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題名 住宅次市場間動態關係之探討 -以臺北市為例
Dynamics of the Taipei Residential Housing Submarket作者 吳侲嶢
Wu, Chen-Yao貢獻者 朱芳妮
Chu, Fang-Ni
吳侲嶢
Wu, Chen-Yao關鍵詞 次市場
動態關係
房價擴散
漣漪效應
Submarket
Dynamics
Housing Price Diffusion
Ripple Effect日期 2024 上傳時間 4-Sep-2024 14:27:30 (UTC+8) 摘要 居住是每個人的基本需求，但隨著國內房地產市場價格近年來直線飆漲，買不起房已成為現今的普遍現象。面對高房價問題，我國政府也試圖透過政策抑制房價上漲，可成果卻不如預期。臺北市作為臺灣的首都，平均住宅價格長年位居全台之首，也是投資者青睞的區域。在國內過往的研究中，大多數都是探討空間或區域次市場的房價擴散關係，少有對於產品次市場之探討，然而產品次市場間的動態關係對於瞭解住宅市場變化也是極為重要的，因此本研究將以臺北市各行政區與三種住宅產品類型之住宅價格指數進行時間序列模型實證分析，藉由同時探討兩種不同類型次市場之動態關係，瞭解住宅各次市場間價格之傳遞關係。 研究結果顯示，臺北市不同住宅產品(公寓、大樓及小宅)次市場間存在長期均衡關係，三者間有相互之動態關係。小宅、公寓對大樓之房價指數為單向因果關係，且其中小宅最容易受到外生變數之影響。而臺北市行政區空間次市場間之住宅價格，由大安區作為中心點，其正向房價衝擊會對其他行政區有正向之影響，顯著影響之時間點則因地理位置之不同而有所差異，即行政區間有漣漪效應之產生，本研究根據實證結果之分析，發現不論是以住宅產品類型劃分次市場，或以行政區劃分空間次市場，次市場之間具有價格動態影響關係。
Having house is a basic need for everyone, but the domestic housing price has skyrocketed in recent years, the inability to afford a home has become a common phenomenon today.In order to cap rising housing prices, our government has introduced many housing policies, but the results have been less than expected. As the capital of Taiwan, Taipei City has long held the highest average housing prices in the country and it’s also a favored area for investors. In past domestic research, most experts have explored the diffusion of spatial or regional submarkets, with little discussion on the product submarket. However, the dynamic relationships of the product submarket are also crucial for understanding the housing market. Therefore, this study will conduct an empirical analysis of time series models on the housing price indices of various administrative districts and three types of residential products in Taipei City. By simultaneously exploring the dynamic relationships between two different types of submarkets, this study aims to understand the price diffusion relationships among different submarkets. The results of the study show that there is a long-term equilibrium relationship between different types of residential product submarkets (apartments, buildings, and small houses) in Taipei City. There are dynamic interactions among these three types. The housing price indices of small houses and apartments tend to influence the housing price index of buildings, and the small houses being the most susceptible to exogenous variables. According to the district empirical results, when Da'an District is the center of the housing prices ripple. It’s positive housing price shock has also caused positive housing price responses from other districts, but the timing of these significant impacts varies are based on geographical location, indicating a ripple effect among the districts. Based on the empirical analysis, this study finds that whether submarkets are divided by residential product types or by administrative districts, there are dynamic price relationships between submarkets.參考文獻 謝博明，2015，「住宅次市場界定及住宅價格空間分析：以新升格之台南市為例」， 『住宅學報』，24(1) : 29-54。 林秋瑾，1996，「臺灣區域性住宅價格模式之建立」，『政大地政學報』，1(1):29-49。 楊宗憲、蕭喻方、張毓蓁、林佩佳、陳巧倫、邱宜芬、林婉茹，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:p277-290。 蔡怡純、陳明吉，2008，「台北地區不動產價格波動之不對稱性探討」，『住宅學 報』，17(2):1-11。 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。 花敬群、張金鶚，1999，「住宅空間次市場價格比例與市場規模之關係」，『都市 與計劃』，26(1):79-94。 朱芳妮、張金鶚、陳淑美，2008，「已購屋者與購屋搜尋者之購屋需求決策比較 分析—兼論顯示性偏好及敘述性偏好之差異」，『都市與計畫』，35(4):339-359。 李泓見、張金鶚、花敬群，2006，「台北都會區不同住宅產品價差之研究」，『土 地問題研究』，9(1):63-87。 陳彥仲，1997，「住宅選擇之程序性決策模式」，『住宅學報』， 5 :37-49。 林祖嘉、林素菁，2009，「住宅次市場定義合理性之探討：因素分析法之應用」， 『都市與計劃』，36(2):133-153。 汪芷均，2019，「臺灣北部區域房價傳遞之研究─以房價漣漪效應觀點」，國立政 治大學地政學研究所碩士論文。 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。 林秋瑾、黃珮玲，1995，「住宅價格與總體經濟變數關係之研究—以向量自我迴 歸模式 (VAR) 進行實證」，『政大學報』，71:143-160。 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。 