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http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/16783Full metadata record
| ???org.dspace.app.webui.jsptag.ItemTag.dcfield??? | Value | Language |
|---|---|---|
| dc.contributor.advisor | 李顯峰(Hsien-Feng Lee) | |
| dc.contributor.author | Lin-Sheng Lee | en |
| dc.contributor.author | 李霖昇 | zh_TW |
| dc.date.accessioned | 2021-06-07T23:45:58Z | - |
| dc.date.copyright | 2014-07-08 | |
| dc.date.issued | 2014 | |
| dc.date.submitted | 2014-06-23 | |
| dc.identifier.citation | 中文部分
王恭棋 (2006)，「房價指數模型建構之研究--以桃竹地區市鎮交易資料為例」， 中央大學產業經濟研究所碩士在職專班學位論文。 吳欣憲、黃俊凱、石豐宇 (2008)，「高鐵營運後對台南都會區房價影響之研究」， 成功大學都市計畫學系會議論文。 李春長、童作君 (2010)，「住宅特徵價格模型之多層次分析」，《經濟論文叢刊》，第三十八卷第二期，頁289-325。 林祖嘉、林素菁 (1993)，「台灣地區環境品質與公共設施對房價與房租影響之分析」，《住宅學報》，第一期，頁21-45。 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》，第三十六卷第二期，頁133-153。 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》，第十六卷第二期，頁1-22。 施甫學 (2007)，「高密度發展對房價之影響-以台北市為例」，政治大學地政學系私立中國地政研究所碩士學位論文。 紀侑廷 (2014)，「科技園區周邊住宅房價影響因素之研究-以新竹科學園區為例」，中興大學應用經濟學研究所碩士學位論文。 張怡文 (2006)，「特徵價格法在住宅大量估價模型中的延伸—分量迴歸之應用」，政治大學地政學研究所碩士學位論文。 張怡文、江穎慧、張金鶚 (2009)，「分量迴歸在大量估價模型之應用-非典型住宅估價之改進」，《都市與計畫》，第三十六卷第三期，頁281-304。 張金鶚、陳明吉、鄧筱蓉、楊智元 (2009)，「台北市房價泡沫知多少?—房價 vs. 租金, 房價 vs. 所得」，《住宅學報》，第十八卷第二期，頁1-22。 許智淵 (2012)，「類神經網路技術與特徵價格法於台北市房價預測結果之比較研究」，臺北大學企業管理學系碩士學位論文。 曾建穎、張金鶚、花敬群 (2005)，「不同空間, 時間住宅租金與其房價關聯性之研究-台北地區之實證現象分析」，《住宅學報》，第十四卷第二期，頁 27-49。 游美姿 (2012)，「探討成人暴力型與非暴力型自殺之相關因素：多層次與時空變異分析」，台灣大學健康政策與管理研究所碩士學位論文。 黃佳鈴、張金鶚 (2005) 「從房地價格分離探討地價指數之建立」，《台灣土地研究》，第八卷第二期，頁73-106。 黃麗燕 (2008)，「資訊不對稱下不動產抵押貸款擔保品估價行為之研究」，成功大學都市計劃學系學位論文。 溫福星、邱皓政 (2009)，「組織研究中的多層次調節式中介效果: 以組織創新氣氛, 組織承諾與工作滿意的實證研究為例」，《管理學報》，第二十六卷第二期，頁189-211。 溫福星、邱皓政 (2012)，「多層次模型方法論: 階層線性模式的關鍵議題與試解」，新北市：前程文化。 廖咸興、張芳玲 (1997)，「不動產評價模式特徵價格法與逼近調整法之比較」， 《住宅學報》，第五期，頁17-35。 英文部分 Adelman, I., & Griliches, Z. (1961). On an index of quality change. Journal of the American Statistical Association, 56(295), 535-548. Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical analysis, 27(2), 93-115. Anselin, L. (2001). Spatial econometrics. A companion to theoretical econometrics, 310330: 1-60. Anselin, L., Sridharan, S., & Gholston, S. (2007). Using exploratory spatial data analysis to leverage social indicator databases: the discovery of interesting patterns. Social Indicators Research, 82(2), 287-309. Armstrong, R. J., & Rodriguez, D. A. (2006). An evaluation of the accessibility benefits of commuter rail in eastern Massachusetts using spatial hedonic price functions. Transportation, 33(1), 21-43. Brown, K. H., & Uyar, B. (2004). A hierarchical linear model approach for assessing the effects of house and neighborhood characteristics on housing prices. Journal of Real Estate Practice and Education, 7(1), 15-24. Bryk, A. S., & Raudenbush, S. W. (1992) Hierarchical linear models: Applications and data analysis methods. Newbury Park, CA: Sage. Cohen, J. (1988) Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Eribaum. Dale‐Johnson, D., & Phillips, G. M. (1984). Housing attributes associated with capital gain. Real Estate Economics, 12(2), 162-175. De Leeuw, J., Meijer, E., & Goldstein, H. (2008). Handbook of multilevel analysis: Springer. DiPasquale, D., & Wheaton, W. C. (1996). Urban economics and real estate markets (Vol. 23, No. 7). Englewood Cliffs, NJ: Prentice Hall. Fielding, A. (2004). The role of the Hausman test and whether higher level effects should be treated as random or fixed. Multilevel Modelling Newsletter, 16(2), 3-9. Follain, J. R., & Malpezzi, S. (1980). Dissecting housing value and rent: Estimates of hedonic indexes for thirty-nine large SMSAs. Washington, DC: Urban