本文係針對中國大陸各省市地區2000年至2007年之房地產指數進行分析，利用雙因子固定效果模型(two factor fixed effects model)探討中國大陸各省市地區房地產指數之重要影響變數，以及這些變數對於房地產指數影響程度之強弱。本文首先將文獻之檢閱做整理介紹，先概述至今國內外討論房地產價格指數文章之重要觀點，了解這些文章作者的研究時間與空間範圍、所使用分析方法、各學者之論點及其變數設定，希望在最後能與本研究之結論相互比較，觀察文獻與本研究之間是否具有一致性。 由於本論文重視各地區變數之影響，故本研究將使用具地域性之各省市數據資料作為變數，經由資料蒐集，將合適之變數（能夠量化及具有地域性之數據）納入研究考量，參閱文獻資料加上能夠取得之數據資料為考量，本文將討論下列變數：各省市地區居民收入、各省市地區居民消費水平、各省市地區城市建設面積、各省市地區固定資產投資指數、各省市地區交通情況、各地區外資投入金額、各省市地區人口數量、各省市地區人口縝密度、各省市地區衛生機構數以及各省市地區進出口總額等十個變數，對於中國大陸各省市地區房地產指數之關係，觀察其影響程度，了解各地區差異，期能提高對各地房地產價格波動之預測與預警水準，並為政府施政提供方向，當政府需要實施宏觀調控，將能夠較為明確的掌握調控之基本要素。

在國外, 消費者信心指數常被用來預測經濟情勢 (如股價、油價、消費支出等) , 縱然有部份學者對此抱持著懷疑的看法。在台灣, 則因缺乏┌消費者信心指數┘的資料, 以往一直無人從事這方面的相關研究。房地產業是火車頭工業, 也是民生工業; 其榮枯不但影響國家之經濟發展, 亦影響到每一個人居住的能力。因此, 房地產業的景氣波動, 是與大家休戚相關的。透過工商時報所編製的┌消費者信心指數┘資料, 與房地產預售屋公開價的變動情況, 我們發現：在大台北區, 兩者之間呈顯著正相關, 而且前者領先後者九個月。 / The forecast of Consumer Confidence Index (CCI) to Economy is usally used in many nations for years, even someone suspects the effect of it. For lack of CCI, there is seldom such study in Taiwan. Estate industry is an important one, due to its influences on national economic development and the capacity for purchasing dwelling house of everyone, therefore, people pay much attention to it in Taiwan. By collecting the data of CCI and open-price of pre-sold housing in Taipei Area, we can recognize that there is obviously positive relation between them. And the former is a leading in- dicator with 9 months Ahead.

本研究對中國大陸房地產市場設立需求及供給函數，並使用中國大陸2001 到2009 年的省級資料進行研究分析，以探討中國大陸房地產市場的所得彈性、價格彈性與貸款成數對中國大陸房地產市場供給與需求的影響。 經由實證估計出來的所得彈性值域為1.77 ~ 3.00，價格彈性值域為0.08 ~ -0.80。相較於過往的文獻與研究，估計到的彈性較高，顯現出即便中國大陸房價持續飆漲，民眾仍肯購置房產，且房價飆漲對於房地產市場的需求量影響有限，乃因民眾對於未來的經濟情勢持樂觀的態度。 而貸款成數方面，在貸放資金大增的環境下，對於中國大陸的房地產需求及供給市場大抵而言有顯著影響，而貸款成數的增加也使得房地產需求及供給量跟隨增加。 / The research constructed the demand and supply function for the market of China's real estate, and used the provincial data in China from 2001 to 2009 to analysis. Based on the information, we discussed the income elasticity and the price elasticity, and furthermore the impacts of the percentage of loans on the China's real estate market. According to the empirical estimate, the range of income elasticity was from 1.77 to 3.00, and for the price elasticity, it was from 0.08 to -0.80. Compare with those previous studies, the higher level of elasticity represented that although the real estate price continued soaring in China, people were still willing to purchase. Moreover, the soaring price had a limited influence in the demand of real estate market, and the main reason was that people were all optimistic about the future. To the percentage of loans, in this environment of the sharp increase in money lending, we found that it affect the demand and the supply market of real estate significantly, and with the relaxation of credit control, it led the demand and the supply of real estate to increase.

