評定合理的房屋價格，有助於維護交易雙方權益，並給予相關稅負有一適當的課稅基礎。惟影響房屋價格的因素眾多，除了房屋內部特徵外，土地價格亦為影響房屋價格之重要變數。由於影響房屋價格與土地價格之有關變數區分不易，過去文獻多僅對房屋價格及土地價格進行個別研究，本文參考評價土地價格相關文獻，利用區位變數將土地價格由房屋價格分離。將影響房屋價格之變數分為房屋內部特徵變數與外部生活機能變數兩類，透過加入兩個交互作用項，並架構於特徵價格估計法上以最小平方估計法分析之。 本文資料採自山東省青島市2008年商品住宅交易數據，實證分析顯示，透過區位變數可分析土地價格之相關區位變數，個別對房屋價格所產生之邊際效果，發現土地價格對於房屋價格有顯著影響。由實證結果發現，在內部特徵變數中，公攤率對房屋價格之邊際效果最大；在外部生活機能變數中，該住宅之行政區位於萊西市對房屋價格之邊際效果最大。 / Evaluating a reasonable housing price is helpful for maintaining the benefits of both buyers and sellers. Also, it can give the tax an appropriate base. However, there are lots of factors affecting the housing price. Besides the inside characteristics, the price of land is the other characteristic. Because it is difficult to distinguish out the factors of housing price and land price, previous studies only pick up one of them to do studies. This study takes the way that previous studies evaluate the land price as reference, and use the location variables to separate the land price from the housing price. The variables which influence housing price are divided into the inside characteristics and outside living standard. By adding two interactions and basing on the hedonic price model, the current study uses the ordinary least squares to do regression. This study uses the housing transaction data of ShanDong Seng QingDao City in 2008, which finds out the location variables can analyze the land price and the marginal effect of housing price. The empirical analysis reveals the land price has significant influence on the housing price. In the inside variables, the pool rate has the biggest significant influence; in outside variables, the house which is located in Laixi city has the biggest significant influence.

The diploma thesis is a summary describing of the current housing situation in the area of the city Brno. It deals with comparing the normal rent of flats in different localities of the city and consider amendments to the lease in terms of amenities and size of dwelling. The figures are divided into flats for a 1+1, 1+kk, 2+1, 2+kk, 3+1, 3+kk, 4+1, 4+kk. Charting the market and its price in the lease is done both text and graphics.

Denna uppsats undersöker hur effektivt det vore att hedga svenska bostadspriser under tidsperioden 2005–2017 med hjälp av ett bostadsprisindex. Uppsatsen undersöker ickeperiodiserade och periodiserade hedgar genom tre olika hedgingstrategier i form av statisk, dynamisk och optimal hedge. Hedge ratios skattas via tre olika hedgingmetoder bestående av OLS, ECM och en naiv hedge. Genom att både använda ett nationellt och regionalt hedginginstrument analyseras skillnaden i hedgingeffektivitet i respektive region som hedgas. Hedgingeffektiviteterna bedöms i termer av reducerad varians vilket har fastställts genom justerad förklaringsgrad samt en alternativ beräkningsmetod för att presentera rättvisande resultat. Med avstamp i resultatet av hedgingeffektivitet och med hjälp av tidigare litteratur genomförs även en analys av förutsättningar för en bostadsprisderivatmarknad i Sverige. Genom studiens uppbyggda analysmodell påvisar undersökningen att ett nationellt hedginginstrument överlag är mer effektivt än ett regionalt hedginginstrument för att hedga bostadsprisrisken i Sverige för den undersökta tidsperioden. Våra resultat pekar på att svenska bostäder inhyser en stor grad av idiosynkratisk risk där den ohedgbara risken är beroende utav vilket hedginginstrument som används. Sammanfattningsvis finner vi det svårt att hedga all form av bostadsprisrisk på den svenska bostadsmarknaden. I dagsläget finns det ingen möjlighet för svenska hushåll att riskjustera sin exponering mot bostadsprisrisken. Ur ett transaktionskostnadsperspektiv anser vi att finansiell bildning kan vara en av de stora anledningarna till att en marknad för att riskjustera bostadsprisrisken inte finns. Dels saknas det kunskap för att applicera en hedge men bostadsägarna kan sakna vetskap om sitt egna behov av att skydda sig mot bostadsprisrisken.

