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.

不動產估價獨立客觀為金融體系穩定的關鍵因素，而客戶影響是探討估價獨立性的重要議題。過去多以問卷調查、實驗設計或深度訪談方式進行相關研究，卻難以證明不動產估值受到客戶影響之真實情形。本文蒐集公開資訊觀測站相同不動產其買賣雙方各自委託之估值及其成交價，在雙方均有影響估值之動機前提下，以獨立樣本t檢定及Wilcoxon-Mann-Whitney檢定驗證其估值溢價率以及估值差異比率在不同變數情況下之差異情形。實證結果顯示，不動產估值因客戶為買方或賣方不同而有顯著差異，另經驗老練客戶將使不動產估值差異更為擴大，而不動產採標售方式買賣者，其估值差異比率遠較採議價方式高。 / The independent objective of real estate appraisal is the key factor of the stability of the financial system, while the client influence is an important issue of the independence of valuation. In the past, more of the relevant research by questionnaire, experiment or interview, but it is difficult to verify the real situation of the client influence. This paper collected the cases of same real estate that both the buyer and the seller commissioned the valuation and the transaction price from MOPS, under the premise that both parties have the motivation to influence the valuation, to examine the valuation premium ratio and valuation difference ratio with the independent sample t test and Wilcoxon-Mann-Whitney test. The results show that the real estate valuation is significantly different from clients, and experienced clients will make the real estate valuation differences more widened. However, the valuation difference ratio of the transactions by auction is much higher than the valuation difference ratio of the transactions by bargain.

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.

主要目的在探討目前我國徵收不動產之扣失補償估價方法有何利弊得失，並研究外國 （尤其是日本）之各種損失補償估價方法，據以研擬合理之損失補償估價基準。 第一章：共分二節，敘述本文研究動機、研究目的、研究內容及研究方法。 第二章：共分四節，首先簡述徵收之一般情況，次闡述徵收損失補償之法律基礎與性 質，並評述我國現行之徵收損失補償標準。 第三章：共分六節，以土地所有權及他項權利、建築改良物、農作改良物、營業等損 失，改葬祭祀費用與共他通常所生之損失等為範圍，探討徵收損失補償應包含之項目 及其合理之估價基準。 第四章：共分十節，研擬徵收市地農地、建築物暨其基地、林地與立木、魚　、墳墓 、他項權利、農作物、工業用地等補償之估價方法，以供作徵收損失補償立法及執行 上之參考。 第五章：結論。

近年來，由於經濟不景氣及市場上之不確定因素，使得投資不動產成為保險業資金投入的新興熱門標的。因投資不動產之行為涉及憲法所保障之財產權，如非有特殊事由不得加以限制。保險法第146條亦有規定不動產為保險業得投資之法定項目之一。惟於現行相關法規規範下，保險業投資發展空間受到限制。究監理機關於此所扮演之角色為何？監理目標為何？於現行法制下，相關法規是否妥適合理等問題，皆有待探求。又隨著不動產投資投入之資金日趨增加，保險業者受裁罰之案例亦較過去為多。究過去裁罰當中，主管機關之處分有無不妥適之處，以及保險業者於投資不動產之相關缺失中，是否有可改善之空間，為本文所欲探討之目標。 / In recent years, real estate has become an increasingly popular investment target for insurers’due to uncertainties in the market and the economic downturn. Legally, investing in real estate is a constitutional right to property and protected specifically by the Insurance Act, Article 146. It cannot be infringed upon unless for specific, legitimate reasons. However, current regulations put a fair amount of limitation on investment in real estate. The adequacy of these regulations, as well as the goals and roles of our supervision system, is to be discussed. In addition, there are a growing number of administrative sanctions as insurance companies put more and more funds in real estate. This article also attempts to investigate if these penalties were appropriate and if there’s room for improvement on the insurers’ part.

