住宅一直是家戶最重要的選擇之一，瞭解家戶對於住宅選擇之行為對於政府政策的擬定有很大的幫助，學者自1960年代即投入家戶對於住宅選擇行為之研究，在1970年代McFadden提出多項羅吉特模型(Multinomial Logit，MNL)後，MNL在此研究領域中即被廣泛的應用。然而MNL模型存在方案間之「不相干獨立替選方案」(Independence from Irrelevant Alternatives，IIA)特性，使得方案間若存在關連性時，會造成估計的偏誤，影響推論的結果與準確性。 既然MNL的IIA特性在某些情況下會造成估計偏誤進而影響推論結果與準確性，因此放寬IIA限制的模型即被提出。巢式多項Logit(Nested Multinomial Logit，NMNL )模型允許方案間存在關連性，放寬IIA的特性限制，因此NMNL是更一般化的模型，更能符合現實情況。 本研究以「中央研究院經濟研究所」民國93年「華人家庭動態資料(PSFD)」為實證資料，以NMNL模型分析家戶對住宅選擇之研究，實證結果發現在選擇次序上，家戶以最先考慮區位、其次為樣式、最後考慮租買的選擇次序最符合實證資料；而在變數上，在家戶屬性方面，家戶所得、家戶人數與戶長年齡對於家戶在選擇住宅時顯著的影響；在住宅屬性方面，坪數、房間數與舒適性對家戶選擇有顯著影響。

價格預期是影響市場價格波動的重要因素，但過去房價預期的研究多半以客觀的市場與環境因素取代購屋者實際的預期，缺乏購屋個體實際的預期研究。股價與物價預期相關研究已從個體預期的角度發現價格預期具有高度的異質性，隱含多元豐富的資訊。故本研究希望釐清個體購屋者預期，是否因身分屬性、搜尋行為、動機與認知產生不同的預期？房價預期的差異與股價預期、民生消費品價格預期有何不同與類似之處？在不同的景氣狀況之下預期之異質性是否因此而改變？ 由於購屋者預期為類別變數，本研究使用多項羅吉特模型。實證結果顯示，購屋者身分、行為、動機與認知使房價預期產生差異，其中女性購屋者看漲機率較男性高與消費市場預期差異結果相同，加上住宅投資消費目的皆較投資者多，推測購屋者對住宅商品之看法仍偏向消費市場。此外，從在不同景氣階段，房價預期之異質性會因此改變，主因是住宅投資與消費價比例發生變化。當市場投資比例增加時，購屋者房價預期差異較大，反之預期差異較小。

由於傳統國人的觀念「安土重遷、購屋保值」，使得一般人都想要擁有自己的房屋，然而房價居高不下，國人大多需仰賴長時間之儲蓄，始有購屋之能力，使得許多人一屋難求，因此，本研究利用79年台北市家戶及住宅普查調查資料，探討家戶住宅租購選擇之影響因素，並瞭解選擇租屋或購屋之對象及其選擇行為，進而估計各類型家戶選擇租屋或購屋之機率。 本研究採用巢式多項羅吉特(Logit)模型分析家戶之住宅租購選擇，及各類型家戶之租購選擇機率，並估計出自有房屋市場與租賃房屋市場間有很高的替代性存在，若忽略租購選擇，則所估計到之住宅需求將會造成誤差，因此，為避免產生錯誤的結果，不應將自有房屋市場與租賃房屋市場分開來討論。 在影響家戶住宅租購選擇之因素方面，所得、住宅價格．利率、家庭因素、預期因素、心理因素等其他因素，皆會影響家戶之選擇行為，而本研究僅就家戶組成來探討住宅之租購選擇。此外，在住宅租購選擇之機率方面，各類型家戶選擇自有房屋之機率均相當高，凸顯台北市高住宅自有率之現象，此種現象正反映傳統國人購屋保值的觀念，房價在高漲的情況下，許多人仍將購屋視為理想目標。

