銀行在面對借款人以不動產申請抵押貸款時，產生對住宅擔保品估價之需求，以為債權之確保。然銀行的估價過程與一般估價最大不同，肇因於其估價前，擔保品本身已先產生一組買賣契約價格。過去研究指出，估價會嘗試以某些較易取得的價格資訊作為定錨點(anchor)，藉以調整並成為最後的價格。而我國不動產交易價格資訊不透明，契約價格往往由借款人提供的情況下，銀行內部估價人員可能因資訊不易取得、定錨效果，在擔保品的鑑估結果上受到契約價格影響，倘有心人士欲藉此獲得高額貸款、牟取不法利益，將損及銀行債權，即使採用自動估價系統降低人為影響因素，因資料來源不佳，只會產生所謂「garbage in garbage out」的結果。據此，如何分辨契約價格是否具有參考力變成為關鍵，亦為本文欲補足的研究缺口。 本文採用國內某銀行臺北市不動產擔保品8,348筆估價資料為樣本，建立以挑選契約價格是否具有參考力的機率預測模型，尋求影響能判定契約價格是否具有參考力的主要因素，並研究在最適的機率界限下，篩選出具有參考力的契約價格樣本。而研究結果所建立的模型，其預測並篩選出的契約價格樣本均較未經模型篩選者，對擔保品價格之估計有顯著提升。因此本研究所建立的契約價格篩選模型確能提升銀行估價準確性，使不動產擔保品鑑估價格的形成過程中，獲得更多可靠的參考資訊，降低人為操縱的空間，並在成交價格資訊不足的情況下，提升估價人員對契約價格的辨識能力。 / In the face of the borrower to apply for a mortgage of real estate, financial institutions have estimated the price of the collateral requirements to protect the debt claim. However, the biggest difference with the general valuation and that of financial institutions, valuation of its causes before the collateral itself has produced a first sale contract price. In the past research that one attempts to estimate the price of some greater access to information act to anchor in order to adjust and become the final price. Because financial institutions are not easy to obtain price information on real estate transactions in Taiwan, price information is often provided by the borrower. A small number of loans borrower deliberate fraud to forgery or false irrigation Contract price sale and purchase agreement in order to obtain high credit. Even with the automatic valuation system to reduce the human impact factor, due to poor data sources, it will only produce so-called "garbage in garbage out" of the results. Accordingly, how to tell whether the contract price to a reference force becomes critical, and also in this article want to complement the research gap. We adopt 8,348 estate collateral valuation data in Taipei City of a domestic bank for the sample to establish a binary logistic regression model. And we try to seek the main factors that determine whether the contract price of the reference force, and find out the optimal cutoff point, filter out of a sample of the contract price of the reference force. The results confirm the model in this paper. The selected samples of the contract price is estimated that the price of collateral significantly improved compared with those without filtering. Therefore, the model established in this study can really improve the accuracy of bank valuation. Enhance the recognition ability of the bank's internal appraisers on the contract price in the lack of transaction price information.

本研究以不動產估價技術規則第19條第7項與第20條之規定，引用相似無關迴歸模式、多變量迴歸模式與典型相關分析等計量模式，對金融機構所做的擔保品估價進行驗證、預測及控制分析。 擔保品估價中會產生兩價，即擔保品的評估市場價格與評估擔保值（價），大部分的人都認為兩價存在一個比率關係。傳統的迴歸分析估價模式係由一組價格影響因素影響一個不動產價格，上述情形是否可能由同一組價格影響因素影響兩個不動產價格？本研究實證結果顯示，在95％統計信賴水準下，有兩個不動產價格受同一組價格因素影響的結果。既然驗證存在同一組價格影響因素影響兩個不動產價格，是否有更具效率的計量估價模式呢？典型相關分析係透過兩組變項之相關關係建構計量模式，除可再度驗證同一組價格影響因素影響兩個不動產價格，並可如同因素分析或主成份分析的功能，對兩組變項各做變項縮減的工作，達到對變項去蕪存菁的效果。 / This thesis is based on Article 19 No 7 and Article 20 of the Real Estate Appraisal Regulation. Seemingly Unrelated Regression Model, Multivariate Regression Model and Econometric Model and so on econometric model are applied. In addition, collateral valuations done by financial institutions are verified, predicted and analyzed. In collateral valuations, there are two-value references: assessed market value and assessed accommodation value. Majority believe that there is a ratio between these two values. The traditional regression analysis of the valuation model is having one set of pricing factors to have impact on the real estate price. However, is it possible that one set of pricing factors will affect two real estate prices? The findings approve that, under statistical confidence level with 95%, more than two real estate prices can be influenced by one set of pricing factors. Further more, this thesis also examines if there are other econometric valuation models to be applied? The canonical correlation analysis is to build a calculation model to analyze correlation between two variables. Other than examining one set of pricing factors can influence two real estate prices, this analysis also provides a similar function of the factor analysis or principal analysis to reduce variables caused by two sets of variable.