房地產巿場預警系統之研究

• Main Author: 陳彥光
• Language: 中文
• Published: 國立政治大學
• Subjects: 房地產預警系統; 檢查值; 預測
• Online Access: http://thesis.lib.nccu.edu.tw/cgi-bin/cdrfb3/gsweb.cgi?o=dstdcdr&i=sid=%22G0095923020%22.

本文為了建立房地產預警系統，針對1992年至2007年期間房地產巿場相關指標為警情指標選取之研究對象，利用層級分析法、因素分析法與擴散指數模型，選擇如同房地產景氣指標四個層面之重要代表性（非先行性）為主變數之警情指標，再綜合確定各指標之權重組成之房地產警情指標，不同於以往將選擇房地產景氣指標利用專家意見的定性方法，且其權重皆設定相同之方式。 警界檢查值是根據房地產警情指標預警值級別判斷房地產警情狀況，檢查值的計算運用變動百分比的方式，拔靴法，3σ法及理想目標值法，評估房地產警情指標之適度警界檢查值。 預測房地產巿場未來情況，採用指數平滑法、Box-Jenkins預測方法，以及灰色GM(1,1)Alpha模型，來預測房地產巿場警情指標及其單指標，並比較其預測績效方式選出較佳模型。據此便可對房地產巿場發生不正常現象的可能性和嚴重程度進行預測和監控。 研究發現，在民國95年第1季至民國96年第1季房地產警情狀況，可以看出其中的建築貸款餘額與全體金融機構放款總額之比值(S7)過高燈號為5分，顯示銀行對於房地產業貸款過熱，而住宅使用率(S19)有過低燈號為1分的不正常情況，並產生出過多的空屋的情形。接著預測民國96年第2季，所選擇的重要性指標來看，延續了前一季的狀況，更可看出其中的建築貸款餘額與全體金融機構放款總額之比值(S7) 過高燈號為5分，臺灣地區住宅建照樓地板面積與臺灣地區建照執照樓地板面積之比值(S8) 偏高燈號為4分，反應出供給多，五大銀行新承做購屋貸款金額與全體金融機構放款總額之比值(S16) 過高燈號為5分，需求過多，然而住宅使用率(S19)有過低燈號為1分的不正常情況，過多空屋，部分也可能含有餘屋的情形，此買了不住的情形，使住宅資源浪費，需加以警戒，並應適時提出修正。 關鍵字：房地產預警系統(Real Estate Early-Warning System) ，擴散指數模型(Diffusion Index Model)，層級分析法(Analytical Hierarchy Process)，因素分析法（Factor Analysis），檢查值(Check Point)，3σ法，拔靴法(Bootstrap)，指數平滑法（Exponential Smoothing）、Box-Jenkins預測方法，灰色GM(1,1)Alpha模型(Grey Forecast)。 === In order to establish real estate early-warning system, We select the main indicators from real estate market relative indexes during 1992 to 2007.Selectction are similar as the four layers of real estate cycle indicators that we have the important not proceeding indicators. The important indicators selection we applied the analytic hierarchy process, factor analysis and diffusion index model, forthmore, we integrated to determine the weights of the indicators and composed of main indicators. Unlike the past, the real estate cycle indicators choose the use of qualitative methods of expert advice, and set the same weight manner. Warning check point is based on the real estate early-warning value of indicators to determine the status of real estate market. Refered as the current cycle indicators, we have the check value calculated by using the range percentage, bootstrap method, and also developed the 3σ and ideal goal methods to determine an appropriate warning check point of the real estate early-warning value. To forecast the real estate market situation, we use black-box model of exponential smoothing, Box-Jenkins methods, as well as the gray GM (1,1) Alpha model to predict the real estate market indicators and their single indexs. Comparing and electing the better prediction performance model which can forecast and monitor the real estate market situation. The real estate market situation are estimate based on the empirical analysis during the first quarter of 2006 to the first quarter of 2007. The construction loans (S7) is too high for the signal is 5 points, which show overheated. The residential usage (S19) show too low for the signal is 1 point, which indicated non-normal conditions and produce too many vacant houses. Thereafter, we forecast the second quarter of 2007. We have continuously the situation of the previous quarter which as the construction loans (S7) is too high for the signal is 5 points. The residential building floor area (S8) is too high for the signal is 4 points, which was reacting too much supply. housing loan (S16) signal is too high for the signal is 5 points, which show too much demand. But The residential usage (S19) is too low for signal is 1 point, which show too many empty houses and some may contain redundant house.To buy and not to live, so that waste the resources of residential. We need to be alert and should be the right time to amend. Keywords: Real Estate Early-Warning System, Diffusion Index Model, Analytical Hierarchy Process, Factor Analysis, Check Point, 3σ, Bootstrap, Exponential Smoothing, Box-Jenkins, Grey forecast.