近年由於物價變動激烈，致使資產歷史成本未能允當表達實際價值，影響所及，傳統 會計所得呈現虛盈實虧。若未有一套具備理論基礎，且屬客觀可行的資產重估價辦法 ，恐傷害企業資本之維持，乃試撰此文。大要如下： 前言：敘述研究動機及目的，研究方法及限制。 第一章：價格變動對傳統會計的衝擊。第一節：價格變動的特質與所得型態。第二節 ：課稅所得型態對經濟影響。第三節：改進傳統會計的各種理論與作法。第四節：本 章彙述。 第二章：物價水準會計。第一節：物價水準會計的介紹。第二節：按物價水準重編報 告的模式。第三節：物價水準重編報告的評估。第四節：本章彙述。 第三章：現值會計。第一節：現值會計制度之依據。第二節：現值會計重編報告的模 式。第三節：現值會計的評估。第四節：本章彙述。 第四章：我國營利事業資產重估價的介紹。第一節：資產重估價範圍。第二節：辦理 資產重估價方法與程序。第三節：資產重估價有關課稅問題。第四節：資產重估價會 計處理。 第五章：我國營利事業資產重估價的評議與構想。第一節：現行重估價制度與重估範 圍之評議。第二節：重估物價指數之抉擇與重估價值計算之探討。第三節：重估價後 課稅所得之分析。第四節：其他重估實務疑難問題之探討。 第六章：彙總與建議。

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.

現行公告土地現值的評估，係採用人工的傳統方法，估價結果誤差甚大且過於主觀，無法達到大量估價客觀、快速、精確之目標。本文首先利用土地經濟理論的分析，探討土地市場價值、交易價格、評估價值之間的關係，釐清常見的混淆概念。並藉由估價比率研究，討論公告現值與市價差距的檢定模型，針對台南市的實際資料進行統計檢定，結果發現平均估價比率落於46.74﹪~48.52﹪之區間，並存在輕微的垂直不公平。 為改進現行公告現值不夠準確之缺失，本研究基於都市經濟理論與估價先驗訊息之基礎，利用特徵價格法與可加性模型建立大量估價模式。實證結果發現，影響台南市地價之因素，以區位、臨街關係、路寬、使用分區最為重要。在部份年度中，亦證實存在基地面積規模不經濟（plattage）現象。 傳統特徵價格法必須預設函數型態，若函數設定錯誤則將使參數估計產生偏誤。可加性模型結合無母數迴歸與母數迴歸之優點，不須預設函數型態、估計結果易於解釋且維持母數迴歸之收斂速度。其可經由修勻法配適出更客觀的函數關係，無論在樣本內與樣本外之估計均較特徵價格法為佳。 研究結果發現，本文所提出的二種估價模式確可達到快速精確的目標，使估價比率接近１，比目前評估效率提高一倍；在公平性方面雖無改善，但亦無嚴重之垂直不公平。其中可加性模型又較特徵價格法為佳，在電腦技術快速進步的今天，應用至大量估價的可行性大為提高，值得後續進一步深入研究。 / The present Announced Land Current Value （ALCV）was evaluated by traditional appraisal method that may result in large errors. Comparing to mass assessment approaches, it is hard to be objective, quick and precise. This research begins with the analysis based on land economic theory to discuss the relation among the market value, sale price and assessed value of land in order to clarify some confusing concepts. Through assessment-sale price ratio study, we analyze the difference between ALCV and sale price, and then use the actual data of Tainan City for empirical study. The results show that the average a-s ratio falls between 46.74%~48.52% with slight vertical inequity. To improve the lack of preciseness and objectivity of the present ALCV, this research uses hedonic price theory and Generalized Additive Model（GAM）based on urban economic theory and appraisal priori information. The results show that location, relations with adjacent streets, road width and zoning are the most influencing factors of land price in Tainan City. During some years, the phenomenon of plattage effect also exits. The function form must be set beforehand in the traditional hedonic pricing, meanwhile parameters bias will occur if the pre-determined function form were wrong. GAM has the advantages of nonparametric regression and parametric regression. The function form needs not to be pre-determined, the empirical results are easy to interpret, and the speed of variable convergence can be maintained. More precise functional relations can also be smoothed by GAM. It is superior to the traditional hedonic price in the sample and out of the sample prediction alike. The results of empirical study show that both of two models can reach the goal of rapidity and preciseness and make the a-s ratio toward 1. As to the equity, although they are not improved very much, the models don't bring serious vertical inequity. However, GAM is better than hedonic pricing when compared to each other. Due to the great progress of computer technology, the application of GAM to mass assessment can be increased greatly and is worthy continuing further study.

