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研究生:楊進興
研究生(外文):YANG, CHIN-HSING
論文名稱:臺灣房地產發展趨勢與價格預測之研究
論文名稱(外文):A Study on Real Estate Development Trend and Price Prediction in Taiwan
指導教授:文一智文一智引用關係
指導教授(外文):WEN, I-JYH
口試委員:龔昶元溫坤禮曾亮陳耀光蘇南潘乃欣
口試委員(外文):KUNG, CHAANG-YUNGWEN, KUN-LILIANG,TSENGCHEN, YAW-KUANGSU, NANPAN, NAI-HSIN
口試日期:2020-07-11
學位類別:博士
校院名稱:國立雲林科技大學
系所名稱:工程科技研究所
學門:工程學門
學類:綜合工程學類
論文種類:學術論文
論文出版年:2020
畢業學年度:108
語文別:中文
論文頁數:161
中文關鍵詞:房地產價格自迴歸移動平均模糊時間序列灰色理論預測
外文關鍵詞:real estate pricesautoregressive moving averagefuzzy time seriesgrey theoryprediction
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長久以來我國因房地產買賣價格的不透明化,造成房地產價格多由建商及房仲業者所掌控,為了獲取高額利潤而任意哄抬價格,造成我國房地產價格的不合理上漲,加重家戶房價的支出,嚴重影響到國人生活品質以及社會居住正義。為了緩和房地產價格的變動以及相關市場經濟的合理發展,政府於民國101年8月開始實施不動產實價登錄制度,以各種電子網絡介面提供國人可隨時查詢參考的相關房地產價格統計資料與資訊,然而這些揭示的資料與資訊並未能及時公告,且須進一步的整理與分析,方能產生有用的資訊與可供掌握的發展趨勢,提供國人住屋買賣與家戶支出的合理規劃與運用。
臺灣房地產從光復以來的歷史變遷與發展趨勢,受臺灣經濟發展、人口成長狀況、重大天然災害以及政府相關政策等等,皆有不同程度上的影響。本研究先行彙整相關文獻資料,再透過政府官網可供查詢取得的房地產相關統計數據作分析比較,從光復後臺灣房地產景氣經過五次波動循環以及各項重大事件與政策的影響,臺灣未來房地產的發展趨勢,在短期(一年)內將有微幅漲跌互見的情形。中期內(一至二年)在新冠病毒疫情、兩岸情勢不明與臺商回流、危老建築重建與人口結構改變等因素影響下,都會區仍有平穩向上的發展趨勢。長期(三年以上)而言,臺灣人口今年(民國109年)已轉為負成長,家戶結構也從民國35年最高的6.09戶量(人/戶)降到民國109年3月的2.67戶量,臺灣地區房地產可能會有結構性的變動。
預測模型是一項重要的學術研究課題與方法,它被廣泛的應用在不同的學術研究領域。在眾多的預測方法中,本研究以時間序列的歷史數據資料,運用「自迴歸移動平均(ARIMA)」、「模糊時間序列(FTS)」以及「灰色理論GM(1,1)」等三種模型來建立相關房地產價格的預測模式與分析比較,並分析比較三種模型預測精度以及成長率。經分析及預測結果顯示,本文研究所應用的三種預測模型,不論以年度別或季別的預測結果,其精度誤差百分比皆在6%以下,都具有良好的預測水準,所預測出的年度成長率與實際值總成長率皆為穩定向上發展趨勢,季別成長率雖有漲跌情形,但大都以向上成長為主,顯示整體臺灣地區的房地產價格在未來的發展趨勢,短、中期內將延續呈現近年的緩和向上發展趨勢。
For a long time, due to the opacity of real estate transaction prices in Taiwan, real estate prices are mostly controlled by builders and real estate brokers. In order to obtain high profits, they arbitrarily bid up prices, causing unreasonable increases in real estate prices in Taiwan and aggravating household housing prices. Seriously affect the quality of our citizen’s live and the justice of social housing. In order to alleviate the changes in real estate prices and the reasonable development of the relevant market economy, the government began to implement the real estate price registration system from August 2012, providing relevant real estate price statistics and information that our citizen can inquire at any time through various electronic network interfaces. However, these revealed data and information have not been announced in time, and further sorting and analysis are required to generate useful information and graspable development trends, and provide reasonable planning and utilization of housing sales and household expenditures for the citizens.

