本文關鍵詞：產(chǎn)業(yè)地產(chǎn)投資環(huán)境評價研究　出處：《西安建筑科技大學》2014年碩士論文　論文類型：學位論文

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【摘要】：產(chǎn)業(yè)地產(chǎn)能夠?qū)崿F(xiàn)產(chǎn)業(yè)、城市、房地產(chǎn)三者的相互融合和良性的互動發(fā)展。投資環(huán)境是影響和制約投資運行效率的各種外部因素的總和，對投資環(huán)境進行分析評價有助于決策者在投資決策之初把控風險；而目前國內(nèi)外對產(chǎn)業(yè)地產(chǎn)投資環(huán)境的研究成果十分有限。鑒于此，本文從產(chǎn)業(yè)地產(chǎn)開發(fā)商的視角出發(fā)，對中尺度地域范圍的產(chǎn)業(yè)地產(chǎn)（工業(yè)產(chǎn)業(yè)綜合體）一般性投資環(huán)境進行探索性的中觀評價研究。 本文在已有投資環(huán)境與產(chǎn)業(yè)地產(chǎn)的相應理論研究基礎上，，從產(chǎn)業(yè)地產(chǎn)開發(fā)商的視角出發(fā)，結(jié)合產(chǎn)業(yè)地產(chǎn)的特征，采用頻度統(tǒng)計、理論分析和專家確認等方法，從經(jīng)濟、基礎設施、社會服務、產(chǎn)業(yè)環(huán)境四個方面篩選出44項具體指標，構建了產(chǎn)業(yè)地產(chǎn)投資環(huán)境評價指標體系，并對各指標進行了定性的解釋和分析；其次將投影尋蹤動態(tài)聚類模型引入到產(chǎn)業(yè)地產(chǎn)投資環(huán)境評價方法中，該方法能夠完全根據(jù)原始數(shù)據(jù)信息的自身特性實現(xiàn)排序和聚類分析，使評價結(jié)果更加客觀可靠；同時采用基于實數(shù)編碼的加速遺傳算法對模型進行求解，編寫了加速遺傳算法的MATLAB程序；最后借助陜西省9個地級市的產(chǎn)業(yè)地產(chǎn)投資環(huán)境對評價模型進行驗證。 實證研究驗證了評價模型的正確性，并得出陜西省產(chǎn)業(yè)地產(chǎn)投資環(huán)境的排序聚類結(jié)果：第一類城市排序依次為寶雞，咸陽，榆林，銅川；第二類城市排序依次為延安，渭南，漢中；第三類城市排序依次為安康，商洛。同時我們結(jié)合陜西省各研究樣本城市的實際資源稟賦情況對產(chǎn)業(yè)地產(chǎn)的類型選擇提出了相應建議。
[Abstract]:Industrial real estate can realize the integration of industry, city and real estate. The investment environment is the sum of various external factors that affect and restrict the efficiency of investment operation. The analysis and evaluation of the investment environment can help the decision-makers to control the risk at the beginning of the investment decision. At present, the domestic and foreign research on the investment environment of industrial real estate is very limited. In view of this, this paper starts from the perspective of industrial real estate developers. This paper studies the general investment environment of industrial real estate (industrial complex) in mesoscale region. On the basis of the existing investment environment and the corresponding theoretical research of industrial real estate, from the perspective of industrial real estate developers, combined with the characteristics of industrial real estate, this paper adopts the methods of frequency statistics, theoretical analysis and expert confirmation. From the four aspects of economy, infrastructure, social service and industrial environment, 44 specific indexes are selected, and the evaluation index system of industrial real estate investment environment is constructed, and the qualitative explanation and analysis of each index are given. Secondly, the projection pursuit dynamic clustering model is introduced into the industrial real estate investment environment evaluation method. This method can complete the sorting and clustering analysis according to the characteristics of the original data information. To make the evaluation results more objective and reliable; At the same time, the accelerated genetic algorithm based on real coding is used to solve the model, and the MATLAB program of accelerated genetic algorithm is written. Finally, the evaluation model is verified by the industrial real estate investment environment of nine prefectural cities in Shaanxi province. The empirical study verifies the correctness of the evaluation model, and obtains the sequencing and clustering results of the industrial real estate investment environment in Shaanxi Province: the first kind of city ranking is Baoji, Xianyang, Yulin, Tongchuan; The order of the second type cities is Yan'an, Weinan, Hanzhong; The order of the third kind of cities is Ankang and Shangluo. At the same time we put forward the corresponding suggestions on the choice of industrial real estate type according to the actual resource endowment of the sample cities in Shaanxi Province.
【學位授予單位】：西安建筑科技大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：F299.23

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