Institute. Goodman, A. C., & Thibodeau, T. G. (1998). Housing market segmentation. Journal of Housing Economics, 7(2), 121-143. Hofmann, D. A. (1997). An overview of the logic and rationale of hierarchical linear models. Journal of Management, 23(6), 723-744. Hofmann, D. A., & Gavin, M. B. (1998). Centering decisions in hierarchical linear models: Implications for research in organizations. Journal of Management, 24(5), 623-641. Jones, K., & Bullen, N. (1994). Contextual models of urban house prices: a comparison of fixed-and random-coefficient models developed by expansion. Economic Geography, 70(3), 252-272. Kreft, I. G., & De Leeuw, J. (1998). Introducing multilevel modeling. Newbury Park, CA: Sage. Kreft, I. G., De Leeuw, J., & Aiken, L. S. (1995). The effect of different forms of centering in hierarchical linear models. Multivariate Behavioral Research, 30(1), 1-21. Malpezzi, S. (2003). Hedonic pricing models: a selective and applied review. Section in Housing Economics and Public Policy: Essays in Honor of Duncan Maclennan. McMillen, D. P., & McDonald, J. (2004). Reaction of house prices to a new rapid transit line: Chicago's midway line, 1983–1999. Real Estate Economics, 32(3), 463-486. Pearson, K. (1901). LIII. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin Philosophical Magazine and Journal of Science, 2(11), 559-572. Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Newbury Park, CA : Sage. Rosen, S. (1974). Hedonic prices and implicit markets: product differentiation in pure competition. The Journal of Political Economy, 82(1), 34-55. Sirmans, S. G., Macpherson, D. A., & Zietz, E. N. (2005). The composition of hedonic pricing models. Journal of Real Estate Literature, 13(1), 1-44. Snijders, T. A. (2011). Multilevel analysis. US: Springer. Snijders, T. A., & Berkhof, J. (2008). Diagnostic checks for multilevel models Handbook of multilevel analysis (pp. 141-175). US: Springer. Snijders, T.A., & Bosker, R. (1999) Multilevel analysis: An introduction to basic and advanced multilevel modeling. London: Sage. Soderberg, B. (2002). A note on the hedonic model specification for income properties. In Real Estate Valuation Theory (pp. 157-180). US: Springer. Tiefelsdorf, M. (1998). Modelling spatial processes: The identification and analysis of spatial relationships in regression residuals by means of Moran's I (Germany). Uyar, B., & Brown, K. H. (2007). Neighborhood affluence, school-achievement scores, and housing prices: cross-classified hierarchies and HLM. Journal of Housing Research, 16(2), 97-116. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/16783 | - |
| dc.description.abstract | 房屋價格近十年來呈現長期上升之趨勢，且不同都會區之間房價差距也逐漸擴大。針對房價之區域分布議題，過去研究受限於房屋實際價格之取得困難，而無法進行完整之估計，我國內政部因而實施「不動產交易實價登錄」，揭露真實房價並減少房屋交易中的資訊不對等。
本研究以內政部「不動產成交案件實際資訊資料供應系統」所提供之房屋交易資料進行分析，資料期間為2012年1月至2014年4月，共納入388,301筆交易於台灣本島333個鄉鎮市區內之交易資料。研究方法分為兩部分：空間分析與多層次分析。空間分析以Moran’s I指標研究房價的空間自相關現象，並以Anselin’s LISA指標分析房價空間聚集現象之熱區與冷區。再以多層次分析，納入房屋個體因素「有停車位」、「有管理單位」、「土地移轉面積」、「是否為住宅區」、「建物移轉面積」、「屋齡」與「隔間多寡」，以及鄉鎮市區的「高等教育人口比例」、「人口密度」及「地區平均所得」，研究區域因素與房價個體因素，對房價解釋力之脈絡效果。 結果顯示房價具有空間聚集之現象，且其熱區與冷區之分布，與區域因素之熱區與冷區分布有類似。多層次分析更指出，區域因素與個體因素，對房價之解釋力具有脈絡效果。 | zh_TW |
| dc.description.abstract | Housing prices have drastically risen in the last ten years, and the price difference among various urban areas rises in Taiwan. The past researches of the distribution of the housing prices are subjected to data availability and result in the incapability of throughout appraisal. Ministry of the Interior, R.O.C has hence recently carried the “Actual Selling Price Property Value Reporting System” in order to reveal correct housing prices and reduce the information asymmetry.
