容積外部對房價影響之實證-以台北市為例

The Impacts of Bulk Externalities on Housing Price in Taipei

吳憶如(Yi-Ju Wu)

指導教授 ： 楊重信

國立臺北大學/公共事務學院/都市計劃研究所/碩士(2008年)

摘要

近年來台北市中心精華區出現交易容積單價達100(萬元/坪)的比比皆是 ，其主因為考量容積移轉及土地可容納容積，以北市住三土地標售行情已達百萬容積價來看，如果沒有利用購買容積來增加建坪，建商標得的土地成本難以打平；因此，建商搶地並大量購買古蹟的容積率或公共設施用地來攤平成本，逐漸成為一種風氣。就台北市目前容積總量而言，允建容積量遠大於法定容積量和現況容積量；容積分配上12個行政區皆尚有法定剩餘容積率，惟大安區、松山區出現負的法定剩餘容積率，在這樣的容積市場之下，大家仍競相爭取高容積。究竟容積率在房價中到底扮演什麼樣的角色與義意呢?
一般直覺上認為宗地本身容積率越高代表房價越高，因為法定容積率對購屋者而言具有投資保值之意義，亦即房屋未來改建或更新時，將可興建較多之樓地板面積；但過多的樓地板面積對周邊住戶而言是一種擁擠外部性(Crowding Externality)，亦即周邊地區之容積率愈大其所產生之負面外部性(如：擁擠、噪音、交通設施容受力不足、開放空間不足等)愈大，此對房價之負面影響將愈大。其次，「容積率」在都市計畫領域裡，指的是建築物總樓地板面積與整個宗地面積之比值，亦即容積率越高代表建築物總樓地板面積越大相對地房價越高、土地持分越小代表房價越低。然而在這樣錯綜複雜的關係下，其對房屋價格將產生何種影響呢? 鑑此，本研究希望藉由實際的不動產交易資料實證容積率與容積外部對房價影響。
本研究以民國88年1月至93年底的不動產實際交易資料運用傳統迴歸方法找出最適的函數型態與影響變數，再應用地理加權回歸分析方法以空間呈現，最後根據估計的參數以GIS展示分析結果之空間分佈圖形，藉以實證容積率與容積外部對房價的影響關係。實證結果發現：
一、房屋總價水準顯著受到房屋之建物面積、屋齡、法定容積率、至最近捷運站(已通車+興建中)之影響；其解釋能力達0.673左右，建物面積越大者其房價水準愈高、屋齡越舊者其房價水準愈低、至捷運站距離越遠者其房價水準愈低，土地持份之係數估計值的顯著水準達到72%；土地持份每增加1%，房屋價格約增加0.003%，法定容積率越高者其房價水準愈高；法定容積每增加1%，房屋價格平均增加0.017%。建物所在街廓所有樓地板面積之係數估計值的顯著水準達到22%；建物所在街廓所有樓地板面積每增加1%，房屋價格約減少0.008%。若以整個地區來看當法定容積率增加1%時，房屋價格淨影響為正的0.006%。
二、房屋單價水準顯著受到房屋之屋齡、法定容積率、建物所在街廓所有樓地板面積之影響；但其解釋能力只有0.025左右，顯示每坪房屋價格與容積率無關，但法定容積對房屋單價有顯著影響且正相關。建物所在街廓所有樓地板面積對房屋單價無顯著影響，意謂著消費者在選擇購屋時擁擠的負外部性對消費者的影響不顯著，亦即消費者在購屋時較不考慮擁擠的負外部性的影響。在這樣的情況下促使生產者樓層越蓋越高，選擇少用土地面積而增加資本的支出，當利潤大於零的情況下仍會競相爭取高容積，而最終的受害者應該是房屋的消費者。
三、藉由地理加權迴歸(GWR)校估出的每個係數估計值應用GIS以圖面呈現可表達空間差異的能力，根據每筆房價資料所在的不同區位及不同的周邊條件提出分析。不同於過去研究將因子影響視為靜態之模型進行分析，將全區視為一個均質的「同質區」，沒有考慮到同一地區內之其他鄰近住宅對住宅價格有著空間相依性的關係。