1978年中國大陸改革開放，吹皺經濟市場一池春水。住房公有制、住房福利制觀念相繼被打破，揭開房產制度改革曙光。鄧小平在1992年南巡講話後，定調「有中國特色社會主義市場經濟」的基本路線，從根本上解決市場經濟意識形態問題，自此中國大陸房地產市場活水澈底被激發。近十餘年來，中國大陸全國商品房平均價格從2000年2,112元人民幣，攀升至2010年5,032元人民幣，部分重點城市如北京、上海、廣州..等更早已突破萬元人民幣大關。 房產市場不對稱發展加深中國大陸社會結構性矛盾與衝突對立，高房價顯然無益其國內經濟健全發展，這也讓大陸中央不得不正視此一嚴肅問題積極採取宏觀調控手段，企圖壓制漲勢不斷的房價期能消彌廣大民怨。而與之同時因為覬覦中國大陸經濟高速發展背後廣大利益而競相投入中國市場的外資也就格外引起大陸政府的關注，因為「外資炒房」的傳言與疑慮一直困擾著中共當局，在高房價、高民怨的氛圍壟罩下，產官學界檢討外資聲浪甚囂塵上；然而，高房價的背後是否一定有外資刻意炒作？值得探究與思考。 本研究嘗試以中國大陸天津做為觀察標的，運用共整合ARDL模型探討外商直接投資（FDI）對房地產價格波動的影響，釐清外資在房地產價格所扮演的角色。實證模型並納入物價指數（CPI）、人均收入（INC）、貸款利率（INT）、匯率（EXC）與股價（STOCK）為解釋變數，以做為觀察總體經濟因素對大陸房地產市場的影響。實證結果表明，在短期關係上外資的確有拉抬房價效果，不過長期關係影響並不顯著，顯示外資不是實際推升房地產價格的主因，角色定位應為「從犯」而非「主嫌」。研究結果並發現，匯率變動對房地產價格有著顯著實質影響，這意味如果大陸政府要運用匯率這項工具來抑制漲勢不斷的房價，就必須讓人民幣適時升值。不過目前中國大陸仍屬以出口導向為大宗的國家，長期而言，人民幣升值將對其出口造成某種程度衝擊，是以在匯率政策的操作上恐陷入兩難（升值或貶值)的困境。 / The reform and opening in mainland China in 1978 had fretted the surface of the water of the economic market. The concepts of public housing and housing welfare system had been broken, leading to a line of hope in the reform of the house property system. After Xiaoping Deng's speech during his south tour, he set up the basic route of the "socialism market economy with Chinese features", resolving the market economy ideology issue from the root. Since then, the house property market in Chine has been activated. In the recent decade, the average price of commercial residential buildings had increased from YMB$2,112 in 2000 to YMB$5,032 in 2010. In major cities such as Beijing, Shanghai, and Guangzhou, the prices had already went over YMB$10,000. The asymmetric development of the house property market has further caused structural conflicts and confrontations in Mainland China. Apparently, high housing prices were not beneficial to the sound development of the domestic economy. And thus the central government in Mainland China had to face up to this serious problem and aggressively took the microscope controlling measure in the attempt to suppress the increasing housing prices to resolve people's complaints. In the mean time, under the desire for the great profits behind the rapid development of the economy in Mainland China, foreign funds had entered the Chinese market one by one, getting some extra attention of the Chinese government. Because the rumor of "foreign funds in real estate speculation" and some doubles had continuously bothered the Chinese government, under the atmosphere with high housing prices and high social grievance, requests for reviewing foreign funds in the industrial, governmental, academic, and research circles were very broad. However, whether there was real estate speculation with foreign funds behind high housing prices is worth thinking and studying. Using Tianjin City in Mainland China as a target for observation, this study attempted to apply the autoregressive ARDL model to explore the influences of foreign direct investment (FDI) on price changes in housing property, in order to clarify the role foreign funds play in real estate prices. The independent variables included in the model were consumer price index (CPI), per capita income (INC), loan interest (INT), exchange rate (EXC), and stock price (STOCK), in order to observe the influences of the macro economical factors on the Chinese real estate market. According to the empirical results, in the short run, foreign funds could indeed drive up housing prices. However, in the long run, the influence was not significant. This means foreign funds are not the main cause driving up real estate prices. The role they played was a "partner in crime" instead of a "main suspect". The study found that there was indeed a significant and substantial influence of exchange rate changes on real estate prices, meaning that if the Chinese government would like to surprise increasing housing prices using exchange rates as a tool, it is necessary to allow YMB appreciation. However, currently, Mainland China is still a country with mainly exports. In the long run, YMB appreciation may lead to certain impact on China's exportation. Therefore, operating exchange rate related policies may lead to a dilemma (to appreciate or depreciate).

