dc.contributor.advisor | 張金鶚 | zh_TW |
dc.contributor.author (作者) | 邱于修 | zh_TW |
dc.contributor.author (作者) | Chiou,Yu Shiou | en_US |
dc.creator (作者) | 邱于修 | zh_TW |
dc.creator (作者) | Chiou,Yu Shiou | en_US |
dc.date (日期) | 2007 | en_US |
dc.date.accessioned | 14-九月-2009 13:54:30 (UTC+8) | - |
dc.date.available | 14-九月-2009 13:54:30 (UTC+8) | - |
dc.date.issued (上傳時間) | 14-九月-2009 13:54:30 (UTC+8) | - |
dc.identifier (其他 識別碼) | G0095257018 | en_US |
dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/32404 | - |
dc.description (描述) | 碩士 | zh_TW |
dc.description (描述) | 國立政治大學 | zh_TW |
dc.description (描述) | 地政研究所 | zh_TW |
dc.description (描述) | 95257018 | zh_TW |
dc.description (描述) | 96 | zh_TW |
dc.description.abstract (摘要) | 住宅為兼具消費及投資之雙重功能財貨,因此若從購屋動機劃分購屋族群,可以分為自住者及投資者,近年來受到國內房市呈現生氣蓬勃之景象及利率持續走低等總體經濟因素影響之下,出現越來越多以投資為主要目的之投資型購屋者,對於金融機構之購屋貸款業務來說,投資者之還款行為相較於自住者是比較不穩定的。故本文之研究目的即藉由探討自住者及投資者之購屋特徵異同,建立投資者之機率預測模型,預測某購屋者成為投資者之機率,提供一較為客觀之機率預測模型,供作金融機構放貸參考準則。接著進一步探討在不同機率界限(cutoff point)下之預測準確率,找出預測準確率最高之機率界限值,提高本模型之預測準確度;並探討金融機構在不同經營方針下之較適機率界限值。 本文使用台灣住宅需求動向季報之已購屋者問卷,建立二元羅吉特模型。研究結果顯示,區位在中心都市、高單價、小面積產品及大面積產品、預售屋及拍賣屋市場屬於投資型產品,而搜尋時間短、搜尋間數少、年齡較長、男性、無固定職業及家庭平均月收入較高者成為投資者之機率較高。接著,運用貝氏定理計算出預測準確率最高之機率界限值,結果當機率界限值為0.70時預測準確率最高,投資者達72.22%,自住者達80.07%。此外,並使用2007Q4的資料作樣本外驗證,投資者命中率為65.52%,自住者命中率為84.51%。最後,為提供金融機構運用,本文模擬兩種預測誤差在不同權重下對於金融機構所造成的損失,找出損失最少的機率界限值,結果皆是以0.70為最適機率界限值。 | zh_TW |
dc.description.abstract (摘要) | Housing is dual function goods, consumption and investment, so if we separate the home buyers by their motives, they can be defined as two groups, owner-occupiers and investors. Recently, because the housing market is vigorous inland and the rates are fairly low, there are more and more home buyers buying houses for investment. To financial institutions, their payment behaviors are more instable, compare to owner-occupiers. So this article is aim to build a probability predictive model of housing investors by discussing the different home buying characters between owner-occupiers and investors. Therefore we can provide financing institutions a more objective method evaluating if they should lend money to the home buyers. Then we discuss the predictive accuracy with different cutoff points, finding the cutoff point with highest predictive accuracy, therefore we can elevate the model"s predictive accuracy. Besides, we also discuss the most optimal cutoff point for financial institutions under different administration principles.<br>This article builds binary logit model by the data of “Housing Demand Survey in Taiwan”. Our results suggests that if the houses in downtown、high unit price、big and small acreage、presale and court auction housing market belong to investing houses. And short search duration、few search items、older、male、non-constant job、higher income are getting higher probability to be housing investors. Then, we use Bayesian Theorem to figure out the cutoff point with highest predictive accuracy, and Our results suggests that 0.70 cutoff point with highest predictive accuracy , at that time, investor predictive accuracy is 72.22%, owner-occupier is 80.07%. Besides, we also do the out-sample test by the 2007Q4 data, the investor"s hit-rate is65.52%, the owner-occupier"s hit-rate is 84.51%. At the end, in order to provide financial institution to use, we give two predictive deviation different weights, to find the smallest loss cutoff point, the result all suggest that 0.70 is the most optimal cutoff point. | en_US |
dc.description.tableofcontents | 目錄 第一章 緒論..............................1 第一節 研究動機與問題 ...................1 第二節 研究範圍與方法....................4 第三節 研究架構與流程....................5 第二章 相關理論與文獻回顧..................7 第一節 購屋貸款提前清償與違約.............7 第二節 住宅消費與投資行為................10 第三節 二元羅吉特模型....................15 第三章 資料說明與樣本分析..................17 第一節 資料說明.........................17 第二節 樣本基本資料分析..................18 第四章 實證分析...........................21 第一節 模型建立與變數選取.................21 第二節 實證結果分析......................26 第三節 機率界限值探討....................31 第四節 金融機構於不同經營策略下的運用......35 第五節 樣本外資料驗證....................37 第五章 結論與建議.........................39 第一節 結論.............................39 第二節 後續研究建議......................41 參考文獻..................................42 附錄一 住宅需求動向調查(2006Q4已購屋者)........i 附錄二 銀行住宅抵押貸款放貸人員訪談記錄整理....iii 附錄三 主要銀行對房市投資客控管概況.............v 附錄四 台灣地區都會區分類.....................vi 附錄五 給兩種誤差不同權重(較高)所造成的總損失...vii | zh_TW |
dc.language.iso | en_US | - |
dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0095257018 | en_US |
dc.subject (關鍵詞) | 投資型購屋者 | zh_TW |
dc.subject (關鍵詞) | 自住型購屋者 | zh_TW |
dc.subject (關鍵詞) | 二元羅吉特模型 | zh_TW |
dc.subject (關鍵詞) | 機率界限 | zh_TW |
dc.subject (關鍵詞) | housing investor | en_US |
dc.subject (關鍵詞) | owner-occupier | en_US |
dc.subject (關鍵詞) | binary logit model | en_US |
dc.subject (關鍵詞) | cutoff point | en_US |
dc.title (題名) | 投資型購屋者機率預測模型之建立 | zh_TW |
dc.title (題名) | The Probability predictive model of housing investors | en_US |
dc.type (資料類型) | thesis | en |
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