我國公部門以課徵土地稅、發放土地徵收補償為目的而進行土地大量估價，並以區段價法與路線價法為主要估價方法。由於此二估價方法之評估基礎為平均區段地價，故是否能準確劃分地價區段乃至關重要。然而，地價調查估計規則中有關地價區段之劃分規定，並無具體規範劃分準則與方式，導致地價人員僅能透過參考各項可得之圖表資料，並根據自身認知與前輩經驗，將地價相近、地段相連、情況相同或相似之土地劃為同一區段。因此，地價區段劃分之公平、客觀、準確性往往遭受民眾質疑。 劃分地價區段之目的係為掌握地區特性，故其實屬劃分同質區之概念。惟地區特性乃難以定義或量化之區域因素的空間聚集特性，致使地價人員難以掌握具體劃分準則，並準確劃分地價區段。而過去相關研究指出難以定義或量化之空間因素存於特徵價格模型的殘差之中，遂有分析殘差之空間特性以劃分同質區者。但是，各種劃分方式皆侷限於已知樣本所在位置的空間關係，導致可能出現無法就整體地區劃分同質區，或出現預測樣本不屬於任何同質區的情況。 由於克利金法可依據樣本的空間變異結構特性推估未知空間位置的觀察值，因此本研究以區域化變數理論為基礎，應用克利金法結合地理資訊系統之空間分析功能，進而依殘差之空間特性劃分空間效果同質區。研究結果顯示該同質區可合理呈現空間效果之同質性，應用於大量估價的準確性佳，且不會扭曲地價高低層次。因此，應用克利金法劃分地價區段確實為合理、準確且可行之方式。 / Public assessors evaluate official land value for taxing and compensating by land value district approach and street value approach. Since the basis of these two approaches is land value district, whether public assessors could define it accurately or not is an important issue. However, there are no specific defining criterions in Regulations on Land Value Assessment; public assessors could only refer to concerning information, especially their own subjectivity and experience of senior assessors, to define district in terms of “close land value, connected relation and similar circumstances of lands”. Accordingly, district that defined by public assessors not only the fairness and objectivity, but also the accuracy are quite doubtful. The main purpose of defining land value district is capturing local characteristics; therefore, it’s similar to the task of defining homogeneous area. Nevertheless, local characteristics are agglomerations of spatial effect, which are difficult to define or quantity. Due to the fact that public assessors are unable to get specific defining principles, they cannot define land value district accurately. A few researches indicate that spatial effect is in the residuals of hedonic pricing model, thus, some researches defining homogeneous area according to the spatial distribution of residuals. However, the defining approaches of these researches are all restricted to spatial location of known samples. Hence, it’s possible to fail to segregate different homogeneous area, or fail to take unknown samples into consideration. For the reason that kriging estimation can predict unknown spatial location’s value in basis of spatial variation structure characteristics, this research apply kriging estimation and GIS to define homogeneous area based on Theory of Regionalized Variables. The research concludes that homogeneous area which is defined by kriging can capture homogeneity of spatial effect. Besides, the prediction accuracy is quite well by adding variables of homogeneous area to hedonic pricing model. On the other hand, predicted land values still remain the exact relation between each other. Therefore, applying kriging estimation to define land value district is a reasonable, accurate and feasible method.