不動產由於具有異質性、不可移動性與昂貴性等特性，相較於其他資產而言，最獨特的風險為「流動性風險」(liquidity risk)，也因此其銷售期間比其他標的較長，議價空間也較大。以往探討不動產流動性的研究大都以在市場上的銷售期間(time on the market, TOM)為主，然本研究認為銷售期間和賣方的表價（listing price）與買方心中的價格（offer price）密切相關，買方決策的過程勢必受到賣方表價與本身心中價格之影響，進而決定該不動產在市場之流動性。故本研究則嘗試以賣方表價與最後成交價(transaction price)間的議價空間，探討不動產市場之流動性。議價空間愈大，表示不動產標的在當時市場之流動性愈低，致使賣方愈能接受低於表價的買方出價及成交價。 本研究透過搜尋理論建立住宅不動產議價空間模型，並以實際市場交易資料進行實證分析，探討房屋本身的屬性、總體市場因素、賣方持有的成本、區位因素與賣方定價因素等，對於買賣雙方議價空間之影響，藉以觀察理論與實證是否相符。其中以房屋總坪數與屋齡代表房屋屬性，以房租成長率與經濟成長率代表市場情況，並以利率代表賣方持有成本。實證結果顯示，屋齡太久或賣方定價過高的不動產，其議價空間愈大，流動性愈差；房租成長率和經濟成長率皆與議價空間呈負相關，表市場景氣愈好，議價空間愈小，賣方在議價過程中較能堅守其表價；又利率與議價空間呈正相關，表賣方持有不動產的成本越高，越能接受較大的議價空間；而總坪數愈大及區位較佳之不動產，其議價空間越小，可能受豪宅市場效應以及區域抗跌性有關。此實證結果與過去利用銷售期間衡量不動產市場流動性的搜尋理論相符，也驗證議價空間實可為衡量不動產市場流動性的新指標，並可降低利用銷售期間分析的研究困境。本研究成果不僅可供不動產賣方定價策略、買方議價時機之參考，亦希望透過本研究對議價空間與不動產市場流動性之研究，期望政府儘速建立與公開不動產交易平台，俾利增進不動產市場之流動性，更能牽動不動產市場與整體經濟市場之成長。 / The study suggests that not only time on market (TOM) but also price concession between the listing and contract prices could measure housing market liquidity. Departed from past studies, this paper develops theory and constructs a model named Residential Housing Price Concession Model to examine whether key factors influenced housing market liquidity significantly from past studies would have the same effect on price concession. The model includes the listing price of house, the macroeconomic data, the cost of the search and other housing characters in empirical model. Results show that listing price, cost of search and age of house have the predicted positive coefficients, and macroeconomic data , squares of house and location factor are found to be negatively related to the price concession. The corresponding conclusion with time on market (TOM) examined by past studies explains that the price concession also could measure housing market liquidity.

由於運用特徵價格模型探討影響不動產價格之變數，國內外相關之文獻數量甚為豐富，但對於影響議價空間比率之變數，較少有研究者探討，由於議價空間比率影響賣方及買方之訂價或出價策略與對成交價格之預期，另對於估價人員採用市場比較法時，僅得待售案例時之情況調整亦有所幫助，故本研究試圖從蒐集相關不動產成交案例，探討有關影響其議價空間比率之變數。 因本研究之資料範圍鎖定成屋且透過仲介成交之案件，故透過與仲介經紀從業人員之訪談並與文獻回顧相關理論作相互印證，廣納可能之變數，並搜集台北市、縣成屋實際委託價格及交易資訊。將訪談蒐集所得之交易價格資料及變數，運用Excel 及SPSS統計軟體，透過敘述統計、相關係數分析、折線圖分析、特徵估價法之多元廻歸模型等方法，分析影響議價空間比率之因素，及其影響之程度。 實證結果發現：影響議價空間比率之顯著變數如下： （一）區位變數有：樹林、林口、新莊為正相關，議價空間比率顯著較高，大安區為負相關，議價空間比率相對較低。 （二）成交時間變數有：97年第四季因金融風暴，該季議價空間比率顯著較高。 （三）物件個別屬性變數有：是否1樓、屋齡、平均委託單價與議價空間比率為正相關，透天產品與議價空間比率為負相關，透天產品議價空間比率顯著較低。 （四）總體因素變數有：北市北縣拍賣移轉件數與議價空間比率為正相關，北縣市買賣移轉件數與議價空間比率為負相關。 本研究建議買賣交易人或投資者應針對交易標的所在區位、交易時間點、標的物個別條件以及總體指標需深入了解，賣方在訂價或買方在出價斡旋時，將更能提高交易成功之機率。物件位於相對偏遠地區，由於條件相對較差，賣方在開價上需預留較大議價空間，並有讓價準備，而買方可多收集相關成交資訊，以減少因資訊不充足而支付較高之價格貼水。 另外，當市場上發生重大事件導致房屋市場變動時，此時仲介角色更具挑戰性， 應提供賣方相關分析資訊，協助使其提早瞭解市場變動情況，避免損失加劇。對於一樓產品，買賣雙方價格認知差異較大，應提供相近條件之成交行情，若是店面或商用產品，可再提供租金收益資訊，易使雙方對於價格較有共識，並促進交易價格之合理性。 而不動產估價師執行台北縣、市之估價案件時，若採用市場比較法，收集之比較案例為待售尚未成交之物件，可參考各區位議價空間比率之平均數，再評估上述顯著變數之正負相關性，酌予上下調整，以增進待售價格情況調整之精確度。 / Although there are abundant sources of references regarding the use of Hedonic pricing models to study the relationship between real estate pricing variables and real estate price volatility, relatively few were dedicated to the research of price concession variables. Price concession variables determine sellers' asking prices and buyers' bid prices, or the expected deal price of a bid/ask strategy and even provide useful indications to real estate evaluators taking the market comparison approach when there are no actual deals to compare. Therefore, in this research we aimed to collect samples of actual real estate deals made to determine the variables that affect degrees of price concession by analyzing the degrees of price concession in proportion to bid/ask prices. In this research, we confined our area of study to completed constructions transacted through real estate agencies. Through interviews with real estate