University of Macau / Faculty of Law

房地產行業俗稱「火車頭工業」，房市的熱絡能帶動上中游非常多的產業蓬勃發展，而房地產代銷業在整體產業鏈當中至關重要，在房價居高不下及政策打房等背景因素下，房市交易轉趨清淡，買賣移轉件數創下近年新低，因此房地產關鍵且重要的行銷策略相形重要。而過去文獻中，多分別探討行銷策略、銷售率、銷售期間，而銷售期間之文獻更多以仲介觀點探討。本研究目的為找出代銷業銷售成功與有利的行銷策略模式、主客觀影響因素，提供房地產業者，成功銷售房地產個案。房地產主要之行銷策略為STP分析、7P理論、4C理論、整合行銷，而規劃合宜的產品定位，制定完善的行銷策略則是銷售成功的主要方程式。銷售成功主客觀因素之研究實證結果顯示，主觀影響因素，依序為地段條件、推案時機、產品條件、品牌因素，但地段條件是先天因素，無法改變，銷售要成功，銷售期間要短，銷售率要高，代銷業者需制定完善的行銷計劃，掌握天時、地利、人和等因素，天時就是推案時機等因素，地利就是地段條件等因素，人合就是產品條件、品牌因素等因素。由銷售期間與銷售率形成銷售成功的實證結果顯示，最重要的因素為產品條件及行銷策略；產品條件的數量、金額，對銷售期間影響較顯著，產品條件的質量如主力產品則對銷售率影響較顯著；行銷策略的成交均價對銷售期間及銷售率影響皆顯著，行銷策略的合作方式則對銷售期間影響較顯著；就區域而言，台北市個案銷售成功，首要因素為行銷策略之成交均價，而新北市個案銷售成功，則著重於產品條件之主力產品。 / The real estate industry, commonly known as "locomotive industry", can up bring many growths in industry development. While in recent years, the housing market becomes dull, and the sales number declining, it’s critical and important for real estate marketing to deliberate strategies at its key point. The real estate agency in the whole industry chain plays an essential part in setting the high prices and policies and other background factors in the market. From the past references, most explored the marketing strategy, sales rate, selling duration, while references on selling duration are more from the real estate agency’s perspective. The objective of this research is to investigate successful marketing and beneficial sales tactics, objective and subjective influential factors, the real estate producers, and the successful real estate selling cases. The STP analysis, the 7P Theory, the 4C Theory, integrated marketing are the main real estate strategies, whereas products positioning planed adequately, and well developed marketing strategy are the fundamental formulation for marketing success.The marketing success objective and subjective influential factors research shown, the objective influential factors are as listed: location attribution, selling timing, product conditions and brand factors. However, location attribution is congenital factor, inevitable, in order to sell successfully, selling duration must be brief, and selling rate must be high, real estate agencies must develop a complete marketing proposal, take control of the right time, the right place, the right social connections and so on. The right time is the selling timing and other factors, the right place is the location attribution and other circumstances, and the right social connections is the product conditions, the brand factors and other elements.From the selling duration and selling rate form subjective influential factors of marketing success, research shown,the product condition and marketing strategy are the most important subjective influential components,product condition quantity and price influences are more indisputable on the selling time, the quality of the product condition as the main product then impact more significantly on the sales rate.Marketing strategy price influences are more indisputable on the selling duration and sales rate, Marketing strategy cooperation influences are more indisputable on the selling duration.About area factors ,taipei City’s success on selling primarily is the price of marketing strategies; as for New Taipei City’s on successful selling is particularly focusing on the main product condition’s square footage.