關鍵字

容積率 ； 容積外部 ； 房價 ； 地理加權迴歸

並列摘要

Taipei center quintessence district appear trade bulk unit price up to 100 (ten thousand yuan / level ground) in recent years Can be found everywhere, its consider because the masters bulk transfer and can hold bulk of land, such as in Taipei city live in three land selling tender quotations up to a million bulk price is it watch to come, is it buy bulk increase and build level ground to utilize already, it is difficult to tie to build the land cost that trade mark has; for building trader and for purchasing a very large portion of historic site bulk rate or it is land used for that it is last cost to come to communal facilities, become a kind of atmosphere gradually. As regards total amount of bulk at present in Taipei, it is far greater than the legal bulk amount and bulk amount of present situation to permit the amount of bulk built; The bulk is distributed and gone to 12 administrative areas all still there is a legal surplus bulk, only the negative legal surplus bulk appears in Daan area, Songshan area, under such bulk market, everybody still competitively strives for the high bulk. What kind of role and justice purpose does bulk really act at the housing price?
Generally person who think case one's own bulk high representative housing price high instinctively, because the legal bulk has meaning of investing and preserving value as to persons who purchase house, namely can build more area of floor area when the house is reconstructed or upgraded in the future.But too much area of building floor is a kind of crowded outside for peripheral household (Crowding Externality) ,bulk of namely surrounding area there is rate outside negative externality heavy (for instance：Crowdedness, noise, traffic facility hold strength insufficient, open space getting not enough), this influences the bigger to reverse side of the housing price. Secondly, Floor area ratio(FAR) means the ratios of the area of floor of total building of the building and area of whole ground in planning the field in the city, namely FAR high total floor area heavy housing price high, land is it divide light housing price the lower representative to hold relatively on behalf of building. But under so intricate a relation, which kinds of influence will it are produced to price of the house? Hence, research this is it influence with bulk externality and FAR too housing price by real estate trade data.
This research use real estate actual trade data of January 1999 to the end of 2004, use traditional statistics technology method to find out the most suit function type and influence parameters, and then use the geographically weighted regression, show the space distribution figure of the analysis result with GIS according to the parameter of the bulk external impact on hosing price. The empirical result is found：
First, the gross price of house is the apparent influence by level to build areas, the room age of the house ,FAR, the distance from MRT station(open to the traffic already + in building)；It explains ability is up to about 0.673, build areas the less heavy their housing price level the less high, housing age is old that housing price is low, the distance is far form MRT station, the price of housing is low, the competence of showing of coefficient estimated value of holding one of land is up to 72%; Land hold increase 1% each time, house price increase 0.003% FAR getting high housing price competence high; The FAR increases by 1% each time, the price of the house increases by 0.017% equally. Build street of all floor of area the floor coefficient estimated value, floor of area show competence up to 22%; The area of floor on the all floor increases by 1% each time to build the street of the thing widely, the price of the house nearly reduces by 0.008%. If see when the FAR increases 1% with the whole area, it is influenced as 0.006% straightly that the price of the house is net.
Second, the house unit price level is apparent influence by the room age of the house, FAR, the area of floor on the all street floor. But it explain ability to be only 0.025 about, show every level ground house between price and FAR rate have nothing to do, but the FAR has apparent influence on unit price of the house and positive correlation. Build street of thing wide the all floor area have to house unit price apparent to influence, not apparent that the crowded one is defeated by the outside impact on consumer that purpose is calling consumers while choosing to purchase house, namely consumers do not consider while purchasing house the crowded one is defeated by outside influence. Impel producer's floor to build the higher in a situation that like this, choose few to increase the expenditure of the capital with the land area, will still competitively strive for the high bulk under the circumstances that the profit is greater than the situation of zero, and the final victim should be consumers in the house.
Third, use GIS with is it can express ability, space of difference to appear pursuing by GWR each coefficient estimated value appeared to estimate, propose analysing according to different positions where each sum of housing price data belong to and different peripheral terms. Different from in the past is it regard factor as static model analyse to influence to studying, regard as one ' homogeneity district ' of quality the whole district, is it consider to near house have space getting interdependent relation to house price while being other the same area to have.

並列關鍵字

Floor area ratio ； Bulk externalities ； Housing price ； Geographical Weighted Regression

參考文獻

26.楊重信，2008，洪災防治之效益-以基隆河整治為例，國科會研究案。

22.張齡方、古建廷、林俊男，2006，以地理加權迴歸分析建立灌溉率與各影響因子之關係，農業工程學報，第52卷，第2期，頁73-82.

23.黃紹東，2004，台南市東區住宅價格之空間自我迴歸分析，國立成功大學都市計劃碩士論文.

12.林家弘，2004，競租價格模型與屬性價格模型對捷運車站周邊房價預測之比較分析，國立成功大學都市計劃碩士論文.

13.林尚德，2003，以反應空間不穩定性為基礎之土地估價模型建立，國立成功大學都市計劃碩士論文.