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| Title: | 住宅租屋市場預警系統之研究
The study on Taiwan rental housing market early-warning system |
| Authors: | 簡嘉嫺
Chien, Chia Hsien |
| Contributors: | 林沛靜
江穎慧
Lin, Vickey Pei Jing
Chiang, Ying Hui
簡嘉嫺
Chien, Chia Hsien |
| Keywords: | 預警系統
主成分分析
變動百分比法
3σ
理想目標值法
差分自迴歸移動平均模型
Early-warning system
Principal components analysis
The range percentage
Ideal goal methods
Autoregressive integrated moving average model
ARIMA |
| Date: | 2018 |
| Issue Date: | 2018-10-01 12:16:12 (UTC+8) |
| Abstract: | 住宅租屋市場過去由於租金成交資訊有限，導致國內住宅租屋市場相關研究相當不足，由於房地產已從高峰反轉直下，租屋市場的需求日益受到重視，但當前可用以觀測租屋市場的指標，僅有消費者物價指數房租類單一指標，能揭露的租屋市場資訊十分有限。本研究為了解目前住宅租屋市場現況，擬從宏觀角度尋找更能反映租屋市場的訊息指標，並嘗試建立一套有效的住宅租屋市場預警系統。本研究從租金市場的供給、需求以及景氣層面歸納出22個變數指標，利用主成分分析法決定權重大小，組合出6種不同的綜合指標，並比較變動百分比法、3σ法及理想目標值法三種警戒門檻值產出方法，選出最適合的警情指標以建立有效的住宅租金巿場預警系統。6種不同的綜合指標與單一指標(住宅租金指數)一同編制警戒門檻值，產製燈號結果後以 3σ法最穩定。而比較單一指標與綜合指標後，差分自迴歸移動平均模型(ARIMA)選出最理想的警情指標是由15個變數編製而成，利用警情指標預測四季(2016年Q3~2017年Q2)變化，租屋市場仍屬於偏冷的黃藍燈居多。
從實證結果可看出住宅租屋市場警情指標的15個變數以供給面變數占大多數；而比較單一指標與綜合指標的預警能力，綜合指標的誤差較小與模型解釋力較佳，較能產生正確的市場警情。目前住宅租金市場的熱絡程度，雖然是黃藍燈的偏冷狀態，但因為租屋需求一直穩定存在，供給下降導致市場有供弱需強的狀況，因此租金仍然持續上漲。供給變弱乃由於國內住宅租屋市場租金投報率普遍偏低，目前市場租金水準仍無法有效刺激更多的私人持有房屋釋出到出租市場，未來如需求突然增加，供給來不及反應，將會讓租屋市場的租金有連續上漲壓力。
為防止租金持續上漲，增加租屋供給是必須的。我們建議政府可以拋出誘因鼓勵屋主出租或是獎勵業者新建出租住宅，透過政府建立專職組織運行，配合公營金融機構提供更低的利率或更長的貸款期限降低出租住宅成本，以及提供租金適中的出租住宅來建構良好自償系統，收益用來支持低收入族群的租金補貼，在財政支出也可以減輕國家負擔。而提高出租住宅的公共設施服務水準，可延長出租住宅壽命及增加使用出租住宅族群消費力，增加居住安定感。
In the past, because of the limited information on rental housing market, the relevant research on domestic rental housing market has been rather inadequate. The demand for renting has been getting more and more attention because of the housing market reversed, but at present it can be used to observe the position of the rental market and only a single indicator, CPI-rent.The market information that can be uncovered is very limited.
In order to understand the current situation of the rental housing market, the present study intends to look for a better information indicator of the rental housing market from a macro perspective and try to establish an effective early-warning system. This study sums up 22 variable pointers from the supply, demand and boom level of rental housing market, by using the principal components analysis method to determine the weight, combining 6 kinds of different synthetic pointers, and comparing the the range percentage method, 3σ method and ideal goal methods, three kinds of warning threshold output methods, the best suitable pointer is chosen to establish an effective early warning system for rental housing market. The 6 different synthetic pointers are combined with the single indicator (residential rental index) to prepare the warning threshold, and the result of the production of the signal is most stable by the 3σ method. Compared with the single pointer and the integrated pointer, the ARIMA results showed the most ideal of the police feeling pointer is compiled from 15 variables, the use of police sentiment to predict the 4 seasons (2016 3rd season~2017 2nd season) changes, rental housing market still belongs to the cold yellow and blue light majority.
