on the market (Days on Market, DOM) obtained from successful cases and analysis was conducted using the multiple regression model. Empirical results show that the average DOM was 103 days. DOM for bulk products is mainly concentrated under 100 days and their price negotiation scope is from 0% to around 40%. Related factors to liquidity, such as “total area,” “price negotiation rate,” “total number of stories,” “housing age,” “parking space availability” and “construction index” are factors of significance.Lastly, this research suggests that in the future the housing market liquidity index and the house price index can be matched for mutual reference. This can enhance accuracy of information when the government departments or related researchers are using the data.參考文獻 一、 中文部分專書王正華、陳寛裕，2021「論文統計分析實務SPSS與AMOS的運用」，五南出版社。林森田，2010，「土地經濟學」，巨流政大書城。林左裕， (2018)，「 不動產投資管理」， 智勝文化事業有限公司。吳明隆，2021，「SPSS操作與應用：多變量分析實務」，五南圖書出版股份有限公司。邊泰明，2011，「土地使用規劃與財產權」，詹氏書局。期刊林左裕，2019，「應用網路搜尋行為預測房地產市場」， 應用經濟論叢， (105)， 219-254。林左裕、 程于芳，2014，「 影響不動產市場之從眾行為與總體經濟因素之研究」，應用經濟論叢， (95)， 61-99。林進益、 林元興，2019，「 不動產資訊科技發展現況與今後問題」，土地問題研究季刊，18(1)， 12-22。林進益，2019，「住宅市場的搜尋理論」，土地問題研究季刊，18(3)，2-13。林元興，2018，「不動產仲介業的國際比較」， 土地問題研究季刊，17(3)， 2-13。李秀蘭、林元興，2018，「藉大數據以提升土地利用的效率」， 土地問題研究季刊， 17(1)， 14-25。李春長、 張金鶚，1996，「房地產仲介市場賣方訂價與成交價和銷售期間關係之研究」， 經濟論文， 24， 591-616。李泓見、 張金鶚、花敬群，1996，「台北都會區不同住宅類型價差之研究」， Journal of Taiwan Land Research, 9(1)，63-87。李春長，2008，「資訊揭露，信任，搜尋成本對委託房屋仲介業售屋意願之實證研究-以高雄市為例」，住宅學報，17(1)，71-104。李春長、張金鶚、林祖嘉，1996，「房屋交易巿場上銷售期間之研究,存活模型之應用」，國家科學委員會研究業刊:人文及社會科學，7(3),420-437。吳森田，1994，「所得、 貨幣與房價-近二十年台北地區的觀察」，住宅學報， (2)， 49-65。范清益，2010，「買屋賣屋「殺」很大！－議價空間與住宅不動產市場流動性 之影響因素分析」，土地問題研究季刊，9(3)，82-91。高慈敏，2014，「經濟波動與房地產交易之價量關係：搜索模型之應用」，住宅學報，23(2)，21-56。陳彥仲、 陳佳欣，2002，「都市土地使用條件對住宅市場流動性之邊際影響效果」，都市與計劃， 29(1)，67-87。張欣民、陳奉瑤，2007，「不動產網站對不動產仲介業產生「去中介化」之研究」，第11屆科際整合管理研討會，295-316。彭建文、康尚德，2001，「網際網路對不動產仲介經營績效影響分析」，都巿與計劃，28(2)，171-186。楊宗憲，2014，「議價空間與住宅次市場關係之研究」 國立屏東商業技術學院學報，16，277-290。廖仲仁、張金鶚，2008，「仲介服務對於住宅價格搜尋之影響」，Journal of City and Planning，35(2)， 155-173。二、 英文文獻Cubbin , John., (1974),“Price, Quality, and Selling Time in the Housing Mark”, Applied Economics, 6(3), 171-187.Famiglietti, Matthew., and Aaron Hedlund., (2020),“The Geography of Housing Market Liquidity During the Great Recession”, Available at SSRN 3587686.et, Applied Economics,6(3), 171-187.Forgey, Fred A., Ronald C. Rutherford., and Thomas M. Springer., (1996),“ Search and Liquidity in Single‐family Housing” , Real Estate Economics, 24(3), 273-292.Garmaise, Mark J., and Tobias J. Moskowitz., (2004), “Confronting Information Asymmetries: Evidence from Real Estate Markets.”, The Review of Financial Studies, 17(2), 405-437.Hirshleifer, Jack., (1971),“ Liquidity, Uncertainty and the Accumulation of Information” , Western management Science Institute, University of California.Kluger, B. D., and Miller, N. G. (1990), “Measuring residential real estate liquidity”, Real Estate Economics, 18(2), 145-159.Kalra, R., and Chan, K., (1994), “Censored Sample Bias, Macroeconomic Factors, and Time on Market of Residential Housing” , Journal of Real Estate Research, 9(2), 253-262.Krainer, J. ,(2001),“ A Theory