梅強、林尚毅，2017，「臺灣總體經濟變動資料對六都房價之影響分析」，『亞太 經濟管理評論』，21(1):33-48。 Kounin, J. (1977). Discipline and group management. Nova Iorque: RE Krieger Publishing. Adair, A., McGreal, S., Smyth, A., Cooper, J., & Ryley, T. (2000). House prices and accessibility: The testing of relationships within the Belfast urban area. Housing studies, 15(5), 699-716. Aksoy Khurami, E., & Özdemir Sarı, Ö. B. (2022). Trends in housing markets during the economic crisis and COVID-19 pandemic: Turkish case. Asia-Pacific Journal of Regional Science, 6(3), 1159-1175. Allen, M. T., Springer, T. M., & Waller, N. G. (1995). Implicit pricing across residential rental submarkets. The Journal of Real Estate Finance and Economics, 11, 137- 151. Bangura, M., & Lee, C. L. (2020). House price diffusion of housing submarkets in Greater Sydney. Housing Studies, 35(6), 1110-1141. Berg, L. (2002). Prices on the second-hand market for Swedish family houses: correlation, causation and determinants. European Journal of housing policy, 2(1), 1-24. Bhavsar, V. (2023). Investigating house price diffusion across eight major cities of India. Journal of Housing and the Built Environment, 38(2), 1241-1261. Bourassa, S. C., Hoesli, M., & Peng, V. S. (2003). Do housing submarkets really matter?. Journal of Housing Economics, 12(1), 12-28. Chen, P. F., Chien, M. S., & Lee, C. C. (2011). Dynamic modeling of regional house price diffusion in Taiwan. Journal of Housing Economics, 20(4), 315-332. Chen, M., Chun, Y., & Griffith, D. A. (2023). Delineating housing submarkets using space–time house sales data: spatially constrained data-driven approaches. Journal of Risk and Financial Management, 16(6), 291. Chow, G. C., & Lin, A. L. (1971). Best linear unbiased interpolation, distribution, and extrapolation of time series by related series. The review of Economics and Statistics, 372-375. Clapp, J. M., & Tirtiroglu, D. (1994). Positive feedback trading and diffusion of asset price changes: Evidence from housing transactions. Journal of Economic Behavior & Organization, 24(3), 337-355. Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a), 427-431. Duca, J. V., Hoesli, M., & Montezuma, J. (2021). The resilience and realignment of house prices in the era of Covid-19. Journal of European Real Estate Research, 14(3), 421-431. Engle, R. F., & Granger, C. W. (1987). Co-integration and error correction: representation, estimation, and testing. Econometrica: journal of the Econometric Society, 251-276. Goodman, A. C., & Thibodeau, T. G. (1998). Housing market segmentation. Journal of housing economics, 7(2), 121-143. Gong, Y., Hu, J., & Boelhouwer, P. J. (2016). Spatial interrelations of Chinese housing markets: Spatial causality, convergence and diffusion. Regional Science and Urban Economics, 59, 103-117. Granger, C. W. (1969). Investigating causal relations by econometric models and cross-spectral methods. Econometrica: journal of the Econometric Society, 424- 438. Hui, H. C. (2011). Cycles in landed and non‐landed housing sub‐markets in Malaysia. International Journal of Housing Markets and Analysis, 4(2), 144-154. Johansen, S. (1988). Statistical analysis of cointegration vectors. Journal of economic dynamics and control, 12(2-3), 231-254. Johansen, S., & Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration—with appucations to the demand for money. Oxford Bulletin of Economics and statistics, 52(2), 169-210. Keskin, B., & Watkins, C. (2017). Defining spatial housing submarkets: Exploring the case for expert delineated boundaries. Urban Studies, 54(6), 1446-1462. Lee, C. C., & Chien, M. S. (2011). Empirical modelling of regional house prices and the ripple effect. Urban Studies, 48(10), 2029-2047. Lee, M. T., Lee, M. L., & Lin, S. H. (2014). Trend properties, cointegration, and diffusion of