From the empirical results, it can be seen that the rental housing market is in the supply side, 15 variables in the supply side of the majority, while comparing the early warning ability of single pointer and integrated pointer, the error of integrated pointer is better than that of model interpretation, which can produce correct market police sentiment. Although the degree of heat of rental hosing market is yellow and blue light of the cold state, but because the demand for rental housing has been stable, supply decline led to strong demand for weak market conditions, so rents continue to rise.The weakening of supply is due to the generally low rental rate of domestic rental housing market, the current market rent level is still unable to effectively stimulate more private housing released to the rental market, in the future, if demand suddenly increases, the supply too late to respond, will let the rental housing market has a continuous upward pressure.
To prevent rents from rising, it is necessary to increase the supply of rental housing. We suggest that the government can throw out incentives to encourage homeowners to rent or reward the owners of new rental housing, and to reduce the cost of rental housing through the establishment of a full-time organization run by the Government and with the provision of lower interest rates or longer loan terms for public financial institutions, and the provision of affordable rental housing to build a good system of compensation, income used to support the low-income groups of rent subsidies, in fiscal spending can also reduce the country`s burden. To improve the service level of public facilities for rental housing, it can prolong the life of renting house and increase the consumption power of renting residential groups, and increase the sense of residence stability. |
| Reference: | 中文參考文獻
一、專書
1.余桂霖，2013，『時間序列分析』。台北：五南圖書出版股份有限公司。
2.何宗武，2014，『Eviews高手：財經計量應用』，台北：頂茂圖書出版有限公司。
3.林震岩，2007，『多變量分析：SPSS的操作與應用』，台北：智勝文化事業有限公司。
4.張金鶚、花敬群、彭建文、楊宗憲，2013，『房地產市場分析理論與實務』，台北：張金鶚。
5.蕭峯雄、洪慧燕，1992，『景氣分析與對策』，台北：遠東經濟研究顧問社有限公司。
二、專書論文
1.林秋瑾，1999，「影響都會區住宅市場互動因素之研究」，『八十七學年度國科會人文處區域研究學門專題研究計畫成果發表會論文集』，國科會人文處。
2.林沛靜，2016，「利用巨量資料於住宅租金價格趨勢之研究---子計畫三:應用政府巨量資料於住宅租金市場重要指標與建立預測模型之研究」，中華民國科技部補助計畫。
3.張金鶚，林秋瑾，1995，「房地產景氣與總體經濟景氣關係之研究」，行政院國家科學委員會補助研究。
4.楊宗憲、林沛靜、江穎慧，2016，「104年度編製住宅價格指數並定期發布(IV)」，內政部營建署委託資訊服務案成果報告。
5.劉正智、林昌明、彭志峰、杜方中，2011，「系統化法應用於都市住宅市場警度界限之研究」，『五屆物業管理研究成果發表會論文集』。
三、期刊論文
1.孫蕾，2016，「基於主成份和灰色預測法的房地產金融風險預警體系研究」，『金融監管研究』，59：24-42。
2. 林祖嘉，1990，「反向巢型多項 Logit模型下的住屋需求與租買選擇」，『經濟論文』，18(1)：137-158。
3. 馬毓駿、林秋瑾，2009，「房地產景氣特性之再確認-多變量馬可夫轉換模型之應用」，『住宅學報』，18(1)：23-37。
4. 張金鶚、賴碧瑩，1990，「房地產景氣指標建立與分析」，『國立政治大學學報』，61：333-411。
5.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』，18(2)：1-22。
6.彭建文，1997，「不動產市場景氣循環、轉變力量與結構變遷翻譯」，『住宅學報論壇』，6：71-88.
7.彭建文，2004，「台灣出租住宅市場與自有住宅市場價格調整關係之研究」，『都市與計劃』，31(4)：195-213。
8. 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』，14(2)：27-49。
9.湯夢玲,王占龍,李志建，2005，「因子分析法求權重評價水質的實例」，『邢台職業技術學院學報』，22(5)：14-16。
10.楊子江，2016，「我國住宅房屋持有及交易簡析」，『Journal of the Chinese. Statistical Association.』，54:129–153。
11.劉正智、伍南彰，2016，「系統化法應用於都市土地利用生態經濟發展警度之研究」，『物業管理學報』，7(1)：57-66。
12.薄有為、鍾懿萍、柯清華，2013，「台北市辦公大樓市場租金與總體經濟因素關聯性之研究—以不同等級商辦大樓為例」，『土地問題研究季刊』，12(1)：12-22。
四、博、碩士論文
1.李如君，1997，「台北地區住宅租金水準之研究」，國立政治大學地政系碩士論文：台北。
2.陳彥光，2009，「房地產市場預警系統之研究」，國立政治大學地政研究所碩士論文：台北。
3.劉正智，2010，「都市住宅市場預警系統動態模擬模式之研究」，朝陽科技大學建築及都市設計研究所博士論文：台中。
4.劉怡吟，1996，「台北市家戶住宅選擇變遷之研究」，國立政治大學地政研究所所碩士論文：台北。
5.簡淨珍，2000，「台北地區出租住宅市場與自有市場替代性之研究」，國立政治大學地政學系碩士論文：台北。
英文參考文獻
一、專書論文
1.Chin, H. W., 2003, “Macro-economic Factors Affecting Office Rental Values in Southeast Asian Cities: The Case of Singapore, Hong Kong, Taipei, Kuala Lumpur and Bangkok”, Paper presented at the 9th Pacific Rim Real Estate Society Conferenceheld at Brisbane, Australia.
