現行公告土地現值的評估，係採用人工的傳統方法，估價結果誤差甚大且過於主觀，無法達到大量估價客觀、快速、精確之目標。本文首先利用土地經濟理論的分析，探討土地市場價值、交易價格、評估價值之間的關係，釐清常見的混淆概念。並藉由估價比率研究，討論公告現值與市價差距的檢定模型，針對台南市的實際資料進行統計檢定，結果發現平均估價比率落於46.74﹪~48.52﹪之區間，並存在輕微的垂直不公平。 為改進現行公告現值不夠準確之缺失，本研究基於都市經濟理論與估價先驗訊息之基礎，利用特徵價格法與可加性模型建立大量估價模式。實證結果發現，影響台南市地價之因素，以區位、臨街關係、路寬、使用分區最為重要。在部份年度中，亦證實存在基地面積規模不經濟（plattage）現象。 傳統特徵價格法必須預設函數型態，若函數設定錯誤則將使參數估計產生偏誤。可加性模型結合無母數迴歸與母數迴歸之優點，不須預設函數型態、估計結果易於解釋且維持母數迴歸之收斂速度。其可經由修勻法配適出更客觀的函數關係，無論在樣本內與樣本外之估計均較特徵價格法為佳。 研究結果發現，本文所提出的二種估價模式確可達到快速精確的目標，使估價比率接近１，比目前評估效率提高一倍；在公平性方面雖無改善，但亦無嚴重之垂直不公平。其中可加性模型又較特徵價格法為佳，在電腦技術快速進步的今天，應用至大量估價的可行性大為提高，值得後續進一步深入研究。 / The present Announced Land Current Value （ALCV）was evaluated by traditional appraisal method that may result in large errors. Comparing to mass assessment approaches, it is hard to be objective, quick and precise. This research begins with the analysis based on land economic theory to discuss the relation among the market value, sale price and assessed value of land in order to clarify some confusing concepts. Through assessment-sale price ratio study, we analyze the difference between ALCV and sale price, and then use the actual data of Tainan City for empirical study. The results show that the average a-s ratio falls between 46.74%~48.52% with slight vertical inequity. To improve the lack of preciseness and objectivity of the present ALCV, this research uses hedonic price theory and Generalized Additive Model（GAM）based on urban economic theory and appraisal priori information. The results show that location, relations with adjacent streets, road width and zoning are the most influencing factors of land price in Tainan City. During some years, the phenomenon of plattage effect also exits. The function form must be set beforehand in the traditional hedonic pricing, meanwhile parameters bias will occur if the pre-determined function form were wrong. GAM has the advantages of nonparametric regression and parametric regression. The function form needs not to be pre-determined, the empirical results are easy to interpret, and the speed of variable convergence can be maintained. More precise functional relations can also be smoothed by GAM. It is superior to the traditional hedonic price in the sample and out of the sample prediction alike. The results of empirical study show that both of two models can reach the goal of rapidity and preciseness and make the a-s ratio toward 1. As to the equity, although they are not improved very much, the models don't bring serious vertical inequity. However, GAM is better than hedonic pricing when compared to each other. Due to the great progress of computer technology, the application of GAM to mass assessment can be increased greatly and is worthy continuing further study.