presale house prices in Taiwan: Can Taipei's house prices ripple out?. Habitat International, 44, 432-441. Liu, S., & Su, Y. (2021). The impact of the COVID-19 pandemic on the demand for density: Evidence from the US housing market. Economics letters, 207, 110010. Luo, Z. Q., Liu, C., & Picken, D. (2007). Housing price diffusion pattern of Australia's state capital cities. International Journal of Strategic Property Management, 11(4), 227-242. Mei-Se, C., Pei-Fen, C., & Te-Chung, H. (2015). House Price Diffusion and Cross- Border House Price Dynamics in the Greater China Economic Area. Meen, G. (1999). Regional house prices and the ripple effect: a new interpretation. Housing studies, 14(6), 733-753. McCord, M., Lo, D., McCord, J., Davis, P. T., & Haran, M. (2019). Measuring the cointegration of housing types in Northern Ireland. Journal of Property Research, 36(4), 343-366. McCord, M., Lo, D., McCord, J., Davis, P., Haran, M., & Turley, P. (2022). The impact of COVID-19 on house prices in Northern Ireland: price persistence, yet d ivergent?. Journal of Property Research, 39(3), 237-267. Pollakowski, H. O., & Ray, T. S. (1997). Housing price diffusion patterns at different aggregation levels: an examination of housing market efficiency. Journal of Housing Research, 107-124. Renaud, B., Zhang, M., & Koeberle, S. (1998). How the Thai real estate boom undid financial institutions: What can be done now. Competitiveness and Sustainable Economic Recovery in Thailand, 2, 103-151. Said, S. E., & Dickey, D. A. (1984). Testing for unit roots in autoregressive-moving average models of unknown order. Biometrika, 71(3), 599-607. Sims, C. A. (1980). Macroeconomics and reality. Econometrica: journal of the Econometric Society, 1-48. Teye, A. L., de Haan, J., & Elsinga, M. G. (2018). Risks and interrelationships of subdistrict house prices: the case of Amsterdam. Journal of Housing and the Built Environment, 33, 209-226. Tian, C., Peng, X., & Zhang, X. (2021). COVID-19 pandemic, urban resilience and real estate prices: the experience of cities in the Yangtze River Delta in China. Land, 10(9), 960. Tsai, I. C. (2018). The cause and outcomes of the ripple effect: housing prices and transaction volume. The Annals of Regional Science, 61(2), 351-373. Zhang, L., Hui, E. C., & Wen, H. (2017). The regional house prices in China: Ripple effect or differentiation. Habitat International, 67, 118-128. 描述 碩士
國立政治大學
地政學系
111257010資料來源 http://thesis.lib.nccu.edu.tw/record/#G0111257010 資料類型 thesis dc.contributor.advisor 朱芳妮 zh_TW dc.contributor.advisor Chu, Fang-Ni en_US dc.contributor.author (Authors) 吳侲嶢 zh_TW dc.contributor.author (Authors) Wu, Chen-Yao en_US dc.creator (作者) 吳侲嶢 zh_TW dc.creator (作者) Wu, Chen-Yao en_US dc.date (日期) 2024 en_US dc.date.accessioned 4-Sep-2024 14:27:30 (UTC+8) - dc.date.available 4-Sep-2024 14:27:30 (UTC+8) - dc.date.issued (上傳時間) 4-Sep-2024 14:27:30 (UTC+8) - dc.identifier (Other Identifiers) G0111257010 en_US dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/153249 - 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) 111257010 zh_TW dc.description.abstract (摘要) 居住是每個人的基本需求，但隨著國內房地產市場價格近年來直線飆漲，買不起房已成為現今的普遍現象。面對高房價問題，我國政府也試圖透過政策抑制房價上漲，可成果卻不如預期。臺北市作為臺灣的首都，平均住宅價格長年位居全台之首，也是投資者青睞的區域。在國內過往的研究中，大多數都是探討空間或區域次市場的房價擴散關係，少有對於產品次市場之探討，然而產品次市場間的動態關係對於瞭解住宅市場變化也是極為重要的，因此本研究將以臺北市各行政區與三種住宅產品類型之住宅價格指數進行時間序列模型實證分析，藉由同時探討兩種不同類型次市場之動態關係，瞭解住宅各次市場間價格之傳遞關係。 研究結果顯示，臺北市不同住宅產品(公寓、大樓及小宅)次市場間存在長期均衡關係，三者間有相互之動態關係。小宅、公寓對大樓之房價指數為單向因果關係，且其中小宅最容易受到外生變數之影響。而臺北市行政區空間次市場間之住宅價格，由大安區作為中心點，其正向房價衝擊會對其他行政區有正向之影響，顯著影響之時間點則因地理位置之不同而有所差異，即行政區間有漣漪效應之產生，本研究根據實證結果之分析，發現不論是以住宅產品類型劃分次市場，或以行政區劃分空間次市場，次市場之間具有價格動態影響關係。 