過去雖有文獻探討國內房地產市場泡沫化問題，卻僅從租金收益的單一角度衡量房價基值，對於自有住宅比例較高的台灣而言，家戶所得不僅代表購屋者的負擔能力，更是構成房價基值的重要因素。有鑑於此，本研究分別從租金收益及家戶所得兩者不同角度下，透過資產市場現值模型，分別建立房價基值模型分析泡沫化現象。此外，過去文獻僅從檢定價格波動穩定性與否或將殘差項視為泡沫來研究泡沫化問題，然泡沫為不可觀察之變數，故本文使用具有可估計不可觀察變數特質的狀態空間模型（STATE-SPACE MODEL），推估泡沫價格，分析在不同時期下泡沫的規模大小。 在實證方面，本研究使用台北市1973Q2至2008Q1共140筆住宅價格資料，發現由租金與所得所計算之房價泡沫規模略為一致。在1988~1990年房市泡沫化時期，所得推估之泡沫規模達到高峰，泡沫價格占市價約47%；而由租金面亦計算出泡沫價格占市價約54%的高比例。而在2008年房價持續上漲的情況下，兩者泡沫價格亦呈現相同上升之走勢，泡沫價格近市價38%，租金推估泡沫價格占市價27%；此結果表示出目前房市有泡沫化之跡象，現階段欲購屋自住者不宜進入市場，宜審慎等待時機。而本文認為房價所得比或是房價租金比皆是作為衡量台北市房地產市場泡沫化現象之重要指標，另外就總體因素分析而言，房價上漲率、貨幣供給額、貸款利率與大盤股價指數皆為影響泡沫之重要因素，且經由實證發現所得所推估之泡沫價格較具有市場代表性。 / The past literatures about Taipei housing price bubble has only been measured the fundamental price by rent. However, the housing owner ratio is so high in Taiwan that housing income is not only regarded as affordability but also an important fundamental factor of housing price. According to the above, we focus on different fundamental models that define market fundamental price to analyses the bubble price from expected present value of both rent and permanent housing income. On the other hand, different from lots of literature testing the housing price volatility or residual to measure bubble prices, because housing bubble is an unobservable variable, we apply State-Space Model which is good for testing an invisible factor to estimate bubble in the housing markets of Taipei. This paper tries to test whether there was a housing price bubble using Taipei housing price index ranged from 1973Q1 to 2008Q1. The findings indicate that there appeared bubble ratio from 1988 to 1990, 47% of the housing price based on housing income and 54 % of the housing price based on rent. In 2008 when housing price continually keeps rising, bubble price ratios are close to 38% and 27% respectively. Those results show that Taipei seems to have sign of a bubble in this moment and housing buyers should concern it with more caution. Secondly, both price-income ratio and price-rent ratio are good indicators to measure housing bubble prices. Beside, we find macro economic factors change, such as the growth rate of housing price, M2, mortgage rate, and stock price index, are important to influence the size of housing bubble. Thirdly, bubble price estimated by housing income has a better performance than rent.

過去由於國內住宅市場的資訊並不流通，以致市場上出現的價格資訊相當混亂，就價格的種類來看，各種名目住宅價格間的差異未有明確釐清，使一般人常會對不同的住宅價格產生誤解。就時間序列來看，不同的時間、地區及住宅類型，到底住宅價格的變動如何，也未能有一嚴謹且量化之指標表示。 　　本研究運用標準住宅的概念編製住宅價格指數，所謂標準住宅乃是指一定時間、地區、類型，市場上成交的住宅中，典型的住宅屬性及其數量的組合，也就是說，觀察市場上成交典型住宅的價格變動情形，作為指數編製的基礎，以控制住宅的異質性，再以特徵價格法來求得各屬性的單價以進一步控制品質。另外，由於住宅成交數量變動較大，因此運用裴氏公式作為指數公式，使加權權重的誤差不致太大。 　　由各地區的指數變動趨勢可得到以下幾點結論：首先，一般所認為的三次房地產價格高峰期（62至63年、68至70年、76至78年），從指數的變動來看並不明顯，只有76至78年的上漲趨勢較明顯，在經過幣值平減後，長期趨勢更顯平緩；其次，長期來看，住宅價格持續上漲，部分時期持平或下跌，但幅度及持續時間有限，故所謂房價下跌，其實跌的是上漲率；最後，就上漲幅度來看，台北縣、市的幅度最大，除台北市外，非都市地區（非省轄市）房價的上漲速度較都市地區（省轄市）為高。 　　最後歸納二個造成一般人對住宅價格變動之錯誤印象的原因。主要是品質未加控制，由於品質會影響住宅價格，且消費的住宅品質及數量會隨時間而改變，一般人未察覺此點，而造成對房價上漲的誤解。其次是未考慮幣值，由於「今天的一塊錢不等於明天的一塊錢」，因此以名目價格觀察住宅價格變動的作法，也會造成對房價變動的誤解。

