隨著人口增加,可供使用土地越來越少,亦使土地市場價格不斷上漲,為抑止投機壟斷土地市場,政府亦企藉由土地增值稅的課徵與調整來穩定土地市場。鑒於80年代房地產市場不景氣,土地增值稅稅收大幅下降,在檢討房地產市場不景氣原因中,土地增值稅稅率也成為市場交易不活絡的原因之一。 影響土地價格之因素,可依照不同的劃分標準區分出不同類型的因素。以往研究大多運用多元迴歸分析進行研究資料分析,較少考量影響變數間不同階層關係進行討論。因此本研究之目的除探討不同地區之相異背景,進行探討建立階層關係,進而探討土地增值稅與其他因素對土地價格之影響效力,並以階層線性模式校估土地增值稅對土地價格。 本研究運用階層線性模式探討階層間相互的影響關係,採用民國88年至99年之各項政府發行之統計資料,並以台灣地區為主要分析研究之範圍。復將選用之因素階層區分為區域層面與一般大環境層面進行實徵分析。 實徵結果顯示,影響土地價格之因素間存在階層關係,需要藉由階層線性模式進行分析,進一步分析發現區域層面因素與一般大環境層面因素具有調節效果,且兩者之間存在交互關係,最終將階層線性模式與迴歸模型進行比較,顯示階層線性模式所估計出之結果對於各樣本預測較為準確。
As the population increases, the availability of land is geting fewer. Result the land market prices are rise. In order to curb the speculative monopoly of the land market, the Government attempting to stabilize it by the imposition and adjustment of land value increment tax. In 1990s, in view of the real estate market downturn, the land value increment tax decline significantly. Review the reason of the real estate market downturn, the land value increment tax rate has become the causes of the market transactions inactively. Factors affecting of the land price, can be according to different criteria for the classification of distinguished different types. Most studies using multiple regression analysis, ther are fewer considering the impact variable between different sectors of discussion. In addition to exploring of the different regions background, exploring the establishment of a hierarchical relationship. And explore the land value increment tax and the other factors that impact on land price, and use hierarchical linear modeling estimated the land value increment tax on land price. In this study, use the hierarchical linear model to explore the relationship between hierarchism. This study use the government issued statistics since 1999 to 2010 years. Selection factors of level distinction as the regional level and the general environment level to empirical analysis. The empirical results shows that the hierarchical relationship between the factors affecting of the land price, as required by the hierarchical linear model analysis. Further analysis revealed that the regional level factors and the general environment factors have moderating effects, and there is reciprocation between hierarchism. Finally, comparing to the hierarchical linear modeling and regression models, estimate of the results of the hierarchical linear model can be used to forecasts more accurate of each sample.
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