互聯網的發展與近年來數據平台受到公私部門重視，資訊的取得與流通變得便捷，中國房地產文化目前有別於台灣，尚無實價登錄機制且地域面積廣大，傳統估價模型可能無法直接應用，面對房地產背後眾多的影響因素，本研究將預測建模目標放在泡沫化尚不嚴重且較具有潛力的中國新一線城市杭州市，自新浪二手房網爬取杭州市房地產數據，並自國家統計局取得各地區行政支出數據，作為實證分析資料。結合自動程序爬蟲抓取數據、統計分析與機器學習方法，期望對中國房地產建立一混合非監督式與監督式學習之模型。 在分群結果之後建構模型採用之技術為C5.0、三層CHAID、五層CHAID與Neural Network，挑選出最適合的模型為使用混合模型後的C5.0決策樹方法，達到降低變數維度亦提升或達到相當的預測準確率的雙贏目標，模型中行政地區、面積、總樓層為最頻出現的重要變數。 另外透過集群分析於行政支出的應用，發現2016年度杭州市投入的行政支出集中於余杭區、蕭山區、濱江區，成為賣屋及購屋者的第二項決策標準。 / In recent years, with the growth of the Internet and the importance of data platform on public sector and private sector. Getting and sharing information are made easily. The culture of real estate in China is different from Taiwan. For instance, there is no actual house price registration system. Furthermore, traditional estimate model may not be directly applicable to China which has the vast geographical area of the mainland. There are many factors to influence house price model. This study focus on Hangzhou city. Because the burst of real estate bubbles is not serious as first-tier cities and it is one of new first-tier cities in China. The research data were crawler from Sina second-hand housing website and National Bureau of Statistics. By using auto web crawler skill, statistical analysis, and machine learning method to build a real estate model in China, which was combining unsupervised learning method with supervised learning method. After clustering Hangzhou second-hand housing data, this study used C5.0, three layers Chi-Square Automatic Interaction Detector(CHAID), five layers CHAID, and Neural Network(NN). The study goal are both reducing dimension and getting better forecast accuracy. Choosing clustering- C5.0 model as appropriate house price model to achieve win-win situation after comparing final result. Administrative region, area, and total floor are the top three high frequency influential factors. Applying Clustering Analysis to administrative expenses data in Hangzhou, the study found that the government resource focus on Yuhang, Xiaoshan, and Binjiang. It can be the second decision-making criterion for house sellers and house buyers.

近年來，房地產之投資及買賣廣為盛行，而房地產依舊為人們投資的方向之一。屬於人工智慧範疇之類神經網路，其具有學習能力，可以進一步的歸納推演所要預估的結果，也適合應用於非線性的問題中，但以往類神經網路的機器學習模型，皆採用中央處理器(CPU)進行運算，在計算量龐大時往往耗費大量時間於訓練上。而圖形處理器(GPU)之崛起，將增進機器學習的速率。 本研究利用穩健學習程序搭配信封模組的概念，建立一類神經網路系統，利用GPU設備及機器學習工具–Tensorflow實作，針對民國一零四年之台北市不動產交易之住宅資料，並使用1276筆資料，隨機選取60%資料作為訓練範例並分別進行以假設有5%為可能離群值及沒有之情況做學習，並選取影響房地產價格之11個變數做為輸入變數，對網路進行訓練，實證結果發現類神經網路的速度有顯著的提升；且在假定有5%離群值之狀況下學習有較好的預測水準；另外在對資料依價格進行分組後，顯示此網路在對中價位以上的資料有較好的預測能力。就實務應用方面，藉由類神經網路適合應用於非線性問題的特性，對未來房地產之估價系統輔助做為參考。 / Real estate investment and transcation prevails in recent year. And it is still one of the choices for people to invest. The Neural Network which belongs to the category of Arificial Intelligence has the ability to learn and it can deduce to reach the goal. In addi-tion, it is also suitable for the application of non-linear problems. However, the machine learning model of the Neural Network use CPU to operate before and it will always spend a lot of time on training when the calculation is large.However, the rise of GPU speeds up the machine learing system. This study will implement resistant learning procedure with the concept of Enve-lope Bulk focus to built a Neural Network system. Using TensorFlow and graphics pro-cessing unit (GPU) to speed up the original Arificial Intelligence system. According to the real estate transaction data of Taipei City in 2015, 1276 data will be used. We will pick 60% of the data in a random way as training data of our two experiment , one of it will assume that there are 5% of outlier and another won’t. Then select 11 variables which may impact the value of real estate as input. As the experiment result, it makes the operation more efficient and faster , training of the Neural Network really speed up a lot. The experiment which has assume that there are 5% of outlier shows the better effect of predicting than the another. And we got a better prediction on the part of the higher price after we divided the data into six groups by their price.In the other hand, Neural Network is good at solving the problem of non-linear. It can be a reference of the sup-port system of real estate appraisal in the future.