由於住宅價格變動毫無預警制度，人民往往憑著個人主觀的判斷而決定何時購屋或售屋，而此種主觀判斷住宅市場利多及利空的觀念，對住宅市場的供需會產生失衡現象，因此是否可從經濟面的訊息找到住宅價格變動的答案，使住宅價格在尚未變動前，政府即已掌握資訊，提前做好穩定住宅價格的因應對策，使民眾依其需要而購屋，則是本研究之主要目的。 　　本研究從文獻中整理出影響住宅價格變動的七個總體經濟變數，這些總體經濟變數包含工資、物價、所得、貨幣供給額、股價、匯率及利率等，並利用向量自我迴歸模式(VAR)進行實證，以便較客觀的獲得變數間的落後期數及暸解變數間雙向、單向及領先、同步、落後情形，且進一步探討住宅價格與每一個總體經濟變數間影響程度大小及影響情形，以釐清各變數之間的關係。 　　本研究利用VAR模型進行住宅價格與總體經濟變數關係的研究，經由實證，得到下列的結論： 　　一、實證結果方面 　　本研究之實證主要有因果關係檢定與分析、變異數分解之分析及衝擊反應之分析三方面，其實證結果如下所述。 　　（一）因果關係檢定與分析 　　由因果關係檢定與分析中，得到股價、物價、匯率、貨幣供給額及利率均能做為住宅價格變動的領先指標。 　　（二）變異數分解之分析 　　由住宅價格之變異數分解中，得知住宅價格自身的解釋程度僅占三分之一，另三分之二被其他的總體經濟變數所解釋，顯示住宅價格受總體經濟變數的影響相當大；而從其他總體經濟變數之變異數分解中，得知住宅價格變動會干擾到總體經濟變數，而使總體經濟變數受干擾而變動變動。 　　（三）衝擊反應之分析 　　從總體經濟變數對住宅價格的衝擊反應分析圖中可以明顯看出除工資外，其餘總體經濟變數變動對住宅價格造成的衝擊均相當明顯，但匯率及利率對住宅價格的衝擊是負向的。 　　住宅價格對所得、股價、匯率及利率的衝擊相當明顯，而其對匯率的衝擊是負向。 　　二、政策應用方面 政府的決策過程中常會有時間落後的現象，而本研究實證的目的則是要使政府能事先掌握住宅價格的變動，並提前做好穩定住宅價格的因應對策，減少政府決策過程的時間落後現象，而實證結果應用至政策方面的內容則由以下說明之。 　　（一）藉由因果關係檢定與分析的實證內容，可以縮短政府對住宅價格不合理變動問題認定落後的時間。 　　（二）從變異數分解之分析的實證內容中，可以使決策者在解決住宅價格問題時，將行動落後的時間減少。 　　（三）由衝擊反應之分析中，可以使政府在執行穩定住宅價格政策時，將衝擊落後的時間縮小。 / Since there is no alarm system in the change of housing prices, people often decide when to buy or when to sell based on personal and subjective judgement. Such concept to judge subjectively whether the housing market is bull or bear will cause unequilibrium in the supply and demend of the housing market. There it is possible to find out the answers to the change of housing prices from economic side so that the government can have enough information and can be prepared in the reaction to stabilizing the housing prices, and so that the public can buy house according to their needs is the main purpose of this project. 　　Seven variables in macroeconomics influencing the change of housing prices have been taken from reative literature, including wage, commodity price, income, money supply, stock price, exchange rate, and interest rate. VAR has been employed to verify so that the more objective lagging period among variable can be known, and the bi-directional, uni-directional, leading, contemporaneous, and lagging situation among variables can be understood. Furthermore, the degree and the status of influence of each macroeconomic variable to the housing price will be investigated to clarify the relations among the variables. 　　The present project investigate the relations between housing price and macroeconomic variables. We have the following findings: 　　I、In Empirical Study: 　　The empirical study in this project includes causal relation test and analysis, the analysis of variable decompositon, and the analysis of impact response. The results are shown in the following: 　　(I) Causality Test and Analysis 　　In the causality test and analysis, we find out that stock price, commodity price, exchange rate, money supply and interest rate all can be the leading indicators in the change of housing prices. 　　(II) The Analysis of Variable Decomposition 　　It is learned from the variable decomposition of housing prices that housing price can only explain one third of the cause in its change, the other two thirds are explained by other macroeconomic variables. It shows that housing prices are subject to the influence of macroeconomic variables greatly. 　　From the variable decomposition of other macroeconomic variables, we know that the change in housing prices will affect macroeconomic variables so that the macroeconomic variables will change. 　　(III) The Analysis of Impact Response 　　It can be obviously seen from the analysis figure of the impact response of the macroeconomics to housing prices, all macroeconomic variables will cause obvious impact to housing prices expect for wage. However, both exchange rate and interest rate have negative impact to housing prices. 　　Housing prices' impact to income, stock prices, exchange rate and interest rate is quite obvious, among which, the impact to exchange rate is negative. 　　