The historical changes and development trends of Taiwan's real estate since its recovery have been affected to varying degrees by Taiwan's economic development, population growth, major natural disasters, and related government policies. This research first summarizes relevant literature data, and then analyzes and compares real estate-related statistics available through the government’s official website. From the five cycles of fluctuations in Taiwan’s real estate boom after the recovery, and the impact of various major events and policies, the future of Taiwan’s real estate Development trend, in the short term (one year), there will be slight ups and downs. In the medium term (one to two years), under the influence of factors such as the new coronavirus outbreak, the unclear cross-strait situation, the return of Taiwanese businessmen, the reconstruction of dangerous old buildings, and the changes in the demographic structure, the metropolitan area still has a steady upward development trend. In the long term (more than three years), Taiwan’s population has turned negative growth this year (2020), and the household structure has dropped from 6.09 households (person/household) of the highest in 1946, to 2.67 in March 2020. There may be structural changes in real estate in Taiwan.

Predictive model is an important academic research topic and method﹔it is widely used in different academic research fields. Among the many forecasting methods, this study uses the historical data of time series, using three models such as "Autoregressive Integrated Moving Average (ARIMA)", "Fuzzy Time Series (FTS)" and " Grey Model First Order One Variable GM (1, 1)" To establish the prediction model and analysis comparison of relevant real estate prices, and analyze and compare the prediction accuracy and growth rate of the three models. The analysis and forecasting results show that the three forecasting models used in this article, regardless of the forecast results of the year or season, whose accuracy error percentage is below 6%. The three prediction models all have good forecasting standards, and the predicted annual growth rate of real estate and the total growth rate of real value are both stable and upward. Although the quarterly growth rate has rise and fall, most of them are mainly upward. This phenomenon shows the overall development of real estate price in Taiwan in the future. It will continue the trend in recent years and keep developing gently upward in short-term and mid-term.
摘要 i
ABSTRACT ii
誌謝 iii
目錄 iv
表目錄 vii
圖目錄 ix

第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 1
1.3 研究方法 2
1.4 研究流程 3
1.5 研究範疇與限制 4
1.5.1 時間範疇 4
1.5.2 空間範疇 4
1.5.3 研究限制 4
1.6 研究架構 5

第二章 時間序列預測方法與應用模型 7
2.1 時間序列與預測 7
2.1.1 時間序列的特性 8
2.1.2 時間序列預測模型 12
2.1.3 預測類型簡介 15
2.1.4 預測的精度評估 17
2.2 自迴歸移動平均模型(ARIMA) 19
2.2.1 ARIMA基本理論 19
2.2.2 ARIMA預測方法 21
2.3 模糊時間序列預測模型 23
2.3.1 模糊理論概要 23
2.3.2 模糊時間序列預測 28
2.4 灰色GM(1,1)預測模型 34
2.4.1 灰色理論概述 34
2.4.2 灰色GM(1,1)預測基本模型 36

第三章 臺灣房地產之發展變遷與未來趨勢 39
3.1 房地產市場理論與特性 39
3.1.1 房地產市場的供需理論 39
3.1.2 房地產景氣循環理論 42
3.1.3 房地產市場四方格象限分析 46
3.1.4 房地產之存量流量模型 47
3.1.5 房地產市場的特性分析 52
3.2 光復後臺灣房地產之發展變遷 54
3.3 臺灣經濟發展對房地產的影響 68
3.4 人口變動對房地產之影響 70
3.5 重大天然災害對房地產之影響 74
3.6 政府政策對房地產之影響 78
3.7 臺灣房地產未來發展趨勢分析 81

第四章 預測模型與分析步驟 87
4.1 預測資料來源與應用說明 87
4.2 自迴歸移動平均模型(ARIMA)預測之研究步驟 89
4.3 模糊時間序列預測模型之研究步驟 101
4.4 灰色理論GM(1,1)預測模型之研究步驟 107

第五章 預測成果與分析 113
5.1 自迴歸移動平均(ARIMA)模型預測成果 113
5.1.1 年度平均單價預測成果 113
5.1.2 季別平均單價預測成果 114
5.2 模糊時間序列(FTS)模型預測成果 118
5.2.1 年度平均單價預測成果 118
5.2.2 季別平均單價預測成果 119
5.3 灰色理論GM(1,1)模型預測成果 123
5.3.1 年度平均單價預測成果 123
5.3.2 季別平均單價預測成果 124
5.4 三種模型預測精度比較分析 127
5.4.1 年度預測精度比較分析 127
5.4.2 季別預測精度比較分析 130
5.5 預測成果分析 133

第六章 結論與建議 137
6.1 結論 137
6.2 建議 138

參考文獻 141
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