從1960年代開始，公部門基於稅務處理需求，使得電腦輔助大量估價（Computer Assisted Mass Assessment，CAMA）成為輔助的工具，大幅提升了估價的效率。在1990年代，金融機構因不動產證券化的發展及不良資產估價等業務，而衍生了對大量不動產進行估價的需求，同時在電腦與統計模型的進步之下，自動估價模型（Automated Valuation Models，AVM）應然而生，並被廣泛應用在金融市場。由此可知因為不動產經濟活動的熱絡發展，大量估價的需求日益增加，其具備的客觀與效率等優點更彰顯其重要性。 雖然大量估價的需求日益增加，然而過去對於估價準確性相關研究，主要著重在估價理論與技術層面、估價行為對估價結果的影響、探討個別估價和大量估價的估值比較，而較少單獨探究影響大量估價準確性的因素。由於特徵價格理論隱含不動產高度異質的特性，不動產價格受到總體經濟、政策、住宅屬性、公共設施、區位等因素影響，然而前述因素是否會對估價準確性造成影響?造成影響的因素為何?為本文所欲探討之問題。 本文在實證部分分成兩階段，首先以特徵價格理論為基礎，利用實價登錄資料建立大量估價模型，以MAPE與Hit Rate來衡量估價準確性，結果指出MAPE達到14.19%，而正負誤差10%的命中率為47.18%、正負誤差20%的命中率為74.75%，跟過往研究所建立的大量估價模型相比具有相當的水準，顯示出官方性質的交易資料具有一定的可信度。在建置大量估價模型後，本文以模型價格及成交價格間的比值作為劃分估價準確程度的依據，以多項羅吉特模型進行實證分析，結果指出住宅大樓、捷運站周遭住宅、大坪數住宅估價結果容易呈現低估情形；而新市區中心估價結果容易呈現高估的情形；另外比較特別的是舊市區中心、北郊區估價結果較容易呈現高估及低估，換言之在這兩個區域估價容易得到不準確的結果。 / Since 1960s, public sector began to take advantage of computer assisted mass assessment(CAMA) based on taxation services and greatly improved the efficiency of appraisal. In 1990s, financial institutions due to the development of securitization of real estate and non-performing asset valuation and other services, generating the demand of mass appraisal. Simultaneously, due to the development of computer and statistical models gradually progress, bring in automated valuation models(AVM) in the financial markets. Hence, with the real estate economic activities gradually booming, the increasing demand for mass appraisal, which has the objective of efficiency and other advantages will be more to highlight its importance. While the increasing demand for mass appraisal, but past studies about the accuracy of appraisal, mainly focused on the theoretical and technical aspects, the impact of behavior on the valuation results, and to explore appraisers and mass appraisal of the valuation. However, past studies less focused on a large number of factors affect the accuracy of the appraisal. Since the hedonic price theory implies highly heterogeneous characteristics of real estate, real estate prices affected by factors of macroeconomic, tax policy, housing properties, public facilities, location and so on, but whether the aforementioned factors will affect the valuation accuracy?Is this research seeking to explore the issue. In this paper, the empirical section is divided into two stages, first with the hedonic price theory based on the use actual price registration to establish the mass appraisal models, and base on MAPE and Hit Rate to measure the accuracy of the appraisal, the results indicate MAPE reached 14.19%, while the margin of error of 10% hit rate of 47.18%, 20% hit rate is 74.75%. Compared with the past studies, this model has established a great performance. This research proved that the official data with reliability. After establishing the mass appraisal models, the research use model prices and the transaction price ratio as the basis for division between the accuracy of the appraisal and use multinomial logistic model to conduct empirical analysis. The results indicated that the residential building, housing around MRT stations, the big area housing was prone to result underestimate valuations, the new urban center appraisal results likely to show overvalued valuations. On the other hand, old city center and the northern suburbs results presented overestimate and underestimate valuations simultaneously, in other words, that is usually get inaccurate results in these two regions.

住宅選擇是每一個家戶都會面臨到的問題，過去文獻發現購屋者先選擇租屋或購屋，若決定購屋，則先決定於何區位購屋，然後再決定購買何種房屋類型之房屋，然而卻未曾提及購屋者於不同市場類型住宅間之選擇。預售屋、新成屋以及中古屋等不同市場類型之住宅，各自隱含不同的效用及風險，影響著購屋者之選擇，因此本研究試圖討論購屋者於不同市場類型住宅間之選擇與偏好。 本研究採用內政部營建署「住宅需求動向調查」資料，利用混合多項羅吉特模型探討不同限制條件下，預售屋、新成屋與中古屋之個體選擇行為。實證結果顯示，投資者較偏好於購買知覺風險較高之預售屋，期待以高知覺風險換取高的報酬；教育程度較高者，因對居住品質要求愈高，因此傾向於選擇設備新穎之預售屋與新成屋；家戶平均月所得較高之購屋者，負擔能力較高，因此選擇總價較高之預售屋機率較高，其次為新成屋。此外，搜尋頻率愈高者，選擇預售屋之機率愈高，因預售屋無實體存在，預售屋購屋者為降低其知覺風險，將花費更高之搜尋成本。在價格彈性分析部分，實證結果顯示預售屋之競爭力最高，但預售屋之受衝擊力亦最高，而中古屋之競爭力於三種市場類型中居次，但中古屋衝擊力最小，因此，當單價屬性發生變動時，較不影響中古屋購屋者之選擇，但卻大幅影響預售屋購屋者之選擇機率。 / Every household would face housing choice, the past housing choice study founded that households decided tenure choice first, if they decides to buy a house, they first decided on what location, and then decided what type of housing to buy, but it has not been mentioned the housing choice among different residential market types. Pre-sale houses, newly constructed houses and existing houses implied different effectiveness and risks, affecting the choice of homebuyers. This article tried to discuss homeowners’ choice among different residential market types. This study use Construction and Planning Agency, "Housing Demand Survey of 2009" data, use mixed multinomial logit model, investigated under different constraints, housing choice behavior among pre-sale houses, newly constructed houses and existing houses. The empirical results showed that investors prefer higher perceived risk in buying pre-sale housing, looking for a high perceived risk and high rewards; higher education level, due to the higher quality requirements for living, so they preferred pre-sale houses and newly constructed houses. Homebuyers which have higher average monthly household income, have more affordable ability, so the probability of choosing pre-sale houses are much higher, followed by the newly constructed houses. In addition, the higher search frequency were more likely to choose pre-sale houses because pre-sale houses for sale no physical presence, pre-sale housing homebuyers in order to reduce their perceived risk, would spend more search costs. In the price elasticity analysis, empirical results showed that the pre-sale houses had the highest competitiveness, but the impact force was also the highest, while the existing houses market, the competitiveness of the third types was the second place, and the competitiveness of the existing houses was the smallest. Thus, when a change in unit price attribute, does not affect existing houses homebuyers, but significantly affected the choice probability of pre-sale houses homebuyers.