網際網路時代資訊接收的便利性，使得大眾容易接收到媒體所發布的媒體資訊，而這些資料具含的意見詞彙間接反應出群眾對特定主題的情緒傾向。在針對房地產的媒體當中，當特定區域的房地產市場具有良好的發展空間而成為交易熱區時，這些針對特定區域且帶含情緒的房市篇章報導或其他影響房市之相關新聞以及廣告往往會影響我們的購屋決策。 本研究將以桃園市及台中市-兩個近五年來台灣房市較為熱門的區域作為研究區域進行分析及研究，期望找出在短期時間新聞輿情及廣告和房市交易價量的相關性以及會影響該房地產市場之因素。首先蒐集桃園市及台中市的實價登錄的房地產交易資料以及廣告後，運用文字探勘分析房市整體輿情與兩都市房地產價量之關聯性，再將新聞分群後找出特徵詞，個別建立時間序列來了解各種情緒及房地產價量的共同移動性，並結合廣告投入量找出房地產市場價量以及影響因素的領先關係。並透過自建的類神經網路模型建立針對桃園市和台中市的交易量預測模型以及針對特定房市熱門區域-青埔和七期的交易量預測模型，並透過計算輸入變數的權重總和來判別新聞情緒對於房地產成交價量的影響程度。 研究首先提供了對於新聞情緒的分類包含區域經濟情緒、區域社會情緒、區域環境情緒、區域政治情緒、稅制情緒、選舉情緒。接著進行時間序列分析指出總情緒序列與成交量的時間序列相關係數都有高於70%以上，桃園市成交量與桃園市情緒的相關係數為0.73，台中市成交量與台中市情緒的相關係數為0.81，皆呈現高度正相關，顯示桃園及台中的房市交易量與情緒現存在高度相關性。在特定新聞類別當中，透過兩個城市的相關係數比對顯示稅制新聞情緒，區域環境相關情緒，區域社會相關情緒，以上三個情緒跟房市的交易量共同移動較為明顯，相關係數皆在0.5左右甚至以上，可見這些類別的新聞能夠適時反映大眾對於特定區域的房地產的看好及看壞。在此階段也透過領先指標驗證了情緒以及廣告是會領先房市交易量，桃園以及台中兩個區域都有情緒領先交易量一個月的現象。針對特定區域的交易量研究包含青埔特區及七期重劃區，也發現到兩地的交易量高峰前一至兩個月都有一波廣告的高峰。 而在類神經網路模型方面的研究結果能夠良好地預測漲跌趨勢，利用桃園資料進行訓練並以台中資料做為測試的模型在19次的漲跌中預測出17次，而將百分之七十的桃園及台中混合資料進行訓練並其餘百分之三十做為測試的模型結果也成功在14次漲跌中預測出10次，顯示模型效果預測能力良好，並透過將輸入權重加總的方式來衡量各輸入變數的影響程度，研究結果指出總情緒，稅制情緒量，區域環境情緒量與兩地房地產市場交易量最有關聯且影響最重。最後利用時間序列得知廣告高峰會領先總交易高峰一至兩個月的特性，利用從2012年10月至2016年2月的青埔特區資料及2012年10月至2013年12月的七期重劃區資料混合進行訓練並以2014年1月至2016年2月七期重劃區資料做為測試資料的模型能夠有效在兩年內預測中三次交易高峰，顯示該模型能透過預測出下一期的廣告投入量做為中介變數進而推估出交易量高峰的時間透過此模型可在未來應用於相關政策投入市場後對市場交易量的影響，也能夠快速有效的得到預測結果，而在針對特定市場我們也可以透過預測廣告以及運用廣告為交易量的領先特性來了解在近期何時會有交易量高峰，如能配合了解市場輿情脈絡，可為房屋仲介以及建商在更精確的時間點投放廣告時機點達到廣告的最大效益。

主要目的為了解造成營建業務波動之因素，營建公司的組成，及營建業在促銷上所反 應出的現象。 全文分四章： 第一章：導論－說明研究動機、目的及方法。還有研究架構、文獻探討。 第二章：說明房地產市場的特性、組成波動的因素、不歷年的主要波動。 第三章：以個案的方式，說明營建公司的組織、融資、決策方式、及在促銷上所顯示 的現象。 第四章：為結論與建議。說明研究結果及以後營建業可能走的方向。