2.DiPasquale, D. and Wheaton, W. C., 1996,”Urban Economics and Real Estate Markets”, New Jersey: Prentice-Hall,Inc.
3.Meulen, P., Micheli, M., and Schmidt, T., 2011, “Forecasting house prices in Germany”, Ruhr Economic Papers , 294, RWI, RUB.
4.Norris, Michelle, Byrne, M., 2017, “Housing Market Volatility, Stability and Social Rented Housing: comparing Austria and Ireland during the global financial crisis”, UCD Geary Institute For Public Policy Discussion Paper Series, WP2017/05.
5.Watkins, C., White, M. and Keskin, B., 2012,”The Future of Property Forecasting”, London:Investment Property Forum.
二、期刊文獻
1.Berlemann, M.and Freese, J., 2010, “Monetary policy and real estate prices: A disaggregated analysis for Switzerland”, Hamburg Institute of International Economics Research paper, 2-19.
2.Cuerpo, C., Kalantaryan, S. and Pontuch, P., 2014,“Rental Market Regulation in the European Union”, Economic Papers, 515, European Commission, Brussels.
3.Garbowski, M., 2017, “Social housing association(PL. TBS) in the realization of goverment housing policy in Poland”, Science and education, 7-14. Verlag SWG imex GmbH,Nürnberg, Deutschland.
4.Granger, C. W. J., 1969, “Investigating Causal Relations by Econometric Models and Cross-spectral Methods,” Econometrica, 37（3）,424–438.
5.Hepsen, A. and Vatansever, M., 2011, “Forecasting future trends in Dubai housing market by using Box-Jenkins autoregressive integrated moving average”, International Journal of Housing Markets and Analysis , 4 (3), 210-223.
6.Huang, F., Wang, F., 2005, “A system for early-warning and forecasting of real estate development”, Automation in Construction, 14(3),333-342.
7.Potepan, M. J., 1996, “Explaining Intermetropolitan Variation in Housing Prices, Rents and Land Prices”, Real Estate Economics, 24(2), 219-45.
8.Roulac, S.E., 1996,“Real estate market cycles, transformation forces and structural change”, The Journal of Real Estate Portfolio Management,1-17.
9.Zhou, J., 2018, “ The New Urbanisation Plan and permanent urban settlement of migrants in Chongqing, China”, Population, Space and Place, e2144.(電子期刊,紙本期刊尚未出版)
參考網頁
1.內政統計查詢網http://statis.moi.gov.tw/micst/stmain.jsp?sys=100
2.主計總處統計專區 - 中華民國統計資訊網https://www.stat.gov.tw/np.asp?ctNode=452
3.Zillow
https://www.zillow.com/research/zillow-rent-index-forecast-11461/)
4.StreetEasy
https://streeteasy.com/blog/methodology-streeteasy-price-indices-2/)
5.永慶房仲網
https://knowhow.yungching.com.tw/article/103
6.財團法人崔媽媽基金會
https://www.tmm.org.tw/contents/text?id=44
7.居住，正義了嗎？系列3之2─台灣租屋市場大解析(104.6.17工商時報)http://www.chinatimes.com/newspapers/20150617000075-260210
8.內政部「整體住宅政策」 - 行政院第3447次院會決議https://www.ey.gov.tw/File/75CB8904E35AA751?A=C
9.https://www.u-trust.com.tw/CC2/CC020101.asp?Pkey=%7B1B1CBE96-0DB4-43DD-8236-5F6A677935B1%7D&CategoryID=%7B67BD928B-5F3A-437E-B9AE-00558B7C7BD4%7D
10.包租公獲利退場 由租轉售增6成6(100.4.15蘋果日報)
https://tw.appledaily.com/finance/daily/20110415/33319656
其他
1.IBM SPSS Statistics Base 22使用手冊。 |
| Description: | 碩士
國立政治大學
地政學系碩士在職專班
104923022 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0104923022 |
| Data Type: | thesis |
| DOI: | 10.6814/THE.NCCU.MPLE.012.2018.A05 |
| Appears in Collections: | [地政學系] 學位論文
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