在資訊較不對稱的不動產市場中，購屋者往往無法有效的利用資訊正確預期房價走勢，而過去的研究多半是以總體經濟變數或是市場環境因素探討購屋者實際的預期，並沒有太多購屋者預期的個體研究。近年來行為財務學的文獻中已經發現個人特質以及身分背景將影響投資人的決策模式，故本研究希望從個體角度出發，研究購屋者是否因個人特質、身分背景或是從業性質影響房價預期的正確性？而在台灣的主要城市中，影響的程度與方向有何不同？ 由於購屋者對房價預期正確與否為一機率變數，因此本研究使用二元羅吉特模型估計之。實證結果顯示，當購屋者最高學歷為碩士或碩士以上畢業時，其購屋時對於房價預期的正確機率顯著較高；另外每當購屋決策者之家庭月收入越高時，其預測房價的正確機率也顯著較高。

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。 / 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. / 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.