II、Policy Application 　　It is a common phenomenon that there often will be lagging in time in government's decision making. The purise of the empirical study in this project is to let the government to know in advance the change of housing prices and to let the government to know in advance the change of housing prices and to let the government be prepared in the reaction of stabilizing the housing prices to minimize the lagging in the decision making process. The contents of application of the empirical study to policy are explained in the following: 　　(I) With the empirical results of the change of the causality test and analysis, the time for the government to recognize the unreasonable changes in housing prices can be shortened. 　　(II) With the empirical results of the analysis of variable decomposition, the decision makers' lagging in the action responding to housing pricescan be minimized. 　　(III) With the analysis in impact response, the lagging in impact will be minimized when the government executing her housing price stabilizing policy.

城市化意味着城市的經濟結構、社會結構和空間結構變遷。隨着中國大陸城市化的推進，城市人口遷移、建成區面積增長，城市發展質量逐步提高，而同期內城市商品住宅價格亦呈現普遍而明顯的上漲趨勢。 過去雖有許多國內外文獻對全國城市商品住宅價格的影響因素進行了探討，但少有文獻從城市化的層面着眼進行研究；在部分集中討論城市化效應的文獻中，亦存在以省域代替城市作爲研究對象，以及採用橫截面數據進行實證研究，未能體現不同城市化和社會經濟發展階段之變動趨勢的缺憾。 有鑑於此，本文使用中國大陸2003年至2011年不同規模城市的相關追蹤資料，以“人口城市化”“土地城市化”和城市發展質量優化作爲城市化的主要體現，用從業人口、建設用地面積、房地產開發投資額等一系列指標作爲自變數構建追蹤資料固定效應模型，分析城市化對不同規模城市商品住宅價格的影響，探究中國大陸社會經濟重要發展來源的城市化能否繼續維持城市商品住宅市場的穩定。實證結果顯示，城市化發展對城市商品住宅價格上漲的貢獻明顯，而人口城市化的作用較之土地城市化更爲顯著；大城市和中小城市在城市化發展過程中商品住宅價格影響因素亦有所不同，城市發展中由政府主導的城市土地開發利用與基礎設施建設應集約化發展，土地城市化必須與人口城市化以及城市發展質量提升相互協調。 / Urbanization means changes of cities ‘economic structure, social structure and spatial structure. With the development of Chinese urbanization, the urban population and construction land area is growing, the urban development quality is improving gradually, in the same period city residential housing prices also presents common and obvious rising trend. In the past, although there were many domestic and external literature focusing on the influence factors of the national urban residential housing prices, but there were few literature based the study on the impact of urbanization; many of literatures in which the urbanization effect discussed, also taken provincial data as the research object instead of city , using cross-sectional data for empirical research, so failed to reflect changes in different stages of urbanization and social and economic development trend. In light of this, this article refer to mainland China from 2003 to 2011, 213 level panel data, related to "land urbanization" and "population urbanization" quality optimization as the main embodiment of urbanization, taking urban working population, construction land area, real estate investment and a series of indicators as independent variables to construct panel fixed effects model, in order to analyze the effect of urbanization on the urban residential property prices. We expect to explore whether if mainland China's urbanization, which is a source of important social and economic development of the nation, will continue to maintain the stability of the urban residential market. Empirical results indicates that the influence of urbanization on city residential housing price is evident, besides population urbanization plays a more important role than land urbanization. In the process of urbanization, the influential factors of residential housing price varying in metropolis and small cities, therefore development of urban land dominated by local government ought to realize intensification, and land urbanization should be coordinated with population urbanization and city development quality.