of Liquidity in Residential Real Estate Markets”, Journal of urban Economics, 49(1), 32-53.Lippman, S. A., and McCall, J., (1986),“An Operational Measure of Liquidity” , The American Economic Review, 76(1), 43-55.Leung, Charla Ka Yui, and Jun Zhang., (2007), “Housing Markets with Competitive Search”, The City University of Hong Kong and The Chinese University of Hong Kong,1-18.Stigler, G. J., (1961),“ The Economics of Information” , Journal of political economy, 69(3), 213-225.Yavas, Abdullah., (1994a),“ Economics of Brokerage: An Overview”, Journal of Real Estate Literature, 2(2), 169-195.Yang, Shiawee., and Yavas, Abdullah.,(1995),“Bigger is not Better: Brokerage and Time on the Market”, Journal of Real Estate Research , 10(1), 23-33.Wu, J., and Y. Deng., (2015),“Intercity Information Diffusion and Price Discovery in Housing Markets: Evidence from Google Searches” , The Journal of Real Estate and Finance Economics, 50, 289-306.Wu, Lynn., and Erik Brynjolfsson., (2015), “The Future of Prediction: How Google Searches Foreshadow Housing Prices and Sales” , Economic analysis of the digital economy , University of Chicago Press,89-118.Wang, Y., and Zhao, L. (2022), “Credit Policy and Housing Market Liquidity: An Empirical Study in Beijing Based on the TVP-VAR Model”, International Journal of Crowd Science, 6(1), 44-52. 描述 碩士
國立政治大學
地政學系碩士在職專班
107923016資料來源 http://thesis.lib.nccu.edu.tw/record/#G0107923016 資料類型 thesis dc.contributor.advisor 林左裕 zh_TW dc.contributor.advisor Lin, Tso-Yu en_US dc.contributor.author (作者) 黃浿綸 zh_TW dc.contributor.author (作者) Huang, Pei-Lun en_US dc.creator (作者) 黃浿綸 zh_TW dc.creator (作者) Huang, Pei-Lun en_US dc.date (日期) 2022 en_US dc.date.accessioned 2-九月-2022 15:18:23 (UTC+8) - dc.date.available 2-九月-2022 15:18:23 (UTC+8) - dc.date.issued (上傳時間) 2-九月-2022 15:18:23 (UTC+8) - dc.identifier (其他 識別碼) G0107923016 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/141703 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 地政學系碩士在職專班 zh_TW dc.description (描述) 107923016 zh_TW dc.description.abstract (摘要) 本研究擬探討不動產住宅巿場成交與否及住宅巿場之流動性變動受何種因素影響且哪些因素為顯著之影響。因而以台北巿12個行政區不動產之資料進行篩選，選擇住宅巿場產品中之公寓、華廈及大樓作為實證研究對象，以瞭解影響住宅巿場之成交、不成交及住宅巿場流動性之因素受何種影響較深。其中以二元邏輯特迴歸模型分析影響住宅巿場成交與否之因素項目；以複迴歸模型探討影響住宅巿場影響流動性因素之原因。實證結果發現，在二元邏輯特迴歸分析中，住宅巿場成交與否之結果顯示出寸土寸金的大台北地區，民眾在預算有限下，價格仍為考量之因素，以「建物總面積」、「有無車位」等具有顯著性；若注重住宅之寧適性、管理狀況者則會購買大樓或華廈之產品。在住宅巿場流動性部分，以成交案例中的銷售天數來研究並以複迴歸模型分析，實證結果發現，平均銷售天數為103天，銷售天數主要集中於100天以下為大宗，100天以內的銷售天數其議價率區間範圍為百分之零至百分之四十左右。與流動性相關之因素為「總面積」、「議價率」、「總樓層」、「屋齡」、「是否有車位」及「營建類股價指數」等因素具有顯著性。最後，本研究建議未來可建立住宅巿場的流動性指數可與房價指數作為搭配互為參考，將可提高政府部門或相關研究人員運用數據時可提高資料之準確性。。 zh_TW dc.description.abstract (摘要) This research examines the types of factors affecting housing market transactions and liquidity changes and identifies which factors are of significance. This is conducted by screening real estate data from 12 administrative districts in Taipei City, followed by selecting the apartments, condominiums, and high-rise apartments among the products of the housing market as empirical research subjects to understand which factors are of greater influence. Binary logistic regression model is used to analyze