zh_TW dc.description.abstract (摘要) Having house is a basic need for everyone, but the domestic housing price has skyrocketed in recent years, the inability to afford a home has become a common phenomenon today.In order to cap rising housing prices, our government has introduced many housing policies, but the results have been less than expected. As the capital of Taiwan, Taipei City has long held the highest average housing prices in the country and it’s also a favored area for investors. In past domestic research, most experts have explored the diffusion of spatial or regional submarkets, with little discussion on the product submarket. However, the dynamic relationships of the product submarket are also crucial for understanding the housing market. Therefore, this study will conduct an empirical analysis of time series models on the housing price indices of various administrative districts and three types of residential products in Taipei City. By simultaneously exploring the dynamic relationships between two different types of submarkets, this study aims to understand the price diffusion relationships among different submarkets. The results of the study show that there is a long-term equilibrium relationship between different types of residential product submarkets (apartments, buildings, and small houses) in Taipei City. There are dynamic interactions among these three types. The housing price indices of small houses and apartments tend to influence the housing price index of buildings, and the small houses being the most susceptible to exogenous variables. According to the district empirical results, when Da'an District is the center of the housing prices ripple. It’s positive housing price shock has also caused positive housing price responses from other districts, but the timing of these significant impacts varies are based on geographical location, indicating a ripple effect among the districts. Based on the empirical analysis, this study finds that whether submarkets are divided by residential product types or by administrative districts, there are dynamic price relationships between submarkets. 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 研究方法與研究流程 5 第二章 文獻回顧 7 第一節 住宅次市場之定義與重要性 7 第二節 住宅次市場之動態關係研究 9 第三節 空間次市場之價格傳遞效果 12 第三章 資料說明與研究設計 14 第一節 研究範圍 14 第二節 研究假說 15 第三節 資料說明 16 第四節 研究設計 26 第四章 實證結果分析 31 第一節 不同住宅產品間之動態關係 31 第三節 行政區間價格之傳遞效果 43 第五章 結論與建議 50 第一節 結論 50 第二節 後續研究建議 52 參考文獻 53 zh_TW dc.format.extent 3416618 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0111257010 en_US dc.subject (關鍵詞) 次市場 zh_TW dc.subject (關鍵詞) 動態關係 zh_TW dc.subject (關鍵詞) 房價擴散 zh_TW dc.subject (關鍵詞) 漣漪效應 zh_TW dc.subject (關鍵詞) Submarket en_US dc.subject (關鍵詞) Dynamics en_US dc.subject (關鍵詞) Housing Price Diffusion en_US dc.subject (關鍵詞) Ripple Effect en_US dc.title (題名) 住宅次市場間動態關係之探討 -以臺北市為例 zh_TW dc.title (題名) Dynamics of the Taipei Residential Housing Submarket en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) 謝博明，2015，「住宅次市場界定及住宅價格空間分析：以新升格之台南市為例」， 『住宅學報』，24(1) : 29-54。 林秋瑾，1996，「臺灣區域性住宅價格模式之建立」，『政大地政學報』，1(1):29-49。 楊宗憲、蕭喻方、張毓蓁、林佩佳、陳巧倫、邱宜芬、林婉茹，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:p277-290。 蔡怡純、陳明吉，2008，「台北地區不動產價格波動之不對稱性探討」，『住宅學 報』，17(2):1-11。 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。 花敬群、張金鶚，1999，「住宅空間次市場價格比例與市場規模之關係」，『都市 與計劃』，26(1):79-94。 朱芳妮、張金鶚、陳淑美，2008，「已購屋者與購屋搜尋者之購屋需求決策比較 分析—兼論顯示性偏好及敘述性偏好之差異」，『都市與計畫』，35(4):339-359。 李泓見、張金鶚、花敬群，2006，「台北都會區不同住宅產品價差之研究」，『土 地問題研究』，9(1):63-87。 陳彥仲，1997，「住宅選擇之程序性決策模式」，『住宅學報』， 5 :37-49。 林祖嘉、林素菁，2009，「住宅次市場定義合理性之探討：因素分析法之應用」， 『都市與計劃』，36(2):133-153。 汪芷均，2019，「臺灣北部區域房價傳遞之研究─以房價漣漪效應觀點」，國立政 治大學地政學研究所碩士論文。 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。 林秋瑾、黃珮玲，1995，「住宅價格與總體經濟變數關係之研究—以向量自我迴 歸模式 (VAR) 進行實證」，『政大學報』，71:143-160。 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。 梅強、林尚毅，2017，「臺灣總體經濟變動資料對六都房價之影響分析」，『亞太 經濟管理評論』，21(1):33-48。 Kounin, J. 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