This study analyzes the dataset of houses trades acquired form Actual Selling Price Property Value Reporting System. The dataset includes 388,301 transactions which happened in 333 administrative districts from January 2012 to April 2014. The study consists of two parts: spatial analysis and multilevel analysis. In the spatial analysis we apply Moran’s I to test the spatial clustering of housing prices, pointing out the hot spots and cold spots of housing prices by Anselin’s LISA. Besides, in order to examine contextual effect on housing prices, multilevel analysis is adopted. In the multilevel analysis we examine individual factors, such as “parking space,” “management units,” ”land size,” “residential area,” ”age of house,” and ”partitions,” and regional factors, such as “high education,” ”population density, ”and ”local average income.” Our empirical findings show that there exist spatial clustering of housing prices and similar distribution of hot/cold spots between housing prices and regional factors in Taiwan. Also, in the multilevel analysis it shows that both regional and individual factors exhibit contextual effect on housing prices. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-07T23:45:58Z (GMT). No. of bitstreams: 1 ntu-103-R01323059-1.pdf: 1579189 bytes, checksum: c44700898f4274d893bb8cd2e7dd704a (MD5) Previous issue date: 2014 | en |
| dc.description.tableofcontents | 謝辭........................................................................................................... i
摘要........................................................................................................... ii ABSTRACT............................................................................................... iii 目錄........................................................................................................... iv 圖目錄....................................................................................................... v 表目錄....................................................................................................... vi 第一章 緒論.............................................................................................. 1 第一節 研究動機與背景……........................................................... 1 第二節 研究目的與架構................................................................... 3 第二章 文獻回顧...................................................................................... 5 第一節 房屋價格之個體特徵........................................................... 5 第二節 房屋價格之區域特徵........................................................... 5 第三節 非傳統特徵價格模型........................................................... 6 第三章 研究方法...................................................................................... 8 第一節 研究流程與研究假說........................................................... 8 第二節 資料來源與研究變數........................................................... 12 第三節 統計分析方法....................................................................... 16 第四章 實證研究結果.............................................................................. 24 第一節 敘述性統計分析................................................................... 24 第二節 空間分析............................................................................... 29 第三節 多層次分析........................................................................... 35 第五章 結論與檢討.................................................................................. 48 第一節 結論....................................................................................... 48 第二節 檢討………........................................................................... 50 參考文獻................................................................................................... 51 | |
| dc.language.iso | zh-TW | |
| dc.title | 房屋價格決定因素之探討：空間與多層次分析之應用 | zh_TW |
| dc.title | Determinants of the Housing Prices: Application of Spatial and Multilevel Analysis | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 102-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 謝德宗,羅光達(Kuang-Ta Lo) | |
| dc.subject.keyword | 房價,空間聚集,多層次分析,脈絡效果, | zh_TW |
| dc.subject.keyword | housing prices,spatial clustering,multilevel analysis,contextual effect, | en |
| dc.relation.page | 55 | |
| dc.rights.note | 未授權 | |
| dc.date.accepted | 2014-06-24 | |
| dc.contributor.author-college | 社會科學院 | zh_TW |
| dc.contributor.author-dept | 經濟學研究所 | zh_TW |
| Appears in Collections: | 經濟學系 | |
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|---|---|---|---|
| ntu-103-1.pdf Restricted Access | 1.54 MB | Adobe PDF |
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