the influencing factors of housing market transactions; multiple regression model is used to examine the reasons affecting the housing market liquidity.Empirical results from the binary logistic regression analysis show that housing market transactions in the greater Taipei area where every inch of the land is worth an inch of gold, for members of the public who have limited budget, price is the factor for consideration. The “total building area” and “car space availability” are of significance in the consideration. For those who place importance on the amenities and management, they will purchase high-rise apartments or condominium products.On housing market liquidity, research is based on the number of days the property is on the market (Days on Market, DOM) obtained from successful cases and analysis was conducted using the multiple regression model. Empirical results show that the average DOM was 103 days. DOM for bulk products is mainly concentrated under 100 days and their price negotiation scope is from 0% to around 40%. Related factors to liquidity, such as “total area,” “price negotiation rate,” “total number of stories,” “housing age,” “parking space availability” and “construction index” are factors of significance.Lastly, this research suggests that in the future the housing market liquidity index and the house price index can be matched for mutual reference. This can enhance accuracy of information when the government departments or related researchers are using the data. en_US dc.description.tableofcontents 第一章 緒論 1第一節 研究動機與目的 1第二節 研究範圍與方法 5第三節 研究架構與流程 8第二章 文獻回顧 10第一節 搜尋理論、流動性理論與網際網路 10第二節 影響流動性/銷售期間之因子 19第三章 研究設計 28第一節 研究方法及研究模型 28第二節 資料說明及變數選取 36第四章 實證結果與分析 44第一節 影響成交因素結果之分析 44第二節 影響不動產住宅巿場流動性因素分析 54第五章 結論與建議 64第一節 結論 64第二節 建議 70參考文獻 72 zh_TW dc.format.extent 2380738 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0107923016 en_US dc.subject (關鍵詞) 二元邏輯特 zh_TW dc.subject (關鍵詞) 複迴歸 zh_TW dc.subject (關鍵詞) 流動性 zh_TW dc.subject (關鍵詞) 住宅產品 zh_TW dc.subject (關鍵詞) 住宅巿場 zh_TW dc.subject (關鍵詞) 成交 zh_TW dc.subject (關鍵詞) 不成交 zh_TW dc.subject (關鍵詞) housing transactions en_US dc.subject (關鍵詞) Binary logistic en_US dc.subject (關鍵詞) multiple regression en_US dc.subject (關鍵詞) liquidity en_US dc.subject (關鍵詞) housing product en_US dc.subject (關鍵詞) housing market en_US dc.subject (關鍵詞) failed housing transactions en_US dc.title (題名) 影響不動產流動性因素之探討-以台北巿為例 zh_TW dc.title (題名) Determinants of Liquidity in Real Estate Markets en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 一、 中文部分專書王正華、陳寛裕，2021「論文統計分析實務SPSS與AMOS的運用」，五南出版社。林森田，2010，「土地經濟學」，巨流政大書城。林左裕， (2018)，「 不動產投資管理」， 智勝文化事業有限公司。吳明隆，2021，「SPSS操作與應用：多變量分析實務」，五南圖書出版股份有限公司。邊泰明，2011，「土地使用規劃與財產權」，詹氏書局。期刊林左裕，2019，「應用網路搜尋行為預測房地產市場」， 應用經濟論叢， (105)， 219-254。林左裕、 程于芳，2014，「 影響不動產市場之從眾行為與總體經濟因素之研究」，應用經濟論叢， (95)， 61-99。林進益、 林元興，2019，「 不動產資訊科技發展現況與今後問題」，土地問題研究季刊，18(1)， 12-22。林進益，2019，「住宅市場的搜尋理論」，土地問題研究季刊，18(3)，2-13。林元興，2018，「不動產仲介業的國際比較」， 土地問題研究季刊，17(3)， 2-13。李秀蘭、林元興，2018，「藉大數據以提升土地利用的效率」， 土地問題研究季刊， 17(1)， 14-25。李春長、 張金鶚，1996，「房地產仲介市場賣方訂價與成交價和銷售期間關係之研究」， 經濟論文， 24， 591-616。李泓見、 張金鶚、花敬群，1996，「台北都會區不同住宅類型價差之研究」， Journal of Taiwan Land Research, 9(1)，63-87。