風水為中國民俗之一大特色，近年來更逐漸受到國人所重視，亦成為對不動產價格造成影響的因素之一。而方位更是風水因子中相當重要的一環，因此本研究以臺北市松山區之住宅大樓為研究對象，探討方位風水因子對於不動產價個的影響程度、不動產業者對於方位風水因子的減價加價態度及購屋者對於方位的選擇及喜好程度。以提供不動產相關業者及政府部門進行推案或估價時的調整，並作為一般民眾購屋時的參考。研究結果如下： 一、一般民眾認為好的方位為坐北朝南，方位的判定方式為房屋的座落窗或大片窗戶。 二、民眾於購買房屋或不動產專業人員於估計房屋價格時，面對好或不好方位的房屋時，認為其價格調整空間為一成以內 三、一般購屋者認為方位會影響其購屋意願及房屋的價格。而雖然購屋者認為好的方位可以提高房屋價格，且願意多花費一成以內的金額去購買方位好的房屋，但對於方位不好的房屋是否應有所降價卻感到遲疑，當面對方位不好的房屋價格下降時，大多數的購屋者仍表示不願意購買。 / Feng-Shui is the most special feature of China's culture. Recently, it’s become more and more important in our mind and it has become one of the effects to the real estate price level. Meaenwhile, direction-position is also the relative important factors to Feng-Shui. The real estate in Taipei Songshan distinct as my research target, we are trying to find out whether direction-position is the factor to effect real estate value. The house-solder attitude was raised or lowered the price toward the direction-Feng-Shui factor. The house-buyer has prefer to choose better direction-position. The house direction-position could be the market pricing information for transaction market and govement. Our research results are as follows: 1. People choice of good direction-Feng-Shui position is facing south/backing north, and the direction-housing is located in windows or large windows. 2. The direction-Feng-Shui position for house-buyer or real estate professionals has cut up its housing price range within ten percent. 3. People think the direction-Feng-Shui position will effect their willing to buy the house and housing prices. Although homebuyers think that a good position to increase housing prices, and willing to spend an amount of money to buy into a position within a good housing, However its not good for the position should be lower prices for housing. They are very hesitant when faced with bad directions and down the housing prices, meaenwhile, the most homeowners are still not willing to buy.

過去已有諸多研究證實位於明星國中學區之住宅相較於普通學區之住宅擁有較高的價格。然而，我國於103 年(2014 年)起實施十二年國教政策，其最大變革即是提供免試入學，則該政策是否會動搖明星國中之優勢，進而衝擊明星國中學區的房價應有探討之必要。本文取自實價登錄資料庫之資料，並以2012年8月至2016年底台北市明星學區及其周遭普通學區為地理範圍，建立特徵價格理論之傳統迴歸模型、空間迴歸模型與分量迴歸模型，探討以額滿學校與高升學率之不同定義下明星國中學區對房價之影響，再結合差異中之差異法，觀察十二年國教實施後是否會打破臺北市明星學區的溢價迷思。 根據實證結果顯示，額滿學校與高升學率學校將分別使學區住宅每坪價格上升1.9%-5.3%與5.3%-14.2%，顯示消費者對於高升學率學校有較高之偏好。然而，隨著住宅價格上升，明星學區的溢價卻隨之下降。而十二年國教實施後，僅對位於明星學區2 的住宅產生顯著負面影響，每坪價格下跌1.6%-2.4%；而對明星學區1 之住宅價格則未有顯著影響。本研究推測原因應為十二年國教對於高中職入學篩選標準之改變對高升學率學校有較大之影響，而額滿學校因多數為完全中學國中部，有特殊之直升管道，故受政策影響不大。此外，本研究也發現使用升學率高低作為明星學校之標準比是否為額滿學校更符合消費者之認知且與國外定義較相近。 / In the past, many studies have confirmed that the house in the popular school district has a higher price than the ordinary school district. However, since the implementation of the 12-Year Compulsory Education Policy in 2014, it provided the exam-free admission, and whether the policy will impact the housing prices of the popular schools districts should be discussed. In this paper, we use the full-school and the high enrolment rate school as the popular school, and analysis the popular school district of housing price by using hedonic price theory OLS, spatial and quantile regression as model, and selecting the sale price of real estate in Taipei city from August 2012 to December 2016 as sample. Besides, we also applied Difference-in-Differences method with spatial regression to analyze whether the 12-Year Compulsory Education Policy will reduce the Popular School premium of Taipei. According to the empirical results, housing price in the full-school district has 1.9%-5.3% premium per floor, and high enrolment rate school has 5.3% -14.2% premium per floor, showing that consumers prefer high enrolment rate school. However, with the rise in housing prices, the premium of the popular school district has fallen. In addition, after the implementation of the 12-Year Compulsory Education, only a significant negative impact on the housing price in the school district of high enrolment rate school, the price fell 1.6% -2.4% per floor; and the housing prices of full-school district were not significantly affected. We speculate that the reason should be 12-Year Compulsory Education of entrance examination of high school changes, resulting high enrolment rate schools have a greater impact. On the contrary, the majority of the full-school is affiliated junior high school, it has a special way to enter a higher school, so little impact on the policy.