agents and reviews of past theoretical references, we attempted to gather all possible variables from the time buyers and sellers approach real estate agencies to the time a deal is made; we also gathered data of bid prices, asking prices and deal prices of completed constructions situated in the various administration districts within Taipei City and Taipei County. Interview results, pricing data and the possible variables we had identified were analyzed using Excel and SPSS; our statistical analysis included descriptive statistics, correlation analysis, line charts and multiple regression of the Hedonic pricing model. The purpose of our analyses was to determine factors that influence the degree of price concession as well as the extent of such influence. Our research results found the following variables that significantly influence the degree of price concession: (1) Location variable: Shulin, Linko and Hsinchuang districts are positively correlated, suggesting a higher degree of price concession; Daan district is negatively correlated, suggesting a lower degree of price concession. (2) Timing variable: The degree of price concession during the fourth quarter of 2008 was significantly higher because of the global financial crisis. (3) Object-specific variables: Whether the property is situated on the first floor and aging variables are positively correlated to the degree of price concession; whether the property is an independent house is negatively correlated to the degree of price concession, suggesting a lower degree of price concession for independent house properties. (4) Macro factors: The number of court auctions in Taipei City and County is positively correlated to the degree of price concession; the number of property sales in Taipei City and County are negatively correlated to the degree of price concession. Through this research, we advise that property buyers, sellers and investors should gain further insights into the location, timing, characteristics and the overall environment relating to the properties they wish to close deals for. These insights will help buyers and sellers set bid/ask prices that are more likely to close deals, thereby reducing the cost of prolonged negotiations. Properties located in remote areas are have a disadvantage; sellers should reserve more room for negotiation and be prepared to make price concessions, while buyers should gather more information related to the deal of similar properties to avoid paying higher premiums due to lack of information. Furthermore, the role of real estate agencies becomes more challenging in the occurrence of major events which cause volatility within the real estate market. Real estate agents should provide sellers the relevant data analysis to facilitate early anticipation of market changes, thereby preventing further losses. For properties located on the first floor, since there are relatively wider discrepancies between sellers' and buyers' expectations, real estate agents should provide more information related to the deals of similar properties to reconcile their differences. If the properties are for retail or commercial purposes, real estate agents may also provide information on rental or revenue to reconcile the understanding between buyers and sellers and give more rationality to deal prices. For real estate evaluators attempting to evaluate properties situated in Taipei City and County using the market comparison approach but lacking deal references, they may consider taking the average degree of price concession across all administrative districts and adjust upwards or downwards based on correlations to the above significant variables and produce a more accurate indicator for the properties pending sale.