李春長，2008，「資訊揭露，信任，搜尋成本對委託房屋仲介業售屋意願之實證研究-以高雄市為例」，住宅學報，17(1)，71-104。李春長、張金鶚、林祖嘉，1996，「房屋交易巿場上銷售期間之研究,存活模型之應用」，國家科學委員會研究業刊:人文及社會科學，7(3),420-437。吳森田，1994，「所得、 貨幣與房價-近二十年台北地區的觀察」，住宅學報， (2)， 49-65。范清益，2010，「買屋賣屋「殺」很大！－議價空間與住宅不動產市場流動性 之影響因素分析」，土地問題研究季刊，9(3)，82-91。高慈敏，2014，「經濟波動與房地產交易之價量關係：搜索模型之應用」，住宅學報，23(2)，21-56。陳彥仲、 陳佳欣，2002，「都市土地使用條件對住宅市場流動性之邊際影響效果」，都市與計劃， 29(1)，67-87。張欣民、陳奉瑤，2007，「不動產網站對不動產仲介業產生「去中介化」之研究」，第11屆科際整合管理研討會，295-316。彭建文、康尚德，2001，「網際網路對不動產仲介經營績效影響分析」，都巿與計劃，28(2)，171-186。楊宗憲，2014，「議價空間與住宅次市場關係之研究」 國立屏東商業技術學院學報，16，277-290。廖仲仁、張金鶚，2008，「仲介服務對於住宅價格搜尋之影響」，Journal of City and Planning，35(2)， 155-173。二、 英文文獻Cubbin , John., (1974),“Price, Quality, and Selling Time in the Housing Mark”, Applied Economics, 6(3), 171-187.Famiglietti, Matthew., and Aaron Hedlund., (2020),“The Geography of Housing Market Liquidity During the Great Recession”, Available at SSRN 3587686.et, Applied Economics,6(3), 171-187.Forgey, Fred A., Ronald C. Rutherford., and Thomas M. Springer., (1996),“ Search and Liquidity in Single‐family Housing” , Real Estate Economics, 24(3), 273-292.Garmaise, Mark J., and Tobias J. Moskowitz., (2004), “Confronting Information Asymmetries: Evidence from Real Estate Markets.”, The Review of Financial Studies, 17(2), 405-437.Hirshleifer, Jack., (1971),“ Liquidity, Uncertainty and the Accumulation of Information” , Western management Science Institute, University of California.Kluger, B. D., and Miller, N. G. (1990), “Measuring residential real estate liquidity”, Real Estate Economics, 18(2), 145-159.Kalra, R., and Chan, K., (1994), “Censored Sample Bias, Macroeconomic Factors, and Time on Market of Residential Housing” , Journal of Real Estate Research, 9(2), 253-262.Krainer, J. ,(2001),“ A Theory of Liquidity in Residential Real Estate Markets”, Journal of urban Economics, 49(1), 32-53.Lippman, S. A., and McCall, J., (1986),“An Operational Measure of Liquidity” , The American Economic Review, 76(1), 43-55.Leung, Charla Ka Yui, and Jun Zhang., (2007), “Housing Markets with Competitive Search”, The City University of Hong Kong and The Chinese University of Hong Kong,1-18.Stigler, G. J., (1961),“ The Economics of Information” , Journal of political economy, 69(3), 213-225.Yavas, Abdullah., (1994a),“ Economics of Brokerage: An Overview”, Journal of Real Estate Literature, 2(2), 169-195.Yang, Shiawee., and Yavas, Abdullah.,(1995),“Bigger is not Better: Brokerage and Time on the Market”, Journal of Real Estate Research , 10(1), 23-33.Wu, J., and Y. Deng., (2015),“Intercity Information Diffusion and Price Discovery in Housing Markets: Evidence from Google Searches” , The Journal of Real Estate and Finance Economics, 50, 289-306.Wu, Lynn., and Erik Brynjolfsson., (2015), “The Future of Prediction: How Google Searches Foreshadow Housing Prices and Sales” , Economic analysis of the digital economy , University of Chicago Press,89-118.Wang, Y., and Zhao, L. (2022), “Credit Policy and Housing Market Liquidity: An Empirical Study in Beijing Based on the TVP-VAR Model”, International Journal of Crowd Science, 6(1), 44-52. zh_TW dc.identifier.doi (DOI) 10.6814/NCCU202201184 en_US