近年來影城結合商場、娛樂及餐飲，如雨後春筍般出現，建商售屋亦常以影城為吸睛廣告，對消費者而言，影城對周邊住宅價格影響是否存有關聯性係其購屋選擇關心條件之一，然而現今影城的類型大不相同，且觀察到影城大多座落於商圈內或是與百貨商場結合。因此，本研究將影城、商圈與住宅價格的相關程度做交叉分析，以初步了解其關聯程度，並應用集群分析以控制異質樣本，使得形成同質性的房屋屬性來看影城效果，再透過複迴歸模型分析，探討不同類型的影城、商圈與周邊住宅價格之影響關係。 透過複迴歸模型實證結果得知，影城對台北市周邊住宅價格是有影響的，以總價1,000萬元房屋，平均而言，正向的價差介於41萬元至310萬元之間，負向的價差則介於30萬元至271萬元之間，並且正向的價差高於負向的價差。進一步將影城依經營模式及服務方式分類進行實證，採連鎖經營模式的影城周邊房價價差約為40萬元，而提供複合式服務或僅提供單一式服務的影城，對房價的影響差別不大。該結果可提供消費者在評估購買房屋時之參考，亦可作為開發商與銷售業者在預售屋訂價策略及廣告遵循原則。 此外，由於消費型態的改變，消費者習慣將看電影結合其他休閒活動，這些由百貨公司以及電影院異業結盟的商圈更得消費者青睞，影城進駐與商圈發展之關係相輔相成，進而影響新北市周邊住宅價格，以總價1,000萬元房屋，平均來說，影城進駐商圈後能提升房價139萬元。 / These years combination-area of cinemas, malls, recreation and catering are springing up as well as being the eye-catching advertisement of building-contractors. In terms of house-buyers, the correlation between cinemas and the neighborhood housing price is one of the conditions they care. Nevertheless, with the variety of the cineplex-cinema currently and mostly locate in business districts and department stores, our study make a cross analysis between cineplex, cinema, business districts with residential price for their correlation. With cluster analysis to control the heterogeneous samples and to evaluate the price-effect of cineplex-cinemas under the homogeneous housing. Furthermore to multiple regression analyze cinema stationed-in commercial-district and their neighborhood housing price. Our study confirmed the cineplex-cinemas are influential to the Taipei City surrounding-area residential price. For the ten million house, averagely the positive-impact is between four hundred ten thousand to 3.1 million. Negative-impact is between three hundred thousand to 2.71 million. Besides, the housing price different positive effect is higher than the negative one. Further to verify type-mode cinemas: the price difference in neighborhood-area is four hundred thousand with franchise management type-mode. However, there is no price difference with complex and single-service type-mode. Housing-buyer can take the result as consideration during purchasing houses as well as real estate developers and salesman in pricing strategy and advertisement principles of pre-sale houses. Additionally, with the change of consumption patterns, consumers get use to watch movies together with other recreation. Thus commercial-districts combining with department stores and cinemas are more favored. The cinema stationed-in and commercial-district development are complemented each other, therefore to affect the neighborhood housing price in New Taipei City. For the ten million house, averagely, the positive price-different effect is one million and three hundred nighty thousand after the cinema stationed-in.